Dr. Brian Arthur and I talk about how technology can be modeled as a modular and evolving system, combinatorial evolution more broadly and dig into some fascinating technological case studies that informed his book The Nature of Technology. Brian is a researcher and author who is perhaps best known for his work on complexity economics, but I wanted to talk to him because of the fascinating work he’s done building out theories of technology. As we discuss, there’s been a lot of theorizing around science — with the works of Popper, Kuhn and others. But there’s been less rigorous work on how technology works despite its effects on our lives. Brian currently works at PARC (formerly Xerox PARC, the birthplace of personal computing) and has also worked at the Santa Fe institute and was a professor Stanford university before that. Links W. Brian Arthur’s Wikipedia Page The Nature of Technology on Amazon W. Brian Arthur’s homepage at the Santa Fe Institute Transcript Brian Arthur [00:00:00]  In this conversation, Dr. Brian Arthur. And I talk about how technology can be modeled as modular and evolving system. Commentorial evolution more broadly, and we dig into some fascinating technological hae studies that informed your book, his book, the nature of tech. Brian is a researcher and author who is perhaps best known for his work on complexity economics. Uh, but I wanted to talk to him [00:01:00] because of the fascinating work he's done, building out theories of technology. Uh, as we discussed in the podcast, there's been a lot of theorizing around science, you know, with the works of popper and Kuhn and other. But there's has been much less rigorous work on how technology works despite its effect on our lives. As some background, Brian currently works at park formerly Xerox park, the birthplace of the personal computer, and has also worked at the Santa Fe Institute and was a professor at Stanford university before that. Uh, so without further ado, here's my conversation with Brian Arthur.  Mo far less interested in technology. So if anybody asks me about technology immediately search. Sure. But so the background to this is that mostly I'm known for a new framework and economic theory, which is called complexity economics. I'm not the [00:02:00] only developer of that, but certainly one of the fathers, well, grandfather, one of the fathers, definitely. I was thinking one of the co-conspirators I think every new scientific theory like starts off as a little bit of a conspiracy. Yes, yes, absolutely. Yeah. This is no exception anyways. So that's what I've been doing. I'm I've think I've produced enough papers and books on that. And I would, so I've been in South Africa lately for many months since last year got back about a month ago and I'm now I was, as these things work in life, I think there's arcs, you know, you're getting interested in something, you work it out or whatever it would be. Businesses, you [00:03:00] start children, there's a kind of arc and, and thing. And you work all that out. And very often that reaches some completion. So most of the things I've been doing, we've reached a completion. I thought maybe it's because I getting ancient, but I don't think so. I think it was that I just kept working at these things. And for some reason, technologies coming back up to think about it in 2009, when this book came out, I stopped thinking about technology people, norm they think, oh yeah, you wrote this book. You must be incredibly interested. Yeah. But it doesn't mean I want to spend the rest of your life. Just thinking about the site, start writing this story, like writing Harry Potter, you know, it doesn't mean to do that forever. Wait, like writing the book is like the whole [00:04:00] point of writing the book. So you can stop thinking about it. Right? Like you get it out of your head into the book. Yeah, you're done. So, okay. So this is very much Silicon valley and I left academia in 1996. I left Stanford I think was I'm not really an academic I'm, I'm a researcher sad that those two things have diverged a little bit. So Stanford treated me extraordinarily well. I've no objections, but anyway, I think I'd been to the Santa Fe Institute and it was hard to come back to standard academia after that.  So why, should people care about sort of, not just the output of the technology creation process, but theory behind technology. Why, why does that matter? Well[00:05:00]  I think that what a fine in in general, whether it's in Europe or China or America, People use tremendous amount of technology. If you ask the average person, what technology is, they tell you it's their smartphone, or it's catch a tree in their cars or something, but they're, most people are contend to make heavy use of technology of, I count everything from frying pans or cars but we make directly or indirectly, enormously heavy use of technology. And we don't think about where it comes from. And so there's a few kind of tendencies and biases, you know we watch we have incredibly good retinal displays these days on our computers. [00:06:00] We can do marvelous things with our smartphone. We switch on GPS and our cars, and very shortly that we won't have to drive at all presumably in a few years. And so all of this technology is doing marvelous things, but for some strange reason, We take it for granted in the sense, we're not that curious as to how it works. People trend in engineering is I am, or I can actually tell you that throughout my entire life, I've been interested in how things work, how technology works, even if it's just something like radios. I remember when I was 10, I like many other kids. I, I constructed a radio and a few instructions. I was very curious how all that worked and but people in general are not curious. So I [00:07:00] invite them quite often to do the following thought experiments. Sometimes them giving talks. All right. Technology. Well, it's an important, yeah, sort of does it matter? Probably while I would matter. And a lot of people manage to be mildly hostile to technology, but there are some of the heaviest users they're blogging on there on Facebook and railing about technology and then getting into their tech late and cars and things like that. So the thought experiment I like to pose to people is imagine you wake up one morning. And for some really weird or malign reason, all your technology is to super weird. So you wake up in your PJ's and you stagger off to the bathroom, but the toilet, [00:08:00] you trying to wash your hands or brush your teeth. That is no sink in the bathroom. There's no running water. You scratch your head and just sort of shrugged in you go off to make coffee, but there's no coffee maker, et cetera. You, in this aspiration, you leave your house and go to clinch your car to go to work. But there's no car. In fact, there's no gas stations. In fact, there's no cars on the roads. In fact, there's no roads and there's no buildings downtown and you're just standing there and naked fields. And wondering, where does this all go? And really what's happened in this weird Saifai set up is that let's say all technologies that were cooked up after say 1300. So what would that be? The last 700 years or so? I've disappeared. And and you've [00:09:00] just left there and. People then said to me, well, I mean, wouldn't there have been technologies then. Sure. So you know how to, if you're a really good architect, you might know how to build cathedrals. You might know how to do some stone bridges. You might know how to produce linen so that you're not walking around with any proper warm clothes and so on. But our whole, my point is that if you took away everything invented. So in the last few hundred years, our modern world or disappear, and you could say, well, we have science, Peter, but without technology, you wouldn't have any instruments to measure anything. There'd be no telescopes. Well, we still have our conceptual ideas. Well, we would still vote Republican or not as the case may be. Yeah, you'd have, and I'd still have my family. Yeah. But how long are your kids gonna [00:10:00] live? Because no modern medicine. Yeah, et cetera. So my point is that not only does technology influence us, it creates our entire world. And yet we take this thing that creates our entire world. Totally. For granted, I'd say by and large, there are plenty of people who are fascinated like you or me, but we tend to take it for granted. And so there isn't much curiosity about technology. And when I started to look into this seriously, I find that there's no ology of technology. There's theories about where science comes from and there's theories about music musicology and theories, endless theories about architecture and, and even theology. But there isn't a very [00:11:00] well-developed set of ideas or theories on what technology is when, where it comes from. Now, if you know, this area is a, was that true? On Thur, you know, I could mention 20 books on it and Stanford library, but when I went to look for them, I couldn't find very much compared with other fields, archi, ology, or petrol energy, you name it technology or knowledge. It was, I went to talk to a wonderful engineer in Stanford. I'm sure he's no longer alive. Cause this was about 15 years ago. He was 95 or so if I couldn't remember his name it's an Italian name, just a second. I brought this to prompts. Just a sec. I'm being sent to you. I remember his name and [00:12:00] make it the first name for him. Yeah. Walter VIN sent him. So I went to see one it's rarely top-notch aerospace engineers of the 20th century had lunch with them. And I said, have engineers themselves worked out a theory of the foundations of their subject. And he looked, he sort of looked slightly embarrassed. He says, no. I said, why not? And he paused. He was very honest. He just paused. And he says, engineers like problems they can solve. It's. So compared with other fields, there isn't as much thinking about what technology is or how it evolves over time, where it comes from how invention works. We've a theory of how new species come into existence since 1859 and Darwin. [00:13:00] We don't have much for theory at all. At least. This was 10, 15 years ago about how new technologies come into being. I started to think about this. And I reflected a lot because I was writing this book and people said, what are you writing about? I said, technology that is always followed by Y you know, I mean, I could say I was maybe writing the history of baseball. Nobody would've said why, but Y you know, what could be interesting about that? And I reflected further that and I argue in my book, the nature of technology, I reflected that technology's not just the backdrop or the whole foundation of our lives. We depend on it 200 years ago, the average length of life, might've been 55 in this country, or 45. [00:14:00] Now it's 80 something. And maybe that's an, a bad year, like the last year. So, and that's technology, medical technology. We've really good diagnostics, great instruments very good methods, surgical procedures. Those are all technology. And by and large, they assure you fairly well that if you're born this year in normal circumstances, Reasonably the normal circumstance through born, let's say this decade, that's with reasonable, lucky to live, to see your grandchildren and you might live to see them get married. So life is a lot longer. So I began to wonder who did research technology and strangely enough maybe not that strangely, it turns out to be if not engineers, a lot sociologists and economists. [00:15:00] And then I began to observe something further in that one was that a lot of people. So wondering about how things change and evolve had really interesting thoughts about how science, what science is and how that evolves. And so that like Thomas Kuhn's, there are many people speculated in that direction, whether they're correct or not. And that's very insightful, but with technology itself I discovered that the people writing about it were historians associates, which is an economist and nearly, always, they talked about it in general. We have the age off the steam engines or when railroads came along, they allowed the expansion of the entire United States Konami that connected his coast and west coast and [00:16:00] so on. So they're treating the technology has sort of like an exogenous effect sent there and they were treating that also. I discovered there's some brilliant books by economic historians and sociologists add constant is one. He wrote about the turbo chapter, super good studies about Silicon valley, how the internet started and so on. So I don't want to make too sweeping the statement here, but by and large, I came to realize that nobody looked inside technologies. So this is if you were set in the 1750s and by ology certain biologists, they would have been called social scientists, natural philosophers. That's right. Thank you. They would have been called natural philosophers and they would have been interested in if they were interested [00:17:00] in different species, say giraffes and Zebras and armadillos or something. It was as if they were trying to understand these from just looking outside. And it wasn't until a few decades later, the 1790s, the time of George cookie that people started to do. And that to me is, and they find striking similarities. So something might be a Bengal tiger and something might be some form of cheetah. And you could see very similar structures and postulate as Darwin's grandfather did that. There might be some relation as to how they evolved some evolutionary tree. By time, Darwin was writing. He wasn't that interested in evolution. He was interested in how new species are formed. So I began to realize that in [00:18:00] technology, people just by and large looking at the technology from the outside, and it didn't tell you much. I was at a seminar. I remember in Stanford where it was on technology every week. And somebody decided that they would talk about modems. Those are the items that just connect your PC. The wireless internet. And they're now unheard of actually they're built into your machine. I'm sure. And we talked for an hour and a half about modems or with an expert who from Silicon valley who'd been behind and venting. These never was the question asked, how does it work? Really? Yeah. Did, did everybody assume that everybody else knew how it worked? No. Oh, they just didn't care. No, no. Yeah, not quiet. It was [00:19:00] more, you didn't open the box. You assume there was a modem who is adopting modems. How fast were modems, what was the efficiency of modems? How would they change the economy? What was in the box itself by and large was never asked about now there are exceptions. There are some economists who really do get inside, but I remember one of my friends late Nate Rosenberg, superb economist of technological history here at Stanford. Rude poop called inside the black box, but he didn't even in that book, he didn't really open up too many technologies. So then I began to realize that people really didn't understand much about biology or zoology or evolution for that matter until this began to open up or can [00:20:00] isms and see similarities between species of toads and start to wonder how these different species had come about by getting inside. So to S set up my book, I decided that the key thing I was going to do, I didn't mention it much in the book, but was to get inside technologies. So if I wanted to talk about jet engines, I, wasn't just going to talk about thrust and about manufacturers and about people who brought it into being, I was going to talk about, you know heat pumps, exactly Sur anti surge systems for compressors different types of combustion systems and materials whole trains of compressors. Oh, assemblies of compressors the details of turbines that drove the compressors. [00:21:00] And I found that in technology, after technology, once you opened it up, you discovered many of the same components. Yeah. So let me hold that thought for a moment. I thought it was amazing that when you look at technologies from the outside, you know, see canoes and giraffes, they don't look at all similar legs. Yeah. But they all have the same thing, basic construction there. And then their case, their memos, and they have skeleton their vertebrates or et cetera, whatever they are or something. And so in technologies, I decided quite early on with the book that I would understand maybe 25 or so technology is pretty well. And of those [00:22:00] I'd understand at least a dozen very well, indeed, meaning spending maybe years trying to. Understand certain technologies are understanding. And and then what I was going to do is to see how they had come into being and what could be said about them, but from particular sources. So I remember calling up the chief engineer on the Boeing 7 47 and asking them questions personally, the cool thing about technology, unlike evolution is that we can actually go and talk to the people who made it right. If they're still alive. Yes. And so, so, so I decided that it would be important to get inside technologies. When I did that, I began to realize that I was seeing the same components [00:23:00] again and again. So in some industrial system, safe for pumping air into coal mines or something, fresh air, you'd see compressors taking in their piping, it done. And and yeah. Again, and again, you see piston engines or steam engines, or sometimes turbines powering something on the outside. They may look very different on the inside. You are seeing the same things again, again, and I reflected that in biology and say, and yeah, in biology save mammals we have roughly the same numbers of genes, very roughly it's kind of, we have a Lego kit of genes, maybe 23,000 case of humans slightly differently for other creatures. [00:24:00] And these genes were put together to express proteins and express different bone structures, skeletal structures, organs in different ways, but they were all put together or originated from roughly the same set of pieces put together differently or expressed differently, actuated differently. They would result in different animals. And I started to see the same thing with technology. So again, you take some. You take maybe in the 1880s some kind of a threshing machine or harvester that worked on steam summer inside. There there'd be a boiler. There'd be crying, Serbia steam engine. If you looked into railway locomotive, you'd see much the [00:25:00] same thing, polars and cranks, and the steam engine there be a place to keep fuel and to feed it with a coal or whatever it was operating on. So once I started to look inside technologies, I realized it was very different set of things that there's ceased to become a mystery. And so the whole theme of what I was looking at was see if I can get this into one sentence. Technologies are means to human purposes normally created from existing components at hand. So if I want to put up some structures and Kuala lumper, which is a high level high rise building, I've got all the pieces I needed. Pre-stressed concrete, whatever posts are needed to create. [00:26:00] Fundations the kinds of bolts and fasteners the do fastened together, concrete, high rise, cranes, and equipment et cetera. Assemblies made of steel to reinforce the whole thing and to make sure the structure stands properly. It's not so much of these are all standardized, but the type of technology, every technology I thought is made with pieces and parts, and they tend to come from the same toolbox used in different ways. They may be in Kuala, lumper used in Seattle's slightly different ways, but the whole idea was the same. So it's technology then cease to be a mystery. It was matter of combining or putting together things from a Lego sets in M where [00:27:00] I grew up in the UK. We'd call them mechano sets. What are they called here? Erector sets or, well, I mean, Legos are, or, but like, I mean, there's, there's metal ones, the metal ones. I think the metal ones are erector sets. There's also like the wood ones that are tinker toys. Anyway, I like Legos, like, like I'm kinda like, okay. Okay. So, and that goes and yeah. And then you could get different sorts of Lego sets. You know, a few were working in high pressure, high temperature, it'd be different types of things of you're working in construction. There'd be a different set of Lego blocks for that. I don't want to say this is all trivial. It's not a matter of just throwing together these things. There's a very, very high art behind it, but it is not these things being born in somebody's attic. And in fact [00:28:00] of you were sitting here and what used to be Xerox park and Xerox graphy was invented by not by Mr. Xerox. Anyway, somewhere in here, but xerography was invented by someone who knew a lot about processes. A lot about paper, a lot about chemical processes, a lot about developing things. And shining light on paper and then using that maybe chemically at first and in modern Sarah Buffy. Electrostatically. Yeah. And so what could born was rarely reflecting light known component of marks on paper, thinking of a copier machine focused with a lot of lenses, [00:29:00] well-known onto something that was fairly new, which was called a Xerox drum. And that was electrostatically charged. And so you arranged that the light effected the electrostatic charges on the Xerox drum and those electrostatic as the drum revolved, it picked up particles of printing, ink like dust and where being differentially charged, and then imprinted that on paper and then fused it. All of those pieces were known. It's and it's not a matter of someone. I think mine's name is Carlson by the way. It's not a matter of what's somebody working in an attic that guy actually, who was more like that, but usually it's a small team of [00:30:00] people who are, who see a principal to do something to say, okay, you know, we want to copy something. Alright. But it could, you know cathode Ray tube and maybe it could project it on to that. And then there might be electrons sensitive or heat sensitive paper, and it could make her copies that way. But certainly in here Xerox itself for zero park, the idea was to say, let's use an electrostatic method combined with Potter and a lot of optics to ride on a Xerox drum and then fuse that under high heat into something that, where the particles stuck to paper. So all of those things were known and given. So I guess there's sorry. There's, there's so many different directions that I, that I want to go. One. [00:31:00] So sort of just like on the idea of modularity for technology. Yeah. It feels like there's both I guess it feels like there's almost like two kinds of modularity. One is the modularity where you, you take a slice in time and you sort of break the technology down into the different components. Yeah. And then there's almost like modularity through time that, that progresses over time where you have to combine sort of different ideas, but it doesn't necessarily, but like those ideas are not necessarily like contained in the technology or there's like precursor technology, like for example there's you have the, the moving assembly line. Right. Which was a technology that was you originally for like butchering meat. Yup. Right. And so you had, you had car manufacturing [00:32:00] and then you had like a moving assembly line. Yep. And then Henry Ford came along and sort of like fused those together. And that feels like a different kind of modularity from the modularity of. Of like looking at the components of technology, M I D do you think that they're actually the same thing? How do you, how do you think about those sort of two types of modularity? I'm not quite sure what the difference is. So, so the, the Henry T I guess like the, the, the, the, the Ford factory did not, doesn't contain a slaughter house. Right. It contains like some components from the slider house. And some components, I guess. Let's see, I think, like, [00:33:00] this is like, I, I was like, sort of like thinking through this, it feels like, like when, when you think of like the sort of like intellectual lineages of technology the, like a technology does not always contain the thing that inspires it, I guess is and so, so there's this kind of like evolution over time of like, almost like the intellectual lineage of a technology that is not necessarily the same as like the. Correct evolutions of the final components of that technology like for yeah. Does that, does that make sense? Like th th th or am I just like, am I seeing a difference where there, there is no difference which could be completely possible? Well, I'm not sure. I think maybe the latter, let me see if I can explain the way I see it, please stop me again. If it [00:34:00] doesn't fit with what you're talking about. I could fascinated by the whole subject of invention, you know, where to radically new technologies come from, not just tweaks on a technology. So we might have we might have a Pratt and Whitney jet engine in 1996, and then 10 years later have a different version of that. That's a good summer different components. That's fine. That's innovation, but it's not ready. Invention invention is something that's quite radical. You go from having air piston engines, which spit like standard car engines, driving propellers systems, 1930s, and you that gets replaced by a jet engine system working on a different principle. So the question really is so I've [00:35:00] begun to realize that what makes an invention is that it works in a different principle. So when Cox came along, the really primitive ones in the 12 hundreds, or a bit later than that are usually made up, they're made with their water clocks and are relying on this idea that a drip of water is fairly regular. If you set it up that way and about the time of Galileo. And in fact, Galileo himself realized that the pendulum had a particular regular beat. And if you could harness that regularity, that might turn into something that can measure time I clock. So, and that's a different principle that the principle is to use the idea that something on the end of a string or on the end of a piece of wire, give you a regular. [00:36:00] Frequency or regular beat. So the country realize that inventions themselves something was carrying out unnecessary purpose using a different principle before the second world war in Britain, they in the mid 1930s, people got worried about aircraft coming from the continent. They thought it could well be terminated and and bombers coming over to bomb England and the standard methods then to detect bombers over the horizon was to get people with incredibly good hearing, quite often blind people and attach to their ear as the enormous air trumpet affair that went from their ear to some big concrete collecting amplifier, some air trumpet that was maybe 50 or a hundred [00:37:00] feet across to listen to what was going on in the sky. And a few years later in the mid thirties, actually the began to look for something better and then. Made a discovery that fact that being well-known in physics by then, that if you bounced a very high frequency beam electromagnetic beam of say piece of metal, the metal would distort the beam. It would kind of echo and you'd get to stores and see if it was just to adore three miles away, made a word, wouldn't have that effect, but it was metal. It would. So that that's different principle. You're not listening. You're actually sending out a beam of something and then trying to detect the echo. And that is a different principle. And from that you get radar, how do you create such a beam? How'd [00:38:00] you switch it off very fast. Search can listen for an echo or electronically how do you direct the beam, et cetera, et cetera. How do you construct the whole thing? How can you get a very high energy beam because needed to be very high energy. These are all problems that had to be solved. So in my, what I began to see, she was the same pattern giving invention guidance began usually an outstanding problem. How do we detect enemy bombers that might come from the east, from the continent, if we need to how do we produce a lot of cars more efficiently and then finding some principle to do that, meaning the idea of using some phenomenon in the case of ear trumpets, it was acoustic phenomena, but these could be greatly amplified for somebody's ear. If you directed them into a big [00:39:00] concrete here, right? Different ways to put out high frequency radio beams and listen for an echo of that. Once you have the principle, then it turns out there's sort of sub problems go with that in the case of radar, how do you switch the beam off so that you can, things are traveling at the speed of light. I just switched it off fast enough that the echo isn't drowned out by the original signal. So then you're into another layer of solving another problem and an invention. Usually not. Well, I could talk about some other ways to look at it, but my wife looking at an invention is that nearly always is a strong social need. What do we do about COVID? The time that [00:40:00] says February, March 20, 20 oh, cur we can do a vaccine. Oh, okay. The vaccine might work on a different principle, maybe messenger RNA rather than the standard sort of vaccines. And so you find a different principle, but that brings even getting that to work brings its own sub problems. And then if with a bit of luck and hard work, usually over several years or months, you solved the sub problems. You managed to put all that in material terms, not just conceptual ones, but make it into some physical thing that works and you have an invention. And so to double click on that, couldn't you argue that those, that the solution to those sub problems are also in themselves inventions. And so it's just like inventions all the way down. [00:41:00] No great point there. I haven't thought of that. Possibly the, if they need to use a new principal themselves, the sub solutions. Yeah. Then you'd have to invent how that might work. But very often they're standing by let me give you an example. I hope this isn't I don't want to be too sort of technical here, please go, go, go, go rotate. Here we go then. So it's 1972 here in Xerox park where I'm sitting and the engineer, Gary Starkweather is his name, brilliant engineer and trained in lasers and trend and optics PhD and master's degrees, really smart guy. And he's trying to [00:42:00] figure out how to how to print. If you have an image in a computer, say a photograph, how do you print that now at that time? In fact, I can remember that time there. There are things called line printers and they're like huge typewriter systems. There is one central computer you put in your job, the outputs it was figured out on the computer and then central line printer, which is like a big industrial typewriter. And then it clanked away on paper and somebody tore off the paper and handed it to through a window. Gary, Starkweather wondered how could you print texts? But more than that images where you weren't using a typewriter, it's very hard to his typewriters and very slow if you wanted to images. So he [00:43:00] cooked up a principle, he went through several principles, but the one that he finished up using was the idea that you could take the information from the computer screens, a photograph you could use computer processors to send that to a laser. The lasers beam would be incredibly, highly focused. And he realized that if he could use a laser beam to the jargon is to paint the image onto the Xerox drum. Then so that it electrically charged the Xerox drum, right then particles would stick to the Xerox, strung the charge places, and the rest would be zero graphy, like a copier machine. He was working in Xerox park. [00:44:00] This was not a huge leap of the imagination, but there were two men's sub-problems in as well. We want to mention, if you look at it there's an enormous two huge problems if you wanted. So you were trying to get these black dots to write on a zero extremity to paint them on a zero Ekstrom. I hope this is an obscure. No, this is great. And I'll, I'll, I'll include some like pictures and this is great. All right. So you suppose I'm writing or painting a photograph from the computer through a processor, send to a laser. The laser has to be able to switch on and off fast. If it's going to write this on a Xerox Trump, and if you work out commercially how fast it would have to operate. Starkweather came to the conclusion. He'd have to be able to switch his [00:45:00] Lezzer on and off black or white 50 million times a second. Okay. So 50 megahertz, but nobody had thought of modulating or doing that sort of switching at that speed. So he had to solve that. That's a major problem. He solved it by circuitry. He got some sort of pizza electric device that's kind of don't ask, but he got a electronic device that could switch on and off. And then he could send signals to modulator for that to modulator, to switch on and off the laser and make a black or white as needed. And so that was number one. Now that kind of, that in your terms acquired an invention, he had to think of a new principle to solve that problem. So how do you, how do you write images on a computer? Sorry, on [00:46:00] how do you write it? How do you write computer images? Print that onto paper. That's required a new principal switching on a laser and. 50 million times the second required a new principal or acquire a new principal. So those are two inventions. There's a third one and another sub problem. The device, by the way, he got to do this was as big as one of these rooms in 1972. If I have my if I have the numbers, right a decent laser would cost you about $50,000 and you could have bought a house for that in 1978 here. And it would be the size, not of a house, but of a pretty big lab, but not something inside a tiny machine, but an enormous apparatus. And so how do you take [00:47:00] a laser on the end of some huge apparatus that you're switching on and off the 15 million times a second and scan it back and forth. And because there's huge inertia, it's an enormous thing. And believe it or not, he, he solved that. Not with smoke, but with mirrors. So he actually, instead of moving the laser beam, He arranged for a series of mirrors under evolving a piece of apparatus, like actuate the mirrors. Yeah. All he had to do was 0.1 beam at the mirror, switch it on and off very quickly for the image. And then the mirror would direct it kind of like a lighthouse beam right across the page. And then the next [00:48:00] face of the mirror exactly little mirror would come along and do the next line. So how do you do that? Well, that was easier. But then he discovered that the different facets on this mirror you'd have to, they'd have to line up to some extraordinarily high precision that you could not manufacture them to. So that's another sub problem. So to solve that he used ope optics if there was so here's one facet of mirror here is the beam. So directs the beam right across the page, switching it off and on as need be. Then the next facet of the mirror comes round switches. The same beam that you want to line up extraordinary. Precisely. Couldn't do it manufactured. [00:49:00] In manufacturing technology. But you could do it with optics. It just said, okay, if there's a slight discrepancy, we will correct that. He did agree and optics. He really knew what he was doing with optics in the lab. So using different lenses, different condensing lenses, whatever lenses do he solved that problem. So it's took two or three years, and it's interesting to look at the lab notebooks that he made. But for me let me see if I can summarize this. There is no such thing as Gary Starkweather scratching his head saying, wouldn't it be lovely to wouldn't it be lovely to be able to print images off the computer and not have to use a big typewriter. And and so he sits in his attic, a star of some self for three months comes up with the solution, not at all. What he did was he envisaged a [00:50:00] different principle. We're writing the image, using a highly focused laser beam onto the Xerox drum. The rest then is just using a copier machine fair. But to do that, you have to switch on and off the laser beam problem. So that's at a lower level to invent a wedge to that. And he also had to invent a principle for scanning this beam across the Xerox strung, maybe whatever it would be 50 times a second, or maybe a hundred times the second without moving the entire apparatus. And the principally came up for that was mirrors. Yeah. And so, and then I could go down to another level, you have to align your mirrors. And so, so what I discovered and see if I can put this in a nutshell [00:51:00] invention, isn't a sort of doing something supremely creative in your mind. It finishes up that way. It might be very creative, but all inventions are basically as problem-solving. Yeah. So to do something more mundane imagine I live here in Palo Alto let's say I work in the financial district in San Francisco and let's say my car's in the shop getting repaired. How am I going to get to work? And or how am I going to get my work done tomorrow? I have no car. The level of principle is to say, okay, I can see an overall concept to do it with. So I might say, all right, if I can get to Caltrain, if I can get to the station I'll go in on the train, but hang on. How do I get to the station? So that's a sub problem. [00:52:00] Maybe I can get my daughter or my wife or her husband, whatever it is to, to drive me. Then the other end, I can get an Uber or I could get a a colleague to pick me up, but then I'd have to get up an hour earlier, or maybe I'll just sit at home and work from home, which is more of the solution we would do these days. But how will that work? Because I et cetera. So invention is not much different from that. In fact, that's the heart of invention. If we worked out that problem of getting worked when your car is gone nobody would stand up and say, this was brilliant yet you've gone through exactly the same process as the guy who invented the polymerase chain reaction. Again, I can't recall his name. Getting older. I can't [00:53:00] eat there, but anyway so what's really important in invention. I think this goes to your mission. If I understand it, rightly is the people who have produced inventions are people who are enormously familiar with what I would call functionalities. Yeah. How do you align beams using optical systems? How do you switch on and off lasers fast? And so the people who are fluent at invention are always people who know huge amounts about those functionalities. I'm trained as an electrical engineer. You're, what's it I'm trained as a mechanical engineer robotics. Oh yeah. Brilliant. So what's really important [00:54:00] in engineering, at least what they teach you apart from all that mathematics is to know certain functionalities. So you could use capacitors and inductors to create, and also electronic oscillations or regular waves. You can. Straighten out varying voltage by using induction in the system, you can store energy and use that in capacitors. You, you can actually change a beam using magnets. And so there's hundreds of such things. You can amplify things you can use using feedback as well to stabilize things. So there are many functionalities and learning engineering is a bit like becoming fluent in this set of functionalities, not learning anything that's semi [00:55:00] creative. What might that be? Yes. Paint learning to do plumbing. Yep. Learning to work as a plumber. Good. A true engineer. So it is a matter of becoming fluent. You want to connect pipes and plumbing. You want to loosen pipes. You want to unclog things you want to reduce. The piping systems or pumping system, you want to add a pump you want, so there's many different things you you're dealing with. Flows of liquids, usually and piping systems and pumping systems and filtration systems. So after maybe three to four years or whatever, it would be a for rail apprentice ship in this, not only can you do it, but you can do it unthinkingly, you know, the exact gauges, you know, the pieces, you know, the parts, you know where to get the parts, you know how to set them up and you look at [00:56:00] some problem and say, oh, okay. The real problem here is that whatever, the piping diameter here is wrong, I'm going to replace it with something a bit larger. So Lincoln's whatever. And here's how I do that. So, you know, being good at invention is not different people. Like Starkweather, Starkweather new, I think is still alive. Knows all about mirrors, but optical systems above all, he knew an awful lot about lasers. He knew a lot about electronics. He was fluent in all those. So if we don't, if we're not fluent ourselves, we stand back and say, wow, how did he do that? But it's a bit like saying, you know, you write a poem and French, let's say I don't speak French. French and support them and it worked, how did he [00:57:00] do that? But if I spoke French, I might, so, okay. Yeah, but I can see, so this actually touches on sort of like an extension of your framework that I wanted to actually run by you, which is what I would describe what you were just describing as talking about almost like the, the affordances and constraints of different pieces of technology and people who invent things being just very like intimately familiar with the, the affordances and constraints of different technologies, different systems. And so the, the question I have that I think is like an open question is whether there is a way of sort of describing or encoding these affordances and constraints [00:58:00] in a way that makes creating these inventions easier. So like in the sense that very often what you see is like someone who knows a lot about. One like the, the affordances in one area, right. When discipline and they sort of like come over to some other discipline and they're like, wait a minute, like, there's this analogy here. And and so they're like, oh, you have this, this constraint over here. Like, there's, there's like a sub problem. Right. And it's like, I know from the, the affordances of the things that I'm, I'm really familiar with, how to actually solve the sub problem. And so like, through that framework, like this framework of like modularity and constraints and affordances, like, is it possible to actually make the process easier or like less serendipitous? Yeah. In, in a couple of ways. One is that I [00:59:00] think quite often you see a pattern where some principle is borrowed from a neighboring discipline. So Henry you were saying that Henry Ford took the idea of a conveyor belt from the meat industry. Right. And and by analogy use the same principle with manufacturing cars. But to get that to work in the car industry, the limitations are different cars are a lot heavier, so you could have a whole side of beef and it's probably 300 pounds or whatever. It would be for a side of beef, but for the car, it could be at 10 and a half. So you have to think of different ways. Yeah. And in the meat industry to do conveyor belts, there's two different ways. You can have a belt standard, rubber thing or whatever it would be just moving along at a certain speed, or you [01:00:00] can have the carcass suspended from an over hanging belts working with a chain system and the carcass is cut in half or whatever and suspended. And you could be working on it pretty much vertically above you both. It was that second system that tended to get used cars as, so things don't translate principles translate from one area to another, and that's a very important mechanism. And so if you wanted to enhance innovation I think the thing would be to set up some institution or some way of looking at things, whereas. They're well-known principles for doing this in area in industry X, how would I do something equivalent in a different industry? So for [01:01:00] example blockchain is basically let's say it's a way of validating transactions that are made privately between two parties without using an intermediary, like a bank. And you could say, well, here's how this works with a Bitcoin trading or something. And somebody could come along and say, well, okay, I want to validate art sales using maybe some similar principle. And I don't want to have to go to some central authority and record there. So maybe I can use blockchain to do fine art sales, in fact, that's happening. So basically you see an enormous amount of analogous principle transfer of principles from [01:02:00] one field to another. And it's we tend to talk about inventions being adopted. At least we do an economic. So you could say the, the arts trading system adopts block chain, but it's not quite that it's something more subtle. You can get a new principal or new, fairly general technology comes out, say like blockchain and then different different industries or different sets of activities in conjure that they don't adopt it then countries. Oh, blockchain. Okay. No, I'm saying the medical insurance business let's say so I can record transactions this way and I don't have to involve a room or, and I particular, I don't have to go through banking systems and I can do it this way and then [01:03:00] inform insurance companies. And so they're encountering and wondering how they can use this new principle, but when they do, they're not just taking it off the shelf. Yeah. They're actually incorporating that into what they do. So here's an example. A GPS comes along quite a while ago. I'm sure. 1970s in principle using atomic clocks. Satellites or whatever. Basically it's a way of recording exactly time and using multiple satellites to know exactly where they are at the same time and allowing for tiny effects of even relativity. You figure out you can triangulate and figure out where something is precisely. Yeah, no, that just exists. But by the [01:04:00] time, so different industries say like Oceanwide Frazier shipping and you conjure it exists. Okay. And by the time they encounter it, they're not just saying I'm going to have a little GPS system in front of, in the Bennett code it's actually built in. And it becomes part of a whole navigational system. Yeah. So what happens in things like that is that some invention or some new possibility becomes a component in what's already done just as in banking around the 1970s, being able to. Process customer names, client names, and monetary months you could process that fast with electronic computers and there most days they were [01:05:00] called and data processing units that we don't think of it that way now, but you could process that. And then that changed the banking industry significantly. So by 1973, there was a, the market and futures in Chicago where you were dealing with say pork belly futures and things like that because computation coming home. Interesting. So the pattern there's always an industry exists using conventional ideas, a new set of technologies becomes available. But the industry doesn't quite adopted it, encounters it and combines it with many of its own operations. So banking has been recording people in ledgers and with machinery, it has been facilitating transactions, [01:06:00] maybe on paper unconscious computation. Now can do that. Yeah. Automatically using computation. So some hybrid thing is born out of banking and computation that goes into the Lego set and actually sort of related to that, something I was wondering is, do you think of social technology as technology, do you think that follows the same patterns? What do you mean social technology? I, I think like a very obvious one would be like for example, like mortgages, right? Like mortgages are like mortgages had to be invented. And they allow people to do things that they couldn't do before. But it's not technology in the sense of, of built. Yeah, exactly. It's not like, there's no, like you can create a mortgage with like you and me and a piece of [01:07:00] paper. Right. But it's, it's something that exists between us or like democracy. Right. And so, so I feel like there's, there's like one end, like, like sort of like things like new legal structures or new financial instruments that feel very much like technology and on the other end, there's like. Great. Just like new, like sort of like vague, like new social norms and like, yeah. Great question. And it's something I did have to think about. So things like labor unions nation states nature. Yes, exactly. These thing democracy itself, and in fact, communism, all kinds of things get created. Don't look like technologies. They don't have they don't have the same feel as physical technologies. They're not humming away in some room or other. They're not under the hood of your [01:08:00] car. And things like insurance for widows and pension systems. There's many of those social technologies even things like Facebook platforms for exchanging information. Sometimes very occasionally things like that are created by people sitting down scratching heads. That must have happened to some degree in the 1930s when Roosevelt said there should be a social security system. But that wasn't invented from scratch either. So what tends to come about in this case, just to get at the nitty gritty here, what tends to happen is that some arrangement happens. Somebody maybe could have been a feudal Lord says, okay, you're my trusted gamekeeper. You can have a [01:09:00] rather nice a single house on my estate. You haven't got the money to purchase and build it. I will lend you the money and you can repay me as time goes by. And in fact, the idea that so many of those things have French names, more, more cash. You know, it's actually, I think the act of something dying as far as my, my school friends would go, I don't know. But a lot of those things came about in the middle ages. There are other things like What happens when somebody dies the yeah. Probate again, these are all things that would go back for centuries and centuries. I believe the way they come about is not by deliberate invention. They come about by it being natural in [01:10:00] to something. And then that natural thing is used again. And again, it gets a name and then somebody comes along and says, let's institutionalize this. So I remember reading somewhere about the middle ages. They it was some Guild of some traders and they didn't feel they were being treated fairly. I think this was in London. And so they decided to withhold their services. I don't know what they're supplying. It could have been, you know, courage, transport, and along the streets or something. And some of these people were called violets. We were, would not be valet again, very French, but so they withheld their services. Now that wouldn't be the first time. [01:11:00] It goes back to Egypt and engineered people withholding their services, but that becomes, gets into circulation as a meme or as some repeated thing. Yeah. And then somebody says, okay, we're going to form an organization. And our Gilda's going to take this on board as being a usable strategy and we'll even give it a name that came to be called going on, strike or striking. And so social invention kind of should take place just by it being the sensible thing to do. The grand Lord allows you. It gives you the money to build your own house. And then you compare that person back over many years [01:12:00] and and put that, put that loan to to its death and mortgage it. So the I think in this case, what happens in these social inventions is that sensible things to do gets a name, gets instituted, and then something's built around it. Well, one could also say that many inventions are also the sensible thing to do where like it's someone realizes like, oh, I can like use this material instead of that material. Or like some small tweak that then enables like a new set of capabilities. Well, I'm not, yeah. In that case, I wouldn't call it really an invention that the, the vast majority of innovations, like 99 point something, something, something 9% or tweaks and, you know, [01:13:00] w we'll replace this material. Well, why doesn't that count as an invention? If, if, if it's like a material, like it's a different, like, I guess why doesn't that also count as, as a new principal, it's like bringing a new principal to the thing. The word to find a principal is it's the principles, the idea of using some phenomenon. And so you could say there's a sliding scale if you insist. Up until about 1926 or 1930 aircraft were made of wooden lengths covered with canvas dope. The dope, giving you waterproofing and so on. And and then the different way of doing that came along when they discovered that with better engines, you could have heavier aircraft, so you could make the skeleton out of [01:14:00] metal, right? And then the cladding might be metal as well. And so you had modern metallic aircraft. There's no new principal there, but there is a new material and you could argue, well, the new materials, different principle, then you're just talking about linguistics. So, so, so you would not consider the, like the transition from cloth aircraft to metal aircraft to be an invention. No. Huh? Not got another, I mean, sure might be a big deal, but I don't see it as a major invention going from air piston Angeles to jet engines. That's a different principle entirely. And I, so I, I've a fairly high bar for different principles. But you're not using a different phenomenon. That's my that's, that's my criteria. And if you have a very primitive clock [01:15:00] in this 16, 20 or 16, Forties that uses a string and a bulb on the end of the string. And then you replace the string where the wire or piece of metal rigid. You're not really using a new phenomenon, but you are using different materials and much of the story of technology isn't inventions, it's these small, but very telling improvements and material. In fact jet engines, weren't very useful until you got combustion systems where you were putting in aircraft fuel. Yeah. Atomizing that and setting the whole thing and fire the early systems down. When you could better material, you could make it work. So there's a difference between a primitive technology and [01:16:00] then one that's built out of better components. So I would say something like this, the if you take what the car looks like in 1919 0 5, is it a very, is it a different thing than using horses? Yeah, because it's auto motive. There is an engine. It's built in. So it's from my money. It's using a different principle. What have you changed? What if you like took the horse and you put it inside the carriage? Like what have you built the carriage around the horse? Would that be an automotive? Well then like, like what if I had a horse on a treadmill and that treadmill was driving the wheels of the vehicle with the horse on it, then I think it would be it would be less of an invention. I don't know. I mean, you're basically say I find it very useful to say that if [01:17:00] that radar uses a different principle from people listening, you could say, well, I mean, people listening are listening for vibrations. So is radar, you know, but just at a electro magnetic vibrations, what's different for my money. It's not so much around the word principle. All technologies are built around phenomena that they're harvesting or harnessing to make use of. And if you use a different set of phenomena, In a different way, I would call it an invention. So if you go from a water wheel, which is using water and gravity to turn something, and you say I'm using the steam engine, I would regard that as you're still, you [01:18:00] could argue, well, aren't you use a phenomenon phenomenon of the first thing you're using the weight of water and gravity, and the fact that you can turn something. And then the second thing you are using the different principle of heating something and having it expand. And so I don't see, I would say those are different principles. And if you're saying, well, there's a different principle, I'd go back to, well, what phenomena are you using? So, yeah, I mean, if you wanted to be part of a philosophy department, you could probably question every damned thing because yeah. I'm actually not trying to, to challenge it from a semantic standpoint. I think it's just actually from like really understanding, like what's going on. I think there's actually like a, sort of a debate of like, whether [01:19:00] it's. Like, whether it's like a fractal thing or whether there are like, like multiple different processes going on as well. Maybe I'm just too simple, but let's start to look at invention. The state of the art was pathetic. It wasn't very good because all papers, well, all the versions of invention, I was reading, all of us had a step, then something massively creative happens and that wasn't very satisfactory. And then there was another set of ideas that were Darwinian. If you have something new, like the railway locomotive that must have come out of variations somehow happening spontaneously, and might've been sufficiently different to qualify as radically new inventions. It doesn't do it for me either because you know, 1930 you could have varied [01:20:00] radio circuits until you're blue in the face. You'd never get radar. Yeah. So what the technology is fundamentally is the use of some set of phenomena to carry out some purpose. The, there are multiple phenomena. So but I would say in this maybe slightly too loose speaking, that's the principal phenomenon you're using or the, the key phenomenon constitutes the concept or principle behind that technology. So if you have a sailing ship, you could argue, well, you know, it, displaces water it's built to be not have water intake. It's got a cargo space, but actually for sailing ships, the key principle is to use the motive, power of wind in clever ways to be able to propel a [01:21:00] ship. If you're using steam and take the sails down you're using, in my opinion, a different principle, a different phenomenon. You're not using the mode of power of wind. You're actually using the energy that's in the, some coal fuel or oil and clever ways and to move the ship. So I would see those as two different principles you could say, well, we also changed whatever the staring system or as does that make it an invention. It makes maybe that part of it, an invention, but overall The story I'm giving is that inventions come along when you see a different principle or a set of phenomena that you want to use for some given purpose and you managed to solve the problems to put that into reality. Yeah. I completely agree [01:22:00] with that. I think the, the thing that I'm interested in is like like to, to use is the fact that sort of, again, we go back to like that modular view then, you're you sort of have like many layers down you, the, the like tinkering or, or the, the innovations are so based on changing the phenomena that are being harnessed, but like much, like much farther down the hierarchy of, of the modularity. Like, like in, in S like sailing ships you like introduce like Latin sales, right? Like, and it's like, you change the, into, like, you've invented a new sale system. You haven't invented a new kind of ship. Right. So you've changed the phenomenon, but yeah, I think the distinction you're making is totally on target. When you introduced Latina sales, you have invented a new. Cell system. Right. [01:23:00] But you haven't invented a new principle of a sailing ship. It's still a sailing ship. So I think you're getting into details that are worth getting into at the time I'm writing this. I I was trying to distinguish, I'm not trying to be defensive here. I hope, but I was just, I'm not trying to be offensive in any way. Wait for me to, I haven't thought about this for 10 years or more the I think what was important in yeah, let's just in case this whole thing that said innovation happens. Nobody's quite sure what innovation is. But we have a vague idea. It's new stuff that works better. Yes. In the book I wrote I make a distinction between radically new ways to do something. So it's radically new to propel the ship by a [01:24:00] steam engine. Even if you're using paddles versus by wind flow. Okay. However, not everything's right. Radically new. And if you look at any technology, be it computers or cars the insides, the actual car Bratcher system in the 1960s would have been like a perfume spray or a spraying gasoline and atomizing it, and then setting that in light. Now we might have as some sort of turbo injections system, that's, that's working, maybe not with a very different principle, but working much more efficiently. So you might have an invention or a technology that the insights are changing enormously. But the, the, I, the overall idea of that [01:25:00] technology hasn't changed much. So the radar would be perfect examples. So be the computer, the computers kept changing its inner circuitry, the materials it's using, and those inner circuits have gotten an awful lot faster. And so on. Now that you could take a circuit out and you could say, well, sometime around 1960, the circuit cease to be. Certainly it seems to be trialed, vacuum tubes and became transistors monitored on boards. But then sometime in that deck, could it became integrated circuits, was the integrated circuit and invention yeah. At the circuit level, at the computer level better component. Yeah. So hope that, that absolutely has I guess as, as actually a sort of a closing question is there, is there like work that you [01:26:00] hope people will sort of like do, based on what you've written like, is, is there, is there sort of like a line of work that you want people to be, to be doing, to like take the sort of the framework that you've laid out and run with it? Cause I, I, I guess I feel like there's like, there's so much more to do. Yeah. And so it's like, do you have a, do you have a sense of like what that program would look like? Like what questions, what questions are still unanswered in your mind? I think are really interesting. I think that's a wonderful question off the red cord. I'm really glad you're here because. It's it's like visiting where you grew up. I am. I'm the ghost of, of books. Oh, I don't know. I mean, it's funny. I was injured. This is just, yeah. I was interviewed a month or two ago on [01:27:00] this subject. I can send you a link if you want, please. Yeah. I listened to tons of podcasts, so, yeah. Anyway, but I went back and read the book. You're like, wow, I'm really smart. Well, it had that effect. And then I thought, well, God, you know, it could have been a lot better written. It had all sorts of different things. And, and the year this was produced and free press and New York actually Simon Schuster, they put it up for a Pulitzer prize. That really surprised me because I didn't set out to write something. Well-written I just thought of keep clarifying the thing. And it went to come back to your question. Yeah. My reflection is this the book I wrote the purpose of my book was to actually look inside technologies. So [01:28:00] when you open them up, meaning have you look at the inside components, how those work and how ultimately the parts of a technology are always using some, none, you know, we can ignite gasoline and a, in a cylinder, in a car, and that will expand rapidly and produce force. So there's all kinds of phenomena. These were things I wanted to stay at. And yeah, the book there's that book has had a funny effect. It has a very large number of followers, meaning people have read that and I think of a field for technology and they're grateful that somebody came along and gave them a way to look at technology. Yeah. But having, let me just say it carefully that I've done other things in research [01:29:00] that have had far more widespread notice than this. And I think it's something tech the study of technology, as I was saying earlier on is a bit of a backwater in academic studies. Yeah. It's eclipsed. Is that the word dazzled by science it's? So I think that it's very hard to we, if something wonderful happens, we put men on the moon, we put people on the moon. We, we come up with artificial intelligence. Some are vaguely. That's supposed to be done by scientists. It's not, it's done by engineers who are very often highly conversant, both with science and mathematics, but as a matter of prestige, then a [01:30:00] lot of what should have been theories of technologies, where they come from, it's sort of gone into theories of science and I would simply point out no technology, no science when you can't do much science without telescopes crystallography x-rays systems microscopes. So yeah, it's all. Yeah. So you need all of these technologies to give you modern science. Without those instruments, we'd still have technology. We'd still have science, but be at the level of the Greeks, which would be a lot of conceptual ideas about how the world works. Anyway, to my surprise, this book came out nature of technology, 2009, I think. Yeah, August. So it's 12 years old [01:31:00] and there was a lot of fuss about it at the time. And then it was kind of like a submarine that appeared and then die. Everything was quiet. Got there, period. It has to be a renewed interest in it this last year. So I have no idea why I suspect I'm trying to keep my own ego out of this. Not very well, but I suspect that a lot of it. Yeah. I think that to start with, and to finish with it has not been fully accepted. That technology is really a worthwhile entity to be thought about in its own. Right. It's more that, oh yeah. Well, we have technology. What more do we want? Well, we can talk about trading [01:32:00] systems. Well, isn't that isn't that economics, well, we could talk about, so things like financial derivatives that I see as technologies that was part of finance. So we tend to subsume these into other fields. There's maybe we can talk about high rise, steel and concrete buildings a hundred or more years ago. Well, isn't that architecture and so on, but actually there hasn't been sufficient attention paid to technology in its own. Right. And so there's been a lot of attention paid to this book, but not so much. I thought it might help give some impetus to get getting the field of study for technology and it didn't not yet. And now I cherish a feeling that after I'm gone this thing, that'll be discovered [01:33:00] mentally F this is very fancy comparison. But it shouldn't have said men to left. I'm thinking of gosh, Mendel, Gregor Mendel, Greg Armento. Yes. Sorry. Okay. Mendel had a theory of genetics and by the time that could properly develop too, you know, it was too late for him. So I don't know, it's a bit of a mystery to me. But I do think I want to stress one or two things that we didn't mention here. And we are moving into we, or leaving as system in the economy said 50 years ago, most things in the economy were produced in factories and I'm thinking of general foods or even general motors. We didn't put this to the factory system. We'd manufacturing, [01:34:00] then we'd outputs. And some of the outputs might be rolled. Steel would be inputs to other factories systems. And then we got a service economy, but now we're moving into an economy that has an awful lot of autonomous functionalities. You use the word affordances and I think that's right. And I hadn't thought of that was a good word. Nationalities is something that does something for you. So being able to navigate your car with a GPS, that's a functionality. And a lot of those, everything, not everything, but we're seeing the economy become more autonomous. So everything from trading systems pretty soon air traffic control systems, autonomous, no human beings involved, supposed to be a lot safer. Similarly, driverless convoys of trucks, trucks, [01:35:00] et cetera. I think if you want to understand those properly, you need to very good understanding of technology and how autonomous systems can work, work, and where technology has come from. I would just simply say that technology is a major part of what we've achieved as humans are, and we need to understand it just as we 300 years ago, we didn't really look very closely at the inside of creatures, animals, or species. By the 17 hundreds, we were well underway doing that. We learned a huge amount. I think we need to do the same in technology. I think technology is very much part of what makes us human. I do think that many technologies let's say kind of social [01:36:00] tech platform technologies think of Facebook or Google for that matter. Another platform technologies, Uber. These are technologies where you can dial into the technology and use it services maybe as a passenger in Uber, or maybe as someone recording information and Facebook, many technologies technology is resisted in many ways because it can produce really nasty things war the automation of war et cetera. And, but I would like to point out that many social technologies of platform technologies like Facebook are neutral. It's what you put in there. Like pipelines what'd you send along the papers differently. That can be benign. It can be [01:37:00] wonderful. You know, I'm a, I'm a great consumer. A late night detective. So that's all coming on the platform of Netflix. And, but you can equally use those pipelines to send a really negative stuff along. So I think we need to be careful. We, we can't just say technology is wonderful. Technology is bad for the most part. Technologies are in some intermediary position between us as humans and the earth, which produces phenomena. It produces metals. If we understand those phenomena produces optics that produces electrical phenomena, magnetic phenomena, and we've learned to harness all those. So they're in the middle and what those are used for is something [01:38:00] not very well. It could be nefarious or could be wonderfully beneficial. I would argue that I used to teach classes here in economic development. And so I had to face the problem. And the first lecture is economic development making an economy say, and Syria or Jordan, is that. Good or bad, and you could argue many ways. But one thing I think is unarguable is that technology has allowed us to live healthier and longer lives. And so I'd come back to the demographic element, morbidity slower, meaning by and large, we're much healthier to give an age and we, and our children are living longer. And I know that if I went back a century ago and looked at my [01:39:00] family, my grandmother died over a hundred years ago of something that would be perfectly treatable. Yeah. Pernicious, anemia, and et cetera. So if the least we can, I've mixed feelings about technology as a, the humanist part of the more practical engineer would say, well, you know, maybe you can, maybe you can criticize technology, but you might be doing it with a swimming pool in your backyard with a Volvo in the driveway with your smart and your hip pockets and, and with your children all alive and a hundred years ago, none of those would have been the case. Is that so [01:40:00] bad? Well, yeah, but we have to be careful. I want to mention one thing if I may, and yeah, this is your platform. The one thing is that yeah, one thing we really, one thing I want to mention is that coming back to the idea of the technology evolves it evolves in the following way that new technologies by and large well, new technologies are constructed from existing technologies. You can't really make a new technology unless you have the components to put it together. So jet engine is made out of compressors and turbines and combustion systems. Those all already existed. And then a new technology becomes available for, to be a component in some other systems. So jet engines are available. That's power [01:41:00] jet aircraft. Yeah. Et cetera. And new amplifier circuits around 1912 using trialed vacuum tubes become available to power radio receivers and res radio transmitters. And it got a broadcasting system which so building blocks each new technologies and principle, a building block for use and further new technologies. So it's as if your Lego set every so often gives you a new block that has its own interesting possibilities. Sometimes that's a, one-off the Solvay process is I think it purchases, what is it sodium? Carbonator this? I thought this isn't the Solvay process. Isn't that for aluminum, then it doesn't look it up or I can not to worry. [01:42:00] Well, this is a process, so, well you can take the Harbor process or any yeah. So the, the Solvay process, oh, it is for, for obtaining setting. Right? Okay. So the. Yeah, Solvay process produces sodium carbonate. But it doesn't mean it's solved a process. Isn't something that is central to a thousand. Other technologies probably is useful in a few hundred other ones. Whereas something like the transistor comes along or even the laser around 1916, nobody, I remember seeing a headline. This was a solution in search of a problem. The laser now it's used, I wouldn't say in everything, but in many, many, many uses. I think so. So what I want to point out is there is a mechanism of [01:43:00] evolution technology that if you take the whole collection of technologies at any one time period, some of those existing technologies in combination are making novel technologies and many of the novel technologies go on to become building blocks for yet further technologies. So if you look at the entire and collection of technologies, it is throwing off new technologies, which may be components and yet further new technologies. The technical word for that is to say either itself creating or the fancy word is Alto ploy, attic, POI ETI. So it's auto poetic. That's the word? I think it came from Umberto Maturana [01:44:00] and The cheese, I'm not sure on anyway to be scanned down. Sorry. All right. So just like, yeah. Sorry. Maturana and Francisco Varela. They at Chilay and philosophers actually of, of technology as well as everything else. So systems' flaws first, but anyway, technology itself producing herself, creating, and but the mechanism isn't Darwinian the mechanism. There's no flood of Darwinian improvement around initial primitive 1825 railway locomotives are still not that difference from ones a hundred years later. Using steam. And there's a certain amount of Darwinian variation and improvement, but [01:45:00] mostly technologies evolve. Bye now of all technologies becoming components and yet other technologies. So the steam engine becomes a component in the railroad locomotive. Yeah. We've stopped in Darlington express around 1820 was a trend of cars. Just train, meaning something that flows out behind you. Yeah. Drawn by horse. And when the railway locomotive comes along, that's a new technology. New technology is adopted in other ways. So you get a whole railroad system and so on. So you go from steam engine to steam, locomotives, to steam trains, to railroad systems. And in that sense technology yeah. At any level, then the technology becomes a component in further technologies. I call that [01:46:00] sort of evolution, competent tutorial evolution. If you can bind things in your Lego set to make new things that are repeated often and encapsulated, then you have a new component. Yeah, for further accommodation, would that be comparable to Darwinian evolution? If, instead of looking at things at the level of species, we look at things sort of at the level of you know, like, like genes or body parts or proteins yeah. That sort of evolution does occur biologically in a fairly primitive bacterial systems are archaic. There's something called horizontal gene transfer. So you're taking genes from one, whatever they are bacterium, and those are getting transferred horizontally to other the actual standard cell for [01:47:00] many creatures evolved out of other cells that become absorbed into our model. That's why we have like mitochondria. Yeah. So this does happen, but but once we get up to like a draft F evolving something, doesn't affect the thing new. Great. If you could take a traps neck and put that onto a horse. Yeah. Whatever, but that's not the way it works. So yeah. When you think about it this time, Evolution by combination is all over the place in biology, but it's quite specific. I think Darwin got it roughly, right by just saying variation and selection. He didn't think in terms of combinations. So to come back to your question, I should let you go here to come back to your question. What what theories could be built out of this and what use could be made out of this thinking as [01:48:00] so many, see if I can give you a, a decent answer, serious answer to that. I think that the book I wrote in 2009, the nature of technology lays out a framework for asking what is the technology? How does technology evolve, how our technology is put together? How does invention work? How does more standard engineering, just pure innovation work? How does tech, how to technologies create an economy? It's it looks at all those questions. I think, I think that, so it's giving a framework for thinking about technology and how to operate. So in our world, yes. I think all of those ideas could be refined or could be challenged. Could be improved upon just so this book, I think, is a first step in trying to set up a theory of [01:49:00] evolution for technology and as such I, I haven't seen that much academically coming up, building on this quite surprising. The other thing I would point out is that this nature of technology dolls positive different method of evolution than Darwin's. So you're not looking at, in Darwin's evolution. Some snail species might evolve say, and bridges of cliffs in Hawaii by being in a slightly different wetter environment and small variations genetically, and those over many, many generations are selected to fit that new environment a bit better until that sort of snail has evolved that to the degree, continue to breed with the old ones and you get a new species. So [01:50:00] Darwin's things, variation and accumulation of small differences. This book puts together a version of evolution that says there's a mechanism. Well, a novel things are created via a combination of the old and become available themselves as building blocks for further combination. Yeah. I am amazed and surprised that I haven't seen that idea taken up. The theory that Darren came up with in 1838, but finally published 20 years later, 1859 got taken up almost immediately argued about bitterly centered resistance celebrated everything you can think of, but there's a different type of evolution competent. I called it competent tutorial evolution. It it hasn't been talked [01:51:00] about in any detail. I'm sure if you go back, you'd find that some people been vaguely aware, but nobody really has written about it in detail. So I think that that would be worth taking up and looking at, in some detail, I'd call it a second evolutionary mechanism. That's certainly not being modest, but I do think that it's different evolution, a different form of evolution. Once you understand it, you see it all over the place. You see new combinations, even in language, certainly in my lifetime the word Munich, he used to be labeled for city in Germany. And now it's kind of label for holes for a type of for a piece of unsavory what's done by unsavory [01:52:00] authorities. So if you try to be accommodating to whoever runs Bella ruse, you could be accused of pulling a Munich and similarly hyphen gate, which came from water gate, you know, truck travel game. That's now a combination and rarely gets rid of an awful lot of components. It's usually official government malfeasance and some minute area of misdoing. And that is but if we want to avoid lengthy explanations, we compress that into a module, something hyphen gate. So concept. Or often encapsulated and then used as components in language, certainly the case in mathematics, [01:53:00] et cetera. It's, it's certainly certainly the case in engineering it's case in science as well. And all those systems build up by having new ideas, concepts, or objects that are created in some competent tutorial where combining way from previous ones and then becoming things in their own ride for further combination that's worth looking at yes. And hopefully this will spawn many arguments about it. Brian, Arthur. Thanks for being part of idea machines. [01:54:00] 

In this Conversation, Jason Crawford and I talk about starting a nonprofit organization, changing conceptions of progress, why 26 years after WWII may have been what happened in 1971, and more. Jason is the proprietor of Roots of Progress a blog and educational hub that has recently become a full-fledged nonprofit devoted to the philosophy of progress. Jason’s a returning guest to the podcast — we first spoke in 2019 relatively soon after he went full time on the project . I thought it would be interesting to do an update now that roots of progress is entering a new stage of its evolution.   Links Roots of Progress Nonprofit announcement Transcript So what was the impetus to switch from sort of being an independent researcher to like actually starting a nonprofit I'm really interested in. Yeah. The basic thing was understanding or getting a sense of the level of support that was actually out there for what I was doing. In brief people wanted to give me money and and one, the best way to receive and manage funds is to have a national nonprofit organization. And I realized there was actually enough support to support more than just myself, which had been doing, you know, as an independent researcher for a year or two. But there was actually enough to have some help around me to basically just make me more effective and, and further the mission. So I've already been able to hire research [00:02:00] assistants. Very soon I'm going to be putting out a a wanted ad for a chief of staff or you know, sort of an everything assistant to help with all sorts of operations and project management and things. And so having these folks around me is going to just help me do a lot more and it's going to let me sort of delegate everything that I can possibly delegate and focus on the things that only I can do, which is mostly research and writing. Nice and sort of, it seems like it would be possible to take money and hire people and do all that without forming a nonprofit. So what what's sort of like in your mind that the thing that makes it worth it. Well, for one thing, it's a lot easier to receive money when you have a, an organization that is designated as a 5 0 1 C three tax status in the United States, that is a status that makes deductions that makes donations tax deductible. Whereas other donations to other types of nonprofits are not I had had issues in the past. One organization would want to [00:03:00] give me a grant as an independent researcher, but they didn't want to give it to an individual. They wanted it to go through a 5 0 1 C3. So then I had to get a new. Organization to sort of like receive the donation for me and then turn around and re grant it to me. And that was just, you know, complicated overhead. Some organizations didn't want to do that all the time. So it was, it was just much simpler to keep doing this if I had my own organization. And do you have sort of a broad vision for the organization? Absolutely. Yes. And it, I mean, it is essentially the same as the vision for my work, which I recently articulated in an essay on richer progress.org. We need a new philosophy of progress for the 21st century and establishing such a philosophy is, is my personal mission. And is the mission. Of the organization to just very briefly frame this in the I, the 19th century had a very sort of strong and positive, you know, pro progress vision of, of what progress was and what it could do for humanity and in the [00:04:00] 20th century. That optimism faded into skepticism and fear and distrust. And I think there are ways in which the 19th century philosophy of progress was perhaps naively optimistic. I don't think we should go back to that at all, but I think we need a, we need to rescue the idea of progress itself. Which the 20th century sort of fell out of love with, and we need to find ways to acknowledge and address the very real problems and risks of progress while not losing our fundamental optimism and confidence and will to, to move forward. We need to, we need to regain to recapture that idea of progress and that fundamental belief in our own agency so that we can go forward in the 21st century with progress. You know, while doing so in a way that is fundamentally safe and benefits all of humanity. And since you, since you mentioned philosophy, I'm really like, just, just ask you a very weird question. That's related to something that I've been thinking about. And [00:05:00] so like, in addition to the fact that I completely agree the philosophy. Progress needs to be updated, recreated. It feels like the same thing needs to be done with like the idea of classical liberalism that like it was created. Like, I think like, sort of both of these, these philosophies a are related and B were created in a world that is just has different assumptions than we have today. Have you like, thought about how the, those two, like those two sort of like philosophical updates. Yeah. So first off, just on that question of, of reinventing classical liberalism, I think you're right. Let me take this as an opportunity to plug a couple of publications that I think are exploring this concept. Yeah. So so the first I'll mention is palladium. I mentioned this because of the founding essay of palladium, which was written by Jonah Bennet as I think a good statement of the problem of, of why classical liberalism is [00:06:00] or, or I think he called it the liberal order, which has maybe a slightly different thing. But you know, the, the, the basic idea of You know, representative democracy is you know, or constitutional republics with, with sort of representative democracy you know, and, and basic ideas of of freedom of speech and other sort of human rights and individual rights. You know, all of that as being sort of basic world order you know, Jonah was saying that that is in question now and. There's essentially now. Okay. I'm going to, I'm going to frame this my own way. I don't know if this is exactly how gender would put it, but there's basically, there's, there's basically now a. A fight between the abolitionists and the reformists, right. Those who think that the, the, the, that liberal order is sort of like fundamentally corrupt. It needs to be burned to the ground and replaced versus those who think it's fundamentally sound, but may have problems and therefore needs reform. And so you know, I think Jonah is on the reform side and I'm on the reform side. I think, you know, the institutions of you know, Western institutions and the institutions of the enlightenment let's say are like [00:07:00] fundamentally sound and need reform. Yeah, rather than, rather than just being raised to the ground. This was also a theme towards the end of enlightenment now by Steven Pinker that you know, a lot of, a lot of why he wrote that book was to sort of counter the fundamental narrative decline ism. If you believe that the world is going to hell, then it makes sense to question the fundamental institutions that have brought us here. And it kind of makes sense to have a burn it all to the ground. Mentality. Right. And so those things go together. Whereas if you believe that you know, actually we've made a lot of progress over the last couple of hundred years. Then you say, Hey, these institutions are actually serving us very well. And again, if there are problems with them, let's sort of address those problems in a reformist type of approach, not an abolitionist type approach. So Jonah Bennett was one of the co-founders of palladium and that's an interesting magazine or I recommend checking out. Another publication that's addressing some of these concepts is I would say persuasion by Yasha Munk. So Yasha is was a part of the Atlantic as I recall. [00:08:00] And basically wanted to. Make a home for people who were maybe left leaning or you know, would call themselves liberals, but did not like the new sort of woke ideology that is arising on the left and wanted to carve out a space for for free speech and for I don't know, just a different a non-local liberalism, let's say. And so persuasion is a sub stack in a community. That's an interesting one. And then the third one that I'll mention is called symposium. And that is done by a friend of mine. Roger Sinskey who it himself has maybe a little bit more would consider himself kind of a more right-leaning or maybe. Just call himself more of an individualist or an independent or a, you know, something else. But I think he maybe appeals more to people who are a little more right-leaning, but he also wanted you know, something that I think a lot of people are, are both maybe both on the right and the left are wanting to break away both from woke ism and from Trumpism and find something that's neither of those things. And so we're seeing this interesting. Where people on the right and left are actually maybe [00:09:00] coming together to try to find a third alternative to where those two sides are going. So symposium is another publication where you know, people are sort of coming together to discuss, what is this idea of liberalism? What does it mean? I think Tristan ski said that he wanted some posting to be the kind of place where Steven Pinker and George will, could come together to discuss what liberalism means. And then, then he like literally had that as a, as a podcast episode. Like those two people. So anyway, recommend, recommend checking it out. And, and Rob is a very good writer. So palladium, persuasion and symposium. Those are the three that I recommend checking out to to explore this kind of idea of. Nice. Yeah. And I think it looks, I mean, I mean, I guess in my head it actually like hooks, like it's sort of like extremely coupled to, to progress. Cause I think a lot of the places where we, there's almost like this tension between ideas of classical liberalism, like property rights and things that we would like see as progress. Right. Cause it's like, okay, you want to build your [00:10:00] Your Hyperloop. Right. But then you need to build that Hyperloop through a lot of people's property. And there's like this fundamental tension there. And then. I look, I don't have a good answer for that, but like just sort of thinking about that, vis-a-vis, it's true. At the same time, I think it's a very good and healthy and important tension. I agree because if you, if you have the, if you, so, you know, I, I tend to think that the enlightenment was sort of. But there were at least two big ideas in the enlightenment, maybe more than two, but you know, one of them was sort of like reason science and the technological progress that hopefully that would lead to. But the other was sort of individualism and and, and, and, and Liberty you know concepts and I think what we saw in the 20th century when you have one of those without the other, it leads to to it to disaster. So in particular I mean the, the, the communists of you know, the Soviet union were were [00:11:00] enamored of some concept of progress that they had. It was a concept of progress. That was ultimately, they, they got the sort of the science and the industry part, but they didn't get the individualism and the Liberty part. And when you do that, what you end up with is a concept of progress. That's actually detached from what it ought to be founded on, which is, I mean, ultimately progress by. To me in progress for individual human lives and their happiness and thriving and flourishing. And when you, when you detach those things, you end up with a, an abstract concept of progress, somehow progress for society that ends up not being progress for any individual. And that, as I think we saw in the Soviet union and other places is a nightmare and it leads to totalitarianism and it leads to, I mean, in the case specifically the case of the Soviet union mass. And not to mention oppression. So one of the big lessons of you know, so going back to what I said, sort of towards the beginning that the 19th century philosophy of progress had, I think a bit of a naive optimism. And part of that, [00:12:00] part of the naivete of that optimism was the hope that that all forms of progress would go together and work sort of going along hand in hand, the technological progress and moral and social progress would, would go together. In fact, towards the end of. The, the 19th century some people were hopeful that the expansion of industry and the growth of trade between nations would lead to a new era of world peace, the end. And the 20th century obviously prove this wrong, right? There's a devastating, dramatic proof though. And I really think it was my hypothesis right now is that it was the world war. That really shattered the optimism of the 19th century that, you know, they really proved that technological progress does not automatically lead to moral progress. And with the dropping of the atomic bomb was just like a horrible exclamation point on this entire lesson, right? The nuclear bomb was obviously a product of modern science, modern technology and modern industry. And it was the most horrific destructive [00:13:00] weapon ever. So so I think with that, people saw that that these things don't automatically go together. And I think the big lesson from from that era and and from history is that technological and moral progress and social progress or an independent thing that we have. You know, in their own right. And technological progress does not create value for humanity unless it is embedded in the, you know, in the context of good moral and social systems. So and I think that's the. You know, that's the lesson of, for instance, you know, the cotton gin and and American slavery. It is the lesson of the of the, the Soviet agricultural experiments that ended on in famine. It's the lesson of the, the Chinese great leap forward and so forth. In all of those cases, what was missing was was Liberty and freedom and human in individual rights. So those are things that we must absolutely protect, even as we move technological and industrial progress forward. Technological progress ultimately is it is [00:14:00] progress for people. And if it's not progress for people and progress for individuals and not just collectives then it is not progress at all the one. I agree with all of that. Except the thing I would poke is I feel like the 1950s might be a counterpoint to the world wars destroying 20th century optimism, or, or is it, do you think that is just sort of like, there's almost like a ha like a delayed effect that I think the 1950s were a holdover. I think that, so I think that these things take a generation to really see. And so this is my fundamental answer at the, at the moment to what happened in 1971, you know, people ask this question or 1970 or 73 or whatever date around. Yeah. I think what actually happened, the right question to ask is what happened in 1945, that took 25 years to sink in. And I think, and I think it's, so my answer is the world wars, and I think it is around this time that [00:15:00] you really start to see. So even in the 1950s, if you read intellectuals and academics who are writing about this stuff, you start to read things like. Well, you know, we can't just unabashedly promote quote-unquote progress anymore, or people are starting to question this idea of progress or, you know, so forth. And I'm not, I haven't yet done enough of the intellectual history to be certain that that's really where it begins. But that's the impression I've gotten anecdotally. And so this is the, the hypothesis that's forming in my mind is that that's about when there was a real turning point now to be clear, there were always skeptics of. From the very beginning of the enlightenment, there was a, an anti enlightenment sort of reactionary, romantic backlash from the beginnings of the industrial revolution, there were people who didn't like what was happening. John chakra. So you know, Mary Shelley, Karl Marx, like, you know, you name it. But I think what was going on was that essentially. The progress you know, the, the progress movement or whatever, they, the people who are actually going forward and making scientific and technological progress, they [00:16:00] were doing that. Like they were winning and they were winning because they were because people could see the inventions coming especially through the end. I mean, you know, imagine somebody born. You know, around 1870 or so. Right. And just think of the things that they would have seen happen in their lifetime. You know, the telephone the the, you know, the expansion of airplane, the automobile and the airplane, right? The electric light bulb and the, and the, the electric motor the first plastics massive. Yeah, indoor plumbing, water, sanitation vaccines, if they live long enough antibiotics. And so there was just oh, the Haber-Bosch process, right. And artificial or synthetic fertilizer. So this just like an enormous amount. Of these amazing inventions that they would have seen happen. And so I basically just think that the, the, the reactionary voices against against technology and against progress, we're just drowned out by all of the cheering for the new inventions. And then my hypothesis is that what happened after world war II is it wasn't so much that, you [00:17:00] know the people who believed in progress suddenly stopped believing in it. But I think what happens in these cases, The people who, who believed in progress their belief was shaken and they lost some of their confidence and they became less vocal and their arguments started feeling a little weaker and having less weight and conversely, the sort of reactionary the, the anti-progress folks were suddenly emboldened. And people were listening to them. And so they could come to the fore and say, see, we told you, so we've been telling you this for generations. We always knew it, that this was going to be what happened. And so there was just a shift in sort of who had the confidence, who was outspoken and whose arguments people were listening to. And I think when you, when you have then a whole generation of people who grew up in this new. Milia, then you get essentially the counterculture of the 1960s and you know, and you get silent spring and you get you know, protests against industry and technology and capitalism and civilization. And, [00:18:00] you know, do you think there, mate, there's just like literally off the cuff, but there might also be some kind of like hedonic treadmill effect where. You know, it's like you see some, like rate of progress and, you know, it's like you, you start to sort of like, that starts to be normalized. And then. It's true. It's true. And it's funny because so well before the world war, so even in the late 18 hundreds and early 19 hundreds, you can find people saying things like essentially like kids these days don't realize how good they have it. You know, people don't even know the history of progress. It's like, I mean, I found. I found it. Let's see. I remember there was so I wrote about this, actually, I hadn't had an essay about this called something like 19th century progress studies, because there was this guy who was even before the transcontinental railroad was built in the U S in the sixties. There was this guy who like in the 1850s or so [00:19:00] was campaigning for it. And he wrote this whole big, long pamphlet that, you know, promoting the idea of a transcontinental railroad and he was trying to raise private money for it. And. One of the things in this long, you know, true to the 19th century, it was like this long wordy document. And one of the parts of this whole thing is he starts going into the, like the whole history of transportation back to like the 17th or 16th century and like the post roads that were established in Britain and you know, how those improve transportation, but even how, even in that era, that like people were speaking out against the post roads as, and we're posing them. No sidebar. Have you seen that comic with like, like the cave men? The caveman? Yes. I know exactly what you're talking about. Yeah. The show notes, but caveman science fiction. Yeah, that one's pretty good. So I'm, I'm blanking on this guy's name now. But he, so he wrote this whole thing and he basically said that. The [00:20:00] story of progress has not even been told and people don't know how far we've come. And if, you know, somebody should really like collect all of this history and tell it in an engaging way so that people knew, you know, how far people knew, how far we've come. And this is in like the 1850s. So this is before the, the, the railroad was built, right? The transcontinental one, this is before the, the light bulb and before the internal combustion engine and before vaccines and, you know, everything. It was pretty, that was pretty remarkable. I also remember there was like an 1895 or 96 anniversary issue of scientific American, where they went over like 50 years of progress. And there was this bit in the beginning that was just like, yeah. You know, people just take progress for granted these days. And there was another thing, a similar thing in the early 19 hundreds, I read where somebody went out to find one of the inventors who'd improved. The the mechanical Reaper I think it was somebody who'd invented an automatic binder for the sheaves of grain and and was saying something like, yeah, people don't even remember, you know, the, the inventors who, you know, who made the modern world. And so [00:21:00] we've got to go find this inventor and like interview him and to record this for posterity. So you're seeing this kind of kids these days type attitude all throughout. So I think that that kind of thing is just natural, is like, I think is sort of always happening. Right. There's this constant complaint. I mean, it's just like, you know, at any pretty much any time in history, you can find people complaining about the decline of morality and you know, the, how the youth are so different and The wet, the ankles, they exposed ankles. Right? Exactly. So I think you have to have some somewhat separate out that sort of thing, which is constant and is always with us with kind of like, but what was, you know, what we're. What was the intellectual class? You know as Deirdre McCloskey likes to call it the clerisy, what were they saying about progress and what was the general zeitgeist? Right. And I think that even though there are some constants, like people always forget the past. Whatever they have for granted. And even though you know, every new invention is always opposed [00:22:00] and fought and feared. There is an overall site Geist that you can see changing from the late 19th century to the mid 20th century. And I think where you can really see. There's a, there's a couple of places you can really see it. So one is in the general attitude of people towards nature. And what is mankind's relationship to nature in the 19th century? People talked unabashedly and unironically about the conquest of nature. They talked about nature almost as an enemy that we had to fight. Yeah. And it sort of made sense you know, nature truly is red in tooth and claw. It does not, it's not a loving, loving mother that has us in her nurturing embrace. You know, the reality is that nature is frankly indifferent to us and you know, we have to make our way in the world in spite of now. Let's say, let's say both because of, and in spite of nature, right? Nature obviously gives us everything that we need for life. It also presents it. It also gives none of that in a [00:23:00] convenience form. Everything that nature gives us is in a highly inconvenient form that, you know, we have to do layers and layers of industrial processing to make into the convenient forms that we consume. David Deutsch also makes a similar point in the beginning of infinity, where he says that, you know, the idea of earth as like a biosphere or a life support, you know, or the ecosystem as a, as a life support system is absurd because a life support system is like deliberately designed for, you know, maximum sort of safety and convenience. Whereas nature is nothing of that. So there was some, you know, so there was some justification to this view, but the way that people just on a, on ironically talked about conquering nature, mastering nature, taming nature improving nature, right? The idea that the manmade, the synthetic, the artificial was just to be expected to be better than nature. Like that is a little mindblowing. Today I was just there was a quote, I was just looking up from I think the plastic is a great example [00:24:00] because plastic was invented and, and, and you know, or arose in this era where people were more favorable to it, but then quickly transitioned into the era where It, it became just one of the hated and demonized inventions. Right. And so in the early days, like in the 1930s I think it was 1936 Texas state had a, some sort of state fair and they had a whole exhibition about plastics and somebody was quote, one woman who was, who, who saw the exhibition, you know, was quoted as saying like, oh, it's just wonderful how everything is synthetic these days, you know, as this is like, nobody would say. Yeah, right. Or there was a documentary about plastic called the fourth kingdom and it was something like, you know, in addition to the, the three kingdoms of what is it like animal vegetable and mineral, you know, man has now added a fourth kingdom whose boundaries are unlimited. Right. And again, just that's just like nobody would ever put it that way. And sometimes, okay. So to come back to the theme of like naive optimism, sometimes this actually led [00:25:00] to problems. So for instance, in this, this still cracks me up in the late 19th century. There were people who believed that we could improve on. Nature is distribution of plant and animal species. The nature was deficient in which species you know, we're aware and that we could improve on this by importing species, into non-natural habitats. And this was not only for like, you can imagine some of this for industrial, like agricultural purposes. Right. But literally some of it was just for aesthetic purposes. Like someone wanted to imitate. Yeah. If I'm recalling this correctly, someone wanted to import into America like all of the species of birds that were mentioned in Shakespeare sun. And this is purely just an aesthetic concern. Like, Hey, what if we had all these great, you know, songbirds in from, from Britain and we have them in America. So it turns out that in importing species, Willy nilly like can create some real problems. And we got by importing a bunch of foreign plants, we got a bunch of invasive pest species. And so this was a real [00:26:00] problem. And ultimately we had to clamp down. Another example of this that is near to my heart currently, because I just became a dad a couple months ago. Thanks. But it turns out that a few decades ago, people thought that for me, that infant formula was like superior to breast milk. And there was this whole generation of kids, apparently that was, that was just like raised on formula. And, you know, today, There's this, I mean, it turns out, oops. We found out like, oh, mother's breast milk has antibodies in it that protect against infection. You know, and it has maybe some, I don't know, growth hormones, and it's like this, we don't even know. It's a really complicated biological formula. That's been honed through, you know, millions or hundreds of millions of years of evolution. Right. However long mammals have been around. Right. And. So yeah, so again, some of that old sort of philosophy of progress was a little naive. You know, but now I think that someday we'll be able to make synthetic, you know whatever infant sustenance that will, [00:27:00] you know, that could be better than than what moms have to put out and given the amount of trouble that some women have with breastfeeding. I think that will be a boon to them. And we'll just be part of the further, a story of technology, liberating women. But we're not there yet, right? So we have to be realistic about sort of like where, where technology is. So this, this sort of relationship to nature is I think part of where you see the the, the, the contrast between then and now a related part is people's people's concept of growth and how they regarded growth. So here's another. One of these shocking stories that shows you going like the past is a foreign country in the, in 1890 in the United States. The, the United States census, which has done every 10 years was done for the first time with machines. With that we didn't yet have computers but it was done for the first time with tabulating machines made by the Hollerith tabulating company. And if it, if it ha you know, the, the, the census had grown large and complicated enough that it had, if it hadn't been known these machines, they probably wouldn't have been able to get it done on time. It was becoming a huge clerical challenge. So, okay. Now, [00:28:00] everybody, now this is an era where. The population estimates are not, are just there. Aren't like up to the minute, you know, population estimates just available. You can't just Google what's the population of the U S and get like a current, you know, today's estimate. Right? So people really didn't have a number that was more like the number they had for the population in the U S was like 10 years old. And they were all sort of curious, like wondering, Hey, what's the new population 10 years later. And they were gunning for a figure of at least 75. There was this one, the way one one history of computing put, it was there were many people who felt that the dignity of the Republic could not be sustained on a number of less than 75 million. And so then, then, so then the census comes in. And the real T count is something in the 60 millions, right? It's not even 70 million. And like, people are not just disappointing. People are incensed, they're angry. And they like, they like blame the Hollerith tabulating company for bundling. They're like, it must've been the machines, right. The machines screwed this. [00:29:00] Yeah, that's right. Demand a recount. Right. And, and so they, they they're like, man, this, this Hollerith guy totally bungled the census. Obviously the number is bigger. It's gotta be bigger than that. Right. And it's funny because, so this is 1890, right? So fast forward to 1968 and you have Paul and, and Erlick writing the population bomb, right. Where they're just like overpopulation is the absolute worst problem facing the entire world. And they're even essentially embraced. You know, coercive population control measures, including you know, and and not, but not limited to like forced sterilization essentially in order to in order to control population because they see it as like the worst risk facing the planet. I recommend by the way Charles Mann's book, the wizard and the prophet. For this and, and many other related issues. One of the things that book opened my eyes to was how much the the 1960s environmentalist movement was super focused on on overpopulation as like its biggest risk. And then, you know, today it's shifted to, they've shifted away [00:30:00] from that in part, because population is actually slowing. Ironically, the population growth rates started to slow right around the late 1960s, when that hysteria was happening. You know, but now now the population is actually projected to level off and maybe decline within the century. And so now of course the environmentalist concern has shifted to resource consumption instead because per capita resource consumption is growing. But, yeah. So just like that flip in, how do we regard growth? Right. Is growth a good thing? Something to be proud of as a nation that our population is growing so fast, right? Or is it something to be worried about? And we breathe a sigh of relief when population is actually level. Yeah, I'm getting like a very strong, like thesis, antithesis synthesis vibe of like we've had, we had the thesis, like the sort of like naive but like naive progress is the thesis, the sort of backlash against that is the, the antithesis. [00:31:00] And then like, now we need to come up with like, what is, what is the new city? Yeah, I mean, I'm not a hit Gelien, but I agree. There's something, there's something. Yeah, sir. Like a police back to two routes of progress, the organization something that I've been just sort of like wondering like Fox is like I feel like sort of a lot of the people. In, in like the, the progress movement in the slack, or like, I would say people like us, right? Like people, people from tech and I've, I've sort of talked to people who are either in academia or in government. And they're like really interested. And I was like, wondering if you have like, faults about like, sort of like now that is sort of onto like the next phase of, of this. I have like, sort of like ways to Rodan broad, like almost like broadened the scope brought in the sort of like people, [00:32:00] I don't know what the right word is like under, under the umbrella, under the tent. And sort of like, yeah, or like just sort of how you, how do you think about that? Cause it seems like really useful to have sort of as many sort of worlds involved as possible. Yeah, absolutely. Well, let me talk about that. Maybe both longterm term and short term. So. Fundamentally, I see this as a very, long-term like a generational effort. So in terms of, you know, results from my work do like direct results from my work. I'm sort of looking on the scale of decades on games. And I think that yeah, I would refer you to a, an essay called culture wars are long wars by tenor Greer of his blog scholar stage which really sort of lays out why this is that the ideas at this fundamental level are sort of they, they take effect on a generational level, just like the, just like the philosophy of progress took about a generation to flip [00:33:00] from, I think, 1945 to 1970, it's going to take another generation to re. Established something deep and new as as the nude psychosis. So how does that happen? Well, I think it starts with a lot of deep and hard and difficult thinking. And and writing and like the most absolutely the, the fundamental thing we need is books. We need a lot of books to be written. And so I'm writing one now tentatively titled the story of industrial civilization that I intend to be sort of. To, to lay the foundation for the new philosophy of progress, but there are dozens more books that need to be written. I don't have time in my life to even write them all. So I'm hoping that other people would join me in this. And one of the things I'd like to do with the new organization is to help make that possible. So if anybody wants to write a progress book and needs help in our support doing it, please get in touch like a list of titles that you'd love to see. Yeah, sure. So I think we actually need three categories of of books or more broadly of contents. [00:34:00] So one is more histories of progress. Like the kind that I do where just a retelling of the story of progress, making it more accessible and more clear, because I just think that the story has never adequately been told. So I'm writing about. The, in, in the book that I'm writing virtually every chapter could be expanded into a book of its own. I've got a chapter on materials and manufacturing. I have a chapter on agriculture. I have a chapter on energy. I have one on you know, health and, and medicine. Right. And so just like all of these things does deserve a book of their own. I also think we could use more sort of analysis of maybe some of the failed promises of progress, what went wrong with nuclear power, for instance what what happened. The space travel and space exploration. Right? Why did that take off so dramatically and then sort of collapse and, and have a period of stagnation or similarly for for air travel and like, why is it that we're only now getting back supersonic air travel, for instance. Perhaps even nanotechnology is [00:35:00] in this category, if you believe. Jason was, Hall's take on it. In his book, where is my flying car? You know, he talks about he talks about nanotechnology as sort of like something that we ought to be much farther along on. So I think, you know, some of those kinds of analyses of what went wrong I think a second category. Of books that we really need is taking the the, just the biggest problems in the world and addressing them head on from kind of the, the pro progress standpoint. Right. So what would it mean to address some of the biggest problems in the world? Like climate change global poverty the environment War existential risk from, you know, everything from you know, bio engineered, pandemics to artificial intelligence, like all of these different things. What would it mean to address these problems? If you fundamentally believe in human agency, if you believe in science and technology and you believe that kind of like we can overcome it, it will be difficult. You know, it will, it's, it's not easy. We shouldn't be naive about it, but like we can find solutions. What [00:36:00] are the solutions that move us, the move humanity forward? You know, how do we, how do we address climate change without destroying our standard of living or killing economic growth? Right. So those are, that's like a whole category of books that need to be written. And then the third category I would say is visions of the future. So what is the, what is the kind of future that we could create? What are the exciting things on the horizon that we should be motivated by and should be working for? Again Hall's book where's my flying car is like a great entry in this. But we could use do you use a lot more including you know, I mean, I would love to see one and it made some of the stuff probably already exists. I haven't totally surveyed the field, but we absolutely need a book on longevity. What does it mean for us all to, to, to, to conquer aging and disease? You know, maybe something on how we cure cancer or how we cure all diseases, which is the the, the mission for instance, of the Chan-Zuckerberg foundation or Institute. We should you know, we should totally have this for nanotechnology. I mean, I guess some of this already exists maybe in Drexler's work, but I just think, you know, more positive visions [00:37:00] of the future to inspire people, to inspire the world at large, but especially to inspire the young scientists and engineers and founders who are going to actually go you know, create those things. The plug is a project hieroglyph which was like, if you've seen that. I've heard of that. I haven't read it yet. Why don't you just say what's about, oh, it was a, it's a collection of short science fiction of short, optimistic science fiction stories. That was a collaboration between, I believe Arizona state university and Neal Stephenson. And like the, the opening story that I love is by Neil Stevenson. And it just talks about like, well, what if we built just like a, a mile high tower that we use that like we've launched rockets out. Like, why not? Right? Like, like you could just, it's like, you don't need a space elevator. You seem like a really, really tall tower. And it's like, there's nothing, we wouldn't actually need to invent new technologies per se. Like we wouldn't need to like discover new scientific principles to do this. It would just take a lot of [00:38:00] engineering and a lot of resources. Yeah. Yeah. And there's a similar concept in Hall's book called the space pier, which you can look up. That's also on, on his website. It does require like discovering new things. Right? Cause the space depends on like being able to build things out of them assignments. The, the space tower just like involves a lot of steel like a lot of steel. So, so you've touched a little bit actually on, this is a good segue into, I've been talking about. But then like, beyond that, you know, the same, the basic ideas need to get out in every medium and format. Right. So, you know, I also do a lot on Twitter. We need, we need people who are good at like every social media channel. You know, I'm, I'm much better at Twitter than I am at Instagram or tech talks. So, you know, we need people kind of on those channels as well. We need, you know, we need video, we need podcasts. We need just sort of like every, every format platform me. These ideas need to get out there. And then ultimately you know, they need to get out there through all the institutions of society. Right. We need more journalists who sort of understand the history on the promise of [00:39:00] technology and use that as context for their work. We need more educators, both at the K-12 level and at university who are going to incorporate this into the. And I've already gotten started on that by creating a high school level course in the history of technology, which is currently being taught through a private high school, the academy of thought and industry we need you know, it needs to get out there in documentaries, right? Like there should be I'm really I'm really tempted as a side project. A a docu-drama about the life of Norman Borlaug, which is just an amazing life and a story that, that everybody should know is just, it's just like an underappreciated hero. I think a lot of these sort of stories of great scientists that had mentors could really be turned into really excellent, compelling stories, whether it's documentaries or I sort of fictionalized you know, dramas. The Wright brothers, it would be, you know, another great one. I, I decided after reading David McCullough's history of them and their invention and, and so forth. Right. So there could just be a lot of these. And then I think ultimately it gets into the culture through through fiction as well in all of its [00:40:00] forms. Right. So optimistic Saifai in, you know, novels and TV shows and movies and everything. Yeah. It's just also, I think I'm not. Science fiction, but just like fiction about what it's like to like what it's actually like to, to, to push things forward. Because I think I, like, I don't know. It's like most people don't actually know. Like researchers do along these lines Anton house had a good post blockbuster two, where he was talking about movies that dramatize invention and was looking for recommendations and was sort of reviewing movies by the criteria. Which ones actually show what it's like to go through the process. Right. And the sad thing about a lot of popular, even the popular treatments of this stuff, like Anton reviewed I guess there was a recent movie about Mary Curie. And there's a similar thing about you know Edison and like the current wars starting Benedict Cumberbatch. [00:41:00] And the problem with a lot of these things is they just sort of focus on like human drama, like people getting mad at each other and yelling and like fighting each other and so forth. Right. And they don't focus on like the iterative discovery process and the joy of, of inventing and discovering. So the, one of the totally you know, unexpected, the sleeper hit of Anton's review was this movie, I think it's actually in Hindi called pad man, which is a drama. the real story of. A guy who invented a cheap menstrual pad for women and that could be made you know, using a sort of like very low capital and then, and be made affordable to women in India. And I mean, he was really trespassing on social you know, cultural norms and boundaries to do this and was sort of like ostracized by his own community. But really pursued this process and the, the movie I saw the movie it's, I, I recommend it as well. It really does a good job of dramatize. The process is process of iteration and [00:42:00] invention and discovery and the trial and error and the joy of finding something, you know, that that actually works. So we need, yeah, we need more stuff like that that actually shows you know, shows the process and and the dedication you know, it's funny, one of the. One of my favorite writers in Silicon valley is Eric Reese who coined this term, the lean startup and read a book at the same name. And he's got this. He has this take that you know, whenever you see these stories of like business success, there's kind of like the opening scene, which is like the spark of inspiration, the great idea, you know, and then there's like, there's like the closing scene, which is. Basking in the rewards of success and in between is, is what he calls the montage, right? Because it's typically just a montage of kind of like people working on stuff, you know, and maybe, you know, maybe there's some like setbacks and there's some iteration and stuff, but it's just kind of glossed over. There's this like two minute montage of people iterating and some music is sort of playing over it. Right. And, and Eric's point is like, the montage is where all the [00:43:00] work happens. Right. It's unglamorous, it's a grind. It's like, you know, it's not necessarily fun and, you know, in and of itself, but it is where the actual work is done. And so you know, his point in that, in that context, it was like, we need to open up the, the, you know, the covers of this a little bit. We need to like teach people a little bit more about what it's like in the montage. And I think that's what we need, you know, just sort of like more broadly for science and. Okay. Here's, here's a pitch for a movie. I believe that the, the Pixar movie inside out right where they like go inside the, the little girl's head that, but for the montage. Right? So like the hall with the montage is that a lot of it is like sitting and thinking and like, not necessarily, it's like not necessarily communicated well with other people or just be talking, but like, you could have an entire internal drama. Oh, The of the, the process as a way to like, show what's [00:44:00] going on. Yeah. Good work. I don't know. I'm so sorry. All of that is so all of that is sort of the long-term view. Right? I think how things happen. A bunch of people including me, but not only me need to do a lot of hard thinking and research and writing and and speaking, and then these ideas need to get out to the world through every, in every format, medium platform and channel and, and institution and you know, sort of that's how ideas get into the zeitgeist. And so then I, you know, I said there's also, so the short term, so what's, so in the short term I'm going to work on doing this as much as possible. Like I said, I'm writing a book. I'm hoping that when I hire some more help, I'll be able to get my ideas out in more formats and mediums and channels. I would like to support other people who want to do these things. So again, if. Any vision that you are inspired to pursue along the lines of anything I've been talking about for the last 10 minutes. And, and there's some way that you need help doing it, whether it's money or connections or advice or coaching or [00:45:00] whatever, please get in touch with me at the roots of progress. And you can find my email on, on my website. And and I would love to support these products. And then another thing I'm going to be doing with the new organization and these resources is just continuing to build and strengthen the network, the progress community finding people who are sympathetic to these ideas and meeting them, getting to know them and. Introducing them to each other and getting them and getting them to know that they all getting everybody to sort of look around at everybody else and say, ah, you exist. You're there. You're interested in this great list form of connection. And I hope through that that there will be you know, a people will just understand, Hey, This is more than just me or more than just a small number of people. This is a growing thing. And also that people can start making connections to have, you know, fruitful collaborations, whether it's supporting each other, working together coaching and mentoring each other, investing in each other and so forth. So I plan to hold a a series of events in the beginning probably be private events. For a, you know, people in various niches or sub-communities of [00:46:00] the progress community to sort of get together and talk and meet each other and start to make some plans for how we develop these ideas and get them out there. Isn't that seems like an excellent, an optimistic place to close. I, I really sort of appreciate you, like laying out the, the grand plan. And just all the work you're doing. It's it's I mean, as you know, it's like, it's super exciting. Thanks. Same to you and yeah, it was great to be here and chat again. Thanks for having me back.

In this conversation, Dr. Stephen Dean talks about how he created the 1976 US fusion program plan, how it played out and the history of fusion power in the US, technology program planning and management more broadly, and more. Stephen has been working on making fusion energy a reality for more than five decades. He did research on controlled fusion reactions in the 60s and in the 70s became a director at the Atomic energy commission which then became the Energy Research and Development Administration which *then* became the department of energy. In 1979 he left government to form the consultancy Fusion Power associates, where he still works. In 1976, he led the preparation of a report called “Fusion power by magnetic confinement” that laid out a roadmap of the work that would need to be done to turn fusion from a science experiment into a functional energy source. References Fusion Power by Magnetic Confinement Executive Summary Volume 1 Volume 2 Volume 3 Volume 4 Fusion Power Associates The notorious fusion never plot Adam Marblestone on technological roadmapping My hypotheses on program design (which were challenged by this conversation!) Fusion Energy Base (a good website on fusion broadly) ITER Transcript  (Machine generated, so please excuse errors) [00:00:00]  In this conversation, Dr. Steven Dean, and I talk about how he created the 1976 S fusion program plan, how it played out in the history of fusion power in the U S technology program, planning and management more broadly, and even more things. Steven has been working on making fusion energy a reality for more than five decades. He did research on control, fusion reactions in the 1960s and seventies, he became a director [00:01:00] at the atomic energy commission, which then became the energy research and development of administration, which then became the department of energy in 1979. He left government to form the consultancy fusion, power associates, where you still want. In 1976, he led the preparation of a report called fusion power by magnetic confinement that laid out a roadmap of the work that needed would need to be done to turn fusion from a science experiment, into a functional energy source. And if I can sort of riff about this for a minute, the thing is. Unlike what I sort of see as modern roadmaps, it lays out not just the sort of like plan of record to getting fusion, to be a real energy source, but lays out all the different possible scenarios in terms of funding, in terms of new technology that we can't even think of being created and lays everything. Yeah. In a way that you can actually sort of make decisions off of it. [00:02:00] And I think one of the most impressive things is that it has several different what it calls logics of funding, which is like different, different funding levels and different funding curves. And it actually, unfortunately, accurately predicts that if you fund fusion below a certain level, even if you're funding it continually you'll never get to. An actual useful fusion source because you'll never have enough money to build these, these demonstrator missions. And so in a way it's sort of predicts the future. This, this document is super impressive. If you haven't seen it you should absolutely check it out there. There are links in the show notes and it's sort of, one of the reasons I wanted to talk to Dr. Dean is because this, this document. Is one of the pieces of evidence behind my hypothesis. That to some extent, program design and program management for advanced technologies is a bit of a lost art. And so I wanted to learn more about how he thought about it and built [00:03:00] it. So without further ado, here's my conversation with Steven Dean. To start off, what was the context of creating the fusion plan? Well, I guess I would have to say that it started a few years earlier in a sense that in 1972 the I was in the fusion office and in the atomic energy commission and the office of men and mission management and budget at the white house put out instructions to, I guess, all the agencies that they should prepare an analysis of their programs under a system, they called management by objectives. And this was some, this was a formalism that was, had a certain amount of popularity at that time. And I was asked to prepare something on the fusion program as a part of the agency, doing this for all of its programs. And [00:04:00] in doing that I looked at our program and I Laid out a map basically that showed the different parts of the program on a map like a roadmap and what the timelines might be and what the functions of those of facilities would be. And when the decisions might be and what decisions would work into into, into what, and that was never published in, in a report, but it w except internally, but the map itself was published and widely distributed. And I have it on my wall and it's in my book. So that was the first, my first venture into. Into doing something that resembled plan, it was not a detailed plan, but it was an outline of decision points and flow this sort of a flow diagram, but it did connect all the different parts of the [00:05:00] program and the identified sub elements, you know, not in great detail and, and budgets were not asked for at that time. So that's how I got into this idea and a little experience in, in the planning area. And then a few years later, we had the gasoline crisis in the U S where there were long lines and we couldn't get gas and people were sitting in their cars over overnight. And the, the white house at that time said that you know, we had to become energy, independent oil you know, the OPEC. And, and so Bob Hirsch, who was at that time about to transition from the director of the fusion program to an assistant minister traitor of Urdu in, I think it was 74, late 74, 75. The, the government decided to Congress decided, or the [00:06:00] administration decided to abolish the atomic energy commission and transition it into something called the energy research and development administration or arena. And the reason for that was to. It create an agency whose function was clearly for all of energy and not just for atomic energy in order to respond to the energy crisis and to get us off of the dependence on foreign oil imports for, for vehicles and things. And so when, when, when that happened, my boss, who was Bob Hirsch at the time he became, he was actually appointed in assistant administrator of errata for basically all the long range energy programs, which included fusion. And as he was at transition, he, he came up with the idea that we should create a detailed long range plan for the, [00:07:00] for the program. And he, he was obviously becoming sort of a senior manager for the many things and he wasn't certainly going to try and do this himself. And so he and I were very close. I was at that point he had three divisions in the fusion program and I was the director of the largest division, which had all of the main experimental programs. And so he asked me to prepare this plan. And if you look at the plan at the very beginning, there's this there's a chart that shows Bob's basically guidance, which was to note that that there needed to be a multiplicity of pathways because no one organization or, or group or division or program was in response could be in full control. And that in order to have a plan that might have some hope of [00:08:00] Last thing that you had to take into account a number of policy variables he said, and technical variables, which meant that he said, because need for the, for the, for fusion and the intent of the government and the funding is all in control by other people in the government. We had to have a number of plans by which the program could be conducted. So he came up with the idea that, well, let's have five plans, which he called logic. So he basically created that framework and turned it over to me at the beginning, I guess, of 1975, I think it was. And to, to create this. This plant. So that's how it all got started. And I had been doing a number of things with the program in terms of the major [00:09:00] experiments that were under my control as a director of the confinement systems, division magnetic confinement systems. I was forcing all, all the people that were that whose budget I had to control over to, to tell me what they were doing and what they needed to do. And so on. It's all right though, I had already been and working on a lot of these things in, within my area, but at that point I took over the responsibility of creating the, in the entire plant. And so I, I, I took it over and I started I created a, a small working group within our office. And we added people that we thought were responsible that could do this for us, or give us the details out in the various parts of the program, all elements of the program. And we created a team and we, we launched this and and this was the result. We were determined to look to these five [00:10:00] logics. They ranged from both, you know, basically a steady level of effort to a maximum level of effort. And and we just started creating these things. During that six months, first six months of 1976, And this was the result. Nice. And did you, so, so each of the logics is kind of a, a wiggly curve. Did, did you go in knowing what the shape of the funding curve for each logic would be, or did you just go in with the framework that there would be five logics and over the course of designing the program, you figured out what the actual shape of those curves would be? Well, we created a definition, a rough definition of what each of the logics was supposed to look like, not in detail, but for example, a [00:11:00] logic to what says moderately. Expanding. But the tech progress would be limited by the availability of funds. But new projects were not started unless we knew that funds would be available. And so we knew that we could not address a lot of problems in parallel. And so we had a general idea that this was a program that was not running at a maximum maximum feasible. Pace. And then the logic three, we said, well, let's look at one, that's a little more aggressive. And we would lay out in that one that as soon as these projects were scientifically justified, they would be in the plan. We would not wait till we knew that there were probably people that wanted them were when he was available. But, and we also said in this scenario, we would address a number of things concurrently rather than in [00:12:00] series. So we assume that the funding was ample. We didn't have a number in mind. At that point, we started laying these things out and asking people. If you had all the money you needed, what could you do if you didn't have quite enough money, what would you do? And people started responding to us that we're working on all of these subtopics. We were mostly working at the beginning laying out what the topics were and what had to be worked on eventually to get to the end point and that these topics could proceed at different rates and with different amounts of risks, depending upon the budget. So this was a sort of an iterative thing that went back and forth with the community and the areas, and our team kept putting these together until they made some sense. Got it. And just to, to sort of step back a second so before [00:13:00] you created this plan sort of all the activities were happening already. Is that, is that right? There were activities in all these areas that were going ongoing. Yes, that's right. They were at, at relatively low level at that stage at the early seventies, the total fusion budget was $30 million. And by the mid seventies, because of the energy crisis, we were told, you know, tell us what you want. And we had raised that budget from 30 million to 300 million. So the program had been undergoing the first five years between 72 and 75, a very rapid expansion. And we had started a lot of new programs. And so the program had been built up quite a bit, although with all of these programs, of course, because they were new. They were at a, still a fairly early stage of their. Their development. The other thing that drove the, the the curves was the [00:14:00] ignition that getting to a fusion power plant required a couple of identifiable major facility steps. And these actually came from that map. I mentioned from 72, which said that from the experiments that we want to do in the near term, which were to build like a physics proof of principle experiment that had to be followed by an engineering step, that was an engineering test reactor. And that had to be then followed by a demonstration power plant. And that those steps were big facilities. Each one, much more expensive than the previous one and making a much more definitive demonstration of fusion that was on a. And, and the wiggly curves that you see, not the, the, the the smooth ones have these bumps on them.[00:15:00]  And those bumps reflect the fact that these major experiments were going to cost a lot of money. And depending on how fast you build them would, would also reflect a different path pace to an end point you know, the faster you build them, the faster you get there, because these major steps really drove the progress and drove the budget. And do you think that sort of, I guess it's hard to think about, but like do you think that the plan helped anything in the sense of. If, if instead you just sort of had continued with the, the program as it started, where I imagined it was like much more sort of bottom up. Do you, do you think that the, the outcome, how do you think the outcome would have been different? I think without the [00:16:00] plan, I don't know what would have happened. I don't think we would've gotten the support that we got in the next few years during the seventies that we got, because the outcome of this cut was that. The plan, the plan was published with all of its detail and all of its budget. It was published publicly. The office of management and budget tried to stop us from publishing this plan because they didn't want budgets out there that said, well, if the Congress would give you so much money, then, then you'd get the job done because that would tie their hands because, you know, they like to be in control of how much money they're going to give to every program. And so they don't want the agencies to put out plans with budgets. And so we had to fight that. And luckily for us, the energy research and development administration, which was fairly new and I [00:17:00] actually only lasted a couple of years before it transitioned to the department of energy, had a a head of it. Bob Siemens, who came from NASA. He overruled the office of management and budget. He said, I'm in charge of this and I'm putting whole plan out. So we probably pushed it. And it got picked up over in the Congress by Congressman Mike McCormick and his staff. And they became champions for this plan and they came. What's the a legislative agenda and they got the Senator from Massachusetts and the Senate to get on board. And and by 1980, I think it was in October, 1980. Congress had passed the magnetic fusion engineering act of 1980, which basically adopted our plan for getting to the end point by the year 2000. [00:18:00] And so the result of our plan was that Congress picked it up. It passed a legislation, making it national policy. And it was signed by president Carter in October 7th, 1980. And we thought at that point we were in that we had a commitment of the United States government at the presidential level to implement this the, the plan for getting there by the year 2000. And so the, the problem, the only problem was that he president Carter signed it in October and lost the election for reelection in November. And as you probably know, whenever there's a change of administration to, especially if it's a change of party in here, Almost everything that the previous administration has decided to do. The other, the new people want to either not do, or they [00:19:00] want to completely reevaluate and start over. And so that's what, that's what happened to this plan in 1981. Got it. And so, because as far as I can tell we've, we've sort of like the, the, the way that it's panned out is that we've, we've sort of followed below logic one, right? Oh yeah. Oh yeah. It was less and less than a, less than ever Guinea. It's the never get their logic. There's one, but there's one caveat to that is that in the 1980s Ronald Reagan was a post to all of this energy stuff until 1985. When he met with Gorbachev. And they decided to work together on fusion and build our first major step that was in our plan. We were going to build this engineering device in the 1980s and he and Grover Jeff decided let's get together and build it together with Europe.[00:20:00]  And this became the eater project, which is under construction in France. So what the program really did to work around this problem of the budget being so low was to say, okay, we're not on our own track, but we're on a wall track and we're all working together. And so they're building this multi tens of billions of dollars engineering test reactor and it's taken them a long time to get it going, but it's hopefully going to be finished in a few years. It's going to turn on by hopefully 20, 25 is plasma, so we're way behind, but, but that was a response to being on this. Their thing was to say, we're all in this together. And we don't have our own plan to get there, but the world has a plan and we'll get there together that that's how this all evolved. Got it. And so I guess the, if, if I'm understanding this correctly, the, the sort of the, [00:21:00] the purpose and the value of this plan was less as a coordination mechanism for the people doing the work and more as a sort of communication mechanism with people sort of outside the organization in terms of. What the work would entail is that, is that accurate? I, I can tell you that when I was doing this plan and I was in a senior management position there, I had responsibility for the bulk of the program. I didn't have the basic pilot, the physics program and universities, and I didn't have the technology part, but I had all the major experiments in my ballywick. And when Bob Hirsch was, I was still reporting to Bob Hirsch and he had all the energy programs in Herta, it was our intent to manage his program, to implement this plan internally. It did turn out that part of the plan, part of our implementation required us getting the money and that all went through this energy building in [00:22:00] Congress. We thought we had the whole thing put together that not only we did, we eventually have the Congress on board, but we also had a management and we had 80 staff in the office then. And And we were prepared to manage the program to, to implement this if we got, get the money in detail. So it was both the management plan for implementation within the within an arena. But of course the other thing that happened in all of this was that Erna was abolished and became the department of energy. So I think Jane and I left, I left in 1979 because I thought we were about to implement this plan. And I formed fusion power associates, and I got a dozen electric utilities. I mean, a dozen major industries like Westinghouse and companies like that to, to form this organization, to bring industry in, to actually bring industry into the, into the implementation phase of this program plan, we were all set to [00:23:00] go. And even in the early eighties, before the whole thing sort of fell apart, I had a dozen electric utilities in fusion, power associates. And so we had both industry that wanted to do this and the electric utilities that were on board and all we needed really was for the new department of energy to to follow through with the management of this thing and try to get, get the money, but. The money never, never came through. And the industries and fusion power associates in the early eighties realized that there wasn't going to be any money for industry because there wasn't any money coming through. And the electric utilities were deregulated by Ronald Reagan and they abandon all their R and D departments, which were the ones that were in, you know, in our organization that were interested in developing fusion. And they became taken over by [00:24:00] business people in the utilities whose main purpose was to make money. And they were not interested in getting involved in brand new technologies. They are only comfortable with the technologies that they had. Yeah. But that makes a lot of sense. And I guess to sort of go, go back to you. You mentioned earlier that this plan was sort of part of a bigger trend of management by objectives. Do you think that that was effective management by objectives? And just because I feel like sort of the, the modern idea, very much like projects, plans like this that like, you know, multi-decade technical plans are at, at best foolish and at worst detrimental. And so, so what do you, what do you think about sort of like big plans for technology projects? More generally, [00:25:00] just sort of say that management objectives was an OMB guidance in the early seventies. And it's soon disappeared from the the roof work, if you will, as the the OMB. One of the things that happens in Washington every two years is that people change in administrations changed. Whatever one group is wanting to do it just a lot, go by the wayside. So by the mid seventies, when the came about there was there was no management by objectives, formalism still going on in the government. Basically they start all over again with how they're going to try. Do these things. And as this thing all evolved, you know, up to the present at the OMB I don't know, probably more than 10 years ago, 10 or 15 years ago, the OMB said to fusion, you are guys are not an energy [00:26:00] program anymore. You are a science program and we are going to evaluate you and have you managed like a science program. And so they stopped even asking us for both aimed towards an energy program. They said that we should go to the scientific community, take unsolicited proposals from the community to do good science, evaluate them under peer review by other scientists. And if it was good science, we should fund. And we should not be trying to make them into seeing we should not evaluate these proposals as to whether or not they are getting us to an energy source. So for over a decade now, the fusion program has not had an energy source as it's, as its goal, and it hasn't been funded or evaluated within the government as an energy program. Now, this has all changed in the [00:27:00] last year, but up until just very recently they're trying to put now the energy mission back into the, into the mission, but it hasn't actually formally happened at OMB yet. Got it. And just, just to sort of pull us back to well mentioned by objectives and just more broadly having very concrete plans W w w do you think it was useful or do you think it was just sort of like a a fad almost. Well, it's been disappointing from him personally. I think that it's been disappointing that like, we haven't actually done the plan. Right? Well, it's just a point. You spent so much effort laying out how you, how you would do it and how you would make decisions and you get everybody that's under your purview out in the in the community of people that you're funding, you get them all set up to try to achieve these things and you try to get them the [00:28:00] money and then it all falls apart. And then somebody tells you that, well, we don't care because we don't really think we really don't care if you ever get there. It's been the attitude until very recently. So it's very demoralizing, you know, to everybody, except the scientific community itself is kind of immune from this to some degree, as long as they get funded for research. As long as the universities getting money for basic research in this area, and they're training students in these trainings and these students can get jobs either in the private sector or they start their own companies, or they go to work at government laboratories, as long as that is moving along at some reasonable degree of success for people getting trained and getting in doing work and publishing papers. There's a certain degree of apathy if you will, or there even a certain degree [00:29:00] of satisfaction in the scientific community since nobody seems to care, if you should never goes on the grid. Yeah. Yeah. And so I guess like counterfactually, if the money had been there, so actually one thing that I, I still do find really impressive about the plan, although it is disappointing is that you basically predicted that. Right. Like you, you said, you said here's logic one. If you're below this line fusion won't happen and indeed you were right. So that's, let's just say like, that is one of the reasons why I'm I'm so impressed by it. Because it, it really did, it made a very precise prediction and that prediction came true, although it is disappointing. If you, if you could imagine that, like the say the money came through, do you think that this plan would have been useful in the sense of like, like how much confidence do you have that you sort of [00:30:00] accounted for all the things that you would need to do over the course of several decades? In order to, to get to fusion as an energy. Well, as it says in sort of early part of the plan, these plans are not bent to be followed blindly in their detail. They are guidance to management and management has to keep updating them and looking to see how they're doing and keeping an eye out for new discoveries and revising the plans in detail to see if new things are emerging or some things are failing. Or the money is coming in in such a way that that the plan schedule has to be changed. That's why you need management structure that's in place and following it, but not blindly following it. Yeah. So I personally believe if the management structure that we had in the [00:31:00] mid seventies had been maintained and, you know, right now I think we had 80 people in the office and they were all management oriented. And right now I think they probably have about, I don't know, maybe, maybe 15 people in the office because they're running it like a research program. So they just taking proposals and getting them evaluating and sending out money. So they're not managing in the way that we would have managed if we had had 80 people and we'd had the divisions that we had divided up and we revise the management structure from time to time. Along the way. And I know if I hadn't been there and what we had in mind, we were going to transition the money starting out into industry to get these things built and to bring engineering oriented people in more into the program, because even in the mid seventies, the pro was dominated by plasma physicists. And we were only in the process at that point of starting to bring in engineering [00:32:00] people, but still the money. The government's laboratories in their technology. People like Oak Ridge has a big technology laboratory. And so there was technology programs being developed in these laboratories. And other a little bit of it was going out into industries as on a job basis for the labs, but we didn't have a big industry program. And you know, one of the things I did just before I left was I brought in McDonald Douglas, which a big aerospace company to build an engineering center at Oak Ridge for fusion. That was sort of the last. Done and you know, and when this whole thing folded in the eight earlier eighties McDonald Douglas basically was told to shut down and they went away. They were, they were eventually bought out by Boeing. So we had started a transition where part of the implementation of this plan was to implement it by bringing industry in to bring [00:33:00] that talent from, we had a bunch of people, for example, in fusion power associates at the beginning, who were the architect engineers that were building nuclear power plants. So, you know, those were the people that we needed to implement the plan, but they were not quite in the program by now, 1980. And when the money didn't come through them, they just all disappeared from any plan that the government had because the government in the eighties and was only interested in trying to make their scientists survive. Yeah. And I guess you don't really see plans like this today. It feels like. And so I get the sense that creating plans like this, and more generally like technology management, like competence, technology management is a bit of a lost art. Do you, do you think that's true or, or am I, or is it like, am I missing something?[00:34:00]  Well, I don't know if it's true or not across the board that they must be out there somewhere. I think when you look at big construction projects and the people that do those projects know how to manage and they know how to cost things out and they know how to, they know the importance of, of keeping things on the schedule and they know how important it is to have pieces of the schedule coming in on the right time timing so that the whole project comes together. And, and we tried to do lay that out so that, that could be done for fusion, but I don't see it being done in the department of energy. And I don't know about any other agencies. I I can I have the feeling that maybe the defense department does it a little better on weapons systems and aircraft systems and fighter systems with some of the big aerospace companies? I mean, I think my observation from a fire department of defense is that [00:35:00] they, they do it the right way, but they're not on top of the cost and schedule and they do get taken to the cleaners by these companies, but somehow or another, they do get the job done, even if it's costing more than it should and taking longer. Yeah. That's, that's the thing that there's sort of been this like wider observation that since the 1970s things just take like sort of complex projects like this take longer and cost, like have, have dramatic like cost and time overruns. And it's sort of like this, there's like this trend of that happening more and more. And so, so I wonder if it's like w what it is about the world. That's changed. Do you have any hypotheses there? Well, you know, I'm not sure if it was ever that good first place ever, because when we, when I was there [00:36:00] in the seventies and we were laying out our plans, we thought we knew how, how to do it and do it right. But at the same time, within the atomic energy commission, there was a a nuclear fission program called the breeder reactor program. And it was a mess. And, and yet the industries out there like Westinghouse and general electric, they were actually building nuclear power plants in those days. And they were building nuclear reactors for submarines in those days. And so those programs were actually working, but at the department, they were working on advanced reactors and they weren't getting them done. And they eventually had to shut down the breeder reactor program because it just wasn't just wasn't seemed to be working. So I'm not sure the government, at least the part that I knew ever did that. Well, you know, when Admiral Rick over wanted to put a. I nuclear reactor in the submarine. The Navy wanted to fire [00:37:00] him as a department of energy, wanted him to put this program into the Lac, their national laboratories. And he had a fight them tooth and nail through his friends in Congress to get put in charge of the program and be allowed to put this out to general electric and Westinghouse. He had to fight them, and this was back in the sixties. So I'm not, I'm not sure that the government itself ever was very efficient at any of these things. Now, I have to say that NASA seems to have a good reputation and I, if it's true, it's I attributed to the fact that Kennedy went public and made it a national priority to get there by the end of the decade. And he demanded that they do it in a way to make it. And he, he had the backing of the Congress and he completely set up a whole new agency focused on [00:38:00] just that. And they got there. So I have to say that that was a success story and it remains a success story today with the evolution of a commercial industry. That's coming out of all of that. All this is quite a few decades later, but nevertheless they seem to have done a good job. I've never, I've, I've never been in NASA. So I only can see it from a farm. I'm sure there's some problems within it, but you know, somehow or another, it proved that we could get it done. And going back further to the Manhattan project for the atomic bomb was clear that when there was a commitment from president Truman, I guess it was, or or maybe it was, maybe it was Roosevelt. To do it and the army set up to take charge of it. They put a general in charge of it and they went to Los Alamos and they forced to deploy the part, the atomic energy commission laboratories to, to work on the problem that was at hand to get it done in a short amount of time. And when you had that kind [00:39:00] of leadership and management, it seems like it can be done, but it all depends on management and it's rare in government. And I would say it's rare even outside of government as well. And, and so, so I guess the upshot of this for me is that and correct me if this is wrong, but that you feel like it's much more about sort of the, the individuals in charge. And then it is about sort of like the, the process of, of planning and roadmapping out the techniques. Yeah, absolutely. I can't tell you how many plans have made since the one that you were looking at that I, that I've gathered dust on shelves. They almost every other year, the program launches a new plan. It finishes the plan. Everybody says whether they like it or they don't, and it's not implemented in a couple weeks [00:40:00] later, they'll turn it over to the national academies to evaluate or proposal new a plan. And I can't tell you, it's countless number of plans in fusion that are gathering dust on shelves over the past 40 years. You mean, it's the managers and the people that are want to implement the plans that, that supervise the plan. And as long as they're there we'll implement the plan, but as soon as they're gone, they, somebody else comes in, maybe makes a new plan or makes no plans at all. You know, just try to keep things alive. And w what do you, what would you think about so I feel like the sort of modern ethos is that planning plenty. Is it that useful? That you should just go and just start doing stuff? So I guess if we, if we think of like a counterfactual world where you just [00:41:00] have a very, like, you, you have consistent management, but they don't have a plan. How do you think that would be. I'm not quite sure what you said, but let me, let me give you an example of this big international project. Either in France, it was, it was, it was started by Ronald Reagan in 1985, but it didn't really get launched as a serious construction project for 2006. And it very rapidly became something that was getting behind schedule and over budget. And it was completely out of control until about 10 years ago. They, they had a management review and they said, we've got to get control of this project. They brought in this guy, that's now the director, Bernard big go. And he, and he took charge of this. And now he's got the thing organized, reorganized. Countries [00:42:00] from all over the world on a schedule to deliver this piece of equipment or that piece of equipment on a certain time schedule, he's got them all being delivered in a sequence and he's having them put together in a sequence. And he's got a great management plan and he's been keeping the thing on schedule now for the last five years. And I have great confidence. He's going to get the job done, but it all started with putting somebody like him in charge that knew he had. Have a plan that was in detail for everybody working together because, and totally took charge every country that had part of the job wasn't controlled with the wrong piece. And there was no, there was no control if they got behind. Sometimes the director in, in France didn't even know until it was too late to get it back on schedule and, and he didn't control the money anyway, each country controlled its own money. So, you know, I think it all comes down to management and then the management [00:43:00] makes the plan. Yeah. And w we'll see, so that's that I do think is worth noting in the sense that there's, there's also sort of a philosophy of management that says management shouldn't actually be imposing a plan on people. They should just like. Let it be very bottom up. Right. And just like, instead of planning, like, you don't know what's going to happen, so you should just sort of like let ideas bubble up from, from the bottom and let people work on what they think is the best thing to work on. Right? Well, you know, managers are managers of people and they oversee people. And so in a company, there's somebody at the top when there's somebody under him, but underneath them, It companies, there are thousands of people they're doing their bit. So a managers is not just say, Hey, we're going to get this done by tomorrow or next week he, he supervises all these people and these [00:44:00] people feed him up the information and help create this plan. And they all have to be on board and supervised properly all the way down the line through it, through a management chain. So it's not like one person does the whole plan by himself or with a couple of people in his office. He supervised the preparation of a plan with the community. So I had, you know, dozens of people around the country who helped prepare this plan. I helped them piece it together. And, you know, I helped organize the structure of the whole thing, but it was, it was an ongoing interaction that went from. And then guidance from top down, it was back and forth through the whole process. Got it. So you could almost think of the plant as a coordination mechanism in a way. Absolutely. Because the managers can actually do the work. Yeah. [00:45:00] Yeah. And they probably like don't, the managers can't know enough to be able to say accurate. They don't know the level of detail. If there's a problem. For example, if there is a problem they can say, okay, let's fix that problem. And they go back to the people that know about it and they tell them, okay, you guys go out and find out how you're going to fix this problem and come back and tell me how you're going to do it. But then the manager has to approve it. You know, if he doesn't, if he thinks it doesn't been done, right. He will go back to them and until they get her. Right. So, and I guess another interesting thing about Th the, the plan is that at some point someone was willing to make a prediction but a decade or more out. And that's sort of an attitude. I, I see people as being very hesitant to make that predictions on that timescale now do you feel like there's, or at least with that amount of w with like that [00:46:00] amount of precision, right? Like people make very, like hand-waving predictions now. Do you think, like there's been some kind of attitude shift around making predictions like that? Well, it's changing in the last year or so. There's been a lot of planning activities going on here and you'll see some time schedules and all of these, like right now there's a whole bunch of the companies that are all saying. But by 2030 or 2040 or 2050 and so on and so forth. And there's sort of a goal that's been proposed to have fusion on the grid by 2050 and in order to participate in the climate change solutions. So there's a lot of thinking about this and there's a lot of people putting out what they think is a reasonable timeframe that is achievable. And it's interesting that these, these timetables are all. One two or three decades out, which is sort of like almost the timescale [00:47:00] with the timescale that we had. So it's not uncommon to think that almost anything that's technically thought to be feasible can be done in 10, 20 or 30 years, depending on how difficult it is. So it's pretty easy for people to think that something can be done in those kinds of timescales and then start backfilling the details to see how it can be done and what it costs. Yeah. I think, I think the thing that strikes me is different between the predictions that I see now. And what you worked on is that. I, I feel like the, the fusion plan was a, the producers were very precise. Like it wasn't like, oh, we'll get this thing working by this time. It was like, okay, we need to show this experiment, this experiment and this experiment. And then there are also like very clear sort of intermediate results and, and like different pathways. All of which I, I don't [00:48:00] see in, in modern modern predictions where there, who are, who it feels like it's like, step one, start project. Step two, question mark. Question mark. Question mark. Step three 30 years later, have this amazing result. And I feel like that well, and you see our times scale to look to around the year 2000. Come out of whole cloth, it was set by the fact that we were in a physics phase and we had just authorized the construction of a physics demonstration called Tokamak fusion test reactor at Princeton. In 1975, we had already launched construction of that, and we knew that to get to a power plant. We had to make two major steps. One was an engineering facility and next was a demonstration power plant. And the time to construct those things is, is kind of known that it takes [00:49:00] five years to build them and five years to run them. So that kind of for each step was a 10 year step. And that gets you to a 20 year timetable. And so that really the time to build those two facilities and operate them, set the timescale. Of 20 years, more or less, depending upon how, you know, give or take a few years how fast the money came in and so on. So you know, we had a, we had a reason that that 20 year time frame was sort of set that we couldn't get there any faster because we couldn't go direct to a power plant. Right. And, and I guess like, so, so two questions one is, how do you think about the difference between a engineering project and a physics project and then two, like how did you know that you couldn't go direct to a. Well, if you [00:50:00] look at all the pieces of a power plant, you'll know that there's an awful lot of stuff in there that is not needed for a physics experiment, you know, a physics experiment, you know, what makes up a fusion plasma, and it has a whole bunch of diagnostics on it, and you're not sure what it's going to do. And so you're, you have to allow for surprises and then you'll have to do theory and computation to see if you understand what's going on. And all of that requires people who, who understand the physics for a power plant. You have to actually have confidence that the plasma that you're making is actually going to sustain fusion for a long period of time and produce heat. That can then be converted into electricity. And that means that these power plant has to be doesn't have room for a lot of diagnostics to be doing experiments, to try to figure out [00:51:00] what's happening. You have to have high confidence that when it turns on it's going to run and not have to be shut down every day or every week to be fixed. Right? So all those things require technology and engineering development, where components, you know, there may be a thousand major components or hundreds if you combine them in the right way into a power plant that has certain functions. And each of these has to be developed by engineers as a company. It has to be run and tested for long periods of time to see you with your breaks, to see how to fix it. How long does it take all of these things have to be demonstrated before you put it all together. Otherwise when you put it all together in Nepal plant it's too late because you can't just hate the far plan a party again, and start over. So the engineering and technology has a whole separate track of development that requires [00:52:00] testing and and development of codes of, of of a manufacture materials have to have codes. How long they'll last in this environment? Yeah. When will they fail? There's a whole skill set called Time to failure and time to repair the engineers, work with that physicists don't work with, if it breaks, it breaks, they just, you know, you know, they, they, they fix it because it's a small piece and they put pieces in, it takes them maybe a few weeks, but it a major piece of a power plant. It might take you a year to take that piece out and repair it and put a new piece in. Yeah. So, so like, meanwhile, you're not making any money selling electricity, electric utility will not buy a power plant like that until someone's shown that every piece works and worked all, all together can be sex if it breaks, you know, in a week. [00:53:00] Yeah. Interesting. So, so in a sense engineering work has a lot more to do with robustness than, than physics. Once, you know, the physics, it's an engineering problem to power commercial aviation. Okay. Yeah. I think that, I guess in my mind that that's still, like, there's still a lot of like research work to be done in engineering problems, even if it is just an engineering problem. There's a, there's a melding of physics in it. That's what they call applied physics and there's basic physics. And so, and there's technology and then there's engineering and all of things. I have slightly different slants and slightly different communities, but they all have, and that's one of the functions of management is to work on a timeframe and with money to meld these things in the proper sequence to get to where you need to. Yeah. That's why a program has to, that's why a program like fusion has to evolve from [00:54:00] totally physicists to mix of physicists and technology, people to a mixtures of engineers, to commercial companies that do costs and schedules and all of this stuff. This all has to be supervised by management. Got it. And so sort of a nitty gritty that I'm interested in is like, how did you think about budgets and like how much things would cost? Cause I feel like there's, there's no good canonical resources about like, how to think about how much research programs cost. Well, the way we did it was we divided it into systems and subsystems. And we went to the people that were working in each area and we asked them to go into more depth and that's, what's in our other volumes. So we had teams of people in all these areas, and [00:55:00] then we use you know, people that from industry and from utilities. Had done similar things. He found, we looked at the cost of nuclear power plants. That was a big part of our, our thinking as to what we knew that the fusion plant had to compete. So, you know, the, the, the skillset was all out there. Technologic technology wise for the power plants because of fusion plant is almost like a nuclear power plant, except a fuel is different in the center. I mean, it doesn't look the same, but it has all the same pieces to get the power. So there, there was a lot of skills out there that we, we were able to draw from. And, and we did the best we could. We know we can't claim that. And we put some contingencies in there, you know, we didn't let them low ball or high ball us, you know, because we had, they had to fit into the different logics as to how much money might be available and stuff like that. So, and we didn't say that this number is where we're, you know, in [00:56:00] stone that they were, they were absolutely. Yes. Yeah. And how did you think about like, places where there's just like, sort of deep uncertainty like where you would need to actually, in terms of a physics problem where you would actually need like some kind of discovery in order to get the thing work? Because it seems like there, there could be a situation where like, you know, it's like you can make that discovery next year, or you could, it could take you 10 years to figure it out. Well, if you look at the say the logic three reference option to page 12 of the blue colored volume you will see. That there are a variety of paths the Tokamak with the lead path and freed, laid out a reference a lot for that to get there by a certain date. But underneath that, there's a path for authentic concepts. And there were decision points, which said that well, if these [00:57:00] things come along and there's even one.at the bottom that says other things that were in very early stages of proof of principle, but we weren't knew that these things might come to fruition. We laid out a timeframe for hoping that we would fund those so that they could be evaluated. And so if those things came to fruition, then they would transition to a next step. And so that would all, that was all sort of taken into account as to the decision point as to when some of these things might, might happen. And, and of course, if, if something really radical were to come along a long, one of these other paths it's listed, I'll say can see one if you'll, I don't know if you have it in front. But under other, you'll see a decision point in 1985 that we're going to try to bring some of those things to a decision.[00:58:00]  If it looked like a positive one, we would proceed to what we call a prototype engineering, power reactor. And so it would take the place of that one up above that was called the Tokamak EPR that would have already been under construction if we kept following the back path. But, but it might still be. But if this other one came along, we would start its own track to compete. 1985 and then it would pick up at its own track and then it would come in later and we'd have to, at that point, if that became the favorite path, or maybe even there'd be three paths, you know, we didn't say that there could only be one winner. So you had a, you could eventually wind up with several of the earliest ones might come on around the year two thousands, but some of these other ones like abandoned 2005 or 2006, if they were better and they'd be a options for the [00:59:00] utilities, if they were better. Got it. Yeah, this is so cool. One of the really big takeaways that like, just like keeps coming through is almost just like consistency of, of management and not so much like the plan, but like of, of a plan. And, and I think like that's what you see. Not happening. And I guess sort of pulling us to today. Do you have a sense of which things that are happening in fusion now that you think are most. Well, you know, I don't want to get out on a limb to pick winners and losers because fusion power associates really is a home for all of these people. And I encourage the ball and there are people that we will not let into fusion, power associates as they're out there because they're so re almost crazy. And their claims are almost crazy that I wouldn't want to be associated with them. There are few and far between [01:00:00] fortunately, most of the alternates that are out there and these little companies they've been formed by good fusion. People who have, who have fallen on bad times because the government started funding all their money into tokamaks and stopped funding their off and net ideas. And so these people branched out and got support on their own. And I know some of these people and they're good people and their ideas need deserve to be pursued. But the truth is that most all of these are at what we used to call the proof of principle stage on their physics. They are not fully thought through power plants and their physics is not fully developed or at least not even far enough along to develop to know how probable their success is. They should be pursued. What was the room in the program for these, because improvements all those come along, any tech technology. So the first thing that comes out is it not going to be the best [01:01:00] thing 20 years after it? So I encourage all these things if they're credible people and you know, right now there are a couple of. Things in the Tokamak area, you know, the Tokamak mainline is the conventional Tokamak that is represented by either, but there are variations. There's one Commonwealth fuse and the systems at spying out of MIT. That's almost the exact same concept as the mainline Tokamak, except they're using high field new superconductors, which make the machine smaller. And which allows them also to be able to disassemble and repair it faster than the conventional tokamaks because the Magnus come apart in a different way. And the exhaust system that they've designed is more efficient. So that may help with some of the materials problems as the conventional talking back. So it does look like a much improved Tokamak and they're getting money and they're trying. You know, [01:02:00] they've got a facility that they're, that they've committed to in Massachusetts, and they're trying to build one step, a physics demonstration followed by a electricity generator. And so I, I have great hopes for them. If they can get money, they're privately funded. Now they're not getting hardly any government money at all. I think the government's helping them a little bit with some support work in the labs, but basically it's a private sector venture. And I think that one of the most promising, and there's another variation of the Tokamak called the spherical story. Or physical Toca Mac the British are going gangbusters on that. They've got one in operation. They've got a company that's also built one and they've got a site for building as a next step one, which they a site where they hope though will the actual power electricity generator. So that variation of the Tokamak is also looking very seeing. And it's the British are way out in front on that. Although [01:03:00] that idea first came. Ascend Princeton is actually had built one of those. And as another one coming in operation in a couple of years, that would support that line. So there's a couple of variations along the token back line that are looking, looking very good. All the other things that you hear about there are at a somewhat earlier stage of develop. They're all doing good work. T a E a tri alpha energy. Your TA in California is probably the most radical of the mall. But they are the farthest along of these alternates. And they've all, they've had success along the way. They built two or three generations. So machine, and they're all trying to get money for a really major step that would really demonstrate most everything they want to demonstrate before going on to a real power producing machine. So, you know, I think I have for them too, there's another company in Canada called general fusion that perhaps is a little bit farther behind, but they're working with the British and a [01:04:00] two. And so that's a promising area. And you know, I hope I have hope that that will evolve. This actually made me think of a question, which is Was sort of now all as, as you alluded to all the fusion development is being done by these sort of separate private companies which sort of stands in contrast to the, the fusion plan, which sort of implicitly is that everything is being at least managed from a central like a central management team. Do you think that w w what do you think about those two, two sort of different approaches towards getting to a technology of like, sort of the, the let a thousand flowers bloom in, in private companies versus a much broader program. Well, I think in the last maybe five years or so times have changed in that regard, you know, in the seventies and up until very recently, it was [01:05:00] only the governments that seem to be able to afford to do this. Those are the timescale and the cost. And so if was to come to pass, the government had to step up or the international governments had to step up and work together. And it was, seemed like the only way to get there was for the government to do it because of the cost. Now it seems that things have come along far enough, especially in the Tokamak area that some private companies are coming up with what they think are. Ways to fund what they want to do to demonstrate what they need to demonstrate because their ideas are at the moment, at least on relatively inexpensive facilities. Now they, they are going to run up against funding problem. If they're successful in the near term, you know, they're getting hundreds of millions of [01:06:00] dollars, some of them from private investors and they're building some things and hopefully they'll be successful, but these will not be powerful. And so they will have to be so successful that they will be able to get much, much larger amounts of money. They may have to be, be bought out by a Westinghouse or something in order to, to become real power plant manufacturers. These are not industries yet, even though they have an industry, what they call an industry association, there are small companies, and if there may be big by some companies standards, but they are not really money-making companies and they don't have their own money. So they have to continue to, to get money from investors and, and even maybe getting a hundred million dollars or $200 million from some billionaire venture capital company is doable. These days, getting a billion for the next step is a much different [01:07:00] problem because there isn't going to be a real fusion demonstration plant built for less than a couple of billion dollars. And private money doesn't come that easily at that Atlanta, unless the thing that's being built is going to make money back fast.  Steven Dean. Thanks for being part of idea machines.'  

Eli Dourado on how the sausage of technology policy is made, the relationship between total factor productivity and technological progress, airships, and more. Eli is an economist, regulatory hacker, and a senior research fellow at the Center for Growth and Opportunity at Utah State University. In the past, he was the head of global policy at Boom Supersonic where he navigated the thicket of regulations on supersonic flight. Before that, he directed the technology policy program at the Mercatus Center at George Mason University.. Eli’s Website Eli on Twitter Transcript audio_only [00:00:00] In this conversation, Eli Durado. And I talk about how the sausage of technology policy has made the relationship between total factor productivity and technological progress, airships, and more Eli is an economist regulatory, hacker, and senior research fellow at the center for growth and opportunity at Utah state university. In the past, he was the head of global policy at boom supersonic, [00:01:00] where he navigated the thicket of regulations on superstar. Before that he directed the technology policy program at the Mercatus center at George Mason university. I wanted to talk to Eli because it feels like there's a gap between the people who understand how technology works and the people who understand how the government works. And Isla is one of those rare folks who understands both. So without further ado my conversation with Eli Dorado.  So just jump directly into it.  When you were on a policy team, what do you actually do?  Well that depends on which policy team you're on. Right. So, so in my career you mean, do you mean the, in sort of like the, the public policy or like the research center think tanks kind of space or in, in, in a company because I've done both. Yeah, exactly. Oh, I didn't even realize that you do like that. It's like different things. So so like, I guess, like, let's start with [00:02:00] Boom. You're you're on a policy team at a technology company and. Yeah. Yeah. So when I, when I started at boom so we had a problem. Right. Which was like, we needed to know what landing and takeoff noise standard we could design too. Right. Like, so, so we needed to know like how loud the airplane could be.  And how, how quiet it had to be. Right. And, and as a big trade off on, on aircraft performance depending on that. And so when I joined up with boom, like FAA had a, what's called a policy statement. Right. Which is, you know, some degree of binding, but not really right. Like that they had published back in 2008 that said, you know, we don't have standards for supersonic airplanes, but you know, like when we do create them they, you know, they're during the subsonic portion of flight, we anticipate the subsidy Arctic standards. Right. So, so for, [00:03:00] for, for landing and takeoff, which is like the big thing that we are concerned about, like that's all subsonic. So we, you know, so that sort of the FAA is like going in position was like, well, the subsonic standards apply to, to boom. And so I kind of like joined up in early 2017 and sort of my job was like, let's figure out a way for that, not to be the case. Right. And so it was, it was basically, you know, look at all the different look at the space of actors and try to figure out a way for that, not to be true. And so, and so that's like kind of what I did. I started, you know, started talking with Congress with FAA. I started figuring out what levers we could push, what, what what angles we could Work work with to ensure that that, that we have we've got to a different place, different answer in the end. And, and so the, like, so basically it's just like this completely bespoke process of [00:04:00] totally like, even trying to figure out like what the constraints you're under are. Exactly. Right. So, so yeah, so it was, there's like a bunch of different, different aspects of that question, right? So there will you know, there's, there is statute, you know, congressional laws passed by Congress that had a bearing on the answer to that question that I went back to like the 1970s. And before there w you know, there was the FAA policy statement. There was, of course the FAA team, which you had to develop, you know you know, relationships with and, and, and, and sort of work with you have the industry association, right. That we remember of that Had different companies, you know, in addition, you know, in addition to boom, there, there were a bunch of other companies Ariane, which is no longer operating. We had Gulf stream, which no longer has a supersonic program. Or actually they didn't Edward admitted to having it announced really dead. They, you know, there was, you know, GE and rolls Royce. And so you had all these companies like coming together, you know, sort of under the, [00:05:00] under the watchful eye of Boeing, of course also. And, and so like the industry association had to have a position on things, and then you had like the international aspect of it. So you had a, there's a UN agency called Oko that sort of coordinates aviation standards among all the different countries you had the European regulators who did not like this idea that there were American startups doing Supersonics because, because the European companies weren't going to do it. And so they wanted to squash everything and they were like, no, no subsonic standards totally applied. Right. And so so that was, that's really the. The environment that, you know, sort of, I came into and I was like, okay, I've got to figure out, you know, I've got to figure out, build a team and, and, and figure out an approach here. And and, and try to try to make it not be the case that the subsonic centers apply. So we, so, you know, basically we tried a bunch of things at first, right. Like we tried to like, get our industry association, like all geared up for like, okay, well, we've gotta, we gotta fight this and they didn't want to do that. Right. So like, like [00:06:00] the other people didn't want to do that. Right. We tried a bunch of different angles in terms of, you know, we, we, what we ended up doing w w we got Congress to get excited about it and sort of, they, they started to, you know, there was a.  Sort of a draft bill that had some, some very forward-leaning supersonic language that we, we you know, worked with Congress on it never passed in exactly that form, but it passed later in the 2018 FAA reauthorization. And then the thing that actually kind of ended up working was I had this idea in late 2017 was, well, you know, what. The, the sub the subsonic standard changes at the end of this year. Right. So, so so the end of 2017, so I was like, well, let's apply for type certification this year. Right. So we applied, like, we are nowhere close to an airplane. Right. And know we're close. Right. Right. And I was like, well, let's just, let's just, let's just like, screw it. We're going to apply like, like in 2017. And I had to like, get the execs to sign off on that. Right. We're going to do it, but we did. [00:07:00] So by the end of, I think December, 2017, we applied, I of course, you know, talk to my FFA colleagues and told them like, Hey, we're going to apply. Just so you know, they're like, well, that raises a whole bunch of questions. And, and that sort of got it, got them working down this path where they were like, well, you only have under part 36 of the FAA rules. You only have five years to to keep that noise standard. If, if you apply today and you're probably not gonna be done in five years. And I was like, that's true. We're probably not going to be done in five years, but we think that part 36 doesn't apply to us at all right. The way it's written. And then they went back and then they looked at it and they were like, oh, Part 36 doesn't apply to them like they're right. Like, you know, Eli's the first person in the history of Supersonics three per 36 and very closely. Right. And so and so then they went back and they like talked to their lawyers and, you know, they, I think came up with a new position in a new legal interpretation [00:08:00] w basically a memo that, that was, that was published that was like, okay, the subsonic standards don't apply and we don't have standards. We can start making some standards. And if we don't have one at any time for any particular applicant, we can make one for that applicant. We can, it's called the rule of particular applicability. So that kind of, once we got that, then in early 2018, like that kind of solved their problem. Like, and I think in in at least th th the domestic part didn't solve the international part, like from, from from Europe and so on. So. I mean, I, so, so if you think about like, what do you do on a policy team? Like you figure out like how, you know, how, how do you solve the problem that you have, that, that you were, that you were hired hard to fix and you just try things, try things until something works. It's part of the answer. Yeah. That's I mean, that's, I really appreciate you going into that level of detail because it's like the sort of like affordances of these things seem incredibly opaque. And just [00:09:00] for, for context, the subsonic standards are the standards that do not a lot, like that set a very like low noise bar. It's very stringent. I mean, the modern, the modern standards are pretty stringent. Like it used to be like, you couldn't, you couldn't basically like stand on a runway and have a conversation while plane's taken off these days. Like, I mean, it's, it's, it's gotten very, very impressive, but they, you know, the, the modern planes have gotten that way because they have high bypass ratios and the engines like big, big fans that move a lot of air around the engine core, not through it. Right. And so so that is, you know, that's just not workable when you're kind of trying to push that big fan through, you know, through the air at mock you know, 2.2 is what we were doing now. Now it's 1.7 that boom. But but but anyway, that's that, you know, that, that just doesn't work as a solution. So that's why, you know, it had to be different. Right. Right. And then did you say it's 30 S w w was it articles 36 [00:10:00] or 36? And volume, volume, volume, 14 of the code of federal regulations, part 36. Yes. Yeah. Yeah. And that's that, that's the part that specifies all the takeoff and landing noise certification rules for bar, all, all kinds of aircraft. Got it. And, and you re and there's like, like particular wording in that part that does not apply to  that didn't apply as it was in, in 2018. I think they've now rechanged some of the definitions. They went through a rulemaking To, to cover some supersonic planes, although interestingly, still not Boone's plane. It covers the plane up to Mach basically between Mach 1.4 Mach 1.8 and below a certain weight limit. So basically biz jets, right. Business jets, small sort of low Mach business jets, but it would be covered under, under the new role, but as part of that, they might have incorporated. [00:11:00] I, I forget the details, but they, they might've changed the definition so that so that boom was at least you know, would, would apply the five-year time limit and stuff like that might apply. Got it. Okay. And so that's so, so sort of like they, at a company, the policy team is like really going after a specific problem that the company has figured out anyway, to, to address that I mean, that, that was, that was how that one was. I mean, I think that there are different, there are different companies, right. And the companies that are playing more in defense rather than offense. Right. So you could imagine oh, I'm thinking of like a company like Facebook, right? Where like the first amendment applies for 30 applies. Like they have like the legal, like they have all the legal permission to operate as much. As as they need to. And they're mostly just like putting out fires right. Of like, like people wanting to like regulate them as utility and things like that. So, so it's, it's, it's more of a defensive mode in those companies, I think. But, but yeah, it's going to [00:12:00] vary from company to company, depending on what it is you need to do. And you just have to kind of be aware of all the different tools in terms of, well, you can go to Congress and get them to do something, and you might be able to get the executive branch to do an executive order, or you might be able to you know, get a new rulemaking or a new guidance or, you know there's, there's just a whole host of different tools in the, in the toolkit. And you've gotta be able to think about them in the different ways that you can use them to solve your problems. And actually so this perhaps getting a little ahead of ourselves, but speaking of those tools, like what in your mind is the theory of change behind writing policy papers? I think that sort of among many people, like you see. Policy papers being written and then, and like policy happens, but like, there's this like big question Mark Black box in between those two things. I think there's, there's, there's definitely different theories, right? I think so before I started at boom, when I was at the Mercatus center, Sam Hammerman and I [00:13:00] wrote a paper on Supersonics and that was, you know, that one I think actually was really influential. Right. So we, we published it a month before the 2016 election, when we thought Donald Trump was going to lose and we titled it sort of as a joke make America boom again you know, so it was like, the slogan was perfect. And and then lo and behold Trump gets elected and that paper like circulated in the, the sorta like when, when his administration got constituted in, in January, 2017 you know, a DLT like that paper circulator and people are like, okay, this makes sense. We need to be very forward-leaning on Supersonics. And, and so, so that, you know, like we still haven't changed the law that we said was most important in that paper. Right. That what we said is that we need to re repeal the Overland ban and replace it with some kind of permissive noise standard that lets the industry got going on Overland, Overland flight. But I think it was  influential in the sense of, it was some reference material [00:14:00] that a lot of different policymakers could look at quickly and say like, okay there, you know, there's some good ideas behind this and we need to support this broadly. And, and, and it's, you know, it's a reputable sort of outlet that, that came up with this and it's, and it's got all the sort of info that we need to, to be able to operate independently and moving this idea forward. Got it. So, so really is like a lot of just sort of like tossing, tossing things out there and hoping like they get to the person who can make, make a decision. Well, I think  you know, ideally you're not just hoping, right? Like ideally like you're, you're reaching out to those people establishing relationships with the right people and and, and sort of getting, getting your ideas taken, taken seriously by everybody that, that matters in your field. And another, so, so this is, again, just coming from [00:15:00] someone who's completely naive to the world is like, how do you figure out who the right person is? Well, I think it depends on what you need to do, right? So like, if you need to repeal an act of Congress, you know, you've got to go to Congress. Right. So, so that's that's an example. So I, so I don't know. I think a lot of times the right person is, is not just one right. Person. I think that there's like a, there's also a move where you're really just trying to go after elites in society. Right. Like if you can get, if you can get sort of like elites, however you define, I don't know what the right, right definition of that term is. But but, but you know, if you can get sort of a consensus among elites that you know, that, that supersonic flight should be allowed over land or that you know that, that we should invest, you know, like the con the government should invest deeply in, in like geothermal energy or that you know, Wait, we need to like have a a Papa program for ornithopter whatever it is. You know, if you convince, like it leads across the board in society that we should do this, [00:16:00] like, it it's pretty likely to happen. Right. It leads still, still sort of control the stuff that at least at least the stuff that nobody else cares about. If it leads care about it, then, then they'll, they'll get their way. One. What sort of pushback to that then I actually wanted to ask you about would be that there's there's this view that in a lot of cases, regulations sort of a codes, a trade-off into a very like a calcified bureaucracy and then sort of like seals it off specifically like an example being you could make this argument that. Nuclear regulation, as opposed to sort of being about health and wellbeing or the environment is actually encoding a trade off that like in order to absolutely prevent any sort of nuclear proliferation at all we basically just make it so that you can't build new nuclear things. What do you w what do you think about that? Do you have technology [00:17:00] regulations? I mean, I think like nuclear is, would be like, I would think that that would be like one of the hardest regulations change, right? The, the, the sort of you're taking an entire agency, like the national the nuclear regulatory commission. Right. And you're saying like, we have to completely change the way, like, like if I were, if I were at one of these efficient startups, right. It'd be like, All right. My job here as the policy lead or whatever, is to completely change the way this entire agency operates. Right? Like that seems really hard, right? That is that's, that's, that's really challenging. And, you know, I don't, I'm not optimistic frankly, about, about their success. And so, you know, so in, in sort of the more like the research-y like nonprofit side of policy that I do now, you know, like a lot of what I'm looking for is areas where it isn't, that it isn't hopeless, right? Where there, where you can work and where you only need like small change and it makes a big difference. Right. And so you're trying to find those [00:18:00] leveraged policy issues where, where you can make a big difference. So that's, that's, that's how I think about it. And it's issue selection. Like when you're, when you're in the nonprofit world and you have the luxury of that, right. Which you don't necessarily in the for-profit world Like that's really, I think that's really important. And that's what separates like good policy entrepreneurs from bad policy entrepreneurs is, is that sort of like awareness of issue selection, and, you know, small changes that make a big difference. And, and so let's dig into that. How did, how do you sort of like, look for that leverage? Like what, what yells to you like that, that you could actually make a big difference by changing a small thing? So I mean like, like Supersonics is a, is a great example, right? That's one that I chose to work on for several years. And that's like, if you could get rid of the Overland band, right. One, one line in the code of federal regulations, the bands over land and flight over land, right. You [00:19:00] would unlock. Massive amounts of aerospace engineering development in a completely you know, new regime of flight that no one else has, no one else is doing. Right. You'd get rapid learning. Then that curve you get like engines being developed specifically for that use case, you'd get, you know, variable, geometry, everything being developed.  For, for airliners and so on and, and you'd make a big difference you know, in, in the future of the industry and, and in the, in sort of this state of the art for, for flight. So I think if you could change that one line, even if you could, even if you couldn't change it international, right. If you could change it just in the U S right, you would get, I think the U S is big enough that, you know, sort of LA to New York and, you know, other plus all the over plus all the transoceanic markets that, you know, sort of the, you know, like a boom is going for now, right. If you got, if you got the combined, combining those two markets, you're at like, you know, DUP say doubling the market size for those planes. And and you'd get a lot more investment. And so, you know, it would be [00:20:00] a, it would be a huge A huge improvement. Right. And so, so I think that's, that's a highly leveraged one, one that I'm working on, you know, a lot more lately, I'm sure you've seen is geothermal, right. Where sort of like, I think there's no like real policy blocker, but the sort of the thing that I've been focused on is permitting, right? So if you want to, if you want a permit you know, there's a huge overlap between like the prime geothermal locations and federal lands. And so, so a lot of it's on, you know, so you need to get the federal government to give you a lease and, and you need to get their approval for it to drill the well. Right. And so that, that approval brings in, you know, environmental review and so on and conveniently the oil and gas industry has gotten themselves exempted from a lot of those environmental review requests. And my argument is like geothermal Wells are like the same as oil and gas Wells. So if they're exempted, like geothermal should be two, and that would speed up the approval time from something like two years to something like two weeks. [00:21:00] Right. So you'd go, you massively speed it up. Right. And so, and so, so just that sort of speed up on federal lands that wouldn't even change anything on, on private lands or on, on state lands necessarily. W w that, that sort of acceleration, I think, would, would, you know, could bring forward sort of the timetable for sort of the geothermal industry as a whole, by a few years. Right. So, so one small change. And so that's, that's, if you think about that, like socially, like, what is the value of that? It's many billions of dollars, right? So if I spend a year of my time working on that and, and get that changed You know, like my ROI for society for that one year is, is many billions of dollars, which is pretty good. It's pretty good. Pretty good. Pretty good way to spend my time. Right. Yeah. Yeah. I mean, there's, I mean, other things you know, like like I'm really interested in, in enhanced weathering, right? So olivine you're using olivine to, to to capture CO2.  And I think it's like, it was the neglected thing and I think policymakers just don't know about it and if I could [00:22:00] educate them and sort of, you know, get them, get them get buy-in for like some sort of, you know, pilot program or, or whatever, whatever would be, whatever the right answer is for for that. And I'm not sure what it is exactly. But if, you know, if you can get them going on that, it's like, oh, we, we, you know, potentially. Capture, you know, many gigatons of CO2 for, you know, 10 to $20 a ton. Yeah. That's, that's pretty cheap and we'd solve a lot of other climate problems. Right. And, and, and it would be maybe the cost of dealing with climate change would go down by something like an order of magnitude. Right. That would be that's, you know, like again, like pretty highly leveraged.  So that's like, those are some examples of like, why I've chosen to work on certain areas. But I think, I think I'm not saying those are the only ones by any means, and it just, just what makes a good policy entrepreneur is figuring out what those are. And, and I guess, like the thing that to put a little bit more is like, how, like, is there something that people could do to [00:23:00] find more of those leverage points? Like it was, it, is it, I guess there's like two, maybe two purchase. One would be just like take an area of interest and like, just like comb through the laws. Like basically like point changes that way to unlock things. Or is, is there a way to like actually sort of like look for potential point changes agnostic of the actual no, it's a great question. So, so, so I've been, so I've been, you know, trying to talk to people about like, what is the way to systematize this. Right, right. So I think that's the question you're asking and, and, and I've been, so I've been thinking about like, what, what is my, you know, what is my system, if I have such as I, such as it exists. And I think that the right answer is to come at, I mean, one is to come at it from the perspective of the entrepreneur. Right? So, so if you, if you think about it from the perspective of, you know, this is a company that is trying to do this thing, or I wish there was a company that was trying to do this thing, like, what would, what would, what [00:24:00] would they run into, right? What is that? What is the actual obstacle? What is the actual policy obstacle that they face? I think that that is the most construct. Way to do it. And, and, and to give you an example of a different approach, right? You can think about some, you know, a bunch of our friends, you know, we're working on this endless frontier, Zack, right. Which is like complete rethinking of the entire like science funding and technology funding thing. Like that is a different approach. And maybe that maybe, you know, we probably need some people working on that and that modality as well. But I, I think it's released for me, it's more effective to do this sort of more bottom up You know, think about it from, from the perspective of here's this thing I want to exist in the world. Like here's the specific narrow problem that they would face if they tried to do it, like, let me work on that as much as possible. Yeah. I think, I think another thing that's really important is you know, the, the policy analyst or whatever should try to learn as much [00:25:00] as possible on from on a technical level about, about the technology and how it works and like the physics of it or the chemistry of it, whatever it is. And I think a lot of, a lot of policy folks don't right. I think that they they're like, well, I'm going to deal with this like legal stuff. And I'm just, you know, I'll go to the engineers if I have a question, but I don't really want to learn it. And I think that that's, that's that's not helpful. I think you want to get in the weeds as much as possible. I mean, Boom. Like I sat people down all the time. It was like, I need you to explain this to me cause I don't understand it. And, and, and I just had tons and tons of conversations with the engineering team and, and, you know, people who weren't on the engineering team, but who understood things better than me and over time, you know, so it got to the point where like, okay, I understand, you know, these airplane design trade-offs pretty well. Right. And, and then, and then, and then when I'm talking to a congressional staffer or, you know [00:26:00] someone at, at a federal agency or something like that, that I can explain it to them. Right. And in sort of in a way that they can understand. So, so I think that you know, thinking from the bottom up you know, try and trying to put yourself in the position of the bottom of the entrepreneur working on it, looking at it from looking at it from you're not being afraid to dig into the technical weeds. I think those are. Those are the things that I would encourage sort of other people working in policy to, to experiment with and to try. And I think that would make them, you know, more, more successful. Yeah. And actually on that note another thing I wanted to ask you about is if you have any opinions about sort of how to get more technical people in to government and policy and like vice versa, help more government policy people like actually understand technical constraints. Cause I just find like very often, like it's like I had this instinct too, where I'm like, I don't understand policy, so I'm just going to like try to avoid [00:27:00] anything that touches government. And, and like that seems suboptimal. Yeah. So it's something that I think about a lot. We're thinking about a lot at the CGO actually is, is, you know, how can we. How can we, you know, either when we train people up, you know, in terms of, you know, young policy analyst, how do we get them to like, engage, you know, like maybe so we're exploring ideas right. Of how we would do this. Right. How could we, could we bring in young policy analysts and like kind of mentor them or like teach them how to, how to sort of, how to self-teach some of the technical stuff, right? Like, like like work through this stuff, or conversely, as you say, like we can take some technical people and, and sort of teach them the road. So policy, if that's what they want to do. Right. And, and, and give them that, that toolkit as well. And cause I think that the overlap is, is really, is really effective. If you can get it, if you can get someone that's interested in playing in both spaces, I think that that is really effective. [00:28:00] And, and the question is like, who are these people that want to do it? You know, there's not, it's not really like a career track. Exactly. Right. It's. And so, you know, if we, if we found a bunch of people that wanted to be that, that you know, in, in that sort of Venn diagram overlap, like we would, we would definitely be interested in training them up. Yeah. W w one thought there is actually sort of what we're doing right now, which is making the, the policy process more legible. In that, like, I, I think it's, it's very silicone valley has done a very good job of like, making people see, like, this is how you change the world by like starting a tech company, whether that's true or not. But it's, it's like very unclear, fuzzy how one changes the world by like helping with policy. So like just making that legible seems very important, you know, I think, I think the other thing about it is that you know, Silicon valley, you know, I think investors and entrepreneurs are too afraid of. You know, what they would call [00:29:00] policy risk, right. Or something like that, you know, like, like, you know, I think it's you know, I think it varies case by case how much of a risk it actually is. But I think it, you know, sort of my view when I was at boom was like, look, there's no way that FAA is not going to let us certify plane. Like, there's no, like, like w we will, they will run us through the ringer. It'll be expensive. Like we'll have to like, spend, you know, new, all kinds of tests and stuff like that, but they are not going to get, we're not going to get to a point where, like, we have a plane ready to ready to fly. And like, yeah, it's not certifiable because of like, something like, like noise. Right. And so, and so like, like there was, or there, you know, there is not like that much policy risk and, and a lot of things you know, I wouldn't feel that same way about like a nuclear startup, right. Like like efficient startup, but but, but sort of being, you know, I think that I wish that The investors were a little bit more savvy about like, what is a smart policy risk to take [00:30:00] and, you know, what, what can be, what can be worked and what can't in terms of policy risks. Yeah. And again, I think it's, it's one of those things where it's like, we need more ways of people actually understanding that of like, like how do you, how do you grok those things? And then I guess, I guess the last thing on, on sort of the regulation front is like, are there historical examples of like sort of like very broad deregulation that enabled technology, like actual, like, it feels like regulation is very much this like bracket where like we keep regulating more and more things. And every once in a while you get like a little bit better, like in the FAA case, but like, is there ever a situation. There's a really big opening up.  Yeah, there, there are a few cases.  Aviation is a perfect example, actually. So, so if you're, I don't know if you've read the book hard landing, but but it's an excellent recommended it if you're, if you're interested in this at [00:31:00] all, but it's basically a history of sort of the aviation industry up through what they call deregulation. Right. Which is there's happened in the I guess the late 1970s. Because up until that point from I don't remember when it started, but there was this thing called the civil aeronautics board that basically regulated routes and affairs. So if you were an airline, you got to fly the routes that the government told you, you could fly and the fares that they, and you, you, you got to charge the fairs that they Told you, you could charge. Right. And you couldn't give discounts or anything like that. Right. Like you had to charge like that fair. Right. And so, so like, what did you have to compete on? Like, like, not very much, right? Like you, you competed actually like on in-flight service and stuff like that. So So, I mean, you had sort of before that deregulatory era, you had a very lavish in-flight meals and stuff like that. And, and super expensive, super expensive, super expensive tickets and not a lot of [00:32:00] convenient route choice and so on. And then And then sort of in the late 1970s under Jimmy Carter, I think I think Ted Kennedy was was the, one of the big proponents of it. So was like getting rid of the civil aeronautics board. They got rid of it, right. Like they got rid of an agency. And so and, and so that sort of deregulated the, the routes and, and the city, you know, city pairs and, and times, and, and the fairs that they could charge. So now, like you can buy like, you know, a ticket to Orlando or Charlotte, or, you know, whatever for like 200 bucks or less. Right. And, and it's and you know, that's all thanks to deregulation. Oh, that's not really exactly an enabling technology, I think, which was your initial question, but it just allowed the industry to move forward and and, and become a whole lot more efficient. And so one could imagine something similar for. Like technology regulations. Yeah. I think in getting rid of an entire agency is pretty rare. But [00:33:00] but, but, but yeah, I think that but yeah, it's, it's not, it's not like a lot of people think like regulations a one way ratchet. That's not totally true. Like there have been, has been times in the past where we got rid of a whole lot of regulation. Yeah. And actually related to that, do you have any good arguments against the position of like, we need regulation to like keep us safe besides sort of well, we also need to like, like there is too much safety. Like I, I find, I wish there was like a more satisfying thing besides like, well, you know, it's like sometimes we'll have to take risks. Right. So I think, I think, I mean, it's, it's true that Like, there's not, there's not like from an economics perspective, like there's not really a good argument for regulating safety, because you would think that the customer could, could make their own choice about how risky they want to live their life. Right. And so so, so it is a little bit awkward from that point of view, I think we're never going to get a situation where the government [00:34:00] doesn't regulate safety and a lot of things, right. They just it's just reality is that you know, the peop the public like sort of wants the government to regulate safety. And so therefore it will. But I think that there is still a difference in the kinds of kinds of safety regulation that we could have. Right. So, so I think one example that I think about a lot is The way planes are regulated versus the way cars are regulated. So if you, if you think so with, with planes FAA sort of type certifies, every plane that is produced or that is registered  model of plane that is produced and you have to get that yeah, it has to get an airworthiness certificate and stuff when you register it. And so that's, that's an example of what's called pre-market approval. Before you go on the market, you have to be certified, right? Drugs are work that work the same way with cars. It's a little different, right? You have car safety standards that, that NITSA promulgates and enforces. But The way that that is [00:35:00] enforced or the way that that is, is dealt with is that the car companies, you know, know that they have to design to these standards NITSA monitors, the market, all right, the marketplace, they sample sample cars that, that and, and test them and stuff like that. And or if they observe a lot of accidents or whatever, they can go back and they can tell the, the car company. Okay. You have to do a recall on this car. And, and make sure, you know, fix all these things that we found that, that aren't up to snuff. Right. Right. And so, so, so that's, that's an example of post-market surveillance, right? So those are both safety regulations, but they have huge structural differences in how they operate in terms of, you know, how, how much of a barrier is there to like getting to market, right. The pre-market approval cases. It means you're, front-loading all of the costs. You're delaying you're, you're making it hard for your investors to recoup any, any returns, just see if the whole thing is going to work, et cetera. So there's like all kinds of effects of that. Whereas in the post-market surveillance model, like you're incentivizing good behavior, but we're not going to [00:36:00] necessarily like verify it upfront. We're going to, which is costly. We're gonna, we're gonna let it play out in the marketplace for awhile. And if we detect like a certain degree of unsafeness, we're going to make you fix it. Right. And so I think of that, I think of that structural difference is really important. And I would, I would like to see. It's more of that that post-market surveillance model. I mean, you could think about it even for drugs too. Instead of, you know, instead of upfront clinical trials, we could say, okay, like you have this technical here. Like we see that it makes sense as a potential treatment for this thing. Like, you know, you would have to test it on people one way or the other. Right. In terms of you know, w whether it's clinical trial subjects or patients who have had the condition we will allow you to use it on this, but we're gonna, we're gonna monitor like, carefully what the side effects are in those early applications of the drug. And if it turns out to be unsafe, we're gonna pull it. Right. And so that that's, that would be a different way of doing it. You know, you can imagine we could do that. Right. But that's, [00:37:00] that's just not where we are. And so I think it is hard for people with You know, sort of bought into the current system to, to think about like how we would get there or how that would be, you know, why we would ever do that. Right. It, it, it does seem much more attractable to just say like, okay, we're still going to regulate, but we're going to do it in a different way though. Like, I, I really liked that and I, I hadn't thought about that very much. I'm going to completely change gears here. And let's talk about GDP, total factor productivity. Your, your stated goal is for GDP per capita to reach 200 a thousand dollars by 2050. And just for the listener context, I looked up some numbers. The current global GDP is $11,000. So we're talking about more than an order of magnitude increase. The highest right now is Monaco at 190 K. So they're not even so I, so I'm, I'm, I'm thinking like S specifically I want to get to 200,000. I want to get everybody there [00:38:00] eventually, but by 2050, I think we, I think we could get the U S so the U S has 63 K right now. Which so, so like we've got a triple it, yeah, we've got it from the blood. And so the interesting thing that I think is like, so the U S looks like it's both low places like Ireland and Switzerland. And like, so, so, so my, the thing that I'd like you to justify is like why high GDP is the thing we should be shooting for, because I would argue that like, sort of on a, like, things that are going on there's like, I would rather be in the U S than Ireland or Switzerland. And so, but like they have higher GDP. Yeah. So like Ireland, is this a special case where like, they have a bunch of tax laws that are favorable and so a lot of like profits and stuff get booked there. So, so I, so I think that that's, I think that's what's going on there. So I would say so GDP is Is it not a perfect metric. [00:39:00] I think that the degree to which it's imperfect, it's often overstated by, by people. So it's, it's pretty good. Even, so I would say I like TFP better as a like, so I, I, I use GDP per capita because I think people are more familiar with it and stuff like that. But I, what I actually think about is in terms of TFP and so total factor productivity is just like, how much can you get more output? From a given amount of inputs. Right? So like, if, you know, if I have in my society, a certain number of plumbers and a certain amount of you know, lumber and a certain amount of, you know, any, all the inputs that you have, right. What can I make out of them? Right. Like how much, how much, how much was the value, total value of all the goods that I can produce out of all the, all the resources I have going in. Right. And you want that number to be as high as possible. Right. You want to be able to produce as much as possible given your inputs. Right. And so that's, that's the, that's the idea of TSP. [00:40:00] And just to like, dig into that, how do, how do you measure inputs? So like, like outputs is just like all, all like basically everybody's receipts, right. So I'll put, so, so in this, there's a very simple model yeah. That people use, right. It's called the, the sort of the solo model. Right. And the idea there is you have you have GDP, which is just a number, right? It's a, it's a dollar value  real GDP is what you're concerned about. And then you have how much, how much labor do you have and how much capital do you have. And then, and then you you take logs actually of it, and then you do a linear aggression. And then the residual, the residual term in that regression is your, is your number for a total factor productivity or log total factor productivity. And so that's, that's how you would do it. Is it, that's a very, very rough estimate right. Of, of how you do it. Sometimes people add in things like human capital levels. Right. So if we if we brought in like a bunch of an educated [00:41:00] immigrants and and brought them in, so, okay. Like labor productivity would go down. If it's measured naively, but if you include in that regression, like a human capital term to, to to reflect education levels, like then, then it wouldn't right. Ideally it wouldn't. So, so anyway, so that's, so that's how you do it is you, you, you, you take labor, capital and output and you figure out the relationship between them and you see that you're getting more output than you used to from ideally hopefully from the given amount of, of labor and capital that that went into it. That's not true in every country. Right. You know, actually our countries where you go down in an output over time. So Brazil, where I. Peaked in total factor productivity in the year of my birth in 1980. And so, so, so it takes about 50% more resources today to produce the same amount of output that they produce that in real terms. Right. And, and, you know, Venezuela is like a basket case, right. They produce way less. So, so so it's, it's, I think it's a [00:42:00] good it's a good concept for thinking about two things bound up together. One is technology and the other is the quality of institutions, and those are the two things that if you improve them, then, then your output, given a certain basket of inputs is going to is going to be higher. Yeah. That's, that's compelling. I buy into the school of thought that institutions are like kind of a social technology that like, should we just actually talk about it that way? And like, to sort of sort of like prime my intuition and like other people's intuition about TFP are there examples. In history of like technologies that like very clearly increased TFP. Like you can like, see like thing invented TFP, like brand of TFP increased shoots up. Yeah. So, so the the guy who's written the most about this is this guy, Robert Gordon. And what he actually would argue is the thing it's like thing invented like a few decades pass [00:43:00] while things like integrating it and figuring it out, then big increase in, in, in, in TFP and GDP. Right, right. And so, and so he, he had this paper and then eventually a book on the five grade inventions. Right. And I, and so things like the internal combustion combustion engine, the idea of. Like sanitation plumbing, et cetera. The idea of pharmaceuticals, chemistry, and pharmaceuticals electricity was probably one and I think that's four, right? And I, and the fifth escapes me right now, but he, he basically argued that we had these sort of five great inventions in the late 18 hundreds. It took a few decades for them to get rolling. And then from 1920 to 1970, you had this like big spasm of growth TFE grew 2% a year. And he basically would argue today that's unrepeatable because we don't have those great inventions. And all, all we really have, according to him is, is progress in it. Right. Like we have, so we have one great invention [00:44:00] and, and that's, you know, it really still hasn't shown up in the productivity statistics. It may still be coming, but he would argue. Yeah. There's just, you know, we've, we've eaten all the low hanging fruit, like there's no more great inventions to be had. And when we just got to settle for a, you know, half a percent a year or TSP crows from here on out, but as I understand you disagreed like I, I certainly share your biases. And so recently you posted a great article about like possible technologies stack that could come down the pike. Do you have a sense, like, and so like through the framing of TFP do you have like, of, of all the things that you're excited about, like which ones do you think would have the biggest impact on TFP and like, what is the mechanism by which that would happen? I mean, so, so, so I think probably the closest, the thing that's like closest to us, where we are now is it's probably like big energy [00:45:00] price reductions. Right? So I've, I'm really bullish on geothermal, I think like 10 years from now. It's totally possible that we would have you know, sort of a geothermal boom, the way we had like a shell boom, right. In energy, in the, in the last 10 years. And then we'll be talking about like, oh man, like energy is getting so cheap. And so energy is something that sort of like infuses every production process in the entire country. And, and so it's difficult to really explain like how exactly it moves iffy. It just moves everything. Right. It just makes everything. You know, if we get, if we get energy costs, you know, down by, by half or something like that, then it makes a lot of things twice as, as productive or, or some, or some maybe not exactly twice, but a lot more productive. So that's, that's one example, but then like other things like longevity, right? Like, let's say we, we we, we fix a fix, you know, extending lifespan and say compress morbidity. Right? Like we make it so that people [00:46:00] don't get sick as much. Right. Well, that manifests as lower real demand for healthcare services. Right. So, so it's like, you don't even go see a doctor until like you're 90. Right. And like, and you don't need to learn because like you're still healthy. Then show up in GDP. They do. Right. But they, but what would happen. See here's where you have to distinguish between real and nominal GDP. Right. So in real, in real GDP, like we would, we would get the same, like with, with proper accounting, right. We would get the same or better. We'd get better at levels of health with fewer dollars spent on it. Right. So we'd be more productive in that, in that sense. Right. And so so we would so we might spend less on health services. But we would also have, we would employ fewer people in those sectors. Right, right. The employ those people would, you know, smart people right now who work in the healthcare sector, those people would all get to do other things like, and they would, they would all become researchers or, [00:47:00] you know, other, other kinds of technicians or, you know, whatever. And, and, and those people would produce things in their new role. So it's like, if, if, if all of a sudden we did not need. As many x-ray tacks or something like that. Right. And all those x-ray techs are out doing new things. That's like getting the x-ray texts for free. Right. It's another way of saying it is like we're getting all that for free, that same output that we used to get, we're getting it for free. And now we are we're taking those same people and, and getting the produce even more on top of it. So, so, so when you think about real GDP, like jobs are costs, right? Like you don't want jobs and you actually, you actually want to reduce as much as possible, like the spending on the need to spend money on things even. Right. And so that's how you actually increase productivity and ultimately real living standards and real GDP. And, and do we actually measure real GDP? Is that like hospital or is it like, sort of like a theoretical concept? No, [00:48:00] we, we, again, it's, it's kind of like the FP, right. We infer it. So we, we sort of And we estimate nominal GDP based on just how we, how we spend, how people are spending their money and how quickly they're spending it and so on. But even that, it's not like we're counting every receipt in the economy and adding tabulating them. Right. It's it's still an estimate. So we're estimating nominal GDP, and then we're also estimating the price level changes. Right. And so you address the nominal GDP estimate by the price level change and that's your real GDP number. Got it. Okay, cool. This is, I really appreciate this because I see all these terms being thrown around and I'm like, what is actually the difference here? Like what's, what's going on. And last question on TFE, can you imagine something that would be like really amazing for the world that would not show up in TFP? Is it like as just like a thought. I think, I think stuff that improves the quality of your leisure [00:49:00] time is unpaid, right? Like, like or that, or that you almost get for free. So like you know, if let's say, let's say open a designer, like an open source video game or something like that. And like, everybody loves it and it gets super high quality leisure time out of it. Right? Like there's no money changing hands. There are utilities going up. Right. So, so like you would, you would think that that would improve living standards without, without showing up in measured GDP at all. Right. So that's, that's the kind of stuff that it's like, yeah, he's got, you got to have that in the back of your mind that, that that's the kind of thing that could, you know, throw off your Your analysis. Okay. And so, and this is actually what some people claim is like, oh, the value of, of the internet, you know, the internet has, has, has increased welfare to something sentence. It's like, okay, yes. To some extent, but, but is it, you know, it's not like a whole like percent, 1% growth a year. It's not, it doesn't, it doesn't account for the reduction in, in TFP that we've seen. Yeah. [00:50:00] Yeah. That makes a lot of sense. Changing gears again make the case for airships air shifts. Yeah. So I think you know, you have. Cargo that is, there's basically two modes that you can take cargo on today. You can take them, put them on a 7 47 freighter, let's say, and, you know, get them to the destination the next day. And it costs a lot of money or you can put them on a container ship and it's basically free, but it takes, you know, a few weeks or even months to get to your destination. And, you know, what, if there was something in between, right? What if there was something that would take, you know, say four or five days anywhere in the world. But it's, you know, like a fifth of the cost of, of an airplane, right? That, that that's like a sweet spot for cargo you know, anywhere in the world. And. You know, so, and then, so with airships, there's an interesting thing about them is that they actually get more efficient, the bigger they get. [00:51:00] And so this is, I think the mistake that everybody's made when designing airships is, they're like, okay, we're going to design this cargo Airship to take like 10 tons to remote places. Well, no, you should be designing it to carry like 500 times, right. Because there's a square. Rule. Right. Right. If you, if you if you increase the length by a certain percentage, the, the volume increases by that factor to the cube, to the cubic power, through the third power and the the surface area and that the cross-sectional area increases by that power or that factor squared. Right. Right. And so your lift to drag ratio is getting better. Cause you, your, your lift is associated with the V with the volume and your drag is associated with the cross-sectional area. And so you're, you're getting more efficient, the bigger you get. And so I think if you designed say a, an Airship to go to carry about 500 tons a time at a time, so it's like four loads for 7 47 loads [00:52:00] at a time. And and, and, and sort of your target. Goods that had a value to weight ratio. That's sort of in the middle of the spectrum. So it's not, not computers or really high value items or, or electronics even, but more of the things like machinery or cars or part, you know, parts for factories and stuff like that. You could that be a nice little business and and you could. You know, provide a new, completely new mode of, of cargo transport. I think that would also be revolutionary for people in landlocked countries. You know, so, so, you know, I, I spent gosh, like a week in, in Rwanda about 10 years ago and, you know, just sort of like studying the country. And and one of the things that we noticed was to access a port on, in Tanzania, like, you know, you'd have to like, it's like 700 miles away or something like that, but you, you have to put the goods on like rail and the real [00:53:00] gauge changes several times between there and the port. And every time the rail gauge changes, like you would have to like pay a bribe to somebody to like move it and stuff like that, like just do their job. And and so that adds up to a lot of inefficiencies. So it's really cheap to get your container to the port on the coastline, but then to, to get it the last 700 miles, it's really expensive. Well, what if you could just get around that by, by taking something in the air ship, right. And so if you, if you designed the Airship for this, like transcontinental or, or Intercontinental. You know, ocean shipping market it would also work for that for that sort of landlord market pretty well. And you could, you know, you could, you could actually bring more than just machinery to a country like Rwanda from from, from that. And then I think there's also a high value remote services market, right. And this is, this is the one that people are going after and sort of like a standalone sense to some degree, like you know, smaller ships that carry 10 or 20, or maybe even 60 tons. It's like, okay, [00:54:00] yeah, you could serve that market, but even better if you design it for a 500 ton model. So, so anyway, that's, that's sort of, my view is like, this is a missing product that we should have. You know, it's over a hundred year old technology. We have way better materials today than we had in the last sort of the last Airship. Yeah. Think about like the, the rigid bot they ships of the past, they'll use aluminum for their internal trusses and you know, carbon fiber protrusions would have something like a six, six fold strength to weight ratio improvement. And let's say you double the, the safety factors. Okay. So your, your weight goes down by a factor of three for your, for your whole structure. You could do it autonomously today. You don't, you don't have to have labs and heads and, and galleys and all that stuff, and you don't have to have bunks. Like you could, you know, if you were on a a manned air ship, like you'd have to have multiple crews because, you know, it's like five day journey. So, or at least some of them would be so do it completely autonomously. [00:55:00] And then another question is like, could you use hydrogen as a lifting gas? Right. Because I mean, so there's a bunch of different arguments for why maybe you could, but if you were on yeah. You know, even, even, even the safety regulator would have to say, well, okay, like this might burn up, but like there's nobody on board. So so maybe it's okay. So, so anyway, I think that there's, I think there's definitely something really interesting there in terms of new, new vehicles that we could have that would enable, you know, a new mode of transportation for at least for Kartra and the so, and you've also written that it's less a technology question and more that sort of like a company that's willing to go all in on, on logistics question. And it seems like th th the way that I see it, it's like the problem is that there's not a like super lucrative niche market to go after. I think it could be super lucrative. And I think the, the, the big market is super lucrative, right? If you're, if you're let's say, you know, [00:56:00] you are. Yeah, let's say you can get 5% of the cargo of the container market, not the bulk cargo, like forget the bulk cargo. Don't don't do that. Like, don't go for the stuff that's already on air freight. Right. You might get some of that anyway, but, but just, just the, the stuff that's containerized today, right. If you could get 5% of that, I think that that would be 4,000 airships. And, you know, if you're, if you're the first one to market, like you have a monopoly right on that, or at least that, that segment of the market, and you could charge it like a decent markup. I think, I think it's like a, you know, you could in revenue, you could make like 150 to 200 billion a year, something like that. Right. And, and then, and then say you get you know, half of that in profit, right. An operating profit at least you know, like it's not a small market. So the culture problem that I see is like that it's, it's worth calling out is like, that is you need to like come out of the [00:57:00] gates at a certain scale.  That would make it very hard to sort of like ramp smoothly, I think is like, it doesn't, it doesn't work with a small airstrip. Like you can't do like a half size Airship and expect to be competitive or like a small company even. Right. Like you just come out of the gates with like a big fleet, right? Like you could say, you could maybe like, say like your first, your first five airships are targeting, like the remote market where they might have a higher willingness to pay. I think that that could be a thing you do, but yeah, you want to just, you want to rent production and just, just churn out you know, hundreds of, you know, hundreds of airships a year, right? Like that's what you want to do. It's hard to call out. It's not like that. There's like this gap here. It's like, there could be this amazing, this like amazing new thing, but it's just like the way that companies start now. Yep. It does exist. Cool. And so in this last part, I want to just do some sort of rapid questions take as [00:58:00] long or as little time as you want to, to answer them. Why is your love of vertical farming? Irrational?  I think it's, I like I am by no means a farming expert. Right. So like, so I, I see these th this sort of technology and I'm like, this is awesome, but I know next to nothing about it. So it's not like an informed like, well considered love it. It's sort of just like, I I think that this would be super cool if we moved to, into our farm. Right. And that's, that's about the extent I would say it's like potentially rational. It's potentially rational, but it's, it's, it's, it's not it's not well grounded. Okay. Why are there so few attempts at world dominance? Oh, man. I wrote a blog post on this a long time ago and I don't remember the answer.  Oh man. I don't know. I think it's, I think I think it's a, I think it's a puzzle, right? You, you see these people who become like globally famous and super influential and they and they just sort of they, they sort of Peter out and they become self satisfied with whatever they [00:59:00] accomplish. But like somebody like there, there are some really talented people out there that you would expect some of them to apply themselves to this problem that I feel like the power influence of like extremely like wealthy, powerful people is like shockingly small compared to what I would expect. Like, I dunno. It's like, I feel like Jeff Bezos actually has a lot of trouble like making the things that he wants to happen with the world happened. And I find that certainly certainly true with like blue origin. Yeah. Yeah. Or just like, sort of like any, anything, like, like you see, you see all of these people who like we think of as like rich and powerful and like, they want things to happen in the world. And like, those things don't seem to happen very often.  And that, that puzzles me. Like I have no, you know, I'd say that it does raise the question of like, whether there are people who actually are having a massive influence, which don't know who they are. Right. The, [01:00:00] the, the gray eminence. Yeah. The person behind the scenes who are, who's like really, really influential. Yeah. Yeah.  Sort of within your field defined broadly, or like, however you want who do you pay attention to that many people may not be aware of? Oh, thank you. Okay. But like in all seriousness  do I pay attention to, I mean, I think I don't know. I'm, I'm blessed to have have people who just like, you know, me out of the blue and like, like tell me things. And, and, and so so I, so I have a, I have a couple of friends, so like one that I worked with for many years who like still texts me, like interesting things all the time. And, and, you know, sort of like the, sort of the private conversations that that could, that could be public conversations. If there were like more public people, but they just like choose to choose to be like totally behind the Steens and choose to be gray. Eminences let's say. And, and like that, I think that that is a. [01:01:00] Like that's who I pay attention to. A lot of the time. Yeah. Yeah. That's that's fair. And I guess just like finally what are, what are some, we've talked about some of them, but like some unintuitive blockers for your favorite technologies, unintuitive Walker. So I think that that, like, I've written a lot about NEPA, right. This, so you may have heard me see me do a lot about this. This is the national environmental policy act. And, and, and so, you know, I think it's like sort of the theory behind it is like, okay, before we decide, we're going to like, Build this highway or whatever we're going to like study it and make sure that like the, what makes sure we understand what the environmental impacts are and that if, you know, if there are negative environmental impacts, we're gonna like study alternatives as well. Right. And, and so what got me sort of worked up about that was I was in a very high level meeting with FAA, got like, seen very senior, very senior people. [01:02:00] And, and, and sort of like the conversation like went to like, well, why can't we just change the, you know, the Overland bed? Like, why can't we do it? And so, and like one of the answers, and it's not the complete answer, one of the answers was like, well, we would have to do an environmental review if we were to change. If we were to change the. Of Berlanti rule and we don't have the data to justify, like, to even say what the impacts are like, what are the environmental impacts of, of Sonic booms on people? Because like, you know, and so this is why like NASA is doing a, a, a study to you know, they're, they're developing actually a many hundreds of millions of dollars.  Airplane T to be a low, low boom demo. And they're gonna fly it over you at the cities and like figure out what the response, the human response is, so that we can have that data so that we can do an environmental impact study. Right. So, so that's [01:03:00] so, so yes. And so, so under so last year there was a rule change in NEPA, sort of in the implementing regulations that said that if you don't have data, that is okay. You just have to say, you don't have the data in the environmental impact statement. That's supposed to be enough. That's supposed to be adequate, like NEPA is not a requirement to go and do science projects. Right. So I wonder if that conversation would go differently if we were having it today. But, but that was the answer at the time was like, we don't have the date. To do this environmental impact study if we were to change it if we were to try to change it today. So, so that they, to me, like that was like that, like that radicalized me on NEPA. Like that is like, that is really, really wrong that that we're like, this is, this is stalling progress in Supersonics, even though it was, you know, it's not really like what it was intended to do or or, or, or this idea of like, you need to go do a you know, $500 million experiment in order to get the data so that you can change one rule, one line, one [01:04:00] line in the regulatory code. That is, and then the question is like, how many other things has depo? Yeah, no, it's, it's when I started looking into it, it's everything it's like, it's like, I think it's it's not the only reason for, you know, the great stagnation, but it is a major factor. Yeah. Let's see. Is there an optimistic, like the one thing I don't want to do is close on a pessimistic note. So like, what is something that people should be optimistic about that they're probably not optimistic about right now?  I don't know, man I think so I would say I would say air pollution, right? So, so the, so the bad news is air pollution is way worse than we thought it was. Right? Like it's, it's like in terms of like the health effects and, and sorta like the, there's like all kinds of like negative effects on crop yields. I just saw a paper about it just, it, the more we look into it, the more terrible days, but we are, you know, I think we're going to make a [01:05:00] transition pretty quickly here to electric fuel. And that is going to significantly improve air pollution. And we're going to get, like, I think all these unmeasured benefits from, from cutting out, you know, especially the diesel emissions are the worst ones. And, and sort of, you know, kids getting asthma and, and, you know, like low birth weight babies, and like a lot of other things, there's just like, a lot of those problems are going to be all of a suddenly and unpredictably and, and, or, you know, seemingly out of nowhere, like reduced because we're going to have a lot lower air pollution, especially. And then if you think about everywhere else in the world too, like, I mean, the U S isn't so bad relative to a lot of other countries. So so yeah, there's just, I think just the switch to electric is going to is going to be a big deal. Yeah. Especially it's, they're all powered by geothermal plants. Oh, yes. Right. That's even better. Amazing. Thank you so much for doing this. [01:06:00] You've really have taught me a lot. And hopefully it will push more, more of these things forward. Well, it's been super fun, Ben. Thanks for having me.  

In this conversation I talk to the Amazing Arati Prabhakar about using Solutions R&D to tackle big societal problems, gaps in the innovation ecosystem, DARPA, and more. Arati’s career has covered almost every corner of the innovation ecosystem - she’s done basically every role at - DARPA she was a program manager, started their Microelectronics Technology Office, and several years later returned to server as its Director. She was also the director of the National Institute of Standards and Technology and was a venture capitalist at US venture partners. Now she’s launching Actuate - a non-profit leveraging the ARPA model to go after some of the biggest problems in American society. Links Actuate Website In the Realm of the Barely Feasible - Arati's Article about Actuate and Solutions R&D Arati on Wikipedia  Transcript [00:00:00] welcome to idea machines. I'm your host and Reinhart. And this podcast is a deep dive into the systems and people that bring innovations from glimmers in someone's eye, all the way to tools, processes, and ideas that can shift paradigms. We see these systems outputs everywhere, but what's inside the black boxes with guests. I dig below the surface into crucial, but often unspoken questions. To explore themes of how we enable innovations today and how we could do it better tomorrow. In this conversation, I talked to the amazing RFE provoca about using solutions R and D tackle, big societal problems, gaps in the innovation ecosystem, DARPA and more. Are these career has covered almost every corner of the innovation ecosystem. She's done almost every job at DARPA where she was a program manager, started their micro electronics technology office. And several years later returned serve as their [00:01:00] director. She was also the director at the national Institute of standards and technology and a venture capitalist at us venture partners. Now she's launching actuate a nonprofit leveraging the ARPA model to go after some of the biggest problems in American society. Hope you enjoy my conversation with Arthur. Provoca.  I'd love to start off and sort of frame this for everybody is with a quote from your article, which, which everybody should read and which I will link to in the show notes. You say yet, we lack a systemic understanding of how to nurture the sort of rich ecosystem we need to confront the societal changes facing us. Now over 75 years, the federal government has dramatically increased supportive research and universities and national labs have built layers of incentives and deep culture for the research role. Companies have honed their ability to develop products in markets, shifting away from doing their own fundamental research in established industries, American venture capital and entrepreneurship have supercharged the startup pathway for commercialization in some [00:02:00] sectors, but we haven't yet put enough energy into understanding the bigger space where policy finance and the market meet to scale component ideas into the kind of deep and wide innovations that can solve big previously intractable problems in society. These sorts of problems, aren't aligned to tangible market opportunities or to the missions of established government R and D organizations today, the philanthropic sector can play a pivotal role by taking the early risk of trying new methods for R and D and developing initial examples that governments and markets can adopt and ramp up the hypothesis behind actuate is that solutions R and D can be a starting place for catalyzing the necessary change in the nation's innovation ecosystem. And so with that, with those, I think I want to test it in a nutshell exactly like that. So can we start with how do you see solutions R and D as being different from other R D and, and sort of coupled with that? How is actuate different from other non-profits. Yeah, I think [00:03:00] that's, that's one of the important threads in this tapestry that we want to develop. So solutions R and D let's see. I think those of us who live in the world of R and D and innovation are very familiar with basic research. That that is about new knowledge, new exploration, but it's designed all the incentives, all the funding and the structures are designed to have that end with publishing papers. And then on the other hand, there's. But the whole machinery that turns an advance into, you know, takes a technological advance or a research advance and turns it into the changes that we want in society that could be new products and services. It could be new policies, it could be new practices and that implementation machinery. The market companies, policymaking, what individuals choose to do pilot practices. I think we understand that. And there are places where the, you know, things just move from basic research over into actual [00:04:00] implementation. But in fact, there are, there are a lot of places where that doesn't happen, seamlessly and solutions, R and D is this weird thing in the middle. That builds on top of a rich foundation of basic research. It has it, its objective is to demonstrate and to prove out completely radically better ways. To solve problems or to pursue different opportunities so that they can be implemented at scale. And so it has this hybrid character that it is at the one on one hand, it's very directed to specific goals. And in that sense, it looks more like. Product development and marching forward and, you know, boom, boom, boom, make things happen, execute drive to drive, drive to an integrated goal. And on the other hand it requires a lot of creativity, experimentation risk-taking. And so it has some of those elements from the research side. So it's this middle [00:05:00] kingdom that I. Love because it has, I think it just has enormous leverage. And I, you know, I, I think a couple of points, number one, it's it requires to do it well, requires its own. Types of expertise and practices and culture that are different from either the research or implementation. And secondly, I would say that it, I think it's overall in the U S in the current us innovation system. I think it's something of a gap. There, there, there, there, there are many, many areas where we're not doing it as well as we need to. And then for some of the new problems, which I hope we'll talk about as well. I think it's actually a very interesting lever to boot the whole system up that we're going to need going forward. Yeah. And so actually just piggybacking right off of that, you've outlined three major sort of problems that you're tackling initially. Climate change sort of health, like general American [00:06:00] health and data privacy. I'm actually really interested in, like, what was the process of deciding, like, these are the things that we're going to work on. Yeah, but this whole actuate emerged from a thought process from a lot of. Bebe's rattling around in the box car in my head in the period as I was wrapping up at DARPA in 2016, at the end of 2016 and going into 2017 when I left and what I was thinking about was how phenomenally good our innovation machinery is. For the problems that we set out to tackle at the end of the second world war, that agenda was national security technology for economic growth. A lot of that was information technology. We set out to tackle health. Instead we did biomedicine. We went long on biomedicine, didn't break their left, left a lot of our serious health problems sitting on the shelf and a big agenda was funding, basic research and, and we've executed on that agenda. That's what we are [00:07:00] very, very, very good at what I couldn't stop thinking about. As I was wrapping up at DARPA is the problems that I think will, you know, many of us feel will determine whether we succeed or fail as a society going forward. So it's not that these challenges, you know, national security or how it's not that those problems have gone away and we should stop. It's just that we have some things that will break us at our. Yeah, arguably, they are in the process of breaking us. If we don't deal with them right now, one is access to opportunity for every person in our society. A second is population health at a cost that doesn't break the economy. Another is being able to trust data and information and the information age in which we now live. And the forest obviously is mitigating climate change. And if you think about it, these, these were not, but these weren't the top of mind issues at the end of the second world war, right? I mean, we had other problems. We didn't really know what to do about. So some of these are all problems that we didn't really know what to do about. Some of these are new problems. And, [00:08:00] and so, you know, now here we are in 2021, if you say what's what really matters those were the four areas that we identified that. Are critical to the success of our society. Number one, number two, we aren't succeeding. And that means we need innovation of all different types. And number three, we, we don't, we're not innovating, you know, we're either innovating at the zero billion dollars a year level, or we are spending money on R and D, but it's not yet turning the tide of the problem and, and that, so that's how we ended up focusing on those areas. Got it. And what could you actually, like, I, I love digging into sort of the nitty gritties of like, what was the process of designing these, these programs? Right. So just to sort of scope this a little bit, these broad areas that I'm talking about, I think of as. But the major societal challenges that we face today, actuate, which is a tiny early stage seed stage [00:09:00] nonprofit organization. Our our aspiration is over time to build portfolios of solutions, R and D programs. In each of these areas. And so very, you know, you, you, you made reference to a couple of the specific programs. One is about being able to access many more data sets to mine, their insights by cross-linking across while rigorously preserving privacy. That's some of the whole set that's one very specific program, but, but think of that as just one program and what will eventually be a much broader portfolio in this area of trusting data and information. So part of what we've been doing as we started actuate in late 2019 was big thinking about our strategy, about the four broad societal challenges that we wanted to work in. And then we've also been doing a lot of work on we've defined a couple of specific programs, but perhaps more importantly for scaling the organization, we've been working through our [00:10:00] art. Our mess, our process and methodology to take, you know, the core idea here of course is our, our founding team has a lot of different experiences, but we met at DARPA and what we our inspiration is really to take what we know from that particular model for solutions R and D. And. Mine, the critical, the essential insights and translate them to these very different societal challenges, not national security, but the ones that actuate is gonna focus on. And, and that, so we've, we've been formulating the four areas, but also thinking through, so how do you get from the question of changing population health outcomes to what are the programs that could be high leverage opportunities to do solutions R and D for that objective? Yeah. And so, so there's, there's sort of like two steps. There's one is like going from like the broad area to a specific program. And then there's another, which is sort of designing the [00:11:00] program itself. And I'm interested in what, what w what do you actually do to design the program? Like what, what is, what does that look like? Yeah. Go ahead. The first two programs that we have built out and defined were developed, were invented and designed by my co-founder Wade Shen he was a DARPA program manager for about five years. That's where we met his areas artificial intelligence and data science. And if you work in that area, you can work on any of the world's problems. And he, he worked on an amazing array of different problem areas as well as. Programs that at darker that drove the AI and data science technology itself forward. So you know, DARPA is a building full at any moment in time full of it. It's got a hundred amazing program managers in it. Wade was one of the really exceptional one people even in that very elite crowd. And so you know, Wade can And this is how he [00:12:00] thinks about the world. As you know, we came together because we share these concerns about these major societal challenges and a passion for bringing this kind of solutions R and D to these problems. And then Wade is the kind of guy who can invent these programs, you know, like he can just go do it. He knows how to think about it. He knows how to go do the research and talk to people and line up a program that could really be very impactful. So we, we weighed spelt these two programs, partly because we wanted to understand what that looked like in these areas. And but you know, that's the, as we go forward, we're going to need a process that engages a community of different people. Because over time, we're going to want to build our cadre of program leaders who will define, and then execute the solutions R and D program. And by definition, they can't all be, you know, they all, they can't all be weighed, right. We need to be able to draw from the talents and insights and the passions. With of people who have all kinds of backgrounds technology backgrounds, deep research backgrounds lived experiences [00:13:00] on these problems. People who have, who really, you know, deeply understand how the systems work that create opportunity or population health or, or take away from those objectives. And so a lot of what we've been doing is figuring out. So that's the question I was, if you want to change the future of health in the U S so that instead of spending twice as much as other developed nations per capita on healthcare, and yet having dozens of other countries that have longer lifespans and lower infant mortality rates, which is just criminal for the world's richest economy, if we want a future where that is radically different, where we don't have a hundred million people who either have diabetes or at risk of diabetes, where we don't have. Can, you know, we don't have a public health system. That's thoroughly incapable of containing and disease. Like COVID-19 unlike many other countries around the world. If we want a different future than you know, That's the landscape. And how do you get from that broad set of what we want to, [00:14:00] what do you do about it? And I think what that process looks like, so it has a top-down part and then a bottoms-up part. So the top-down part is understanding that landscape it's, it's the kind of, you know, it's understanding what, how big the problem is. What is the nature of the problem what's who's doing what I mean, these are big complex systems, right? There are many, many, many different kinds of actors. Actors practices, culture that you have to understand. You have to have some notion of how all of those complex systems components are operating and interacting. And then you can start thinking about where there are gaps or opportunities, but still at a very strategic, broad level. And that's about it for top-down because then of course, the model emulating a lot of the power we found in the way DARPA works is then to flip it, to bottoms up. And so then we go find people who are experts. In some aspect of this, again, they might have deep research expertise, deep knowledge of the specific problems or the way the system works. What you want is people who either [00:15:00] know or are willing to go learn enough about what the boxes, and then be willing to live outside of it and figure out how to recast it in a different way. And And, and then, you know, similar to DARPA, there's a process of nurturing and coaching, but allowing these smart individuals to bubble and brew program concepts from, you know, like a couple of bullets on a chart eventually to a full executable program, you know, a process that I think even for someone who's super good at this take six months or a year. So that's what we're just starting to embark on. Got it. And so that's sort of the beginning of of programs. I'm also interested in sort of like, What you hope to happen at the end of them? Sort of you're, you're in a slightly different position that DARPA, which sort of has a, hopefully a way being customer in the DOD. That's one of the funniest ideas on the planet. I just love it when people say, Oh, [00:16:00] well, It's easy because DARPA has DOD waiting for it. All right, please. Yeah. Let's, let's talk about how, yeah, I, okay. So yeah, let's, let's talk about that. And, and yes. And then what do we do? Right. So at DARPA, I first of all, think about six decades of history at DARPA in. Two halves for across generations of that agency. About half of what it has done is prototype military systems, things that were just crazy, that the services would never have tried by themselves, but were very directed at a specific military platform or capability. The other half has been. Sparking core enabling technologies. And that was out of a recognition that if you build your new military capabilities out of just the same old ingredients, you're only going to get so far and you need some very disruptive core technologies. So what came out of military systems? Iconically, of course, it's stealth aircraft. There's a much, much, much longer list, but that's the [00:17:00] easy one that everyone knows. A lot of people know that story in the national security world. Of course, what came out of core, enabling technologies. Well, arguably the entire field of advanced material science, but also ARPANET and the internet the seeds of artificial intelligence, advanced microelectronics, Microsystems, huge numbers of technological revolutions. So if that's what's going on at DARPA the first thing to point out is that half of it and some of the most transformative. Core technology, things that have come out of DARPA did not transition to the world because DOD went and bought a bunch of it. Right. And so and, and so the transition for most of the core enabling technologies is out to industry to turn into products and services. And, you know, we've seen. We've seen many, many stories and how that works often, what it looks like is a project that darkened funds at a university and or company. And then those individuals beyond DARPA funding go forward, identify markets, raise capital, build businesses, [00:18:00] build product lines, build industries, changed the world. Right? So we, that that's that that's not trivial. In itself. And then, but I think I just want to also be clear that even for the half of DARPA, that's been about building prototype military systems by and large DOD is not excited about someone they'd start. I'll tell you just, just one story. When I came to DARPA, we had just started just before I arrived, we had started a program. A great partner manager had been a Navy officer. I was serving at DARPA and he said, you know, wouldn't it be great if the Navy had an autonomous vessel, a ship that could leave the pier and navigate across open oceans for months at a time without a single sailor onboard, not a remote control vehicle, but one that just had sparse supervisory control, radically different tools for the Navy, if something like that existed. And maybe we can actually do that. And the Navy got when the DARPA was. Trying to do this and the Navy thought, but I observed this. And what they thought was that is a [00:19:00] really bad idea. And they tried to shut it down there. Important element of DARPA is the Navy doesn't actually get to tell their people what to do. And my predecessor appropriately said, I don't know if she said, thank you. But she definitely said, we're just doing this. By the time I got to DARPA, the Navy had gone from outright hostile to merely deeply skeptical, which is pretty important because that's the stage. It was. People will tell you what, you know, all the reasons that they don't believe it. And they say, well, how is it going to meet call rags, which are the rules of the road for navigating, you know, in dense areas. And how's it gonna last that long at sea in that harsh Marine environment, they had the entire long, difficult list of challenges. So then we knew then, you know what you gotta do, right. So fast forward before I left DARPA I got to Christen the first ever self-driving ship. See Hunter that we put in the water. And at that christening ceremony, by that time, we were paired up with the Navy and the Navy was a partner with us for awhile. And I think now is taking the effort [00:20:00] forward. And you know, now we have a working prototype. Now the Navy can say, Oh, let me figure out. Do I want to use it to hunt sea mines? Is it a cheaper, safer way to trail quiet diesel submarines? You know, there's a lot more that has to happen to really figure out how you take this and move it forward. So that's a success story, but I think that stealth is another great example. These things were not only not embraced or asked for, or, or. Welcomed when they weren't delivered from DARPA, they were, you know, they were spat upon often. But it doesn't matter because if it's radically better enough and, and the stars align and you get like, I mean, a lot of things she can't control, but that is how big changes happen. And you have to be able to do those things, even when there isn't a customer standing there waiting for it. I appreciate that. And so, yeah. Do so, so like then let's how does that then translate for you guys for actually, yeah, so I think the way to think [00:21:00] about it for any, any, so look, I mean, anytime you're setting out to make. To spark a radical transformation. You, it's not going to happen unless you really think about the entire system of what it's going to take to, to create the change that you want to see in the world. And so let me just take one really specific example. One of our programs, Dave safes at actuate. But one of these is one of Wade's programs that he's built. The objective there is to use privacy technologies that are emerging, that are currently being used ad hoc to build a new architecture and infrastructure that would allow for multiple data sets to be provided on an encrypted basis. And then what would allow researchers or policymakers, anyone who wants to analyze the data and cross-link among those data sets for the insights that they hold would allow them to do that entire process while rigorously preserving privacy. And that includes. The CR the linking, the cleaning and the [00:22:00] linking, you know, all the sort of, or ugly data science stuff that has to happen before you can actually start seeing the insights. So it's a soup to nuts full system. That's the ambition of that program is to demonstrate something that's that's that's. Robust enough and flexible enough to handle many different kinds of data and data problems. So the future that we want to see is that instead of today, where research is, you know, you ended up doing research or policy in halafu, it's sort of a lamppost problem, right? You do a lot of interesting research with the data you happen to be able to get a hold of, or that you happen to have permission to link to other data, but all the really interesting problems what, what. Happens in K through 12, but that leads to different kinds of life outcomes. How has that to other environmental factors in a kid's neighborhood or the way that, that education and that child is going to end up interacting with the criminal justice system? How, how do all of those things tie to the progress of the [00:23:00] economy and jobs and the things that lift people up and allow them to pursue opportunity? That's you know, to answer those kinds of questions, you need 53 different agencies at state local and federal levels, and you need private company data. And you know, like it's all just it's it exists, but that doesn't mean you actually can get at it and start using it. So we want to see a future where you could answer those kinds of questions. Well, so what's it going to take the piece that the program will do when we're able to get it going is to demonstrate a prototype system that allows for radically different kinds of data owners to put their data together, you know, run some real examples and. And do applications show that are demonstrations of what this new data capability would look like, but that's probably not going to be enough. Right. And so the other things that need to happen you know, my dream is there's a future where there's NIST or other standard for the kinds of. [00:24:00] Procedures and processes that would allow the legal counsel of the firm or the organization that owns the data to say, okay, if we comply with this regulation, if we meet this certification, I can now sign off and know that I'm protecting the data properly, but I can, I can make that decision tomorrow, not in six months or a year, like it usually takes today. And, and, and then over time with, you know, with a lot of different players and. An infrastructure for regulation and certification, you can start to see how you could, you could have the kind of rich data future that, you know, w we all talk about these days, but actually isn't quite happening yet. So, so I think that, I don't know if that's a useful, for example, but what the pic, the general picture is. Think about all the entities, all the actors that are going to have to. To do something to change their minds, take an action. And you may not be, I mean, we're not going to go fund all of that. We're going to fund a piece that would allow them to change their minds. And that's really, our [00:25:00] objective is a prototype and demonstrations that cause them to say, okay, we can, we can now do something in a different way. Do you see encouraging them to change their minds as part of the program in that there's sort of like a very there's there's a spectrum of from just like demonstrating the prototype and then washing your hands of it too. Like. Push like knocking on their doors for years. And I assume it's somewhere in the middle. Yeah. There's a lot of leading horses to water recognizing that you can't make them drink. What I, what I think is really clear for many, many years of experience at DARPA and other places is that if you're not deliberate and thoughtful about. Who those players are, what would cause them to change their minds and then doing the active work to engage them all along the process. For sure. If you don't do those things, the chances are pretty, pretty slim. If you do them, you might have a shot. Right. And [00:26:00] and so I think we're as we're designing programs that actually we're being. Very explicit about that engagement process, which starts by you have a lot of conversations with people who are like, most often, they're like, yeah, sure. You're in fantasy land. If that stuff existed, it'd be awesome. I'm like, that's not the reality. And let me tell you what I really need. So that's at the beginning. And then as a program starts, you know, during the execution of a program, that's really when it starts going from. Just, you know, something that the program leader believes in to something that now is starting to be palpably real potentially. Right. And so you want to bring those. Decision makers whose minds need to be changed, but at least could be investors. They could be entrepreneurs. They could be policymakers. I mean, a whole different sets of who those, those, those adopters need to be the ones that are going to take it to scale. But the places where we can bring them to the table are you know, you continue to call them up and tell them what's going on. But. But you [00:27:00] create demonstrations and updates where you bring them to the technology or you bring the technology to them and you say, look, did you, did, you know, this was possible. Look what we can now do. And, you know, ideally they get dazzled and then they say, Oh yeah, but they hear the next three things. That would be a problem. And that tells you what you need for the next phase. So that's what, that's a parallel track to the three to five years of technical work that's going on in the program. That makes a lot of sense. And in terms of the technical work, do you plan on having it be mostly externalized to the organization? The same way that DARPA does. I would say w there there's a very important piece of intellectual work and management and leadership that happens with the program leader and that individuals tiny little team within actuate, very much like at DARPA. But you know, the vast majority, the overwhelming amount of the funding goes out to the, the companies, the [00:28:00] universities, the nonprofits who are doing the different components of R and D and. Testing and demonstrations and all the people who are doing all of that work. And that's for a couple of reasons. Number one you know, these are three to five year projects programs, and we w w what we want to do is we don't want to hire them all and put them under our roof for that period of time just as a practical matter. But the other really important thing is when the program is over. What you want is, you know, a successful program and w a program starts with a program leader who has this vision. Yeah, they are, they are, you know, they're calling people to try to do this really difficult, new thing, and. At the end of a program, what you want is that entire community that you've been funding and working with that, they get the vision. Not only that they built, they delivered it, right? Like they've actually built this thing and they become the most important [00:29:00] vectors for moving it out into the world and getting it. Actually implemented. So the world starts changing. And so for both of those reasons up front and at the back end I think that's, I think that's one of the powers of the DARPA model is, is tapping these amazing talents wherever they are. Yeah. So something that I've actually wondered about with the DARPA model, that I've never been able to find any good information on is what do you do when you run into a situation where You need, like there there's multiple groups that have been working on different pieces and there's like, is there ever contention over, who's going to take it forward or like, like, how do you, how do you sort of coordinate it so that the outcome is the best for the world where like, which might involve like like squashing someone's ego or something like that. I was like, shocked. I'm shocked. So are you thinking I would say they're somewhat different answers if those junctures happen [00:30:00] during a program versus after a program. So, you know, let's say you have a program that that had different university groups working on dunno some advanced chip for doing machine learning or whatever. And, and, and it, I mean, this just happened. I think that there were multiple very good research results, but then were commercialized in different ways by the performers. So at that point, you know, it's like, great. Let them drive it out. Hopefully they, they. But they may compete with each other. They might go after different market segments, but there, there are multiple shots on goal to commercialize something coming out of a program. And I would characterize that as something that DARPA would not particularly, I certainly wouldn't control, probably doesn't even have much influence. Conversely, if you're in a program at the early stages of a program, a lot of the that's a lot of what the core management Work is for the program manager at DARPA or the program leader as we're calling them at actuate [00:31:00] is, you know, so let's back up. Number one, you're trying to do something that achieves huge impact sad, but true that involves taking risks because all the low-risk things have already been done. And so the, the whole art of this. Business is how do you intelligently take and then manage and drive down and eliminate risks. And one of the, one of the really effective tools in the toolkit for managing risk is a to S to S to plant a number of different seeds. And to deliberately have competitive efforts that might, you know one of our programs at actuate, for example is built on the idea that we have all kinds of research that could be better at real-time incentives to help people make better. To develop healthier habits. So, you know, it, when we get that program going, we're going to deliberately have multiple teams who are working on different kinds of incentives, themes, and then a core [00:32:00] management challenge in a program like that is going to be, you know, you, you may choose to start four, but you, you know, at some point you're, you're going to want to down select and go to two. And what is the right point? When is it. Point where you want to say, you know, I'm going to put more of my eggs in these baskets. And so I think that that's integral to the design and then the, the day to day or week to week management of the program. And I imagine that there might be one more situation where at the look you're actually sort of building a system and you have different groups working on different pieces of like different components in the system. And so. What, what, how do you, how do you manage that at the end? Where it's like, okay, like at the end of the day we, we want the system. Yeah. That's exactly right. Yeah. And I, I let's say maybe just one small point at DARPA. DARPA's running 250 or 300 programs at any moment in time. Right? So full-blown huge agency [00:33:00] relative to the scale that we're starting at zero right now at actuary, but in the DARPA portfolio, you will find programs. You know, the self-driving ship program was a systems development program, Gantt charts, milestones, boom, boom, boom. Right on the very other end of the spectrum might be a very much more research oriented program. That's highly exploratory. There's a new physical phenomenon that looks like it could be interesting down the road, but right now you just want to have vibrant research and people pursuing the question in lots of different ways. So there, there are many, many models. Yeah. Somewhat in the middle is probably where is, is what I would characterize where actuate will start and what we're finding in the kinds of programs that we're exploring is over and over again. Here's the pattern there. Number one, there's a, there's a problem for which we think there's a radically better solution. That's possible. The reason we think it's possible is because not because of one new research result, but because there are a handful of different research areas that are advancing in interesting ways. But they [00:34:00] haven't yet those advances have not yet really been applied to the right problem or critically to your point, integrated together into a system that can actually follow the problem. They're just like threads or hopes. Right? Yeah. And so that becomes, I think this is a classic template. For solutions R and D program at DARPA or an actuate. So a great way to manage those kinds of programs is, should think in terms of different tracks of effort. And the first track is to advance the research itself. So it's applied research where you're, you're building on these, these threads and nuggets, which you're really aiming at the specific new capability that your, that the programs. The program's goal is to demonstrate that, right? So track one is applied research. The second track is building prototypes and that's often that's a different kind of performer. It's someone who can integrate the different pieces and you can, you know, you can imagine a process where every seat. Three or six months, there's a drop from applied research into building prototypes. Right. And so, [00:35:00] especially for software tools, this is like the classic way you would do it. So every three to six months to see what's coming out of applied research, that's baked enough to put it into the prototype. And so that that's. That's becomes a very good way to flow things. That's tracks one and two track three is now you got to figure out if this stuff is doing anything. So then it's, it's testing, evaluation and working, you know, trying to show that it works for the application or applications that you're going after. And while there are different tracks, they interact, right? Because as you're learning what works and as you take the integrated prototype, so an integrated prototype for. But tool to help individuals choose healthier habits throughout their days and their weeks. So it's going to integrate a whole host of these different advances that are coming from different areas of a lie, including incentives, as I mentioned before, but, you know, ideally every six months or so as the prototype strop to testing, you start getting real feedback about this, this combination of. [00:36:00] Sensing and coaching and personalized incentive. Is it working or is it not working? Right. And then, then you go through these iteration loops. So I think that's So, yeah, I mean, I think what, what, so what the program looks like when it's underway is you'll see some researchers, universities, or companies you'll see prototype developers, typically more companies there you'll see people who do the tests or the demonstrations. It could be a clinical trial. If it's health-related it could be, I mean, it could be whatever, whatever the form of the prototype or the application is. And then throughout the whole thing, and the management challenge is. You know, you have a plan and then reality is going to happen. It's going to be something different. So how do you keep that whole engine moving forward? That is, that is an amazing description. I really appreciate you going into those details. Cause I think that that's something that. People don't think about it enough is, is sort of like how, how to manage those tracks. I want to actually go back to something that you said earlier, which is that the people that you want sort of as [00:37:00] performers in the program are the people who can see where the boxes and then, and think about, think outside of it. And do you have any, any strategies for finding those people and, and sort of teasing that out of them? Yeah, I, I think I said it more in the context of program leaders. And then, and, you know, by the way, at DARPA, one of the best ways to go find great new program managers or potentially great new program managers. Cause you don't really know until you give them a shot. Is to find, go through the performer base. Right? And there, there, there at DARPA I found there were always, there were always performers who were very, very good at their piece of it and they loved their piece of it. And you have to have those people, but then once in a while, you'd see a performer who started seeing the whole picture and they could help the, you know, they would start being creative about like, we could go here. And when you start seeing that, those are the, those are the signs. So I have a set of. Criteria that I thought about in terms of [00:38:00] DARPA program managers. And it's very similar for Dar for, for actually future program leaders. Number one, it's people who are driven to make a change in the world which like, I mean, this is where I live and breathe, but it. Over time. It has finally dawned on me that not everyone gets out of bed in the morning to make the future a better place. All right. Like that's just like what the culture and the whole point of the exercises. They have to find people who are driven to do that. I'm always looking for domain expertise because you need to be deeply rooted and deeply smart about something that's relevant to the problem it's going to work on almost by definition. You won't be a domain expert on everything that it takes, because these are big systems complex. Thanks. So the next thing I'm always looking for is the ability to understand the whole, the big picture of the system, and then to navigate seamlessly, you know, from, from forest to trees, to bark, to cells, right. And then back up and you have to be able to do that whole thing. And that means you may know a little, a lot [00:39:00] about how you know how some aspect of behavioral science works in a very specific context, but you also, I'm also looking for people who can then extrapolate up to how might that and other advances to be harnessed, to, to move the world forward. Right. And that that's that's I would tell you that's one of the harvest characteristics to find, cause of course. W w you know, there are lots of people who have domain expertise, but that ability to navigate from systems to details is, is actually a very precious commodity that I always love when I find I'm looking for people who, the overall thing I'm looking for is people who have, you know, head in the clouds feet on the ground, because you need to be able to dream, but you actually have to be able to go execute. And in this case, execute by managing other people on projects. Yeah. You know, it's not an individual contributor role. And then the final thing that matters deeply is an ethical core, just because you know, that that's important for how you treat people on [00:40:00] a day-to-day basis. But it's also important because we're talking about really powerful technologies and someone who we need people who are willing to be explicit and thoughtful about the ethical considerations that they'll be weighing in. Yeah. That that's great. I want to change gears just a little bit and sort of talk and talk about money for a little bit. So, so, so you spent many years in venture capital, and so I assume you, you know, the, the, sort of both the upsides and the downsides of, of startups and for capital organizations and you decided to, to start as a nonprofit. And so, so I'd love to sort of understand the thought process behind that because I definitely, I, there, there's sort of a line of thinking that. You know, it's like, if it, like, if it can be done, it should be done as a company, as a, like a startup. And so I'm interested in why you, so I would say that [00:41:00] simple minded and, and to the extent you think that's, if that's your worldview, I would say the things I think need to be done, that I can make a contribution to cannot aren't companies. They're not there. There's not a visible market. And so it's not, it's not a company today. Some of the things we want to work on will part of getting them out to the world will involve markets and therefore companies, including startups, but you know, coming back to these major societal challenges that we have none of them are simply going to be solved. By companies, building new products, services, and profits. And I do think that some of the solutions will ultimately will include companies having really interesting new market opportunities. But it, you know, this is the stuff that the market doesn't do and, and. But, you know, th the, so if you think about us, R and D we spend about half a trillion dollars a year in the U S economy on research and development. [00:42:00] The majority of that of course, is companies doing product development and but about a hundred, I think it's about 140 billion a year that's that's federally funded, R and D and and, and the, the. But areas in which actually is focusing are places where they are not market driven opportunities and, and they are not, I think they are not yet the places where we have the federal R and D machinery and yeah. But so those things need to happen for our ultimate dreams to come true. Right. Is to make the difference that we want more. And, and ideally it seems like you, you'd almost sort of like pull both of those both of those leavers, like towards a certain direction, right? Like that's, that seems like a, a place that you could sit getting opportunities for them. Right. I think that's the biggest pull as you show them something that, that changes their minds. Yeah. And are you funding the organization as like actually as an organization, as a whole? Or are you funding [00:43:00] each sort of program? Like, are you funding it as a program by program basis? We're still at a seed stage just to be really clear, but we spent a lot of time on this strategic question about whether first of all, let's be really clear that what we're trying, we think philanthropy has an important role to play because of the fact that market and government are not. For various reasons, stepping up to the plate on these topics that said that what we're trying to do in the social sector is there isn't a template for it. It's not what philanthropy has, has done at least in the last, you know, Six or eight decades. Very interesting stories about Rockefeller foundation and the green revolution and how they, how they funded the research. But, you know, if you go back and read how they thought about it in the methodologies that they developed, it looks a lot like solutions, R and D and then those. Actually those human beings, those exact people went into whenever bushes organizations on during the second war. And, and [00:44:00] I mean, that's the template for solutions R and D is right. We have an existential crisis and we have things we can do about it. And it's all hands on deck and integrating everything. And. Building radar on the bomb. Right? So, so anyway, so, but it's been decades since part of philanthropy, I would say, was really seriously focused on this kind of solutions, R and D. So with that, that is the significant caveat. So everything we're doing is going to be a big experiment in the social sector question you're to get now to get to your question. That we spend a lot of time thinking about whether we should try to build a program, build a program, go raise money for it. Or if we should try to do something that's even harder, which is to raise a fund, to do multiple programs and build a portfolio we've settled on the ladder. And the reason for that is simply that, first of all, I think, you know, sometimes doing an impossible thing. It's better to do the more impossible thing that actually. Can make an impact. I think this comes back to risk management and we talked about risk management within [00:45:00] a program, but a lot of, you know, how to start have one or two things, every single decade that literally is changing the world. Well, it certainly isn't because all the programs succeed, it is because you have a portfolio. And because it's a very deliberately managed diversified portfolio, it's diverse in. Aspects of national security it's that it's targeting, it's diverse in the technological levers that it's pursuing, it's diverse and timeframes to impact. And so at the end of the day, we concluded that for actually to make a dent on any of these met massive societal challenges that we needed to be able to build portfolio. Yeah, no, that makes a lot of sense. And so teaching to do tracks again and just talk to you a little bit about, about the Pat, like your, your, your, your career, which has included some like amazing things. Like when, when you became the DARPA director, like how, [00:46:00] how did. You know what to do? Like did they, I'm sorry, this is a silly question, but as you say, it seems like such a big role. Yeah. I've been super lucky in the things that I got to do. But I th the luckiest day, I would say in my professional life was the day that Dick Reynolds, who ran the defense sciences office at DARPA in the 1980s. He said to me at a workshop of there that I happened to be attending. He asked if I wanted to come to Darko as a program manager, and I was 27. I had been out of graduate school for. A year. Oh, maybe I was 26 at the time. Anyway, I had only been out of graduate school for about a year. And I was in Washington on a congressional fellowship at that time because I had decided I wanted to do something other than research on the academic track, but I didn't know what that was. It's like on a [00:47:00] Lark, I went to Washington for a year, which was critical because even when you leave the trot, you know, th the, the path you are supposed to be on, that's when you don't know what's going to happen. But one of the things that can happen as amazing new possibilities occur. And that's what happened when Dick asked if I wanted to come to DARPA. So at a very early stage of my career, I landed at DARPA and it was the first place I had ever been. I mean, I had worked. Two summers at bell labs who put me through graduate school. I'd worked at Lawrence Livermore one summer as a summer student. I'd worked at Texas tech and the laser lab as an undergraduate. I'd done this graduate work at Caltech and then I'd been on at the office of technology assessment. And the honest congressional fellowship I got to DARPA and all of a sudden, it just made sense to me, right? Like everything that I thought and believed in the way I was culturally oriented, which was you go find really hard problems. And then the contribution we get to make as technologists is we get to come up with a better way to solve a really hard problem. And we get to [00:48:00] blow open these doors to new opportunities. I just, it just resonated so deeply. So I spent seven years at DARPA the last couple of years, which we're starting with micro at that time, it was the micro electronics technology office, which we spun out of the rail defense sciences office at that time. And I, I, you know, I loved it. It was, it was a crazy ride. Right. I got to do all kinds of things that were very, very meaningful then. And that, you know, for the 30 years, since then, it's been just. Such a delight to see so many things, but have come into the world that trace back to some of the early investments that we got to make. And I would tell you that while I loved it, everything else I got to do after DARPA and I treasure it and I needed those experiences, I never really got over being at DARPA. It was just like, it was my home. It was my place. It was what made sense to me. And So when I got the call in 2012 to go back and lead it I, you know, it was just a dream come true. And [00:49:00] when I got there, it was, you know, being a program manager and then being an office director at DARPA, which I had done in the eighties and nineties, and then going back as director, those are three very different jobs, but so there was a huge amount of learning and growth in every stage. But they are all. Lined up to this mission and vision of an organization. That's just like, I'm wired the way that DARPA is wired. So, so I, I have to say it's, it was the most satisfying job that I've had so far, I'm trying to make actually even more. It was very hard. It was very meaningful, but I have to tell you, it just felt natural. It felt instinctive and natural in a way that none of my other jobs really did. I have to say, I mean, you know, And they were all. Okay. And I think there are other jobs. I think I was good at their other jobs. I was horrible at that matter, but DARPA was the place where it just sort of, it just felt natural to me. Yeah. And, and so sort of to provide on that and, and in closing do you [00:50:00] think that there are any ways to improve on the DARPA model that you're trying to implement going forward? So we talk about this all the time. I mean, I think for small, if the work that we're starting an actuator can have anything like the kind of impact that DARPA has had in, and, and, you know, any subset of its programs Then I can die happy, right? Like if we can really make a contribution to these big societal problems, that's, that's, that's going to make, that's just going to be deeply meaningful to me. We've talked about some of the things that I think are difficult in the DARPA model. One of them is about the more radical the innovation and advanced the harder typically is to get anyone to. Change the way that they work in order to adopt it and get the benefits of it. So I think being we're, we're trying to be even more deliberate about how would you get decision makers to change their minds and implement in the design of our programs have actually, I mean, I think DARPA does that, but that's something we're trying to put [00:51:00] special focus on. I think DARPA's done a huge amount of work to make it easier to, they have legislative authorities and good practices about being able to hire people who. Many of them normally wouldn't consider public service for many reasons, but especially of course, low compensation levels. And while dark was not fully market competitive, we w we were able to move very quickly and had a little bit of a salary cap relief. So, you know, the nonprofit sector is not going to be the place that you make your billions obviously. But I think being outside of government has that advantage and something that we'll, we'll definitely take advantage of. And they're, you know, they're things that are simply not appropriate for the government in a market economy. To do. And so there, there are things that you can do for national security, but that, that unless we have a radical change in our thoughts about industrial policy, which by the way might be happening, I can't quite tell, but there are ways in which government has not [00:52:00] chosen in the past to work with industry or with finance that I think are less, you know, those are not as significant on a limitation for the work we're doing in the social sector. Nice. Excellent. Well, I want to be really respectful of your time. How can, how can people find out more about what you're doing? And like if they, if they think this is interesting, like what, what should they do to, to help out. Well, thanks so much for talking about this. I love the fact that you, that you care about these issues and you've done more than anyone I've seen from outside DARPA to really understand the agency. So that it's been so much fun talking with you, Ben, about that. I think you're going to provide the link to the issues in science and technology. And our website courses, if it's all brand new. So take a look and you know, we're so early right now, but I'm, I'm always looking for people who have a deep passion for these societal challenges who see new opportunities to do things that are radically better way. [00:53:00] And please reach out to us from our website. If you, if, if it resonates, we'd love to hear from you. Thanks for listening. We're always looking to improve. So we'd love feedback and suggestions. You can get in touch on Twitter at Ben underscore Reinhardt. If you found this podcast intriguing, don't forget to share and discuss it with your friends. Thank you.

In this conversation I talk to Ilan Gur about what it really means for technology to “escape the lab”, the power of context to shape the usefulness of research, the inadequacies of current institutional structures, how activate helps technology escape the lab *by* changing people’s context, and more. Ilan is the CEO and founder of Activate, which is a nonprofit that runs a fellowship enabling scientists to spend two years embedded in research institutions to mature technology from a concept to a first product. In the past, he has also served as a program director at ARPA-E and was a cofounder of Seeo, where he commercial new high-density battery technology. Links Activate Ilan on Twitter Ilan on My Climate Journey Podcast  Transcript In the past, we've talked about the, how the whole process of really turning hardcore scientific research into products that have an impact on people's lives is fairly abstract to people outside of the system. Since you've both walked the path and now help other people do the same, let's round the conversation. would you go into detail on what the actual actions you need to take to go from say, being a graduate student who just published a paper on a promising battery technology to an improved battery in a car. That's that's a great place to start. let me try and answer that from a few different dimensions. I'll, I'll start by answering it, just from an anecdote about my personal experience, which I've shared in other places, but, you know, I basically. Went into my PhD program because I felt like the field I was studying material scientists, material science could, be the biggest way to make a big impact on climate change by basically taking new science and turning it into the next generation of all the technologies. We need to have a sustainable economy. And, I was working in nanotechnology, joined. Kind of the world, the best research group in the world that that was working on how nano materials could improve solar cells. and this is before the, the enormous solar market that exists today exists. There was a sense at the time that, you know, we needed a completely new generation of technology to make solar ubiquitous and cost effective. And so, you know, we had this great mantra around how we were going to print solar cells like newspapers, using these small colloidal nano, semiconductors. and the research was phenomenal. we were driven by the fact that what I like to say is, you know, we wrote a science paper where the first paragraph, like any, talked about how the research was going to change the world. And it wasn't until I randomly got connected with some business school folks at Berkeley, where I was doing my PhD. and they actually. It didn't take long. they put me through just a few cycles of digging one level deeper into, how solar cells were actually made, how they were sold, what determined their, their costs and the cost of energy they produce. and I ended up, you know, over the course of a few weeks with a spreadsheet that I still have somewhere, which told me that. If we hit all of our targets and our research in terms of what we thought could change the world. we would end up with a solar cell where even if you gave it away for free, it couldn't compete with the existing state of the art Silicon solar cells at the time. and it was a really. Simple idea, which was, we were making dirt cheap solar cells, but they probably wouldn't last very long. And we didn't think that was such a big deal. You just print some more. and yet, certainly at the time, and it's still true. It's such a, such a predominant amount of the cost of solar energy came from the balance of systems and installations. And I bring up the story because, for me, it was a tipping point. We had so much excitement about our research. It was even published in Forbes, you know, so a business magazine, and. It just showed how it showed, how easy it was to think you were doing something productive and successful. I it's not that I, I, I was in academia, but the reason I was there was to try and get something productive that could turn into a product. Right. And I had missed the boat so much, even with that intention. and so that was a shock to me. And so. That was kind of the first lesson around how, you know, institutions matter and incentives matter. but what I ended up doing was then leaving academia and jumping into an early stage startup, which was an amazing vehicle to think about how this transition happens and, you know, basically the learning there, and, This is what we now, you know, this is a lot of what we now indoctrinate and try and help people understand in the fellowship we run, was that, you know, the depth and multitude of elements that determine whether a technology can actually make it from the research stage to a product in the market. You know, first of all, you know, the idea is like, you know, the easy part in some regard. but yeah. You know, the number of levels deeper, you have to go to understand, okay, how is it going to be, how is it actually going to be valuable? Who's going to buy it. Why are they going to buy it? You know, how does, how does the whole system get built to make it, it's it's a month multi-dimensional problem where everything needs to line up between finance and the team you have in the market yet. And it's technology. and. You know, for me, I think, you know, this we've talked before, one of the biggest things that I've come to realize is we've got, you know, we've got hundreds of billions of dollars that government spends to do the idea and ideation. We've got hundreds of billions of dollars that the private sector spends to basically take the early prototypes and the idea of a product and scale it. and we've got really very little, that goes into how you do all the really hard stuff of translating one to the other. Yeah. So, so let's like what I'm going to actually continue to poke at. Like, what is that actual stuff? So the, the start that you joined did w what, what sort of was the origin of the technology that you were working on? I assume it came out of a lab somewhere. I, yeah, I was involved in two startups. One was after that epiphany moment in my PhD work, I basically threw out the work we were doing, and shifted gears and ended up developing the technology. That was the basis for, for actually a solar startup thinking about sort of thin-film, nanocrystal based, solar cells, Basically realizing that the, that the lifetime was so important, we just threw out all of the organics that we were working on and focused on. Like, you basically just need a new manufacturing approach to make something that looks like a traditional solar. So, that was a company that I kind of helped establish, but then ultimately didn't go. I was, I was meant to be sort of the founding, you know, grad student turn CTO. and then, for a number of reasons, didn't end up jumping into that as a startup and instead, through. Just some of the serendipity of being in the Bay area and Silicon Valley ended up, on the founding team of a battery startup that came out of another research lab at Berkeley. and this was funded by, Samira and Vanessa who, when, when coastal ventures was just going to start it. yeah, so, so like let's so. W when we say coming out of a lab, I think it's actually worth almost disecting what that means. Cause I suspect that it means different things to different people. and so, so someone in the lab. Did some research, figure it out. Okay. We think we can extend it was, it was a lifetime, et cetera, extended battery lifetimes, or, this was about making or energy batteries, higher energy density, batteries that were still safe and stable. using basically solid electrolytes. so, so they like publish her paper, like, like I assume that there's like, like they do some experiments. They come up with like the core. sort of process improvement. It's like, okay, we, we make batteries this, this old way, and now we need to make batteries at different way that will eventually make the battery into something useful. then what did, like, what did they need to do? What do you, what did you all do? Yeah, the origin story of CEO is I think a great one. So ingredients in this case and, and some, and there are some universal, I think things that you can pull out of this, you had a couple of graduate students and a professor at Berkeley, Natasha Bulsara, doing research, basically a polymer expert who starts doing research in terms of how polymers can be applied to batteries. the, the business as usual or the incentive structures within universities generally, you know, would say for Natasha to be successful in his career, he needs to make some new discoveries. He needs to write some great papers. he needs to advance, you know, as an academic, right. And he was doing that. and. In this case, it took this moment where, you know, Natasha was a dreamer and had, you know, just had a sense of, well, wait a second, I want this to be useful. I think this can be useful. He kind of had a zero with order idea that there's this problem in batteries, where, you know, you can, if you try and use high energy density, electrodes, like lithium metal, they can short across and lithium metals, flammable and combustible. And so, you know, There's this idea that you could make a high energy density battery. Unfortunately, it starts to look more like a bomb than a battery. and he, you know, to zero with order, the polymers that he's making could solve that problem, right. It could be robust and strong mechanically and still be highly conductive, for ions and. Tasha to his credit is audacious enough to say, Oh, and this is a time to, we have to recognize when venture capitalists are interested in funding these things at the early stages. Right? So it takes Natasha being audacious enough to say, I think we can, we can start something. And then it takes someone in this case, like the node who is as audacious as it comes in saying, well, I think batteries are going to be a big deal. I think this is a really smart team and they'll figure it out. And so like, let's start a company here. it turns out and, you know, I don't really, I don't know if anyone will be upset at this point to say this. Right. Like I joined the company, not being a battery expert. I kind of was the entrepreneurial scientist who jumped in to kind of help start it. You know, I had a meeting with severe, he said like, all right, buy a cell phone. And like, you'll be employee number one, like just, just go and let's start. So there's a whole nother story about that, but, It wasn't until I was in the, you know, and coastal ventures decided to fund it. and then I actually saw the diligence, the early diligence that coastal ventures had done on the idea. And it was like battery expert. I won't say who world-renowned battery expert, who I now highly respect. Basically said, this is total BS. You know, like there is no way this, this idea and this technology could solve this problem for these 10 reasons. and what I love about the node, and what allows him to really catalyze new things, things is, he just said he just ignored it. He said like, all right, the experts don't think it's possible. Fine. you know, and invested in any way, a couple of million dollars to go, you know, to go start this company. And so. You know, you have me as a scientist, who's motivated to be entrepreneurial, but has no experience. And you have a, like really incredible, you know, genius academic professor out of Berkeley and two of his students that are really entirely scientific in their thinking at the time. and now all of a sudden, like we're in a startup and we're meant to go develop a product. And so this question of like, well, what does that actually take? Like, you know, we just got thrown into the deep end, about that. but you know, the first thing is some people to just be audacious and say, there could be value created here. Let's take these individuals. And this was one of the reasons why I found it cyclotron road and now created activate like the origin story was let's take these individuals and get them into a different mode of how they're thinking about their R and D. And there was just an, there was an entire phase transformation that happened where all of a sudden, you know, Natasha and Mohit and Hani, and I, are now in a startup and. Our only reason for existence is figuring out how you make a product that could be impactful and get out to the market. And jeez, like, you know, I mentioned zero with order before, because like at the first order, all of the assumptions around why that technology could have been valuable in batteries were not all of them, but most of them were wrong. and, and yet now there was no choice. We were all, I mean, Antosz was still a professor, but the rest of the team is basically now in a mode. We're like, okay, we got to figure out how to make something valuable for batteries. You know, ideally starting with the technology that we have. and you know, it's funny, you're I think your audience, a lot of your audience is sort of scientific and technical. So what I like to say now is like, if you want to move science to products, you need to live for some time and a superposition of those States. before you can kind of collapse the wave function and understand like, where, like, what is it that you have. And for me, like what was so lucky was because of the node was there to be able to put that speculative money in those first 18 months of co, like we weren't a research project anymore, but we certainly weren't a company. and we had to figure out like, Okay. Which parts of this are just still interesting research that Natasha can keep doing this lab, which he did. And he benefited from, and which of these might actually turn into something that could be valuable to the market and a product. I'm not actually sure. I'm answering your question. I think you are. I think we're, we're getting to it. I'm going to like, sort of tease it out. Cause I think it's actually really like, I, I love this because it, I think that it is probably different for every situation, but then there are these similarities where it, so actually, so like during that, that 18 months, what did you spend your time on? So I assumed there was some amount of like going and talking to battery companies and like trying to figure out their end and then some amount of like, It's like, like you were still doing experiments, like, Well, first of all, it's worth noting. You know, there's so much value in taking some smart folks and putting them in a different mode of working. But the idea that the way to do that aggressive applied research was to be in a startup. There's a, there's a bunch of activation barriers there that we have to cross. So luckily even node and the financing, wasn't one of them, he made that easy. but then it's like, Oh shit, like where are we going to do this work? and it's like, we can't meet up at a Starbucks and open our laptops and start prototyping. So like, my job as employee, number one, as unsexy as it was, was like buy a cell phone and like figure out like, Where are you guys going to work? You know, like, Oh, we got to find space. We gotta, you know, I gotta go call em Bron and negotiate, you know, glove glovebox order. and see whether I can find some way, because frankly for them to build us a custom blood box is going to take three to five months. And like, I want it in six weeks, you know, like, how are we going to do that? so that's, you know, like that's number one that was kind of just table stakes, you know, then. And I think this is the tough part, in this transition, especially when you start with venture capital, which is, you know, the team has certain assumptions, those zeros or assumptions around. Here's how we think it was valuable. So obviously like the node wasn't going to fund without at least like, Some plan around like, okay, we're going to take your money. And here are the experiments or the development we were going to do. Right. So the development was like, okay, we've got a polymer that can do X, Y, and Z. And we need a polymer that could do a, B and C. And so the first part of this effort is going to be to, you know, we're gonna. Take the synthesis. We're going to make these better polymers. We're going to show that we can get the properties you need for it to be valuable. we're going to show that we can develop a process where this can actually be a scalable polymer to produce because otherwise it's going to be way too expensive. Yeah. And then we're actually going to figure out, like, how do you make a battery so that we can show that you can put this into a battery. And what's interesting is you look at all three of those things. If we were to go back and look at the early plans and experiments that we had on those. Like they were totally, you know, they're pointed in the wrong direction because we didn't understand what the real problems or, and so you then set out on, you set out on building the, the lab infrastructure and the experiments to go do that. I mean, all you can do is March in the direction that you is your current assumption. Right. and so that's what we did. we found. We were, I remember like the node was blown away just in terms of the rate at which we were able to make progress. and then alongside that, you know, my job as the kind of person to start thinking about how the technology met the market within the team, you know, like I started going to industry conferences and shadowing people. On the technical side that I knew just walking around with them and asking questions. And I started to realize, Oh, you know, we, in our, in our thinking, we said that X innovation was going to make the battery five times better. Like once you actually understand how the battery gets made and what the convention is and the different, like, it turns out that five times better as closer to like 1.2 times, it's better. Like, you know, the differences were that big. Yeah. and so, So then this really hairy and stressful process of, okay, w we want to make progress against the dimension, but here we are learning things where that suggested like that vector, you know, our assumptions were wrong and maybe it's not actually as important as we thought. And now you've got to figure out, okay, well, how do we change our experiments to actually work in a direction that matters with a lot of limited information and. You know, that's, it's an insane process. it's an insane and hairy process from so many elements because it's, you know, imperfect information. you know, sometimes you don't, it's, it's a lot of unknown unknowns where like, you're not, you're not totally even missing the thing that's going to kill you. meanwhile, you have different people on the team, who all are at different levels of, of their own understanding and perception around, you know, Maybe difference in the, in the spectrum of like a dreamer of like, yeah, people are telling us it won't be as good, but like radically can be, you know, and it's like, yes, theoretically, it can be, I'll give you an example from, from the CEO days, which is. You know, theoretically lithium metal as a, as an anode and a battery can give you enormous energy densities. cause rather than sticking the lithium ions and holding them within some other material. Right? So in the battery, normally Lithium's, intercalated into some other compounds. So you have to carry the weight and volume of that compound around in your battery. Even though it's not playing an active role, lithium lamp metals, pure lithium. So you don't have to carry any of that baggage around. And so you can have a really lightweight small battery. You know, one of the big epiphanies for us was like, yes. if you can essentially have a two micron thick. Piece of lithium in your battery. and the way we were thinking about making the batteries, it was like you take a piece of lithium foil that would make it so easy and you'd have lithium foil, the, one of the electrodes that you've turned into the battery, you know, so one of the aha moments as stupid as it was, was like, all right, let's go source a five micron thick foil of lithium, right? No, like, that's not like no one makes that. because guess what? Like, you know, lithium at those, you can't even handle with them at that thickness. Cause we've just burst into flame, you know? well, no, cause it was just too, you know, Lithium's not very robust mechanically. Okay. So like you can buy a hundred microns, thick foil of lithium and handle it nicely and easily. And you can buy that really cheap. You start talking to people about like, can I source 10 microns of lithium? And, you know, and they say, yeah, you can, there's one person, there's one group in the world that can do that for you. And it's going to cost a thousand, 10,000 times more than a hundred microns. Now, all of a sudden, like the entire, the entire proposition goes away. and then you're stuck saying like, okay, does this kill the idea? Or actually it probably means we got to figure out a different way to get two to five micron thick lithium. Which is like an entirely new development path and expense that we just never thought about, you know? And is it gonna work? Is it gonna be possible? So this is like, you know, this is that there's this multi Plex, you know, divergent and crazy, you know, optimization that has to happen in terms of like, okay, what do we do next? And, and at the. End of it where you actually building batteries or were you like ha ha like, what is the, like, once you figure out the, like, once you've sort of like gone through that optimization, you actually even have a process to make batteries better. Like, how does that process end up in a device that's using a battery, Yeah. great question. So in that multi sort of, multi-dimensional. Development that we had to be doing. You know, one of the questions basically was like, okay, let's imagine we can make this phenomenal electrolyte, which could enable this phenomenal battery, like a, how are we going to actually prove that the battery is better? You know? you could imagine partnering with battery companies to do that existing battery companies, but like they have no idea how to handle our stuff. Yeah. And they don't have the equipment. So like, no, we can't do that. Like we got to figure out how to make batteries ourselves. So now, like all of a sudden, like you've got an innovation, which is a materials innovation for a component of the battery that you can think Naval and stuff anymore. And all of a sudden, like we have to figure out how to make batteries okay. And produce them. and then we need to think about, are we just going to produce them as at the pilot stage? And then we'll teach a partner how to produce them, or are we going to actually have to build in our little startup, like the entire battery manufacturing capability for this entirely new batteries. And this is where that multidimensional optimization and what we like to tell our fellows now is like, You know, everything has to align in terms of the way you're going to go build this. So for instance, you can have, and I like to think about this in thermodynamics and kinetics. Like the thermodynamics can be great. Oh, we actually do have a magic material that could build a magic battery and we think it's possible. Like we know it's possible. And then the kinetics could kill you. Meaning for us as a small company to actually build out and figure out how to manufacture these at scale, it might take $200 million of capital to do the development, to figure that out. if at that moment in time, the venture capital community doesn't have the appetite to put $200 billion into a battery manufacturing company, then that's not going to happen. And that's going to be the reason why that entire vector doesn't make sense. so this is, you know, like the idea that to navigate how something is going to get to a product into the market. There's a lot of strategy, but there's a lot of dynamic optimization that needs to happen as you learn more and you understand your context. And one of the things you and I have talked about this a little bit, but one of the things that's that for me is the biggest challenge in this, especially for hard technologies that take infrastructure and capital and manufacturing is. There's in theory, there's a really broad, in terms of ways, you can take an idea from research and get it out to market. And what I mean by that is you can, from the university lab, Natasha could have licensed the technology to a big company, right? he could start a company which he did and that company could raise venture capital money to go try and become the biggest battery manufacturer in the world. and raise a lot of money or it could spend some time working and developing technology and then it could license it to a big company where it can be acquired by a big company. Yeah. Interestingly w the lesson learned for me was one, the only way we were able to get that group of people into the mode where we were working aggressively in the supplied R and D way was because of a node gave us a couple million dollars. Yeah. But now the entire organization of the startup was founded on the nodes venture capital money. And the node is notorious in the best possible way of being an amazing venture capitalist. In the sense that his view is if I'm going to invest in you as a VC, my goal is to make you the next multi-billion dollar company. That is the industry leader in this space. And my incentive is to make my bets count. Yeah. And so I would rather do everything. I would rather do everything we can to get it, to beat that you you've learned and adapted this so that it can be the biggest battery company in the world or fail Trump. and there are no other sort of off routes. And what I mean by that is, you know, we recognized, we recognize probably two years in a CEO that. The idea that we could line up all those stars and create a battery. You know, I, I remember learning, you know, the battery manufacturing industry, you looked at the most successful battery manufacturers like Panasonic's battery manufacturing business unit had like 5%, margins in terms of like income net income, or operating margins. And. You basically said, like, I don't know how to justify a big, massive multi-billion dollar business. That sucks. It's a shitty business. Right? Like, so, so we started, then we started thinking like, and we even have a chance at that. Like we gotta go figure out how to we had enough money to go figure out how to manufacture an entirely new battery chemistry. Like I w how are we going to do that? And so it started to realize like, And we had developed, we had some really amazing ideas. We had a really amazing kind of early development and validation, and we had people from big corporates coming to us with a lot of interest, including one that ultimately came through an intermediary. We got the sentence. Well, maybe there's an acquisition here that someone would want to do. And this was early in the company. and you know, the VC board that we had basically said, like, okay, we know we put, we put $5 million in, you could get acquired for $30 million. The technology could end up in a company that actually has the ability to manufacture and the distribution channels. and their view was no, that's not good. That's not, I mean, you know, basically if you think about it, if I'm a venture capitalist and I funded you out of a $500 billion fund, right. what I need you to do is. Build a company that's big enough that returns to me and to my investors. You know, something on the order of hundred, a billion dollars, right? If you returned to me $30 million after three years, it's an amazing return on investment from a, from a pure kind of, we gave you X, you turned it into Y, but in absolute terms, I've spent three years with you. And all you're giving me back for my fund is, you know, 30 million bucks, which doesn't move the needle. You basically proven to me that I just wasted the last three years from you, because you're not the thing that's going to make the fund successful. Yeah, it's a really long-winded story. But the point of the story is to suggest that, even though there are a lot in theory, there are a lot of different ways to get the technology out to market and to get it to scale. Capital sources and institutional structures and incentives. They all act as band pass filters. and they cut you down where all of a sudden, like your only option to be successful is in some narrow range. and. That's. Yeah, it's just an interesting thing too, to think about relative to how we encourage more of this. Yeah. Oh, well, I really appreciate you going down into the nitty gritties because I think it's, one just valuable to sort of have out there. Like, I feel like people don't go into that and what it does is it then sort of frames the work that you're doing both at activate and that you did do at RPE, because I feel like both of those, like that sort of your whole. career has now been trying like the hypothesis of my life. Yes. Yeah, exactly. It's like, so it's like, how do we play with those constraints? actually one question I have about this idea of the phase change, and sort of let's, let's look at the phase change, the, the super position. Do you feel like the idea of, of kind of. Burning the boats in a way of like taking that VC money, that then sort of really focused you on, on the product. Do you think that that is. Essential or like, because like, I, I actually, I've sort of like mixed, like I have very mixed feelings because as you pointed out, like it both focused you, but at the same time, it, it put those constraints on you that you sort of like needed to go big and go home or go home. and so sort of like left. Didn't leave a lot of room for playing. So, so where, where do you, where do you come down on this sort of, like going all in or like, is there, is there like a point in time when it's correct to do that? We got to think about a few they're few different pieces. So I think what you're asking, you know, One is, is it essential to get into a different institutional mindset or structure or incentive structure to do this translational work? The other is, is it essential that you're all in, so to speak, right? That that's your entire life? I think with, with very few exceptions, the first is essential. and the second is important. and. But, but maybe, you know, but, but maybe not essential. I mean, the, you know, the, this is the, I mean, this is the reason why we created activate, and this is the reason why, you know, we started cyclotron road as, as kind of the precursor experiment, which was, The first thing we have to recognize is like, we've lost big picture, at least in the U S but I think this is true globally. Like we've lost a really critical modality of how we do research, with which is a place with amazing people who understand science and engineering at the earliest stages of technology development and who are incentivized to create a product to create something practical. And, you know, I think you, you know this, right? Like, you know, this story, but the best research in the world used to happen in companies. and, and you know, whether it was the companies themselves funding it, you know, think about bell labs, whether it was the companies themselves funding it. Whether it was funding that was really government funding through a monopoly that allowed them to fund it, or whether the government funded it directly, which the government used to fund a lot of research within companies, because guess what that's where the best research in the world happened. you know, we had, we had people who were thinking about who understood cutting edge science, and yet we're in an organizational structure that cared about products. and now we have, frankly, startups are one of the only places that we really have that in an intense way. Yeah. Oh, and I was going to, and that startups, you, you don't have as much of sort of that continued institutional knowledge, right? Like you have, a bunch of people, like a bunch of people who, as you said, like, are sort of new to that phase and that, that way of thinking, And I guess so, so we could almost sort of think of, of activate as allowing people to be in that super position longer. Would that be an accurate way of describing it? I mean, that's exactly it, which is why we take right. Our fellowship takes people who have the motivation to go figure out how the research gets out of the lab. It basically simulates for them what the first couple of years. Would look like in a, the node funded early stage startup. but without the constraints of that funding, meaning they have all of a sudden, it's like, Oh shit, they've got two years. Their entire life and success in their life at that moment is can I figure out how my research turns into something valuable and yet there's not the supposition that they already have something valuable, right. That would be putting the cart before the horse. Right. They're really allowed to explore that and figure out, Oh, you know what. You know, we've got a fellow who spent time in the program and then said, actually, no, like this isn't for me. And he's now a professor at Oxford, you know, we've got other fellows in the program who have said, actually this isn't for me and now they're running groups at Apple. and, and then we have a lot that spend that time and they say, Oh, you know what? This is a start. This is the majority of them. Like, and the startup is the right vehicle to move this forward. But then we get this additional classification that comes in, where some of them are saying, you know what? And this is, and I want to go build a VC, go big, go home startup. And I'm going to go raise money from Linode or someone like him. But we have others who have taken the time and that superposition to say, there's something valuable there. We built here, but the traditional venture capital funding model, you know, the time counts, it's not going to work well for me at this stage. So now I'm going to try and build something in a different way, you know, partnering with corporations and selling early things to them. If you think about, you know, we focus at activate on, you know, hard. Physical natural science technologies and with really focused on industrial markets. And what I like to say is, if you think about the biggest industrial companies in the world, it's hard to find many that have their origin stories in a financial VC funding landscape, right? Yeah. most of them have had to build, you know, what I like to say is in industrial markets, it would harder technology. The incubation period between proof of concept or sorry between proof of interest in the marketplace and proof of value is very high, meaning, it can take a long time for something that could be valuable. To actually be accepted by the industry as we actually believe this is valuable because we've, de-risked the technology enough, it's been in the field for, you know, a hundred thousand hours or whatever else. And so those, what you find is that to get technologies that can go into those markets and companies that can have the reputation of being able to deliver with the reliability, et cetera, that you need. Oftentimes it's the slower growth companies that just took a lot more time to incubate. We don't have right now capital. We don't have capital structures that allow people to build those kinds of companies. I'll stop. No, send me like riled up in different dimensions and this, this is so good. so the extreme version of that argument would be that just the, the timescale and the return expectation of. The venture cap, venture capital as a, as a institutional structure, just doesn't align with the, sort of the necessities of a lot of hard technology like that. That's, that's the extreme version of, and you're, you're welcome to push back on that, but. That's that that's the hypothesis. In other words, often align, it only aligns in these magic convergent moments where, you know, the market is, you know, way out of equilibrium and wants to move on, you know, a hundred times faster. And, and there, there are great counterpoints to all of this. Right. you know, the counter points are, you know, like if, if the market is not, Let me think about what the counterpoint yeah. Look at. Look at the automotive market, right? it wasn't until the automotive market basically. Realized like, Oh, like internal combustion vehicles may be dead. Like our entire business and capabilities may be dead. That all of a sudden they're willing to make big investments in acquisitions and take things on. and you know, I think from the financial lens, the argument would be, okay, well, don't just push the technology. Don't, don't push on a rope, meaning like to innovate in the electric vehicles before anyone actually has any appetite, but. As you know, it's not like then the world will never change, you know, like I never push it. the story I love on this is, You know, Dick's, Watson's at our board at SunPower. Dick was the founder of a company called SunPower. that was one of the first kind of biggest solar companies in the world when the solar market took off, it still is, Dick left his job as a tenured professor at Stanford in the mid to late seventies, mid seventies to go start a solar energy company, the world expert in the field. and he basically. It felt like university wasn't the right mode for him. and at the time, the idea of like building a solar company that put solar cells on your roof, like, you know, th the cost of solar energy with the Silicon solar cell would probably, you know, it was like, I don't know, somewhere between 50 and a hundred X, what would make economic sense? And this is in the mid seventies, you know, Dick founded like. Against all odds built a small team with government grants with early investors was able to spend 15 to 20 years building a, like a suite of technology around solar, getting the data points around validation and everything else. And I think a lot of people, I imagine a lot of VCs and others who interacted with them at the time, just thought, you know, This guy's nuts. Like he's going to be spending his whole life just kind of building and tinkering and it's, this is not a real business, you know? And then in the mid nineties, all of a sudden, you know, Japan and Germany decided let's make solar real and they put some real incentives and subsidies in place. And all of a sudden there was this market and it just so happened that Dick and his company and the way he says it is like he was at the time he got to the point where he understood how to. You understood how to make the cells work, how to make them cheap, how to manufacture. what he didn't understand was how to make them, like, you know, how to make a million cells a day, a million wafers a day. and it just so happened that at, at that moment, you know, the market was starting to turn on. He bought TJ Rogers is running Cypress semiconductor who says like, Oh, well we know how to manufacture things at scale. we don't do a million wafers a day, but, but that's an interesting challenge. and now all of a sudden, there's an opportunity to take everything Dick's done and create a massive business and a massive new industry. Yeah. and so, you know, like, Is there a way to encourage people to be as like ridiculously, you know, naively, you know, whatever, like whatever it was that you would call J Dick in the, in the first part of that journey. Tenacious. I don't know. But what I know is there are a lot of people and we've now seen this in the fellowship program. Like, and you've probably seen this, like, there are a lot of scientists, engineers who are willing to commit to decades of their life to go develop a field, to develop something that can make that impact. And. what I know is if Dick would have decided to commit those two decades at Stanford, he would have learned 0.0, zero 1% of the things he needed to learn, to figure out how that technology was going to be productive and valuable. No, no. Dig on Stanford. Just again, it's like that's, that's how academia doesn't incentivize like actually going out and like building the same thing over and over again. So I've spent a lot of time thought thinking about this, and frankly, where I end up is you can't, you can't blame VC or wall street, right? Like, you know, frankly, the earliest investors in Dick's company, a company that might take, you know, Edison talked about like you can't beat compound interest. Like those investors are not going to make their money, no matter how successful it happens. If the success starts 20 years later. and so. The only way to encourage that type of work at that stage is to start to think about Dick's company in the early stages, as a research lab, as a really interesting applied research lab and what I'm hoping and what I've been really working toward is how do we get to government to realize that like startups, a network of startups, you know, a constellation of starting, however you want to think about it, like. Like that should be the most powerful way to do applied research, I think in today's world. Yeah. there are a lot of problems with how you do that. There are a lot of challenges in how you think about government funding, startups as research labs, but, but I think that's, I think that's a really compelling, you know, direction, from a policy person. w what do you think some of the biggest challenges are. I'm trying to think of who, who pointed this out for me? I think it was Nathan Coons, who, I don't know if you've met Nathan, but he, He started a company called Kymeta, out of intellectual ventures, which interestingly enough, if you argue, there are not enough modalities around how to do applied research in the world or in this country, you know, intellectual ventures is one of those strange modalities and experiments. but I think he's the one who pointed out to me initially. what has all this stuck in my head as the biggest challenge, which is. When you fund research, you know, as you know, science technology can be easily used irresponsibly, in research funding, let's put it this way. R and D funding could be used irresponsibly either because you're going to go develop an evil technology, or because you're going to go squander the money by like, you know, buying yourself a Ferrari on the side. Right. Yeah. When the governor buying expensive equipment, when it's not even when it's not necessary. Right. Like, I think that's the most insidious one where it's like, not even clearly fraud, but just like, do you really need like a million dollar fem, right. yes. and I think Nate, when Nathan pointed out, you know, when government funds things, you know, if the government sends a check to Virginia tech to do research. there are a lot of guardrails and bounds, that would make it very hard for that money to be spent in a really irresponsible way. or to, for that project to be ethically misled. It happens. Yeah. you know, one of the benefits of startups is there they're much less tightly bound and they have a lot more dynamicism in terms of how they get led. The incentives are different, but it also means you now have less control mechanisms. And that's one, that's kind of stuck with me as, as a challenge, right? not that it's not, yeah, I think about it as like, it's one that's worth thinking about how to manage or you think about it as like, well, you know, there's a risk reward to everything you do. one of the interesting things, I don't know if we, you and I have talked about it. One of the interesting things I've found is that when we think about science, whether it's. Frankly, whether it's government, any of the actors who fund science and engineering and research, you're willing to take insane amounts of technical risk and scientific risk. there's very little willingness to take kind of institutional risk, or, or modality risk. you know, I, I, I had an interaction with, you know, a large private foundation that funds a lot of research. and I, you know, basically noticed most, if not all of their programs, you need to be, you need to be a large university to apply. and when I sort of asked them about like, well, why is that? You've got now a lot of interesting research that could even the applied stuff, even in their applied programs. And part of it is like, well, you know, no one ever lost their job funding. You know, stamp a Stanford professor or Stanford to do research. Whereas like, I don't know, like God forbid I accidentally fund Theranose to do research, that that's, you know, that's death. So that's another one that we got to figure out how to get around. I call that asymmetric career risk where nobody nobody ever gets fired for, Funding the safe thing, right? Like the, the sort of like mean results. But when we're talking, like the things that we're talking about are we're, we're counting on the outlier results. And the problem with outlier results is that they can be outliers on the good side, or they can be outliers on the bad side. And so if you. Fund a outlier result on the good side, then, then you're the hero. Yes. But then if you fund an outlier on the bad side, then you get fired. And so the sort of like expected career value for, for a funder, if the government or large, large. Yeah, exactly. And it's like ed, unlike a VC who gets to participate in the outside's upside of a positive outlier, Someone in the government or a, another large funding organization, like sure. They'll, they'll like, people be like, yeah, you funded it. But then they won't get that much, participation in the upside if it, if it really pays off. And so, so that's. What? Yeah, but I think the sweats really well thing, but let's think about like, you know, and again, all day long, that makes sense. If you look at it through the downside risks, but let's think about all the upside opportunity that we're giving up. And I'll give you the example here, which is Saul and other lab, you know, like I was a good friend. I like, I think he's one of the most brilliant people on the planet in science and technology and engineering. You could do good. other lab. You know, the amount of upstream swimming, Saul has had to do for other labs to exist. And the idea that like, you know, you have to be a one in 1,000,000,001 in a hundred million type of person to be able to end up doing something like other lab, which is essentially a different modality for doing R D you know, in a very different way than activate. It sits somewhere between a startup and a research lab. and you know, I think the question we need to be asking ourselves is like, you know, how do we allow? Not just like, how, how do we make it possible so that not just Saul could go run another lab. Right? But that the hundred or thousand top scientists and engineers who have the same motivations assault, Could think about that as a career path for themselves and something successful to do, you know, salt takes a lot of risks. Other, you know, like other lab is in some ways an insane proposition, and yet it allows them to do the work that he does. And it's like, frankly, it's, it's gotta be, you know, one of the most productive research labs in terms of applied research in the world on a per dollar basis. So, so, so, so big that I would propose. Which is like, this is going to be like a deeply unsatisfying thing, but I'd love to get your take on it is that the missing ingredient is trust. And I realize that sounds very sort of like Woohoo, but I mean, it, in a very, like if you look at, if you just sort of like, look at history and you look at the people who do these crazy things, like what ends up happening is like, it comes down to like one person trust another person they're like, look. I trust you to go spend this money responsibly. And then they sort of like take the guard rails off. And, I was, I was talking to Donald Raven the other week, and he ran, this, this program called venture research for BP in the eighties where they funded. Crazy scientific research. So it's like less on the applied side, but more on the, like, like, like literally scientific research that couldn't get funded. And the thing that struck me was that he spent sometimes up to a year getting to know the scientist who was applying for funding. And so, and I think that what happened there is eventually it got to the point where I just trusted them as a person. And so I was wondering like, well, think about, think about Bob Taylor at DARPA ARPA. Right. Like, you know, his whole mode was, let me find the smartest people, let me spend enough time with them where I can understand which of them, you know, does the best job of calling bullshit on the rest of them, you know? And then let me go give them money. Yeah. And yeah. Right. I think that's, I think, I think you're, You know, I think one of the, one of the challenges with the, with the trust and with some of those modes is, you know, I think right now, one of the really important questions in science and research is, you know, how do you think more inclusively? and how do you make sure that you're not. Just super, you know, reinforcing biases in terms of what it means to be good and excellent, that, you know, like that's, that's one of the things that you, you really need to then struggle with, which is, you know, trust is a very efficient mode. but, you know, people build trust quickly based on their biases. So. I just point that out because it's worth, you know, absolutely being part of, part of it, thinking it's like something that's sort of like really grappling with hard. Yeah. now that said, yeah, I mean, I don't, I don't think that prevents you from. You know, th the two things that I think for us is special about activate one is, we're, we're, we're supporting people outside of the normal incentive structures. Right. and two is we, we fund people, who have great ideas and who have the right motivations, you know, but the whole reason back to the superposition, like the whole reason we exist. Is to give someone a chance to go through that first 18 months where they realize all their assumptions are wrong. So we don't pick them based on our, their assumptions. Right. We pick them based on other attributes, and. You know, I'd say in the last five years, we're learning a lot about how improve processes to do that with less bias. and you know, one thing we can't control is unfortunately, one of the limitations of an entrepreneurial though, is you're dependent on. You know, you're not that you're to be successful at gaining resources to support your project. You're not dependent on a really clear pipeline, you know, within your university or whatever else. You're dependent on pulling resources from a bunch of parts of the world and the ecosystem, and those places all have biases. So yeah. You know, if I think of our women fellows, our fellows of color, you know, I would say if. You know, blind tests, their strength is individuals and ideas at the beginning of the fellowship. And how much progress do they make in two years? No question. There's a deficit there. and you know, I think the work we need to do is we need to do a better job at, you know, Not being biased and picking those people and instead, right. Because you've worried they're going to fail or whatever it is. Right. But instead, like we need to be working really aggressively to make sure that we counter all the other biases. I feel like it's starting to happen. Yeah. and, and sort of, so I got on the, on the note of the sort of, the 18 months of the fellowship, do you feel like. And this is, this would be, it's very hard to do counterfactual. So it's just based on your feeling, like, do you feel like on the margin, like that is sort of the correct length and amount and the amount of money that you give them? Or like what, what, what is, what is the marginal value of say keeping people in that superposition longer or, giving them more resources while they're in it? Yeah, that's a great question. it's super tricky. when we sort of penciled out the model, that's now, you know, activate and synchrotron road, on a napkin, There was a question of like, all right, well, as you know, my initial model for this was very different. and the model we have was really bound by some of the constraints in terms of how much funding we have. And so we looked at the amount of funding we had and we said, Oh, we could probably support people for two years. And I walked around and talked to folks and. Well, it was interesting is anyone I talked to who was on the entrepreneurial kind of VC side of the spectrum was like two years way too cushy. Like that's ridiculous. You're just going to get people into a really relaxed state. The anyone I talked to on the more science, academic research side that realized like the only way you're going to get funding for this is through a grant or something, you know, like. Funding cycles happen on the order of years. Yeah, they were all like, so, so the entrepreneurs said like, Oh, you should make it one year. The academics all said, like, it has to be at least three years. and so we landed on two. the I've actually found it to be pretty appropriate. we do is really unique in the sense that we believe that there's value in. The science and engineering expert inventor actually understanding the connection point to how what they're doing could be valuable. Yeah. Right. Absolutely. And so what that means, because they can then be a mobile, the dimensional player, and they can start to connect the dots and innovative ways that they wouldn't otherwise in the applied research perspective. You know, the other mode is you take the inventor scientist and you just hybridize. You pair them with someone who, is thinking more about the practical market and you find a way to make that work. But at least for us, there's this sense of like, we can be creating these rare breed of like, You know, super, super scientists who are thinking applied and are still cutting edge experts. There are many people who have that capacity, but if you create one and we've seen this kind of time and time, again, like those people can be really powerful drivers. Okay. So for us, a big part of the two years is we found that. There's so much nuance in debt. Like the things that we've been talking about here to really understand, like, how did the capital markets work w w VCs fund things a certain way. how does manufacturing work? How do you think about techno economics? Like, Oh, wait a sec. Yeah, just like that, that mental transition. It takes time. You know, what we find is we tell people things in the two year fellowship, and every year we bring in a new group in the beginning of the fellowship and basically the way the fellowship works is there are a lot of ways it works, but one way is every week we get the cohort together and we're exposing them to ideas for founders and others, founder stories. Here's how venture capital works, et cetera. And we do that for them in the first year. And then we often are repeating some of that stuff for the new cohort that comes in next year. And the second year fellows will sit in on a session and be like, Oh shit. Like you told me all this stuff before, but I didn't, I just wasn't even a place where I could even understand where to which bucket to put that into my brain. But like, now that I've been now that I've been hit with this stuff so many times, like I'm starting to understand it. so that's one and then the other is. You know, I wish we were a program that could find the talent that we have. And then once they're in the program, if they're doing well, six months in, I give them a two or $5 million grant to keep working on it, but we don't have that luxury. I don't control the purse strings. So our fellows will, all we're giving them is sort of the institutional, you know, Umbrella and the support and the runway. So the other reason that two years ends up being important is given how speculative, what they're working on is, you know, the amount of time it takes to get a grant proposal together into a funding agency, get it funded. So you have cash in the bank to do the work or on the venture side or the corporate side to have an engagement with a corporation, that's going to get you funded or to develop a pitch. That's strong enough that BC's like. Like those things, any of those things happen on a time constant of roughly a year. Yeah. and so the idea that in the first year of our, of our fellowship, you know, fellows are basically shifting their mindset and. Like building the foundation for where that those funding and resources are going to come from. So that in the second year of the fellowship, they're actually able to hit the ground running, in the best cases, you know, in some cases that first year, it doesn't, you know, like what, what they're working on doesn't resonate or they miss the window on the grant, like whatever it is, it seems to work and this is sort of like, something that I. Struggle with, and I, and you know, much more about it than me. this idea of a sort of push versus pull on the people coming to the program. And what I mean by that is, There's there's one school of thought that says that people need to be like intensely, intrinsically motivated. They need to like, be like banging down your door to join the program. And then there's another school of thought that, there are people who like don't even know that they should be banging down your door and that you need to go out there and sort of like forcefully opened their eyes and then they will be amazing. Where do you, where do you sort of fall on that spectrum? When you're thinking about where the best fellows come from, this is a really, this is a really hard, it's a really good question. and there are a few different things that come to mind for me. One is what's, what's the risk of like, what's the risk of a program like ours? You know, one could argue that. Being an entrepreneur is not something you should just fall into. Like you will not succeed as an entrepreneur unless you woke up and said, Oh, like, there is no other thing I could imagine doing then this startup, you know, and it's right. and, and argument that has been made that I think is, is reasonable, which is to say, you know, if you give people a nice path, To start thinking about themselves as an entrepreneur. Like you're basically setting them up for failure because those people probably shouldn't be doing it. Like the Darwinian selection that occurs in terms of what there's, someone actually is willing in the middle of their piece deprogram to say, you know what? I'm just gonna put everything aside. I'm gonna figure out how to go raise venture capital money and find the person who's going to help me do that. Like, that's an important selection because the other people just shouldn't be entrepreneurs. It's one thing that keeps me up at night, which is, you know, what we found is actually quite the opposite. you know, because it's so hard, like it's almost stupid for someone who's got a PhD, in science to take that, right? Like, let's just think about a few pieces of this. Traditional entrepreneurial story in the software space. like what's the, what's the, what's the calculus you're doing here. First of all, like, you might be 20 years old and decided to basically go to do, do this. Right? Meaning you don't have a family, you know, you haven't already, you're not in your mid thirties, right? Like where you have to actually figure out like what your life has looked like. So it's a different calculus already from the get-go then it's like, well, what does it take to go get learning cycles? Okay. Like, I'm going to stop going to class and I'm going to meet up with some friends and I'm going to start prototyping. but like I could argue that I'll probably get some really, I'll probably get some really satisfying learning cycles, like on the order of months. Right. Yeah. So that's number two. and then number three, and by the way, like I'm gonna, I'm gonna find other people to do it with, they're going to be my co-founders. Like, I don't have a lot of like vested interest in these ideas. Like they're brand new, I'm just in the, in the early creative. So if you now contrast that with someone who. Let's just say they've spent the last, you know, five to 10 years becoming a cutting edge expert in material science. they've developed an idea that they've been working on as a research program for probably five years. That is now the basis of something that they think could be valuable. Okay. So now what's their calculus. okay. Let's see. I want to just go be an entrepreneur, first of all. I'm later in life, we already covered that. Yeah. so my, my analysis is different. And the other people I need to do this with me are probably all still later along life, they're more expensive. They have other constraints. So that makes it harder. Second of all, if I decide to step away, I stopped going to my grad school classes and do this. well, how am I going to start doing it? Like, where am I getting my learning cycles to do it? I need to raise enough money to get a. Venture capitalists to fund me. Then I got to go negotiate with Ember on an order of glove boxes and wait the six months for the like potentially it's like a year of my life before I'm actually in a startup. Yeah. Which is a big deal and a big opportunity cost. and then, you know, and then thirdly, like I have a watch a different amount of vested interest in what are like, I've already spent five years on this idea. so. The thought that we should imagine that people are just going to jump into these things at the same rates or paces, as in other areas of entrepreneurship, I think is sort of ridiculous. And frankly, even if I think about, so you say like, Oh, you're giving them a fellowship. That's really cushy. Like, I look at it like the people who are cutting into our fellowship are people who could get professor jobs at any university in the country. A lot of them. Like, so for me, it's not like cushy, like they're basically deciding to do something that on the face of it is totally stupid, which is like walk away from that path and like go into this fellowship where like, who knows what's next. and my read is like, if we have, if we have amazing individuals who w who are willing to take that step, like. The least we could do is provide that about a cushion. I've I've totally forgotten the question here. I think, I think we we're, we're really attacking it, which is this idea of like, do the best. Do you need to. Filter people by the ones that are willing to bang down your door or do you need to go out and find the best people and open their eyes to what they should be doing? yeah, from our experience, the ones that bang down our door are in fact, the most entrepreneurial of our fellows and they are the ones that make the most progress, and, and have sort of the highest likelihood to succeed. what's interesting for me is the other ones, the folks who come into our program, who didn't bang down our door, who basically looked at it and said like, yeah, I've always wondered. You know, maybe I don't know, I want to be a professor. and the example that I would give is Raymond White at camp who came out of Bob Grubbs, his lab, Nobel Laureate at Caltech. You know, Raymond, it wasn't until after he got into our first cohort where he basically said, you know what, this is kind of a hedge for me. I figured because you're connected to the national lab. I'll keep publishing. And like, I'll kind of probe this. but I can, I can still go be a professor. And what we found in that case was like, what he found in that case was he had no idea what the other. World would look like, and he found himself like so excited and motivated by, you know, by a more entrepreneurial path. and my take is like, okay, you know, Ray the way I talk about this, you know, Ray was working on, he had stumbled in a way in his PhD to take olefin metathesis chemistry. We can make, which can make some of the strongest polymers in the world. And most corrosion resistant toughest. He found a way to make those polymers light, the synthesis light activated so that you could turn that into a 3d printing technique. and I think he would agree, had he not come and done the fellowship. Like those ideas would still be in the world of publications, you know, instead, you know, he's proven those as 3d, you know, he's selling products as 3d printed resins in the world. And more importantly for me, like Raven would have been a professor somewhere, still with kind of not having been exposed to this other mindset. And now I think the fact that he still has that, you know, he could still go back and be a professor. I think about folks like, You know, like a number of actually folks from the great industrial, you know, from, from bell labs or IBM research that are now in universities, right? Like they, they can pop back if they want to, but they have just this enormous new muscle, and percept, you know, skillset and perception. That's going to allow them to do really amazing things is, is important to me. And this is actually, both related to, to pulling and, and your experience on R and R B E, which we did not wait, sorry. Can I mention one last thing here? I absolutely realized, I realized when I gave you the story about Raymond, that that's like my third story or case study about a guy. So let me also mention I'm a little bit different, but Sarah Richardson, who you may have crossed paths with who's, you know, one of the smartest people I've ever met, Sarah was really interested in thinking about how you create infrastructure, meaning research infrastructure, to industrialize how we think about, how we think about the power of, microbial organisms to be productive, basically like bio manufacturing. how do we, how do we take advantage of, genetic engineering and microbes to do important things for the world? And, You know, Sarah was within the national lab system, and came into the fellowship basically saying like, this is BS. Like I'm not an entrepreneur. and if, you know, Sarah, like her words were stronger than BS and, R you know, everything, all, all the external addition had pointed towards the fact that actually Sarah is about as entrepreneurial as he gets, in her personality and mindset and having a different structure, for her to, to thrive and be successful. as a researcher, cause she is like incredible researcher in the field, but just this idea of like the different modality. and, and you know, I think if you, if you. If you interview Sarah, I think she would say, well, I think I probably would have just as rather you, you know, someone had just given me a bunch of money to run my own lab within the national lab in my own way. My view is like, yes, you're talking about doing something entrepreneurial, which doesn't exist very often to that. So, like, I dunno if that was possible. but you know, she's now been able to, she's now been able to like, build a lot of the pieces and his running lab within her, her company to do it. So, yeah. And so, one thing I, I wonder when I sort of look at, at activate as sort of juxtaposed with ARPA E And the ARPA model as a whole. Do you, do you ever find that, that people are working on sort of like one piece of what should be a bigger system in that like, like activate really sort of focuses on sort of individual individual level projects and sort of, ideally it seems like RPE would focus on. sort of like systems level projects where you would get a bunch of these individual projects that come together to be bigger than the sum of their parts and sort of like, how would you, how do you think about that? Yeah. well, I'm glad you mentioned it because like, as excited as I am about what we're doing with activated, it's like, you know, It's a puny experiment, you know, like it's, it's, it's a puny mechanism relative to what you could do, and what's needed. And what I mean by that is, and this is largely because of funding constraints and funding and flexibility constraints. there are a lot of, There are a lot of problems where we might still accept a fellow. but what they really need is something much bigger, right? Like the idea that two years to transition to what, you know, who's going to fund them next. Like, what they need is five years, to think really much more systemically about a problem. And they need, you know, 10 times the amount of money that either we would get them at phase zero or a venture capitalist or anyone else, would you have them at the next phase? that's really clear. you know, we can only, you know, so if you look at our fellows, you know, a lot of them are starting and we have a number that are more sick, some lied, but those are the ones that are really, you know, bold and. You know, like w we're just betting on the strength of the individual to go figure out how the rest of that comes together. You know, it's, it's a lot more suited to component technologies that can unlock, you know, bigger, bigger things, or only certain spaces, you know? I'll give an example, which is, you know, we basically turned down an exceptional individual for the fellowship, a couple individuals working on a nuclear. energy innovation, because we basically said, well it began, we basically said like, we don't see how our support can make any substantive difference on your trajectory. It's just not, it's not at the level that it needs to be. And so given that slot, we're better off giving that slot to someone where we think our support is actually going to shift the trajectory. And it's just because of the scale of what you need to do, what you're doing. And so there, you know, I think, you know, I think you and I are both excited about right. Adam's ideas around, you know, these focus, research institutions, organizations, and whether it's, yeah, I mean, I, I it's, it's really interesting to think about how you would do that. how you would provide. This is the right type of incentives, but at a bigger scale of funding, probably the biggest question being, are the incentives, are, are the incentives and funding more aligned with the private sector or more aligned with them? the public sector. .

In this conversation I talk to Luke Constable about the complicated tapestry of finance, funding projects, incentives, organizational and legal structures, social technologies, and more. Luke is the founder of the hedge fund Lampa Capital and publishes a widely-read newsletter full of fascinating deep dives. He’s also trained as a lawyer and historian so he looks at the world with a fairly unique set of lenses. Disclaimer: nothing Luke says is an offer to buy or sell a security or to make an investment Links Luke on Twitter Lampa Capital Theory of Investment Value (John Burr Williams) 1,000 True Fans (Kevin Kelly) Quantum Country Patreon Lampa Capital’s Open Questions The Empire of Value (André Orléan) Who Gets What and Why (Alvin Roth) The Mystery of Capital (Hernando de Soto) I, Pencil (Leonard Read) The Crime of Reason (Robert Laughlin) Andrew Lo’s papers Transcript 0:01:05 BR: So if technology creates a lot of wealth, why does it feel like most people in finance are hesitant to invest in technology?   0:01:19 Luke Constable: So that's an interesting place to start. I think you have to understand, no one invests in technology. If you think about investors, investors invest in businesses that use technology, and so that's probably the first frame I would use. Investors aren't hesitant to invest in technology, investors never invest in technology. What investors do is they invest in these products that are going to generate cash flow streams, and so that's sort of the first thing. And then the second thing is, a lot of the technologies that you and I think about, they seem obvious at a macro scale, where you take a high level view and you say, "Well, it would be so much better if we had a blank sheet of paper," and I said, "We should do X."   0:02:10 LC: For instance, you could make an argument about housing technology in San Francisco, and you could say, “All of these houses built in SF, they're old Victorians, they don't really have washing machines and laundry machines, you could probably change the structural engineering, probably build them higher”. And if you look at them and said, "Oh, I have a better prefab housing technology," or "I have a better way to do it," you'd miss the point, which is just because you've invented the physics, and this is the other thing, you actually have to sell it into a market. You have to work within the market, and so that's usually where I see a lot of the interesting technical products fall down.   0:02:53 BR: So the thing that I want to poke at in the assertion that people invest in businesses is that people invest in things that are not businesses as well, people invest in gold, in currencies and other, I guess, assets would be the high level thing, and so I guess the question is why isn't technology itself an asset, and there's probably a very obvious answer to this, I just...   0:03:25 LC: Sure, so let's take a step back and talk about the various asset classes, there's sort of a couple of ways to break them down.   0:03:32 BR: Okay.   0:03:33 LC: One way people do this is they'll say there are real assets, these are things like real estate, some people put commodities in there, and then there are sort of these yield assets, these are debt that is putting out a cash flow stream, and then you have equities, and there's some argument that cryptocurrency is sort of its own asset class, and then currencies might be their own asset class too. And what you'll quickly find is these things kind of blend together. A lot of them are different ways of financing sort of the same project. And then you have the ones that are just traded for their own sake. So there's sort of two questions you're asking, the first is, why isn't "technology" the same as like gold or silver or real estate, for instance? And so there's a use value to all of those commodities, and that's why they have value, and that actually is a cash flow stream, we actually do use gold, we do use silver, and that's how that works.   0:04:43 LC: But if you think about what's valuable, there's sort of something that's value... And I should have started with this. When you think about what value is, there's value in exchange and then there's value in use. So the value in exchange ones, these are often, you could argue, cryptocurrency or a lot of currencies, gold is actually usually thought of as a medium of exchange, that actually is valuable for cash flow purposes just probably not in the ways that you think. So what happens with these currencies and these stores of value is they sort of become Schelling points where I just know there are enough people transacting in that thing that I can find the liquidity, I can actually go convert to cash, and I can go basically get that cash when I need it. That actually is a cash flow need. It's just not often thought of that way.   0:05:40 LC: Now, liquidity is really valuable because you might be invested in the best business of all time, and it might have a very, very, very high net present value and be doing a lot of good for the world. But if you take a step back and say, "Wait a second, I have to pay off student loans," or "I have to pay off my mortgage," or "I just want some cash to go on vacation" or whatever you want to do with it, you look at this and say, "Gosh, I do need some liquidity," and that's what those other sort of trading assets are for.   0:06:10 BR: So basically, technology contributes to the use value of an equity asset, is that the right way to think about it?   0:06:22 LC: I don't think of technology that separate from... It's sort of so baked into the environment that it's just difficult to disentangle. Technology, lazily put, is just ways of doing things hopefully more efficiently than we're already doing them. And so if you think about why certain assets become tradable, either they're creating these cash flow streams, or there is some value in exchange. I mean, the way that I often frame investing for the people who I invest for is there's sort of two sets of flows that determine an asset's price. There is underlying asset's cash flows and then there are the capital flows of all the investors. So you have sellers for some reason, maybe they have liquidity needs, maybe they can't hold an asset for a regulatory reason or a legal reason, and then you have buyers who come in, because they're interested in that asset, and it could be because they think it's an interesting thing to invest in, it could be because the regulators told them that they have to buy it, it could be... You laugh, but this is actually...   0:07:32 BR: What sort of things do regulators mandate that people buy?   0:07:37 LC: Sure, so if you go look at banks and sovereign debt, well, actually banks and all debt. So you have the bank regulators set risk weightings on various types of debt, which is sort of a nice way of saying, there are all of these different cash flow streams, and the regulators are saying to you that certain cash flow streams are riskier or less risky. And shockingly, they often argue that their sovereign debt is less risky than some other cash flow streams.   0:08:13 BR: I'm shocked.   0:08:14 LC: In practice, that may or may not be true. It's a weird thing to think about, but, in some cases, a multi-national corporation might actually be a better credit than a country. But that's not how these things work, and so what happens is a bank regulator will sometimes go to a bank and say, "The risk weighting on the sovereign debt is far lower than the risk weighting on this corporate debt,” which effectively is pushing the bank to go buy a certain type of debt, which then goes and funds all of those projects. So then coming back to all of this, if you think about investing in sort of these two sets of flows, like that underlying asset's cash flows and then the capital flows of all the investors, you basically, in practical terms, want to think about markets in terms of what's driving someone's action.   0:09:05 LC: And when you think about that, that's when market prices start to make sense. They won't make sense to you if you think that you're just going to sit down and solve an analytical equation where you just sort of put in a few inputs, you make a few estimates and then the price gets spit out. It's much more of a socially constructed thing.   0:09:25 BR: And going back to your point about liquidity, it feels like there's this... I don't know how to describe it, like sort of a weird effect where it feels like there's a consensus that investing in... I won't say technology, I'll say investing in a business that is proposing to build a technology with a very long-term time scale, there's consensus that that will eventually create something... Will eventually create a lot of value, but then at the same time, because of these liquidity constraints, very few people are doing that, and that's the argument for why people are not making those investments, but it seems like that would be a point where you could arbitrage. It seems like there should be some people who are willing to not get cash flow for a couple of decades, and they would be able to reap the rewards of making these sorts of investments, but you don't see that, so I assume that those people are smarter than I am. And so the question is, why don't you see people doing that?   0:10:50 LC: So you actually do see people doing this literally all the time, but it's not for the sexy technology concepts that you are thinking of. So go look into the public markets right now. You'll see a handful of software businesses that are trading at very high multiples to sales. So the idea is that you sort of have this trade-off: you could get free cash flow after taxes right now, or effectively more free cash flow down the line from some company that's growing quickly, and so what you do is you pay some price based on that free cash flow multiple. What happens when the free cash flow is really, really far down the line, we don't even use the free cash flow number, we actually just use the sales number. And sales is obviously much higher than just free cash flow, 'cause free cash flow is after all of your expenses and taxes. So when you go look, and you see some company that's trading at 15 or 20 or 25 times sales, the stock market is betting on that business being around and generating free cash flow over a 25 or 30-year period. That's the only way that math works. In practice, the reason the stock gets priced that way has something to do with those cash flows and also a lot to do with the capital flow landscape, but that is what's happening.   0:12:15 LC: These companies are getting funded on a 30-year time scale, and so the right question shouldn't be, "Why aren't good projects getting funded?" They actually are. The right question is, "Why aren't other good projects getting funded?" And so I think it comes down to... I think it comes down to what is legible to institutional finance, and so you might look out into the world and say, "There are trillions of dollars of capital... " I mean, there's just oceans of money out there, and it seems like someone could raise billions of dollar to go trade a building with someone else or something else that seems like it isn't actually moving the world forward and this sort of simplistic take. But why can't we take that billion dollars and put it towards some technology, something that might be obvious in your opinion toward moving the world forward?   0:13:15 LC: So the first thing is you have to understand what matters is, in practice, even though it looks like there are trillions of dollars of capital out there, risk-adjusted or uncertainty-adjusted, there's actually very little capital available. And the right way to think about it is to say, what type of product are the capital allocators buying? And so this isn't, again, a place where we have an analytical equation and you just pop your numbers into the equation and you say, "Well, the return to society would be X percent higher if we invested in this type of technology that will have a payoff in 25 years." The right way to look at it is to have empathy with the person who is in this capital allocator's seat, in this investor's seat...   0:14:08 BR: I.e you.   0:14:08 LC: Well, me or anyone else. But again, I'm not trying to paint myself upfront, there's the intellectual side of capital allocation, and then there's the reality that a lot of people are using an element of gambling in this. But it's to understand what they're buying. And so the reason people are comfortable investing in that real estate or investing in an enterprise software company is someone has come up with a set of metrics that has convinced the market that those cash flow streams are durable, that they will exist and be predictable 20 or 30 years out. And so what you've done is you've created this yield product, and what you've really done is you've created a sense of certainty. And I think what people don't like is uncertainty, they really want to essentially have something that they don't have to do too much intellectual work to understand and that they feel like they can trust. And so the problem is actually sort of one of search costs.   0:15:20 BR: A really dumb question is, What does it mean for something to be risk or uncertainty adjusted? Because you said that there's trillions of dollars out there, but there's actually not that much when they're risk or uncertainty adjusted, and is that basically just say that capital allocators don't have the incentive to spend most of that money on anything that they perceive to be risky or uncertain?   0:15:50 LC: Not exactly.   0:15:51 BR: Okay.   0:15:52 LC: It's two things. So first, in terms of how most people think about risk, so the way that you might think about this before you start really looking at it is you'd think, Well, we're just trying to sort of predict the future, the future is relatively predictable, and we can make some educated guesses about probabilistically what is going to happen, and then we can sort of model out those payoffs, those defaults, and sort of go from there. And so sort of the canonical text in finance for equity evaluation is called The Theory of Investment Value, and it's written by a guy named John Burr Williams. I can send you links after this. It's written by a guy named John Burr Williams after the Great Depression, and he was basically trying to sort of scientifically estimate the value of all free cash flows. You may have heard of this concept of discounted free cash flows?   0:16:48 BR: Yeah.   0:16:48 LC: He's arguably the person who invented it or at least codified it. In practice, though, you quickly find it is unbelievably difficult to figure out and to actually estimate the cash flows of something, even four, five or six years out. The world just changes really quickly, competitive positions tend to change really quickly, and so you actually could come up with this range of outcomes, but they become somewhat uncertain. So you take that as sort of the investing reality, and now let's look at sort of the funding reality. A lot of the people who fund investment funds or who are making investments, they have cash flow needs. They have sort of real cash flow needs, and then they have sort of intellectually forced cash flow needs. The real cash flow needs are, look, we have to fund our endowment, we pay X percent out per year so that the college can function, so that the hospital can function.   0:17:53 LC: And then the intellectual cash flow needs are, look, here are the risk models that we use, and when we see the prices of our investments fall 8%, we consider that as fundamental information that our investments aren't performing well, and so we need to sell out. And so they actually don't just need cash flow to look good, they need the pricing information in the market to look good. So we're talking about arbitrages. This is probably one of the biggest arbitrages that exists in the market, but it's unbelievably difficult to capture. So let me give you an example, imagine that you had a row of 10 houses in a neighborhood and they were all... Let's just say for these purposes, valued at $100. So let's say one of the neighbors, they are in a rush and they need to sell their house because they got a good job offer somewhere else, so she sells her house for $97 because she'll just get whatever she can get. And then another neighbor gets a similar job offer, and she sells her house for $95, and suddenly some other neighbor along the street looks around and says, "Oh no, prices are falling on our houses, everything else is getting sold off, we need to sell." And so they might sell just because they're scared, because they think there's sort of fundamental information in those transactions, in saying, "Okay, the market price has fallen."   0:19:22 LC: So you've seen the marked prices fall from a $100 down to $95. The problem is the market shows the prices of transactions, they don't necessarily tell you the fundamental value behind those transactions. So as a result, you being a portfolio manager, say you're invested in houses, you might have a view and say, I think that those houses that sold off, those were forced sellers. That doesn't mean that the price of the assets have actually fallen, these prices will come back up. Someone else might say, "No, no, that's pretty arrogant of you. The market has spoken and job opportunities have changed and people are going to leave the neighborhood." Now, it's really difficult to capture that sort of arbitrage, and arbitrage isn't even the right word, but capture that valuation spread, because it actually comes effectively down to who is right, and that ends up being a grounded matter of opinion, but effectively a matter of opinion.   0:20:32 LC: You can do a lot of diligence, and then you can maybe figure out if you're generally more right or generally more wrong. Ideally, if you get really, really good at sourcing information on the asset cost that you're investing in, and then you go around looking for these situations where the market has sold them off, but you recognize that they're sort of incorrect in doing it. But for the big portfolio managers, again, there's an information search cost. Every single time one of their fund managers underperforms, fund manager is of course, going to come back and say, "No, no, it's temporary. We're right, the strategy will come back. Don't pull your money."   0:21:12 LC: And so the difficult thing for the allocators to funds is they sort of have to diligence the fund managers who are then diligencing the investments. And so you can see that as you sort of go down this line of information being passed from person to person, the search costs just rise. Whatt it really comes down to is basically trust, where the investor is investing in a company or in some operator, and then the allocator is investing with the investment fund. And all along those links in the chain, it's so expensive from an information perspective to figure out who's being honest and who isn't. That trust is actually the fastest way to figure out what is a good investment and what isn't.   0:22:06 BR: Yeah. Correct me if I'm wrong, but then I sort of extrapolate that to the thought that it's actually very hard to build up trust in someone who's proposing to make, say, a 25-year bet, because you would need 25 years to build that trust, right?   0:22:31 LC: Sure, and this is actually the problem. And so if you look at it, most fund cycles for the investment funds themselves, they typically have about a three-year window to prove themselves. So if they can't show marked prices rising within two to three years, or they can't show cash flows coming out in those two to three years, it's in practice really difficult for that fund manager to go raise more money from an allocator. The best allocators, they really get it. But in practice, most people are sort of looking at each other trying to understand what we all think is valuable and what we don't, and people are actually pretty good at it. But if you're not seeing results within three years, it's difficult to go raise the next VC fund, the next private equity fund, or just to raise more money for whatever your next fund vehicle is. And so what happens in practice is, people don't go spend their time investing in projects that are going to take a really, really long time and won't get marked. So what that means is, for an entrepreneur or for someone who's trying to get funding for something, getting that asset mark is unbelievably important because that's what lets the great investors go invest in you.   0:24:00 LC: So it's really important that for the VC company to get that Series B or the Series C or the Series D done. That single mark in time is hugely important because everyone can sort of concentrate on that, take it as a market price, even if it's not a perfect market price, and then write that in their books, measure it, sort of trust it to some degree, and everyone can sort of coordinate around that because you have a market clearing price there. And so if you think about it, just on the equity side of it, every founder's equity actually is a product in and of itself. I always find this interesting because I think most people don't think of it this way.   0:24:42 BR: I don't.   0:24:45 LC: But when you start a company, you're actually... You're selling two products. The first is sort of your individual product. This is the thing that you think you're starting. And the second is your company itself. And so your company can turn into a product where you sell your debt or you sell your equity or you sell some other sort of financing scheme, but that's a product, too. And the way that product is priced is, in the private markets, you have one-off auctions where you sort of game the options as much as you can to get the highest price. This is where everyone in their C, Series A, B, C... Well, not so much in C, but in A, B, C, you basically create auctions where you try to get all of the partner meetings on Monday morning to be talking about you, put all of your meetings into a week, and then you get everyone to bid all at the same time, and then you maybe don't go to the highest bidder, but you go with some mix of the highest bidder plus the people that you want to work with.   0:25:35 LC: Then the public markets are actually a totally different mechanism, it's a different distribution method where it's a continuous auction, where there's bids and asks continuous in time, at all times. And so you can't actually create these small little one-off auctions where you can rig the price up because the bids and asks, they just keep coming. But the benefit is, if you know how to... If you do well in that channel, you then have a lot of liquidity and you can usually get a higher price and arguably more capital. It's not actually even clear that you need to do that, but that's sort of the argument. And so I think if you start thinking about it that way, you can start to recognize, "Alright, that's why some projects are getting funded and some aren't." It's because the projects that are getting funded, they are products that work well in that market, and they are actually products, it's not just a throwaway phrase.   0:26:37 LC: I was chatting with someone about this earlier. I think it's probably good to take the emotion out of whatever project you're working on and think about this for unemotional things. So one of my friends is trying to get a research project funded, sort of like an arts VR research project funded. And we're talking about this and she's like, "Oh, now I get it. I should think about this like soap." So imagine you are a soap manufacturer, and you have made the best soap in the world. You think it's better than any other soap. You wouldn't expect to sell that just because you've created it. You'd think, "Okay, how am I going to get it out there? Am I going to get it on to Amazon? Am I going to start a store on Shopify? Am I going to go to the people at Costco or Walmart and cut a deal with them so it's distributed?” Because I might have the best soap in the world, but some mediocre soap that gets into the Costco channel and then works with those constraints, they are going sell more than I am. That product is going to do better. And if you care about people using your product and you're sort of not just cash flow-driven, but you actually care about the impact, you really, really need to think about that distribution channel and how you're going to get it out there.   0:27:50 LC: What you quickly find is that often the constraints that people place on their products, it's not that they don't realize they're making their products worse, it's that they want those products to get distributed and they think the tradeoffs are worth it. And so the really interesting new products, they recognize that, "Oh, there's some constraint or there's some tradeoff that a lot of other people made with their existing product lines, and I don't have to do that," because the way you distribute it has changed, or some assumption that they've made, they actually don't have to make that tradeoff. And I use something like soap because it's boring and unemotional to at least most of us, but it's almost definitely true with research funding. And so you and I talk about this a lot, but I mean, if I were trying to go raise money for research, it would depend what I was trying to do, but I think there are probably new distribution channels out there, so I mentioned with small scale... Sorry, you were saying?   0:28:50 BR: Oh, there's just three different directions that are really exciting to go with this.   0:28:56 LC: Oh, please.   0:29:00 BR: Yeah, so I think what I'm going do is I'm going to lay... Actually, I will lay out the places that I think are all tied into this that are all really interesting, and you let me know how you want to weave through them. So one is actually this... So both this point about a project as a product is a little bit mindblowing, and I think that it's tied to an earlier point that you made that I wanted to dig into about what it means to be legible to institutions. And if I am understanding correctly, the marking of valuations is one of the ways in which... At least, in the startup world, venture capitalists make themselves, their firm, as a product legible to other institutions. And so Shopify comes along, and you can now distribute your soap through an online store that you never could. What would be the project funding equivalence of that new distribution channel?   0:30:17 LC: So I absolutely don't think that this is that new, but it seems to have come somewhat in vogue, and I think it's just patronage. And so if I were trying to go do research where I was trying to make, say, call it $100,000 a year or something along those lines, basically enough that you could live a really good life, afford rent in any city and sort of have basically time to yourself, I think the obvious way to do it is to try to build an online following. And this is not a new idea. Kevin Kelly wrote that old essay, I think it was 1000 True Fans, where he said, “Look, at 1000 True Fans paying you $10 a month, that's enough.” I think a mutual friend, Andy Matuschak, who has Quantum Country has done a great job with his Patreon. I think it would be really, really, really difficult to do this. But I would think a lot about what really causes someone to say, "I'll pay $5 a month to go read this newsletter, or to go basically fund some research I find interesting." And this distribution mechanism didn't really exist before, and so I actually think in some ways, we're still pretty early on. And all I would do is think, "Alright, I need to get 2000 people to sign up all over the world." The Internet rewards niche behavior, and so how do I get into the community of these people find it just sort of interesting, and this is sort of entertaining to them, and I would think a lot about how I could create something around there.   0:32:01 LC: For the larger amounts, I would actually do the opposite. So for the larger amounts, I would go become friends with everyone in the funding world. So they have incentives too. And what you'd want to think through is normally... I guess I'll put it this way, and I was chatting with my friend about this. Normally, the way that the great researchers I know think, they're almost... They're quite dogmatic, to be honest. They say, "Okay, my project is the best project. This really will advance the field." But in practice, what might make it easier to sell the project is to understand what gets the person funding the project promoted? What makes the funder feel good?   0:32:40 LC: What will get to that next level of funding for the person above them too? And then if you're able to map that out, you can represent it in a way that basically works for everyone. And she was actually pushing back on me and saying, "Look, I don't want to lie. I don't want to represent my project that way. That seems sort of fake or it seems like a veneer." But the truth is, is that the project that she has in her head only exists in her head and doesn't exist in anyone else's head that way. And if she doesn't communicate it in a way that actually makes sense to them, then it's not going to get anywhere.   0:33:21 LC: So I think the really frustrating thing to come out of this is that basically everyone's in sales in some way, shape or form, and I think a lot of people don't want to be in sales or think that it is a sort of a difficult thing to go do. And so as a result, they just sort of shy away from it. And so this is, again, why I think the distribution analogies really, really can work well, because it sort of takes the emotional weight out of it. And then if you look at this and say, "Oh, this isn't the best grant maker in the world, this is just Costco, and I'm just trying to get into the new line," I think it can feel a lot less heavy. And you can maybe treat it, and maybe the field might open up to you a little bit more.   0:34:05 BR: Okay. I guess, the tension I see there is building up trust with the people who are the capital allocators, almost feels like the opposite of figuring out a different way of making yourself legible to an institution. Institutions are obviously made up of people, so these aren't two separate things. But I think that there's something to the fact that you need trust when you're doing something that is not institutionally legible. So it's like you don't actually have trust with a lot of the companies that are publicly traded that you invest in, but they are... They've packaged themselves in a way that is sort of institutionally legible if that's... And I think this might actually be a good point to really... What do you mean by something being institutionally legible? What does that mean?   0:35:20 LC: It's a vague handwavy way of saying you just need to be recognizable to the people who are buying your product, and you just have to understand, in practice, how those relationships work. And once you understand the practicalities of whatever market you're working in, then you'll be able to understand how to craft a product for the people who actually want it. And, again, I think the difficult thing here, this is not intellectually that challenging, it's much more of an ego thing where we have to put aside what we think are the best products that everyone should be buying or what everyone should be doing. So if you think about it, since we're talking very abstractly here, what capitalism really rewards is, and actually this is true of all non-violent selection, it rewards behavior change. And so what we're really saying is how do you get someone to sort of change that behavior. And when you think about it that way, what's legible in your head, if someone else hasn't learned all the same things you have, they're going to end up using some sort of abstraction, some sort of shortcut.   0:36:41 LC: And that's sort of what I mean by saying intellectually... Or sorry, institutionally legible, is you understand the abstractions they use, you understand basically the mental models they're using to try to understand what's going on, and then you are able to fit your product into that. So I can give you a couple of examples and findings that are...   0:37:02 BR: Yeah, please.   0:37:04 LC: So I don't know how deep into accounting you are, but there is a metric that's really commonly used called EBITDA. And effectively, it is a free cash flow proxy metric. And it was invented by some people in the cable industry who wanted to raise a lot of money to go roll out cable systems all across the US. And they wanted to be able to quickly raise debt to go buy these sort of small cable operators and then put them all together. And with this metric that they invented, all of these other investors suddenly had a Schelling point. Suddenly, all of these investors had a new unit of measurement to look at this type of business. And because they accepted it, they were willing to go fund those purchases. Suddenly, a whole wave of those purchases were done, and basically a whole wave of these projects were financed because someone figured out a way to make that institutionally legible.   0:38:11 LC: And a similar thing has happened in the last 10 or 15 years with what we call enterprise SaaS companies, where we now have a new set of metrics that weren't really in use 20 years ago. These are metrics, I'm not sure if you're familiar with them, these are metrics like...   0:38:26 BR: The CAC.   0:38:27 LC: Gross churn... CAC, gross churn, net dollar retention. And if I went to someone today and I said, "Oh, I'm investing in a business that has an average customer life time of six years, an LTV to CAC of 4:1, it has 98% gross retention and 127% net dollar retention, and I think those numbers are going to persist for the next four or five years, that is something that I almost wouldn't even have to explain what the product is. If something met those metrics and truly met those metrics, it's a company that would get a huge valuation in today's markets. And it's again, it's because it's now institutionally legible. Someone has basically convinced the world of that. So then the question should probably be, why do these things get institutionally legible? And what I find is that, we're actually re-using the same math over and over again and finding new situations where we didn't realize that math applied. And so usually what's happening is, we're finding relationships that are really durable, that are really, really, really resilient.   0:39:40 LC: So I have this little questions page on my website, and the first one is, "What is the next durable customer relationship that we haven't really seen yet?" So what happens is, once the market recognizes that there is a durable customer relationship, and you can build that into our models. These models actually should come from how we model these bonds that last 20 or 30 years. If you can fit the customer relationship into that model, suddenly, all of the bond investors and sort of the bond valuation metrics that we used as proxies, they drift into the financing world. And people say, "Oh, this is also a durable relationship, so we should go fund it." And coming back to your first question to say, how do some of these huge technology projects get off the ground, it's because someone has convinced a set of investors somewhere that there is this long durable, and that's important, resilient set of cash flow streams 20 or 25 years out, and then we discount that forward, so that's how that works.   0:40:45 BR: Oh, man. Okay, so to riff on that and to go back to your analogy to products and distribution channels, what basically... You could almost think of it as someone coming in being really good at sales and arguably like marketing, and basically changing taste and creating a new product category where people didn't know they wanted gluten-free things, and then they go and they create that new marketing category, and now customer tastes change, would that be...   0:41:29 LC: And it's funny you use the word taste, that is... It's both fundamental reality of, Oh, in a true Bayesian universal sense where we're updating our priors correctly, imagine we had all knowledge, that does matter. But then taste does matter too, that's exactly right. There's another book I'd recommend called "The Empire of Value" by a guy named Andre Orlean, who is this really interesting French economist. And so in this book, he makes this argument that prices are completely socially constructed, and it's like you're saying, it's taste. As a side note, it's totally unclear to me why all of the people who are coming up with the socially constructed value theories are all these French people. It makes one wonder what's in the water in Paris. But similar is to say, actually, I think, and everyone else thinks, and we're all sort of self-referentially thinking, therefore, the thing exists, the price exists, the value exists.   0:42:32 BR: Yeah, yeah, that makes sense.   0:42:33 LC: It exists as this organizing principle, which everyone else then cites as a real reference and then it takes on a momentum of its own.   0:42:44 BR: And what... And so, I guess, do institutional structures like C-corps and LLCs, do those relate to institutional legibility? In my head, they do, but I might be going a step too far.   0:43:04 LC: Yes, they do, but I want to backtrack in terms of what you're saying.   0:43:12 BR: Yeah, do it.   0:43:14 LC: So what they do is they basically... The legal structure sets the landscape for markets. I should completely confess my own bias here. I am massively, massively pro-markets. I think virtually, no other social mechanism that we know of has raised so many people out of poverty. But as much as I love markets, I recognize that it's not sort of this shallow teenager's love of markets where I overdosed on Ayn Rand. It's more of on the lines of...   0:43:45 BR: Be nice to the little libertarians.   0:43:49 LC: No, I was once one when I was 14 too, I get it. And I think the problem is, you have to understand markets are these amazing and emergent phenomena that pop up basically naturally everywhere, people trade with each other. But efficiently functioning markets are actually very, very expensive public goods to maintain. And that means that you're depending on the bias of all the regulators to try to make the best guess as they can to create and maintain these liquid markets to make sure that people are transacting fairly. To give you another book recommendation, there is an economist named Alvin Roth, who wrote a book called "Who Gets What and Why," and a lot of his students went on to go work at Uber and Airbnb to sort of create these marketplaces. And if you look at it, they're actually quite intentional about how they're sort of creating the markets. So now, let's take one step further back and say, “Alright, all of the countries are creating markets themselves, too, and they're creating the balance of these markets.”   0:44:54 LC: So as you know, I'm a lawyer and was a history major and sort of loved looking into this stuff. I would argue that one of the least appreciated social technologies of the last few centuries is the concept of limited liability. And so it used to be, before we had easy access to creating limited liability organizations, if you started a business and it went bankrupt, you personally went bankrupt. Maybe you were thrown in jail, maybe your family went bankrupt, and so you couldn't go that far out onto the risk curve. And so, socially, if you were thinking about this sort of like an agent-based modeling perspective, if you could basically increase the variance of what agents could do, if you could basically socialize some of the risk, then you let people take a little bit more risk. Maybe it doesn't work out as well for a few people, but socially, you get to that higher hill in the hill-climbing analogy. And so you're asking about how C corps work and LLCs work. Do you want me to just run through the history really quickly?   0:46:01 BR: Well, I guess more what I'm poking at is just talking about how, at the end of the day, these aren't laws of nature, the structure of organizations and...   0:46:14 LC: Not at all. So why do we have Delaware C corps? Coming back to limited liability, in the late 1800s, New Jersey created a charter that let anyone go get a corporation. And then after that, later in the 1800s, New Jersey passed a set of laws that are colloquially known as the “Seven Sisters,” And these were these terrible laws in the view of all the businesses who were registered there, so they were looking for other places to register. Delaware saw this as an opportunity, so around 1900, Delaware lowers their taxes, lowers their registration fees, and they bring a lot of corporate registrations in. And then they set up their court systems so that they specialized in registrations, at which point Delaware becomes the de facto place. You get a runaway phenomenon, then all of the good corporate lawyers want to go practice in Delaware or they want to be corporate judges in Delaware, and all of the interesting cases go to Delaware. And it's literally gotten to the point where everyone in the US references Delaware corporate law, and non-US companies will create charters saying they'll defer to Delaware corporate law, and countries who are still forming their legal systems will effectively copy and paste a lot of Delaware corporate law. And so coming back to your point, it's not a law of nature. These are people doing the best they can to optimize the landscape, and that's how it works.   0:47:47 BR: And so my thought would be that that does relate to institutional legibility, because if I went to someone and said, "I'm using a B corp structure," they'd be like, "What the heck is that? I'm not touching that with a 10-foot pole." But if I say that I am using a Delaware C corp, then that is a legible abstraction, so I guess that would be my argument for why institutional structures matter.   0:48:24 LC: They do, and I think what it comes down to is you have all these degrees of freedom when you're starting any organization or any project, and you just want to think about where you want to innovate and where you don't want to innovate. So you look at US business organizations, I should say this, since I'm a barred attorney, this is not legal advice. There are basically four options. You default into being a partnership where you actually have unlimited liability. You can be a limited liability company, which is done state-by state. You could be an S corp, which is a tax status of LLCs, or you can be a C corp, which is the one that you're talking about.   0:49:03 LC: And what you go see when you run through all of these things is, well, there might be a better way to do this, but for the company that I'm starting or the project I'm starting... So the fund that I run, we have a Delaware LLC. I could argue to you that there are things we could do that would actually be better for the investors and better for the whole strategy, but you then look at this and say, "Hmm, it's just not worth the marginal effort given the payoff of actually trying to overcome that sort of legibility hurdle." And so I think what ends up happening is you end up getting these innovations around the edges where someone says, "Okay, here is one use case that's a little bit better, and we'll keep everything else the same except for that," and then the new standard arises. I don't think it ends up being worth saying, "I want to create a new legal structure and a new product and do physics research all at the same time," just because there's not enough time in the day.   0:50:11 BR: Yeah, I guess it just... It makes me wonder, because it feels like these legal structures do impose certain constraints, it just makes me wonder out in the landscape on a completely different optimization mountain what other constraints could be imaginable.   0:50:40 LC: So probably the most difficult cost to measure out there is opportunity cost, because it's so difficult to say, what could things be if we organized everything differently? And one hopes that when you have 50 states, that's how federalism works in the US, one hopes you get people experimenting with regulation, and you can get maybe a new project started off the ground somewhere else, if not in the state that you live in, and then of course, with more countries, you can maybe go overseas and do it too. And it's interesting, you brought up Spotify a little bit earlier, it's unclear to me that Spotify could have gotten started in the United States, given the state of music laws at the time. But then what happens is all of these European customers start using the product, and that has an institutional legibility of itself, and people say, "Oh, okay, I can see it's working in that country, it will probably work here," and I wasn't involved in the record label negotiations, but I assume that's basically what they were looking at. And then you look and say, "Oh, okay, then the laws can change."   0:51:52 LC: The other thing that I just want to point out is that when a law is set, that's a much more fluid thing than I think most people realize who haven't spent a lot of time looking at this. So in practice, a lot of times, there are sort of these gray areas of the law, and I'm not saying people should go break the law. But there's a gray area of the law where the products that you're working with don't really fit into the regulation, or customer demand is just so massive that the regulators will actually change their mind once they see that demand. Now how far you want to push that boundary is really up to you. There are arguments that Uber or Airbnb were illegal when they were first started. There are arguments that they're illegal right now. I don't think so, and I think they did the right thing, and I think the world's a better place for giving everyone the options. But it’s also really, really important to realize is there are these constraints, but the constraints, when you read a law, it's not a law of physics. And the other thing that you have to understand is laws are executed by regulators, so understanding why they are enforced or what they actually want to enforce is also really, really important.   0:53:09 BR: Yeah, and do you think there's... So to your point about there being different regulations in different places, do you think that it's then problematic that you see so much copying of Delaware law and sort of copy-pasting that around the world? 'Cause wouldn't that then sort of make everything... Wouldn't that be a very strong attractor?   0:53:37 LC: I think what ends up happening is it's a good enough baseline. So I can't remember what the book is called right now, but there is another famous economist named Hernando de Soto who wrote about just the importance of property rights and how if you are able to sort of import the property rights regimes from the US into a lot of different countries that don't have them right now, it would be a huge driver [0:54:00] ____.   0:54:00 BR: It wouldn't necessarily work.   0:54:01 LC: And so I don't think we live in a world where we figured everything out so perfectly that all we need to do are these sort of minor experiments. I think we live in a pretty uneven world where if we just had relatively good legal functioning across the world, not just in terms of the laws that are written down, but sort of culturally how they're practiced, we could make life a lot better off for a lot of people. So it does make a lot of sense to me that if you and I were trying to start up a corporate law and corporate practice in some small country somewhere that was just starting to figure it out, or just decided they really wanted to change their system, I think we would go look at best practices. I think that's normal. It's unclear to me though that we are actually doing enough experimentation on the regulatory side, it's just really, really hard to say how much because it's just sort of this abstract opportunity cost question.   0:55:03 BR: Yeah, it's... And I guess these are sort of the same thing where I think of it as it's very hard to talk about counterfactuals, and actually, to riff off of the point about opportunity costs, my impression about... Of one of the reasons that large long-term projects don't get funded is because the opportunity cost is so high in that if I see that the stock market is increasing at a... It's like the number in my head is 5% of... I think of stock market 5%, I'm not... Is that roughly...   0:55:47 LC: I think nominally, the numbers, depending on the timeframe you look at, are along the range of 8%-10%.   0:55:56 BR: Oh, wow, okay.   0:55:57 LC: But there are actually a lot of people who right now think that 5% is what you're going to see for the next 10 years.   0:56:03 BR: Okay, well, let's...   0:56:05 LC: Anyway, doesn't really matter. Let's say 5%.   0:56:06 BR: Yeah, exactly. So in order to make the argument for something like the opportunity cost of investing in an illiquid thing is the compounding returns that you would get from 5% growth in the stock market, plus the amount that... Like the liquidity that you're giving up, which is, as you pointed out, a really big deal. And so it's... And then put uncertainty on top of that, so it's not even a guaranteed in the future compounding... Like you need to be... So it just... It seems like it's a fairly straightforward... It's actually a very, very large opportunity cost to propose an alternative investment to just the stock market.   0:57:07 LC: So I think it is and it isn't. First of all, I think you framed all of that correctly, that everything is subject to an opportunity cost. And so, of course, when I'm looking at whatever investments I'm making, and you are too, or deciding where to spend your time, you're going to look at your other alternatives and then choose. I don't think that necessarily should mean that it's impossible to go find a project worth working on. I think what it means is you just need to really, really understand what you're building. So that you understand why it's really valuable, and you have to go after sort of basically big projects or you have to have really, really fast experimentation, so you can just try out a lot of things and say, "Okay, maybe the opportunity cost is high over five to seven or eight years or 10 years, but I am going to try 2,000 different types of Shopify stores programmatically, I'm going to figure out which ones work, and then I'll have the revenue stream that I want once I've tested out and pulled out to the best 25, and then go on from there."   0:58:16 LC: So I do think that that it's definitely doable, you just have to recognize the opportunity cost. But you're right, there is an opportunity cost. I just think you shouldn't sell yourself short. I think implicit in what you're saying is that the world is relatively efficient, and because the world's relatively efficient, how on earth could I earn more than 5%? But I have to say, I look around everywhere and see a lot of products that, they were built on the constraints of the past distribution channels, they were built on the constraints of the past production approaches, or they were built on social relationships that have broken for whatever reason.   0:59:04 LC: So you look at this and say, huh, I think there's probably a better way to do it. And if I'm right, and if I really, really focus on figuring out what's wrong and how we can do this better, you're going to find that the returns you earn are massively more than the stock market. I just think you have to be really focused and intentional in how you're doing it, and I think you have to spend a lot of time understanding the people behind the process. If you ever... I'm trying to think. Have you ever read that essay "I, Pencil" by Leonard Read? There's this idea that if you look at any sort of product in front of you, so you look at a pencil, an uncountable number of relationships went into building that product. So for the pencil, someone had to chop the wood, someone had to mine the metal, someone had to refine it, someone had to put it all together, someone had to paint it, someone had to build the eraser, and someone had to invent all of that and patent all of that, and start all of those companies and then figure out how to market it, and then figure out how that distribution channel worked, and then figure out how consumer tastes were changing, and just look through all of that.   1:00:11 LC: There are so many relationships there, and if you think about it, there's just... There's no... It's extremely unlikely that we've reached the global maximum for almost any product, because you only need one of those relationships not to have been done perfectly, not to have been optimized, to have an opportunity to do things better. And then you look at the constraints that they used to have 80 years ago versus what we have now... Software has changed so much in the last 15 or 20 years, the Internet has changed the world a lot in the last 20 to 30 years. You look at this and say, there probably are better ways to organize these things or to sort of optimize things. And I think that's true... I'm looking around my apartment now, when you look at, I don't know, a glass, or you look at a countertop, or you look at any art or any hardware, I actually think this is true for almost the most mundane object in your life.  And actually I find... Once you start getting into the details of all of these mundane objects, it's not mundane, it's totally...   1:01:19 BR: My concern is actually the opposite, where I think that there are tons and tons of dollar bills on the ground, but the payoff you need to convince someone of becomes inordinately larger, the better the stock market is doing, it feels like, because of the opportunity cost.   1:01:45 LC: So yes and no. If you look, for reasons that are separate from this conversation, at demographics and the way that capital is structured, interest rates are low and look like they're going to stay low for a while, which means the required return for a project is going to keep falling. So yes, when the stock market is doing really well... Imagine the stock market were returning 40% a year, it would be harder and harder to get new projects funded because people would just put their money in the stock market. But as those returns fall from 8% to 5%, or you used to be able to get 6% or 8% over a 10-year period in a 10-year bond and now you're getting 2, 2 1/2% a year, you actually are more and more willing to go out onto that risk curve and sort of fund something new. So I actually don't think the problem is as much opportunity cost, especially today. Socially venture capital is so popular that I don't think the problem is opportunity cost. I actually think the problem is alpha. And so if you think about what alpha is in the finance world, it's basically, you're looking for an information advantage, and it's going back to cash flows and capital flows.   1:03:07 LC: You're looking for an information advantage on what's going on with those cash flows, with the product, the customer sort of thing, or what's going on with capital flows. So your alpha could be, you understand there's going to be a forced seller here or a forced buyer there, and then you bridge the liquidity into that market. And to throw one more book out there, the best book I know of to think about information sourcing is a book by a Nobel Prize-winning physicist named Robert Laughlin, it's a book called "The Crime of Reason." Have I ever mentioned this one to you?   1:03:39 BR: No.   1:03:39 LC: So it's really interesting. Frankly, it's a shocking book when you really process it. He basically argues that all economically valuable information is kept secret. And so you think that you really understand a lot about the world, but you actually understand, say, 98% about some topic, but that last 2% that really matters to get the project off the ground, to get the product built, to actually get funded, that's really kept secret. So the reason I think this is interesting is we've turned an opportunity cost problem of, "Well, there's really nothing I can do about it, I hope I come up with a good idea," to an information sourcing problem. So the way I think about this is I say, okay, there are really two places that you find information in the world. It can either be recorded or it can be in someone's head. So recorded could be like written and natural language, or in numbers in a database. And I often find, unless we're talking about you going and coming up with some new fundamental algorithm, all you really need to be doing is collecting all of that data and joining tables. It's not actually that complicated from an intellectual perspective, but it's really about finding those tables and joining them. And then on the side of, oh, it's in someone's head, it just ends up being about building relationships with people.   1:05:01 LC: And to your point about there being lots of dollar bills on the sidewalk, there are, but it's almost like they're invisible, so you need to go find the information to really understand, oh, that's a real one, that's a fake one. And it just ends up being a shoe leather exercise where you say, "Okay, I'm just going to go reach out to a lot of people, become friends with a lot of people, talk to them about their work, really try to understand what they're going through, and then I'll recognize what they want and what they don't want, and then I'll find effectively that alpha." And I think that's probably a more useful way to think about it than opportunity cost, because it's more empowering once you think about it that way.   1:05:38 BR: I like that. To change tracks a little bit drastically, but to just get to a point that I think it is really important to talk about... So you invest primarily in public equities, right?   1:05:52 LC: Mostly public, but public and private companies, yeah.   1:05:55 BR: Yeah, and so there is a argument that... I'm on like... There's basically an argument that short-term is like short-term thinking on the part of public investors has sort of pushed public companies to slash R&D costs and basically caused the fabled death of corporate labs. I think it is pretty clear that corporate labs don't sort of have the sort of world-changing output that they used to. However, I'm agnostic about the cause and still trying to figure that out, so... What do you think about that argument?   1:06:42 LC: So, I think it's complicated. I also think I'm not sure, but I can think it through with you.   1:06:50 BR: Yeah, let's think through it.   1:06:51 LC: Sure, so if you look at the valuations in the public markets today, they are very high by any historical measure. And so high valuations do not imply short-termism. They imply that the market is placing very, very high prices on companies. Now, it just turns out that a lot of that has to do with the way capital flows work today, not just with cash flows. And so what's going on effectively is we changed the retirement system in 2005. So we default decided to put a lot of people's money into index funds. Index funds just blindly buy a set of equity as a set of stocks, just as capital flows into them, and so we've had more and more retirement flows, so you see all of these stocks get bid up. That has been a huge reason for valuations going up. But anyway, you look at this and say, alright, so just on a project basis, companies are actually getting huge valuations. Now quarter to quarter, companies face unbelievable pressure to sort of make a mark that Wall Street thinks is good or bad. And what ends up happening is people are definitely optimizing over the quarters, because the research analysts, it's so difficult to see inside the companies that these are the metrics that they use to measure what's going on.   1:08:13 LC: So it's sort of a mixed bag. We are getting really high valuations, but there is still a lot of quarter to quarter pressure, but at the same time, I mean I look at this and say... I think it's actually closer to the journalism and editorial arguments, where it used to be that these newspapers were monopolies and then separately or sort of for social reasons, they were also safeguarding these unbelievable journalists, and it was this huge benefit to society. The reason it worked was the newspapers were monopolies, so they really didn't face competition, and then culturally it became normal for them to sort of support journalists. And then it was like a social competition, like "Who is going to win the Pulitzer price this year? Who's sort of funding the best journalists?" If you go look at the big corporate R&D labs, you find that it was a set of funders that were basically semi-government entities. They were such great monopolies, and culturally, the people who are running those companies also wanted the R&D labs, maybe out of the sense of patriotism, maybe out of some other sense, but I think that's sort of how they came to be. And when those monopolies were broken up, they basically weren't able to keep funding the R&D labs.   1:09:40 LC: I do think that some of today's monopolies and oligopolies, these are the Facebooks and the Googles and the Microsofts of the world, they are able to fund big R&D labs, and we could argue about whether it's the same as Bell labs or PARC... But they're definitely trying, they have been inspired by those old examples, and my friends who work there, I do think are quite brilliant. So I do think that the ones that you're talking about and that I've read that you've written about, I think that it was basically this really nice side effect of monopolies that also doing it. But at the same time, not every monopoly... And in fact, almost every monopoly isn't going to have that cultural imperative. And then on the flipside, let's look at the ones that aren't monopolies, and this is again, partially a narrative problem and partially a reality problem. People haven't come up with a good metric for outsiders to know that research projects are going to do well long-term, so the outsiders feel comfortable funding them.   1:10:48 LC: So an example is that over the last 15 years, you can go look at pharmaceutical companies, and you'll see that their R&D budgets are getting cut. And what happened was a lot of investors were looking at the returns on that R&D over a three-year basis and a five-year basis, and they were saying, "Look, we're not seeing any returns here, it really doesn't make sense for you to be spending money." And of course, people trot out the worst examples when they're making arguments, but there was a set of pharmaceutical companies that maybe was abusing the R&D line. Maybe they were basically not really doing great research, and they were paying themselves a lot of money to not do great research. And some hard-charging Wall Street hedge funds came in and really, really pressured those companies to stop spending on R&D. Now, you'd say socially, that's terrible outcome. We could say, "Look, maybe the R&D is a public good, not a private good, so we need some way to incentivize that and we can have that conversation." I think it's possibly solvable if we come up with a new set of metrics that everyone actually believes.   1:12:07 BR: Yeah, so this goes back to the legibility point.   1:12:09 LC: It does. So you and I have spoken about this one privately before, but there's a professor at MIT named Andrew Lo who proposed that you bundle cancer research projects together or any pharmaceutical projects together. And say you take 100 of these projects or 200 of these projects, you bundle them together, you give each of them, say, a couple million dollars, and then you bundle all the payoffs together. And so the idea is that, hopefully, that's institutionally legible enough that someone would be willing to fund it because they think, "Okay, there's actually a good chance that of these 200 projects, one or two of them will hit, and then you'll have this unbelievably valuable drug that will really be good for the world, and maybe that's a good way to push us out on the risk curve." I haven't seen this type of thinking really take hold because we're still very much in that project-based milestone-based financing approach where it's like, okay, you have the metrics that makes sense for your series A, for your series B and C and D.   1:13:18 LC: And there's also an argument that maybe the smartest biotech investors and pharma investors are already cherry-picking the best companies, the best projects. So maybe you'll sort of have this adverse selection where maybe of the top 200 projects, this would have worked, but if the best five are just going to go off on their own, you're just not going to get the good ones. And this is again, sort of that information sourcing problem, and no one has really solved it. But I do think that usually the arguments I hear are, "We need some regulatory apparatus, we just need more government funding," but some really, really clever salesperson, someone who understands how to position the soaps so that it gets into a Costco and Walmart, they could figure it out. I bet there is a way to position R&D products so that they could get just a bit of funding from the universities or from some other source. One thing that I'd mentioned there... Oh, sorry, didn't mean to interrupt you.   1:14:16 BR: Oh no, no. Please, please.   1:14:17 LC: Because one thing I'd mentioned there is, you have to remember, not everyone is just looking for just dollars and cents. The universities care about reputation. So maybe there's a way to make the universities look good. Maybe there's a way to get the students access to some field, a less prestigious university their students can't get access to. Maybe there's a tax incentive that exists that people don't recognize exists, and you could work that way. My guess is there actually are probably a couple degrees of freedom, and if you look at it really closely from the university's perspective, there might be some creative way to sort of re-form the way that we're approaching all of these project funding issues. Because it does seem wrong to me that brilliant people that I follow are not getting funded for what to me, look like unbelievably interesting projects. And I'm not in a position to fund them yet, maybe, knock on wood, but one person will never be able to fund all of these things. You really need a new product in the sense of a new way to convince all of these funders that they should go back this kind of project.   1:15:27 BR: Your point about the pharmaceutical company that was wasting money to do bad research, and also the legibility that they thought is what it seems like we need is a way of measuring the fact that the research being done is good research, which I think is different, by the very nature of research, is different than the outcomes of the research, right? So you can have...   1:16:00 LC: How...   1:16:00 BR: What's that?   1:16:00 LC: How would you approach it?   1:16:04 BR: I don't know. This is the thought, like the thought that you just provoked, which is like, is there a way to legibly say that there's good research going on, independent of the outcomes of the research? Because I think really good research projects can still fail just because that is how research is. Do you think that the increase in finance since arguably like the '80s has been a net good thing or a net drag on innovation?   1:16:40 LC: That is a really, really difficult question to answer. And so the first thing...   1:16:46 BR: It's probably both.   1:16:48 LC: What the question is, is it on net good or on net bad? I think it's on net good, but it's just so difficult to measure. So there's a whole set of professions that all they do is coordinate people.   1:17:03 BR: Right.   1:17:03 LC: If you assume that markets are perfectly efficient, then there's no value in coordinating people. And they're a huge net drag, so basically, all salespeople, all lawyers, all accountants and compliance people, all investors, all analysts, all of these people are working with information that ends up coordinating us. And if you look at this, you might say, none of these people are creating the product, there's no value in anything that they do. And so if we have more and more of society going into these professions or more and more smart people going into these professions, it's a net negative.   1:17:44 LC: I think that there are net positives and net negatives. I think the real problem is not that more people are going into finance. It's that sometimes the abstractions are just not a net good anymore. I think that we have become addicted to the idea that we need basically an infinite amount of liquidity or an unbelievable amount of liquidity. It's not clear to me that the world would function that much worse if we only had the markets open for a few hours a day and trades were batched so that they only happened in five-minute increments. I think that there are also things you could do where you change the way that information reporting works, so that people who are effectively free riding on the market and maybe not adding that much fundamental information about the underlying cash flows and projects, maybe you can make it a little bit harder for them to do it, and therefore sort of reward what you think is socially beneficial behavior.   1:18:50 LC: I think that there's also a set of gambling that's going on that arguably is not net good for the world. The problem is, I don't know where to draw that line, and truthfully, almost no one does. Again, markets are emergent phenomenon. A well-functioning market is an extremely expensive public good to maintain. But it's always sort of the error bar, the error part of your estimate that holds all of the new interesting insights. And so I'm really hesitant to say, "Well, we should stop all the experimentation," or, "All of this experimentation is net negative."   1:19:41 LC: And I do think that there is fraud in the markets, both sort of hardcore fraud, but also forms of soft fraud. I do think that there is abuse in markets, but it's difficult for me to argue that it's a net negative. I actually think it's sort of the opposite, where the people who think it's a net negative, they want to hold humanity up to this perfect moral standard where everyone is great all of the time. And I think the better way to think about it might be, "Well, assuming that there will be gambling, that people won't be perfect angels all of the time, is this still a good coordinating mechanism?" And I think the answer is yes. I think there are still flaws and things that we could do to structure it better, but I think it is on net actually a very, very strong net positive.   1:20:24 BR: Cool. So we talk about social technology a lot. What are some social technologies that you would like to see developed? Or you can even pose them as research or development questions.   1:20:44 LC: Oh, there are so many interesting things going on right now. Number one, just from a policy perspective, any better way to measure opportunity costs would be fantastic. As someone who has just gone through the legal theory, I just don't see it mapped out that well for practitioners. I don't see limited liability and sort of the values and drawbacks there, I don't see that mapped out that explicitly for policy makers. And then I think on the contracting side, I actually think that there's going to be sort of a huge new wave of value created by new sets of contracts. And so if you go look at what's going on in the cryptocurrency world, not just this sort of Bitcoin-as-religion set of people, but people who are really looking at reducing transaction costs. If you can really reduce transaction costs, you usually open up a whole new set of actions that we can all take. One that I thought was really interesting is, they were brought up by two people, one is an ex-lawyer named Nick Szabo, and one is Balaji Srinivasan, who's very big in the crypto world.   1:21:57 LC: And there's this argument that we'll have a machine-readable web, where right now, if you look at the way webpages work, most of them monetize with ads. And because they monetize with ads, if you have a computer loading a webpage instead of a person, that ad actually has zero dollars of value. And the highly trafficked webpages basically do what they can to paywall their information. The more that we can do to effectively create these contracts that allow microtransactions between computers and a machine-readable web, I think that you could actually create automatically pricing markets and a huge, huge, huge number of digital goods. Those are some of the things that I think are really interesting that I'd love to read about, if anyone is reading about these things.   1:23:01 LC: And the other one, these are a set of things that I will be working on very closely over the next three to five years, are basically new fund and financing structures, especially for pre-IPO companies to public companies. I look at the types of financial products that have been invented over the last 15 years, and it's clear that there are the kernels of some really, really, really interesting things. But just like what you've been talking about where it's really difficult to get funding for research projects that don't sort of fit into a narrow box, there's a certain set of transactions that basically everyone is used to doing. They're institutionally legible, and they just sort of push the button and check the box and you're done. And I don't know that we have reached the global maximum of like, "This is how companies should be funded," at all. I have a whole set of ideas around that, and if anyone's reading anything on that or working on that, I'd love to hear from them.   1:23:44 BR: Excellent. And how can people find you on the Internet and share those things with you?   1:23:49 LC: Sure. My Twitter handle is @l_constable. And you can also just email me, my email is luke at lampacapital.com.    1:24:07 BR: Thanks for listening. We're always looking to improve, so we'd love feedback and suggestions. You can get in touch on Twitter @Ben_Reinhart. If you found this podcast intriguing, don't forget to share and discuss it with your friends. Thank you.  

In this conversation I talk to Donald Braben about his venture research initiative, peer review, and enabling the 21st century equivalents of Max Planck. Donald has been a staunch advocate of reforming how we fund and evaluate research for decades. From 1980 to 1990 he ran BP’s venture research program, where he had a chance to put his ideas into practice. Considering the fact that the program cost two million pounds per year and enabled research that both led to at least one Nobel prize and a centi-million dollar company, I would say the program was a success. Despite that, it was shut down in 1990. Most of our conversation centers heavily around his book “Scientific Freedom” which I suspect you would enjoy if you’re listening to this podcast. Links Scientific Freedom Transcript audio_only [00:00:00]   This conversation. I talked to Donald breathing about his venture research initiative, peer review, and enabling the 21st century equivalent of max Planck. Donald has been a staunch advocate for forming how we fund and evaluate research for decades. From 1980 to 1990, he ran BP's venture research program. Where he had a chance to put his ideas into practice. [00:01:00] Considering the fact that the program costs about 2 million pounds per year and enabled research, that book led to at least one Nobel prize and to send a million dollar company. I would say the program was success, despite that it was shut down in 1990. Most of our conversations centers heavily around his book, scientific freedom, which just came out from straight press. And I suspect that you would enjoy if you're listening to this podcast. So here's my conversation with Donald Raven.     would you explain, in your own words, the concept   of a punk club and why it's really well, it's just my name for the, for the, outstanding scientists of the 20th century, you know, starting with max blank, who looked at thermodynamics, and it took him 20 years to reach his conclusions, that, that matter was, was quantized. You know, and that, and, he developed quantum mechanics, that was followed by Einstein and Rutherford and, and, and a [00:02:00] whole host of scientists. And I've called, in order to be, succinct Coley's they, these 500 or so scientists who dominated the 20th century, the plank club. So I don't have to keep saying Einstein rather for that second. I said, and it's, it's an easy shorthand. Right. And so, do you think that like, well, there's a raging debate about whether the existence of the plank club was due to sort of like the time and place and the, the things that could be discovered in physics in the first half of the 20th century versus. Sort of a more or more structural argument. Do you, where do you really come down on that? The existence of the plank club? [00:03:00] W well, like, yeah, so like, I guess, I guess it's, tied to sort of like this, but the question of like, like almost like, yeah. Are you asking, will there be a 20th century, 21st century playing club? Do you think, do you think it's possible? Like, it's sort of like now right now. No, it's not. because, peer review forbids it, in the early parts of the 20th century, then scientists did not have to deal with, did not necessarily have to deal with peer review. that is the opinions of the, of the expert of the few expert colleagues. they just got on, on, Edgar to university and had a university position, which was as difficult then as it is now to get. But once you got a university position in the first part up to about 1970, then you could do then providing your requirements were modest, Varney. You didn't [00:04:00] need, you know, huge amounts of money. Say. You could do anything you wanted and, you didn't have to worry about your, your peers opinions. I mean, you did in your department when people were saying, Oh, he's mad. You know, and he's looking at this, that, and the other, you could get on with it. You didn't have to take too much attention. We pay too much attention to what they were doing, but now in the 21st century, consensus dominates everything. And, it is a serious, serious problem. Yeah. So I, I seriously believe that keeps me what keeps me going is that it is possible for there to be a plane club in the 21st century. It is possible, but right now it won't take, it won't happen. I mean, re there's been reams written on peer review, absolute huge, literature. and the, but, but most of it seems to have been written by, by people who at least favor the status [00:05:00] quo. And so they conclude that peer review is great, except perhaps for multidisciplinary research, which ma, which might cause problems. This is the establishment view. And so they take steps to try to ease the progress of multidisciplinary research, but still using peer review. Now. Multidisciplinary research is essentially is, is absolutely essential to venture research. I mean, because what they are doing, what every venture researchers, the researcher is doing is to look at the universe. and the world we inhabit in a new way. So that's bound to create new, new disciplines, new thought processes. And so the, when the conventional P, when the funding agencies say, there's a problem with multidisciplinary research, they're saying that's a problem with venture reserves. Yeah. And so therefore we won't have a plank club until that problem is [00:06:00] solved. And I proposed the solution in the book. Of course. Yeah, exactly. And so I guess, so with the book, I actually think of it as it's just like a really well done, an eloquent, almost like policy proposal, like it's, it's like you could, I feel like you could actually take the book and like hand it to. A policymaker and say like do this, I guess you could, so, I guess to put it, but like clearly nobody's done that. Right? did you, do you ever do that? Like, did you actually like go to,  government agencies or even  billionaires? Like the, the amount of money that you're talking about is almost like shockingly small. what, what are, what are people's responses of like, why not do this? Patrick Collison as being the only billionaire who has responded, I've met about, I don't know, half a dozen billionaires. And, they all want to, they all want to do things [00:07:00] their way, you know, they all want to, which is fair, which is fair enough. They all want to, sees a university through their own eyes. They are not capable of saying opening their eyes and listening to what scientists really want to do and to get what scientists really want to do. You've got you. You just can't just ask them straight off. You've got to talk to them. For a long time before they will reveal what they want really want to do. And then only a few of them will be capable of being a potential member of the plank club of the 21st century state. But it's a wonderful process. It's exciting. And I don't know why. well, I, I think I do actually, why the conventional authorities do not do this. And I believe that for, the reason this is more or less as follows that, for 20, 30 years following the expansion of the universities in about, about 1970 for political reasons. [00:08:00] no, not at all for, for scientific reasons that, there was a huge expansion in the universities and, and, and a number of academics. I really really mean it's factors of three, two, three, four, or something like that, depending on the country. Really huge. And, so therefore the old system where freedom for everyone was more or less guaranteed, which is what I would advocate freedom for everyone as a right. So, what we have done now is to develop absolute selection, rules, absolute selection rules for selecting venture researchers. And, and, and that's taken, you know, that's taken some time to develop them, but they work well. And, and, and open up the world to a complete ways, new ways of looking at it. Yeah, look, I mean, the, the, the track record seems very like very good, right? Like you, you, you, you [00:09:00] enabled research that would not have happened otherwise and led to Nobel prizes. Right. Like, I don't, I don't see how it could, what evidence one could present that your method works more so than that. and so it's, so yeah. Well, well, over the years you see, the, the scientists to work in for, for the funding agencies. they have advised politicians on the ways to ration research without affecting it. And they have come up with the way, the method of peer review, which is now a dairy girl, you know? it's absolutely essential. Yeah. Every to every funding agency in the world, I've not come across one that does not use it well, apart from our own operation, of course we don't use it. but we, we find ways around it. And that's the conventional wisdom is that there are no ways around, [00:10:00] there are no way. peer review is regarded as the only way to ensure research excellence. People keep saying that it's the only way, but we have demonstrated with the BP venture to search you and this and that UCL, that there is another way. And, and I guess so is, is, is the response from, people that you would propose this to simply that , they, they don't believe that.  they don't believe that it can work because it doesn't, it isn't peer reviewed, , is that the main contention? Any, any ideas now must, must, must survive. Peer review and venture research of course would not. And so therefore what we're saying is therefore not admissible. And now a few people, in like the 50 or so of my, my, of my supporters, very senior supporters, re regard what we [00:11:00] are doing as essential, but their voice is still tiny compared with the, you know, the millions of, researchers and, and the, I I'm the funding agencies. Now the funding agencies kept on saying that they have advised politicians over the years, that the only way to ensure to ensure, that the, that the scientific enterprise is healthy is to, is to, is to a DIA to peer review. Now. They cannot. They cannot now say, ah, yes, Raven points out. There's a serious floor. They cannot do that. And so they say they do, they do not acknowledge that I exist or that the problem exists. This is so, so just because like they have, have doubled down so hard on peer review being the acid test for research quality. That they, they just like, they can't, they're like they're [00:12:00] lash to that, man. Okay. Okay. And, so I, I know at least in the NSF, I think actually shortly after your book came out originally, so in. 2008, 2009. I read about an initiative to try to do more. I think they, they termed it like transformational research and the NSF, that was the NSF, initiative. it was pioneered by Nina fedoroff. Nina fedoroff, who's a great, another great is one of my supporters. and, she was the, she was the, I think she was the chairman of the science board or something like that, which controls the NSF. And so she set up a special task force to look at. Mainly what I was trying to do. And so, she invited me to go to Washington on three occasions and we sat in this huge room at the national science [00:13:00] foundation headquarters. And we, we, we, we had three, two or three day meetings, venture research, and they concluded that it was the only way to go. And so that's what they recommended to the NSF. But what did the NSF do with decided that th that, that, that they would accept Nina Federoff's recommendations, but they should be administered by each of the divisions separately. Well, that's, that means they don't do anything that they wouldn't do normally. and so, I guess one, one thing, I'm not sure if you mentioned it in the book, what do you think about, like HHMI Janelia and. Like sort of the effort that they do, because it is much closer to your recommendations, how Hughes you mean? Yeah. Howard Hughes medical Institute. Yeah, exactly. and specifically like they're their Janelia campus where my understanding is [00:14:00] that they give people sort of, whole free funding for five years and really just sort of let them. Explore what they want to explore, but they have to, but they, I think they insist on them going to the central laboratories. Yeah. Yeah. That is a problem. How's that? Because, well, because, scientists all have roots and they all, I've ended up where they have, you know, wherever it is and that's where they prefer to work. And so therefore in venture research, that's why we allowed them to work in their old environment. Yeah. But now with total freedom and they'd radically transformed, you know, a little segment of the, of what it's done, but they transformed it and that they would've transformed even more. Had we been allowed in 1990, if BP had allowed venture research to continue. They were th th there [00:15:00] would be more than 14 major breakthroughs because, in 1990, when BP closed us down, then we, these people no longer could rely on venture research support the, the, the essential, feedback that we gave them, the meetings that we arranged, you know, of all venture researchers, which we had to work out how the hell to do that because, you know, w th the just scientists and engineers, scientists, and engineers all came together. Yeah. And, I don't know that's been done. but anyway, right. we were no longer allowed to provide that support. And so therefore they were on their own and exposed to the full rigors of peer review in applying to funds before they were ready for it. Yeah. The successful ones are venture research, you know, people they can suddenly, you know, it's, with his ionic liquids, then he, he, he, jumped over the line [00:16:00] of, of, of, into mainstream science and it became then part of the mainstream. Yeah, and same with similar Polica and all the other people who, who, who, who was successful. But, but th th there were, there were a few groups, you know, who were left high and dry and, and they had to manage, they had to, cut their class according to the funding. Yeah.  do you keep track of people who today would make good venture researchers? but, but don't like, like, do people still still send you letters and say like, I want to do this crazy thing. No, I'm afraid. I can't, I can't do that because I would be, raising their hopes, way beyond what Mabel to provide, UCL. We've done that and we've met one person. we supported one person, Nick lane, whose work has been prodigious digitally successful. Now he could not get support. He couldn't get support from anybody. [00:17:00] Before we, we felt a bit, before we backed in. And, so I persuaded the university to cover up 150,000 pounds over three years, which is trivial amount of money. Totally. Totally. And since that time, since then, he's, now he's more or less stepped over the line and he's now become mainstream. And he's, since that time as you're right. 5 million pounds, 5 million compared to the 150,000. So that's, that's profitable, you know, as far as the university is concerned, they're profitable, but even so even with UCL, it's still not caught on. Yeah. And, do you, do you, so when I, I guess I also have a question about like, what about the people who might make really be really good researchers, but , don't even make it through to the point where, they would even like be able to [00:18:00] raise venture, venture research money in that , There's also the fact that. in venture research, you were entirely supporting people except for, believe one case, you were supporting people who are already in academia, right?  they'd already sort of gone through all the hoops of getting a PhD and, getting some sort of, some sort of position. And so do you have a sense of  how many possibly amazing people get weeded out? even before that point? Oh, I mean, to be a venture researcher, you you've got to have a university position, I would guess. or, I mean, as with, was, with, with, with the only engineer we supported, he was working for a company and we enabled them to leave and I took great care to, to, to inquire of him. He w he would have to give up his job because, you know, industrial company couldn't support him if he was working for another company. And so I had to be sure that [00:19:00] he really was serious about this. And so we arranged that he, we arranged for a university appointment for the nearest university to where he lived, which was sorry. That's was just down the road, so to speak. And, but even that created problems, he was never really accepted by the university hierarchy. And w why do you think the university, association is, is important? as opposed to just someone just, you know, just doing research, right? Like what if they had, they built like a lab in their basement or. we're doing mostly theory. And so they just sort of, they've done that. You know, people, you know, like the guy, the guy at shop, I mean, if they, if they'd done that, then of course we listened to them, but they must be, they must be reasonably proficient in, in, in, in, in what I mean, they're, they're coming with a proposal to do something. Right. And to some that you've got to, [00:20:00] you've got to have done something else. You've got to, you've got to prepare the ground, so to speak. Yeah. So getting a university appointment today is no more difficult than it was say in 1970, you still had to get up. You still got to get, you know, go through a degree. PhD may be, and, and then, convince the university that you're worthy of, appointment. But then as I said before, You, you had automatic, you automatically qualified for this modest amount of funding, at least in Britain. you automatically qualify for that, but now you're quantifying for nothing. Once you pass the, you know, you're appointed by the university, you then start this game of trying to convince funding agencies to support you. And if you don't, you're dead. Yeah, you don't get, you don't get anywhere. You've got no Tanya. So you, you, you, you, you just disappear. It says it's an unforgivable system and it's extremely [00:21:00] inefficient. Yes. Do you think, I guess the question is like, is, is efficiency even the thing that's worth shooting for it. Like, it seems like it's, it's going always going to be inherently inefficient. Because of the uncertainty. like I guess I always worry when, when, like, when efficiency comes up as a metric around research, because then you sort of start having to calculate like, okay, like how much value is this? What is our like, return on investment? Like how efficient is that? And it's just, do you think that's the right way to think about it? Well, it's certainly not a bad way. but, but mines are closed, you know, I, I've been in touch with so many people over the years, you know, I've been at this now for 20 years since, since BP terminated my contract, so to speak and I've never, and I've always, and I've always tried every single minute [00:22:00] of that 20 years to find new ways of doing this. I mean, it's big, it does sound a bit, you know, that, that th th th th th th th the, the, what I do as a large element of the crank about it, but I'm so convinced of the value of this eventual research and its contribution to humanity, so to speak. I'm so convinced that it will make an enormous contribution that I keep on going. Yeah. No. I mean, I have no money. Yeah, no, I'm not paid to do this. And the first person that I've met of the many, very rich people I've come across has been factored colorism. Who, offered to publish my book and at a fraction of the price, why only were charging for it? Why do you charge $75 for a paperback he's charging less than $20 for a, for a hardback? Yeah. Well, I think he realizes that it's important for people to [00:23:00] actually read it. That's that's good. he, took part in, just before he met me, he took part in a, in a, in a, in a, in a blog or something like that. it's on, it has a YouTube thing. And, he said he was very impressed when he met me and I, I, I, I changed the way he looked. Yeah. I changed the way he looked at the world. You know, and, and he made this, joining an hour long speech to these fellow billionaires, but no, one's come forward. No one said, you know what you wanted. Yeah. Well, I hopefully like, I mean, hopefully between the book being out and, like. You know, I, I try to recommend it to everybody. I know. so, so hopefully like we'll, we'll start to, sort of get it more into people's heads. Do do you have any good stories about, people who applied and didn't make it right, [00:24:00] because I assume that, like, I always noticed that the sort of a line between like brilliant ideas and like completely crackpot ideas is very, very thin. so did you get any, like really, really ridiculous applications? It's not that thin. I don't think it is, there were lots of people who came to me and said, you know, similar dynamics is bunk. And, and one thing that they really hate is to be asked you say, okay, I agree that it is, what do you want to do now that completely floors them. So people was a crackpot idea are automatically. disqualified because they never return. They never say what they want to do, or if they do, you know, you cannot, you keep on re repeating the same question and they eventually gave up. Now venture was search the venture researcher. I may not say we, we, we, we may not say yes for the first meeting. It [00:25:00] might be five, six, seven, eight meetings with Dudley, Hirsch, Nobel laureates. It took more than a year. Because, you know, I met him very shortly after he got his Nobel prize and he came to a meeting that I took part in a meeting of the American physical society in New York. And, and I gave a talk and there, I noticed this guy in the front row scribbling away and he came to me after each day. He said that I think I've died and gone to heaven. This was Dudley Hurst back three days after winning the Nobel prize. And he had an idea which no one would listen to. And, well, we, we, we, backed him, but I can't think of an, of people who come with crazy ideas and gone on to be, you know, to, to, to fizzle out and die if you like. And, and, and so w I w I want to dig into that because that's, that's really, I feel like you're, you're saying something like very important. [00:26:00] and that, so to sort of repeat back to you, it sounds like the people who are not crackpots are able to like go to a level of precision about action that other than people who don't know what they're talking about are not able to do. Would that be accurate? Okay. That's exactly right. And, I'm, I'm asking like very much, because I, I'm trying to do similar things and like looking at, it, and like very much in the same position where it's not always in my exact field. and so it's, it's like, what is that? That you can. Do to sort of like tease out the, like the, the difference between a good, crazy idea and a crazy, crazy idea. Well, all venture research has courses outside my own field, all of it, because I'm, you know, that was a [00:27:00] long time ago when I was a practicing scientist. And so everything is outside my, my competence, so to speak, which is another reason why the mainstream venture is searchable. The mainstream, funding agencies tend to disregard what I say because it doesn't pass peer review. Of course. Yeah. So, we are accustomed to being uncomfortable with talking to people we are, but we try to engage them in conversation that reveals what they really want to do, what they, and try to assess, what they want to do compared with the state of knowledge in that field right now, you know, they want to transform it. and you don't have to be a fellow expert to understand that. I mean, I am an expert now, but in general, things like science, you know, or engineering that the broadest possible approach to these study subjects. And I can, I can tell on everyone else can tell who was involved with this process because when I came to BP, [00:28:00] they gave me two or three very, talented people, more or less people like yourself, you know, high flyers young in their early 2030s. And they joined me for two or three or four years. And, they were, they were mainly chemists. and so, yeah, chemistry, I think, I think, only there was only one physicist. This, I think that the BP provided me with, and it didn't matter because they were all fairly, talented people, very talented people, in fact, and you didn't have to say anything twice to them, you know? And, and they took it like a duck to water. Now we never had to discuss. But, you know, when we're sitting around our little table and people would, were coming with their ideas, we never had to discuss whether someone was what we called a venture researcher. It came to immediately obvious to anybody to have to everyone around the table, that he was someone who wanted to transform thought processes in that particular [00:29:00] field, they would do something important and that, and, and then once we made up our mind, then. Then, we backed them and then they could then do absolutely anything they wanted and, you know, nothing, you know, we're not bound by the proposal they wrote to us. Yeah. But that was mainly the agency, which, which caused it caused us to, to, to select them. And mostly, I mean, most people did of course, you know, but, some of them didn't. And do you feel like. So, so I'm, I'm really interested in sort of like the, the landscape of the like untapped potential in research. And so, venture research sort of goes after people who already have an idea and know what they want to do and need money and support to do it. And I wonder if there's sort of another [00:30:00] class of people who. Could do amazing things, and, and have the right skills and mindset to do it, but almost have like either not even thought of the things yet, or, or sort of have suppressed them in order to fit into the peer reviewed research box. and they could be unlocked by either like putting them in contact with, with other researchers or, Sort of like shifting their focus. Do, do you have a set, like a sense of like, whether those people actually exist or am I just making, making that up? Well, I'm not quite clear what you're saying. I mean, the people that, most venture researchers we came across, I mean, they knew precisely what they wanted to do well eventually, and they would eventually admitted to us. What they really wanted to do, you know? but there must be mutual trust. Trust has been lost entirely in funding. [00:31:00] Nobody, you know, funding agency doesn't trust the researchers, but I found that trust is absolutely essential in this, you know, that they must trust you and we must trust them because I've said once we back someone, they can go anywhere or anywhere in any direction they want. You know, you can only do that. If there's mutual trust. And if they come across problems as inevitably, they will, you know, things, you know, then they come to us and say, look, we've got this problem, you know, can you help us to solve it? And, you know, and we, we took up a problem with a university or something like that. And we w we helped them along. And this is why when venture research was closed down in 1990, this service was no longer available. And so the 14 people who made, who made major breakthroughs subsequently, and now that's a minimum. I think there are, it would be much greater if we'd have had the support, if I was able to provide this, the support right through, right through to today. Yeah. [00:32:00] And, and, and so. Can we dig into how you build trust with researchers. So you, you have a lot of conversations with them. and like, but can you, can you unpack that? Like, I, I want to we'll have the same problem in everyday life. You know, you meet someone and you talk to them, you know, not about research. You don't have to think about research, you know? do you trust somebody? how do, how, how do you, how do you reveal your trust? How do you express it? How do they express it? And, it has to come, you know, by, by a multi, by a multitude of routes, just like in everyday life, you have to make up your mind that you would trust somebody eventually, and we'll go along with most things that they say. So it is no more of them. It's no different from that. And, in fact I've found that, that the people who've been most receptive to what I've been saying have been nonscientists, you know, people like the [00:33:00] secretary I used to be, I wish to work in the cabinet office, the secretary of the cabinet Burke trend when I was there, he was, you know, he was instantly, so what we were trying to do, you know, and, I had an ally there. Yeah. And, and yeah. And I guess why, why wasn't he? So, so if there were people in the government who were excited about this, like what sort of stopped him from being able to push through? He was, he, he, that was before I CA I went to BP and before I'd worked out the ideas of venture research, but I, I talked to him and I realized I could trust him. You know, and, which is very unusual, you know, for a very senior person, to be able to do that. And, so we didn't discuss venture to search them because I hadn't, I hadn't taken up the cudgel so to speak when I, when I in, on the April, the eighth, 1980, I went to VP. Yeah. Got it and sort of [00:34:00] switching gears a little bit, something I loved and it's like, give me a completely different way of seeing the world in the book is, your. Sort of your take down of the idea of high risk research, biggest high risk. Well, there's not, I, venture search was the lowest possible risk you can imagine, because I was convinced that they would, that the venture researchers would do something of value. You wouldn't be able to predict what it was, but they would do something of value and only had to do with, to keep talking to them. Yeah. And, unfortunately we, we can't show the graph, but I, I just, the, the sort of concept of, like with cutting edge research, the fact that there's just uncertainty about what the like, underlying probability distributions even look like. Right? Like, and, and the fact that the researcher actually knows that distribution so much more than anybody else, [00:35:00] It's just a, a much, much better way of thinking about it. because I think that people really do think of like high risk research as sort of just like this like portfolio approach to, to the world. And it's like, okay, well, like what's our expected, and this actually goes back to the, the, the idea of expected value where, why it's, it's so hard to talk about it in those terms, because. we don't know the underlying distributions. Right? Well, have why should a funding agency support high risk research? When what they're really saying is that we expect you to fail. That's what high risk means we expect you to fail. So why, why should either we measure as the researchers or the funding agency do that? I mean our approach, which goes to the lowest possible risk and the highest possible gain, much more. It's much more accessible now. Not everyone of course can be a venture [00:36:00] researcher, but, I'm convinced that every serious minded researcher, at least once in a, in, in, in a scientific career will come across an idea that will transform his is a local, field. Yeah. he, he, he did not share it or he did not reveal it to the, to peer review because he hasn't yet proved it. Yeah. Peer review only works sort of ad hoc, you know, sort of, so, after the events, so to speak, when you've actually shown that it will work, then they can say whether it's good or not. Yeah. And I guess, I don't know if you've been paying attention to sort of like the, the meta discussion around scientific stagnation. but there's, there's sort of like the argument that we've picked, although the low-hanging fruit of, of the physical world, What do you w what, and like, I get the [00:37:00] impression that you don't agree with that. So I, I always looking for sort of good counter arguments. I don't agree with that because, we're looking for people who will, grow new types of fruit. Or if you take the continental view, you know, that, that our field is a bit like a country. And, when the, you know, people like Einstein discover that country, then it leads to, to, to a wave of new research, but eventually. Then the field becomes paid out and it gets more and more difficult to make it difficult to make a new discovery. What if somebody comes along and says, there's a new continent there and I want to, I want to create it. Yeah. And they can convince you that it exists. So, either the, you know, the low hanging fruit thing, which is, works very well for particular types of fruit. But what if you come up with a new idea for fruit and you come. And that actually sort of ties back to [00:38:00] the peer review argument, which is that, peer review is very bad at allowing, sort of like new fields and therefore new contents orchards to exist. We are, we all an endowed with a w w w with a creative spirit. You know, and, it's this fundamental creative spirit that we all have and scientists, you know, perhaps, to a, to a greater degree than others though. I'm not convinced of that. and, you cannot expect that their view of the world  completely individual view of the world. Just if I have consensus. Yeah, not immediately. You can't do it right away. I mean, no, the scientists, you know, major discoveries have not been greeted with a claim. Science, Einstein Einstein's discovery were called in the times newspaper. And one is when he wrote his famous paper on relativity and the front to come and sense. That's what the time [00:39:00] said. And so it was, it wasn't a front to common sense. Yeah. It should be fair. It's still, it's still sort of is right. Like you in like, you know, it was like the universe is curved and it's like that, that actually doesn't. Even jive with my comments, that I'll be honest. university's a very, very big place. Yes. It's very, a very weird place, right? Like when you start really looking at it. Yeah. And you know, the, the, the, the, the gravitational constant, which is, the Hubble constant. So, is, is, is one 80, sorry. Th well, there's some dispute about what it is, but it's a tiny amount per million years, you know, it's a tiny difference from what we see now, how the, it would be difficult to detect. Have you, have you said, you know, [00:40:00] you've gotta look for this and this is what people are doing. Yeah. Yeah. It's something, something that I wonder. And I'm not sure like how this sort of jives with, with venture research. But what I feel like it has also happened is, people have become so specialized in like theory or experimentation or, like engineering or development, that. And like part of where, these, these new fields come from is people, sort of interacting with people that they wouldn't normally interact with. and, and so did you, did you find yourself sort of beyond that, like they're, they're the, the sort of group meetings, of the venture research community, but did you, did you find yourself, Sort of like pushing people to interact in ways that they wouldn't have otherwise interacted with. No, we never push people that way. there, there were always [00:41:00] any new interaction that came from our meeting, from the meetings they were derived in exclusively from the scientists. I mean, we w we may make one or two suggestions about a group. Well, I did, we would do, we did. In fact, the, the ant people. we're working, in the field of distributed intelligence. Now I happen to know there was a unit at the university of Edinburgh that was doing this, that this very work distributed computing, and they weren't, there were one or two experts. So I went to them and said, do you have somebody who might be interested in joining the group? And they did a man called Tuft and he went down and he, and he did, and it was a very productive exchange. But that was very rare. I mean, that, that happened, but they, the user can the other way, got it. I don't know. Maybe, maybe, or the, you know, I forgotten, it's a long time ago, you know? but, I'm just always interested in, sort of improving my mental models of like, how, like, sort of [00:42:00] the, the question that everybody have of like, where do ideas come from, right. Like, and it's like, how important is sort of like cross cross-pollination, to sort of, to, to creating new areas. Actually I'm really interested in what the day-to-day of running VPs venture research was like. I kind of in my head, imagine you like just flying around the world, and sort of like meeting scientists and, and sort of, did you, have you ever watched the Avengers? I imagine you like Nick fury. Sort of like going around to different superheroes, and sort of say like, alright, like we're gonna, we're gonna form a team. yeah. W w what was that like? Well, I'll tell you, it was a, a very difficult problem because when I first arrived, I was, you know, a single person in a single room. and, so, the research director and, you know, the [00:43:00] guy who was responsible for BPS main research activities. And he spent about 2 billion pounds a year, you know, on, on he had, and he had 2000 people working for him and, he he's, he thought I was mainly harmless, you know, so, but as the, as the, as the decade wore on, then, it became obvious because I always try to involve, see in here. BP, directors in what we were doing. I always invited them to our conferences, for example. And even the chairman, you know, came down and other senior directors came down and they could see for themselves that what I was talking was not bullshit, you know, what really serious. And, and so the idea became. embedded in, in, in, in, in a few recess director's minds that the bang, the Braven was having a bigger bang for book Cadogan will the research director and it's 2 billion a year that he was [00:44:00] spending. And, and this fed back on, on, on to the research directors approach to me and the increasingly saw me as a threat rather than as an opportunity. And eventually. In 1990, he won and we were closed down. I got a phone call from New Zealand on, on March the eighth, 1990 from New Zealand, the man, the guy, the guy, or just the, the, research director, had just retired Bob  and he gave me all the freedom. I, what I wanted. And he retired in, in 1989, it was succeeded by Buzzle Butler and Buzzle Butler was, well, I won't say what I think of him. he, he phoned me for ISA from New Zealand and, and his first thought first was hello, Don venture search has closed down. The BP can no longer afford the drain on its resources, BP. I was spending then [00:45:00] five millions a year when, you know, managing directors didn't get out of bed unless it was at least a billion. Yeah. Yeah. It, it's funny how people can get very attached to like even comparatively small amounts of money. Well, people see, tend to see value in cost. You see? and so, if a university adopts venture to search, which I hope they will then, you know, they can't release, there's no glory in spending nothing a year. Even if you have an arrangement for looking for these people, you know, even a UCL, you know, it's been 150,000 in 10 years, but you know, that's about the right number. You know, we're talking like 500 people in a century over the whole world. So any single university is likely to have one or maybe two in a decade. And so, [00:46:00] but if the arrangements were set up so that people could come forward with their ideas to talk with senior people in the university, people who had given up their own research, like I have, and you take the carrier's pleasure and their discoveries. Then, if you can find some people such that such people than ever few universities were able to do this, then it would solve the problem. And so that's what we're working on right now. So as soon as I get my book, I'll be sending it to various, which is the due to come this week. I think, you know, my, the 50 copies that Stripe send me and I can send these out. I'm not very optimistic. I'm afraid. Okay. Okay. Well, I am, perhaps I, I realized I realized, foolishly, but my, my, my theory is that, If you're not optimistic, then you're sort of doomed to failure. Like, like the, the, the [00:47:00] non optimism makes like, it, it, reinforces itself, right? Like, so, so if you're pessimistic, then it will make itself come true. we sort of, yeah, so, so we, so we need to be optimistic and, and I guess like, with, with the universities today, Like, what did they, so like if, if the money were there, like if, if the money were coming in to, to researchers, universities don't have any problem with sort of people being there, doing work as long as, as the funding is coming from somewhere else with no strings attached. Right? Yep. Yeah. Okay. So let me, every university has, I mean, UCL, I mean, it has a budget of a billion pounds, you know, it's a big university, so we're asking for a tiny amount of money, you know, the, and even that is an over estimate, you know, because most years expenditure will be zero, [00:48:00] you know, and it's only, but to have that, to be able to call on occasionally. something like 150 200,000 pounds a year. So 200,000 pounds for three years, sorry. it would be no big commitment for them to enter into it, especially if you could re entertain the hope that in a few years, the, the scientists would, would make the transmit that the transition into the mainstream and then back external funding. And just what I've done. And so, so I know that a couple of universities do give, like new professors a year or two of funding. Well, that's right. I mean, people taking new jobs are in th th their, their maximum creativity then. And so universities, it's very good investment for them, but, but, but, but, an academic now has to look [00:49:00] forward to what will happen when this, when this funding ends. Right. You know, and will he be well-placed and he's got to engineer his, his position to be well-placed to, to attract funds. And so a year or two, not enough to do venture research. And so they just, you know, it, it works, but only to a very limited extent. Yeah. And the, and the, the incentive sort of cascade backwards. Right. Cause you're looking at, and you're like, okay, well, I'm going to need to get grants. And in order to get those grants, I'm going to need to have, do you have done peer reviewed research? So I better get started on that now so that it has to start thinking about that. Yeah, no, that, that makes a lot of sense. so I guess in including, What besides simply just like reading the book and thinking about, venture research. What, what is something that you think people should be thinking about [00:50:00] that they're not thinking about enough? Well, I, I don't think you can, so you can say it like that. I mean, if you're, if you're a venture researcher or a budding venture search, then you'll have an idea and you'll, always wants to be returning to it. So, I mean, the creation of venture research therefore is a happy coincidence. You know, that it's a meeting of, of, of similar minds, if you like. And I provided the opportunity to those, 40 people, that we backed over the 10 years to do their thing. but it was a partnership. It was a partnership between us and them. I was taking a risk, you know, with BP and, you know, having to solve the problems. I had to do all the other things you say, you know, like travel the world and give, to get, cause I didn't believe we could advertise. I didn't, I didn't think we could advertise, you know, in journals and say, you know, we wanted good ideas. I had to go to universities and give talks about venture to search while people were doing. [00:51:00] And the state of science now. Yeah. And then invite proposals and then sit and listen to what people came up with. And, at each university I might get in an afternoon, I may get 20 or 30 proposals. I mean, most of them just so you as a new source of money. You know, and that was always a problem we had, even with venture researchers was convincing them that even though we were the BP venture research unit, we were not interested in getting oil out of the ground. This could help the research director and I wouldn't trust us on what he was trying to do. So I had to find a way of, of, so our strategy was completely different. So the search directors. So we're not in direct competition, but he did see me as getting direct competition because they, the senior directors, you know, were saying the bang for buck that blaming God is higher than yours. I didn't say that they did. And actually, what, what was, what is the thinking behind, Not putting [00:52:00] out a sort of like a broad, call for, for applications, but instead, going and giving a talk and then, and talking to people because my, like my gut says that makes a lot of sense, but I'm not quite sure I can pick apart. Why. Well, venture searches, Nobel prize winners are a very special breed, you know, they do not respond to a, to opportunity. I mean, they create, they create their own opportunity and they are convinced of their particular view of the world will eventually be proved. Right. And hopefully. well, for the few we managed to help, we, we enable them to do that, but, other people just happened to have to cut their clamps according to the phones that are available and have to keep doing that. So I, I, I think that, people's view is, is, of the world is, is created within what the, within themselves, in this, within their thought processes, [00:53:00] their thought processes on their creative spirit, you know, this thing, which we, we all have, allows, them to do. I mean, people like, like Einstein, you know, w w when he, he looked at the, at the world without any feedback from anyone. You know, he didn't read the literature, he didn't cite any publication in this, you know, it didn't say anything. And his Anna's mirabilis is three papers and max playing catheter to decide whether or not he was the editor of the journal that he submitted his papers to. And he had to decide whether we should publish these or sub subject them to the usual references, but he didn't and he just published them. And th th they attracted a lot of criticism that said the times for it, it's always in the front of common sense, but there's other two work, you know, for the, for the conductivity and, Brownian motion. I mean, they were major pieces of work, but he did that [00:54:00] without talking to anybody. I mean, apart from, you know, mathematical advice and things like that, which had got from various people from time to time. Yeah. So, so to, to, to sort of pull that back to the strategy for teasing out, the, like the, the high quality applicants, the hypothesis would be that like, they, they don't even, they wouldn't even be reading the, like the journal where you'd be advertising that you want applications and you sort of have to like really go and, and get in their face. Yes, you have to let it be. You have to create the environment that allows them to write to you or to contact you, or to give you a phone call and say, I want to do this. You know? And, I remember I got a phone call once from, from a lunatic who said, I have a way of launching satellites, which has been much cheaper than, than anyone else has ever had. And I said, Oh, what do you want to do? He said, Oh, I want to build a building a hundred miles high. And, and then throw out of [00:55:00] the window, you know, there's, there's been, these objects and there were, there would be an arbiter immediately. And so they would, but I asked him about, you know, what do you think are the limits on, on growth, you know, on, on, on, on building, what would the foundations for a hundred mile high building? And there was just silence then, you know, cause he realized that the Earth's crust would not support. And what's the highest structure on the world Mount Everest high is that five miles. So Y you know, w how are you going to construct something which is a hundred miles high? So, I mean, that doesn't matter. It's just an anecdote of a, of a phone call I got, but that's what, that's what constituted, And initial approach to us. That's all we asked for, that the person would, was a ring or write a paragraph or whatever it was and saying, this is what I want to do. Yeah. And you would then take them there. Yeah. That's. [00:56:00] I like that a lot, because it's, it's very, it's almost the opposite of the approach that I've seen, that you you've seen, that there are like many other places. Like you look at how, like DARPA or bell labs does it, did it. and they it's like almost the opposite where they would only ever go and be like, you, like, we want you to come and like do some awesome stuff. And so it was it's like that, like push versus pull in, in getting people into the. The the organization is, is an interesting dynamic. well, I spoke, I spoke to somebody in the cabinet office recently about that, but, you know, he, he, he knew about it. He learned, he learned about the publication of the book and he said that the government was saying the British government were thinking of creating DARPA in Britain. And I didn't think it would be a very good idea. Because not for venture to search, it would be good for other things. but, DARPA 10, it's a bit like venture capital, you know, they know what they want to [00:57:00] do. And, when I was no venture to searcher would be able to point to specific benefits flowing from their work, right at the beginning, it would not be able to do that. And so therefore they would be disqualified from applying. Yeah, I think it's actually, a really important distinction that you just made because that like what, when people say like, like research is broken, my, my hypothesis is that there is actually that they're describing at least two completely distinct problems where there's the one problem of sort of, like making more. Technology. And then there's the other problem of like discovering more new areas of knowledge. And, and venture research is very much targeted at the latter. and w distinctly from the former new fruits and new continents, you know, that's what we're concentrating on. You know, [00:58:00] the people, you know, completely new consonants, completely new fruits. So there'll be low hanging fruit will come from that, from that fruit. And, yeah, that's what we're trying to do. I think one of the biggest takeaways from this conversation for me, that I just want to double click on is. Donald's assertion that the line between genius and crack pot is not as thin as I. Used to believe. And that it may be possible to tell the difference. Bye. Really. Paying attention to how precise someone's ideas. Uh, I'm still sort of processing that, but it's. An important thing for us to think about [00:59:00]

A conversation with Adam Marblestone about his new project - Focused Research Organizations. Focused Research Organizations (FROs) are a new initiative that Adam is working on to address gaps in current institutional structures. You can read more about them in this white paper that Adam released with Sam Rodriques. Links FRO Whitepaper Adam on Twitter Adam's Website Transcript [00:00:00]   In this conversation, I talked to Adam marble stone about focused research organizations. What are focused research organizations you may ask. It's a good question. Because as of this recording, they don't exist yet. There are new initiatives that Adam is working on to address gaps. In current institutional structures, you can read more about them in the white paper that Adam released recently with San Brad regens. I'll put them in the show notes. Uh, [00:01:00] just a housekeeping note. We talk about F borrows a lot, and that's just the abbreviation for focus, research organizations. just to start off, in case listeners have created a grave error and not yet read the white paper to explain what an fro is. Sure. so an fro is stands for focus research organization. the idea is, is really fundamentally, very simple and maybe we'll get into it. On this chat of why, why it sounds so trivial. And yet isn't completely trivial in our current, system of research structures, but an fro is simply a special purpose organization to pursue a problem defined problem over us over a finite period of time. Irrespective of, any financial gain, like in a startup and, and separate from any existing, academic structure or existing national lab or things [00:02:00] like that. It's just a special purpose organization to solve, a research and development problem. Got it. And so the, you go much more depth in the paper, so I encourage everybody to go read that. I'm actually also really interested in what's what's sort of the backstory that led to this initiative. Yeah. it's kind of, there's kind of a long story, I think for each of us. And I would be curious your, a backstory of how, how you got involved in, in thinking about this as well. And, but I can tell you in my personal experience, I had been spending a number of years, working on neuroscience and technologies related to neuroscience. And the brain is sort of a particularly hard a technology problem in a number of ways. where I think I ran up against our existing research structures. in addition to just my own abilities and [00:03:00] everything, but, but I think, I think I ran up against some structural issues too, in, in dealing with, the brain. So, so basically one thing we want to do, is to map is make a map of the brain. and to do that in a, in a scalable high-speed. Way w what does it mean to have a map of the brain? Like what, what would, what would I see if I was looking at this map? Yeah, well, we could, we could take this example of a mouse brain, for example. just, just, just for instance, so that there's a few things you want to know. You want to know how the individual neurons are connected to each other often through synopsis, but also through some other types of connections called gap junctions. And there are many different kinds of synopsis. and there are many different kinds of neurons and, There's also this incredibly multi-scale nature of this problem where a neuron, you know, it's, it's axon, it's wire that it sends out can shrink down to like a hundred nanometers in [00:04:00] thickness or less. but it can also go over maybe centimeter long, or, you know, if you're talking about, you know, the neurons that go down your spinal cord could be meter long, neurons. so this incredibly multi-scale it poses. Even if irrespective of other problems like brain, computer interfacing or real time communication or so on, it just poses really severe technological challenges, to be able to make the neurons visible and distinguishable. and to do it in a way where, you can use microscopy, two image at a high speed while still preserving all of that information that you need, like which molecules are aware in which neuron are we even looking at right now? So I think, there's a few different ways to approach that technologically one, one is with. The more mature technology is called the electron microscope, electromicroscopy approach, where basically you look at just the membranes of the neurons at any given pixel sort of black or white [00:05:00] or gray scale, you know, is there a membrane present here or not? and then you have to stitch together images. Across this very large volume. but you have to, because you're just able to see which, which, which pixels have membrane or not. you have to image it very fine resolution to be able to then stitch that together later into a three D reconstruction and you're potentially missing some information about where the molecules are. And then there's some other more, less mature technologies that use optical microscopes and they use other technologies like DNA based barcoding or protein based barcoding to label the neurons. Lots of fancy, but no matter how you do this, This is not about the problem that I think can be addressed by a small group of students and postdocs, let's say working in an academic lab, we can go a little bit into why. Yeah, why not? They can certainly make big contributions and have to, to being able to do this. But I think ultimately if we're talking about something like mapping a mouse brain, it's not [00:06:00] going to be, just a, a single investigator science, Well, so it depends on how you think about it. One, one, one way to think about it is if you're just talking about scaling up, quote, unquote, just talking about scaling up the existing, technologies, which in itself entails a lot of challenges. there's a lot of work that isn't academically novel necessarily. It's things like, you know, making sure that, Improving the reliability with which you can make slices of the brain, into, into tiny slices are making sure that they can be loaded, onto, onto the microscope in an automated fast way. those are sort of more engineering problems and technology or process optimization problems. That's one issue. And just like, so Y Y Can't like, why, why couldn't you just sort of have like, isn't that what grad students are for like, you know, it's like pipetting things and, doing, doing graduate work. So like why, why couldn't that be done in the lab? That's not why [00:07:00] they're ultimately there. Although I, you know, I was, I was a grad student, did a lot of pipetting also, but, But ultimately they're grad student. So are there in order to distinguish themselves as, as scientists and publish their own papers and, and really generate a unique academic sort of brand really for their work. Got it. So there's, there's both problems that are lower hanging fruit in order to. in order to generate that type of academic brand, but don't necessarily fit into a systems engineering problem of, of putting together a ConnectTo mapping, system. There's also the fact that grad students in, you know, in neuroscience, you know, may not be professional grade engineers, that, for example, know how to deal with the data handling or computation here, where you would need to be, be paying people much higher salaries, to actually do, you know, the kind of industrial grade, data, data piping, and, and, and many other [00:08:00] aspects. But I think the fundamental thing that I sort of realized that I think San Rodriquez, my coauthor on this white paper also realized it through particularly working on problems that are as hard as, as clinic Comix and as multifaceted as a system building problem. I th I think that's, that's the key is that there's, there's certain classes of problems that are hard to address in academia because they're system building problems in the sense that maybe you need five or six different. activities to be happening simultaneously. And if any, one of them. Doesn't follow through completely. you're sort of, you don't have something that's novel and exciting unless you have all the pieces putting, you know, put together. So I don't have something individually. That's that exciting on my own as a paper, Unless you, and also three other people, separately do very expert level, work, which is itself not academically that interesting. Now having the connectome is academically [00:09:00] interesting to say the least. but yes, not only my incentives. but also everybody else's incentives are to, to maybe spend say 60% of their time doing some academically novel things for their thesis and only spend 40% of their time on, on building the connectome system. Then it's sort of, the probability of the whole thing fitting together. And then. We see everyone can perceive that. And so, you know, they basically, the incentives don't align well, for, for what you would think of as sort of team science or team engineering or systems engineering. yeah. And so I'm like, I think, I think everybody knows that I'm actually like very much in favor of this thing. So, I'm going to play devil's advocate to sort of like tease out. what I think are. Important things to think about. so, so one sort of counter argument would be like, well, what about projects? Like cert, right? Like that [00:10:00] is a government yeah. Led, you should, if you do requires a lot of systems engineering, there's probably a lot of work that is not academic interesting. And yet, it, it, it happens. So like there's clearly like proof of concepts. So like what what's like. W why, why don't we just have more things like, like certain for, the brain. Yeah. And I think this gets very much into why we want to talk about a category of focused research organizations and also a certain scale, which we can get into. So, so I think certain is actually in many ways, a great example of, of this, obviously this kind of team science and team engineering is incredible. And there are many others, like LIGO or, or CBO observatory or the human genome project. These are great examples. I think the, the problem there is simply that these, these are multibillion dollar initiatives that really take decades of sustained. government involvement, to make it happen. And so once they get going, and [00:11:00] once that flywheel sort of start spinning, then you have you have it. And so, and so that, that is a nonacademic research project and also the physics and astronomy communities, I think have more of a track record and pipeline overall. perhaps because it's easier, I think in physical sciences, then in some of these sort of emerging areas of, of, you know, biology or sort of next gen fabrication or other areas where it's, it's, there's less of a, a grounded set of principles. So, so for CERN, everybody in the physics basically can agree. You need to get to a certain energy scale. Right. And so none of the theoretical physicists who work on higher energy systems are going to be able to really experimentally validate what they're doing without a particle accelerator of a certain level. None of the astronomers are gonna be able to really do deep space astronomy without a space telescope. and so you can agree, you know, community-wide that, This is something that's worth doing. And I think there's a lot of incredible innovation that happens in those with focus, research organizations. We're thinking about a scale that, [00:12:00] that sort of medium science, as opposed to small science, which is like a, you know, academic or one or a few labs working together, Or big science, which is like the human genome project was $3 billion. For example, a scope to be about $1 per base pair. I don't know what actually came out, but the human genome has 3 billion basis. So that was a good number. these are supposed to be medium scale. So maybe similar to the size of a DARPA project, which is like maybe between say 25 and. A hundred or $150 million for a project over a finite period of time. And they're there. The idea is also that they can be catalytic. So there's a goal that you could deliver over a, some time period. It doesn't have to be five years. It could be seven years, but there's some, some definable goal over definable time period, which is then also catalytic. so in some ways it will be more equivalent to. For the genome project example, what happened after the genome project where, the [00:13:00] cost of genome sequencing through, through new technologies was brought down, basically by a million fold or so is, is, is, how George Church likes to say it, inventing new technologies, bringing them to a level of, of readiness where they can then be, be used catalytically. whereas CERN, you know, It's just a big experiment that really has to keep going. Right. And it's also sort of a research facility. there's also permanent institutes. I think there's a, is a, is a, certainly a model that can do team science and, and many of the best in the brain mapping space, many of the sort of largest scale. connectomes in particular have come either from Janelia or from the Allen Institute for brain science, which are both sort of permanent institutes, that are, that are sort of, nonacademic or semi academic. but that's also a different thing in the sense that it's, it takes a lot of activation energy to create an Institute. And then that becomes now, a permanent career path rather than sort of focusing solely on what's the shortest path to. To some [00:14:00] innovation, the, the, the permanence. So, so the, the flip side of the permanence is that, I guess, how are you going to convince people to do this, this, like this temporary thing, where. I think, someone asked on Twitter about like, you know, if it's being run by the government, these people are probably going to get, government salaries. So you're, you're getting a government salary, without the like one upside of a government job, which is the security. so like what, what is the incentive for, for people to, to come do this? Yeah. And I think, I think it depends on whether it's government or philanthropic, philanthropic fro Faros are also definitely. An option and maybe in many ways more flexible, because the, you know, the government sort of has to, has to contract in a certain way and compete out, you know, contracts in a certain way. They can't just decide, the exact set of people to do something, for example. So, so the government side has. Both a huge [00:15:00] opportunity in the sense that I think this is a very good match for a number of things that the government really would care about. and the government has, has, has the money, and resources to do this, but philanthropic is also one we should consider. but in any case, there are questions about who and who will do Froy and, and why. and I think the basic answer though, it, it comes down to, it's not a matter of, of cushiness of the career certainty. it's, it's really, these are for problems that are not doable any other way. this is actually in many ways, the definition is that you're only going to do this. if this is the only way to do it, and if it's incredibly important. So it really is a, it's a medium scale moonshots. you would have to be extremely passionate about it. That being said, there are reasons I think in approximate sense why one might want to do it both in terms of initiating one and in terms of sort of B being part of them. [00:16:00] so one is simply that you can do science. that is for a fundamental purpose or, or, or, pure, purely driven toward your passion to solve a problem. and yet can have potentially a number of the affordances of, of industry such as, industry competitive salaries, potentially. I think the government, we have to ask about what the government can do, but, but in a certain philanthropic setting, you could do it another aspect that I think a lot of scientists find. Frustrating in the academic system is precisely that they have to. spend so much work to differentiate themselves and do something that's completely separate from what their friends are doing, in order to pay the bills basically. So, so if, if you don't eventually go and get your own appealing, you know, Tenure track job or, or so on and so forth. the career paths available in academia are much, much fewer, and often not, not super well compensated. And, and [00:17:00] so there are a number of groups of people that I've seen in sort of, if you want critical mass labs or environments where they're working together, actually, despite perhaps the. Incentive to, to, differentiate where they're working, does a group of three or four together. and they would like to stay that way, but they can't stay that way forever. And so it's also an opportunity if you, if you have a group of people that wants to solve a problem, to create something a little bit like, like a seal team. so like when, when I was, I'm not very generally militaristic person, but, when I was a kid, I was very obsessed with the Navy seals. But, but anyway, I think the seal team was sort of very tight knit. kind of a special forces operation that works together on one project is something that a lot of scientists and engineers I think want. and the problem is just that they don't have a structure in which they can do that. Yeah. So then finally, I think that, although in many cases maybe essentially built into the structure fro is make sense. We can [00:18:00] talk about this as, as nonprofit organizations. these are the kinds of projects where, you would be getting a relatively small team together to basically create a new industry. and if you're in the right place at the right time, then after an fro is over, you would be in the ideal place to start. The next startup in an area where it previously, it's not been possible to do startups because the horizons for a venture investment would have been too long to make it happen from the beginning. Well, that's actually a great transition to a place that  I'm still not certain about, which is  what happens. After it fro, cause you, you said that it, that it's a explicitly temporary organization. And then, how do you make sure that it sort of achieves its goal, right? Like, because you can see so many of these, these projects that actually sound really great and they like go in and possibly could do good work and then somehow it all just sort of diffuses. [00:19:00] so, so have you thought about how to sort of make sure that that lives on. Well, this is a tricky thing as we've discussed, in a number of settings. So, in a, like to maybe throw that question back to you after I answer it. Cause I think you have interesting thoughts about that too, but, but in short, it's, it's a tricky thing. So, so the fro. Is entirely legal focused there isn't, there's no expectation that it would continue, by default and simply because it's a great group of people, or because it's been doing interesting work, it's sort of, it is designed to fulfill a certain goal and it should be designed also from the beginning to have a, a plan of the transition. Like it could be a nonprofit organization where it is explicitly intended that at the end, assuming success, One or more startups could be created. One or more datasets could be released and then a, you know, a much less expensive and intensive, nonprofits, structure could be be there to [00:20:00] host the data and provide it to the world. it could be something where. the government would be using it as a sort of prototyping phase for something that could then become a larger project or be incorporated into a larger moonshot project. So I think you explicitly want a, a goal of a finite tune to it, and then also a explicit, upfront, deployment or transition plan, being central to it much more so than any publication or anything. Of course. At the same time. there is the pitfall that when you have a milestone driven or goal focused organization, that the funder would try to micromanage that and say, well, actually, not only do I care about you meeting this goal, but also I really care that by month six, you've actually got exactly this with this instrument and this throughput, and I'm not going to let you buy this other piece of equipment. Unless, you know, you show me that, you know, [00:21:00] and that's a problem that I think, we sometimes see with, externalized research models, like DARPA ARPA models, that try to. achieve more coordination and, and, and goal driven among otherwise, somewhat uncoordinated entities like contractors and, and universities that, that are working on programs, but then they, they, they, they achieve that coordination by then, managing the process and, with an fro, I think it will be closer to. You know, if you have a series, a investment in the startup, you know, you are reporting back to your investors and, and they, they, at some level care, you know, about the process and maybe they're on your board. but ultimately the CEO gets to decide, how am I going to spend the money? And it's extremely flexible to get to the goal. Yeah. Yeah. The, the micromanage, like [00:22:00] figuring out how to avoid, Micromanagement seems like it's going to be really tricky because it's sort of like once you get to that amount of money, I like, have you, have you thought about, like how, like, if you could do some kind of like actually, well, I'll, I'll give her the, the, the, the, the, the thing that the cruxy thing is like this, I think there's a huge amount of trust that needs to happen in it. And what I'm. like I constantly wonder about is like, is there this like fundamental tension between the fact that, especially with like government money, we really do want it to be transparent and well-spent, but at the same time, in order to sort of do these like knowledge frontier projects, sometimes you need to do things that. Are a little weird or like seem like a waste of money at the time, if you're not like intimately connected. and so there's, there's this sort of tension [00:23:00] between accountability and, Sort of like doing the things that need to get done. I agree with that and Efros, we're going to navigate that. Yeah. I agree with that. And I think it relates to a number of themes that you've touched on and that we've discussed with, which has sort of, has to do with the changing overall research landscape of, in what situations can that trust actually occur, you know, in bell labs, I think there was a lot of trust. throughout, throughout that system. And as you have more externalized research, conflicting incentives and so on it, it's, it's hard. It's hard to obtain that trust. startups of course, can align that financially, to a large degree. I think there are things that we want to avoid. so one of the reasons I think that these need to be scoped as. Deliverables driven and roadmaps, systematic projects over finite periods of time, is to avoid, individual [00:24:00] personalities, interests, and sort of conflicting politics, ending up. Fragmenting that resource into a million pieces. So, so I think this is a problem that you see a lot with billion dollar scale projects, major international and national initiatives. Everybody has a different, if you say, I want this to be, to solve neuroscience, you know, and here's $10 billion. Everybody has a different opinion about what solved neuroscience is. And there's also lots of different conflicting personalities and, and leadership there. So I think for an fro, there needs to be an initial phase, where there's a sort of objective process of technology roadmapping. And people figure people understand and transparently understand what are the competing technologies? What are the approaches? What, what are the risks? And you understand it. and you also closely understand the people involved. but importantly, the people doing that roadmapping and sort of catalyzing the initial formation of that [00:25:00] fro need to have a somewhat objective perspective. It's not just funding my lab. It's actually, you, you want to have vision, but you, you need to. Subjected to a relatively objective process, which, which is hard because you also don't want it to be a committee driven consensus process. You want it to be active, in, in a, in a systematic, analysis sense, but, but not in a, everyone agrees and likes it, you know, emotionally sense. and so that, that's a hard thing. but you need to establish it's that trust upfront, with, with the funder, And that's a hard process and it gets a hard process to do as a large government program. I think DARPA does it pretty well with their program managers where a program manager will come in and they will pitch DARPA on the idea of the program. there'll be a lot of analysis behind it and, but then once, once they're going, that program manager has tremendous discretion, and trust. To how they actually run that [00:26:00] project. And so I think you need something like a program manager driven process to initiate the fro and figure out is there appropriate leadership and goals and our livable as reasonable, Yeah, that seems the way, at least the way that it's presented in the paper, it, it feels a little bit chicken and egg in that. so with DARPA, DARPA is a sort of permanent organization that brings in program managers. And then those programmers program managers then go, start programs, whereas, The look at fro it seems like there's this chicken and egg between like, you sort of, you need someone spearheading it. It seems like, but then it, you sort of like, it, it seems like it will be very hard to get someone who's qualified to, to spearhead it, to do that before you have funding, but then you need someone spearheading it in order to get that [00:27:00] funding. yeah. Like, yeah. How, how are you thinking about. Cracking that that's, that's sort of the motivation for me behavior over the next year or two, is that I'm trying to go out and search for them. And, a little bit of it is from my own creativity, but a lot of it is going out and talking to people and try and understand what the best ideas. Here would be, and who are the networks of, of human beings behind those ideas, and trying to make kind of a prioritized set of borrows. Now, this kind of thing would have to be done again, I think to some degree, if there was a, larger umbrella program that someone else wanted to do, but, I'm both trying to get a set of, of exemplary. And representative ideas and people together, and try to help those people get funding. You know, I think there can be a stage process. I agree that, in the absence of a funder showing [00:28:00] really strong interest, people committing, to really be involved is difficult, because it is a big change to people's normal. Progression through life to do something like that. but just like with startups, to the extent that you can identify, someone who's. We spiritually just really wants to do this and we'll kind of do anything to do it, the sort of founder type, and also teams that want to behave like that. that's obviously powerful, and also ideas where there's a kind of inevitability, where based on scientific roadmapping, it, it just has to happen. There's no way, you know, for neuroscience to progress unless we get better. Connectomics and I think we can go through many other fields where, because of. The structures we've had available and just the difficulty of problems now, where arguably Faros are needed in order to make progress in fields that people really care about. So, so I think you can get engagement at the level of, of discussion, and, and, and starting to nucleate [00:29:00] people. But, but there is a bit of a chicken and egg problem. In the sense that it's, it's not so much as here's an fro, would you please fund to me it's we need to go and figure out where there might be Faros to be had, and then who is interested in those problems as well to, to fund and support those things. So, yeah. So I guess to recap what I see your process that is, is that you're going out, you're sort of really trying to. Identify possible people possible ideas, then go to funders and say, here, like sort of get some, some tentative interest of like, okay, what, which of these things might you be interested in if I could get it to go further and then you'll circle back to. the, the people who might be interested in sort of say like, okay, I have someone, a funder who's potentially interested. Can we [00:30:00] sort of like refine the idea? and then sort of like, like you will drive that loop hopefully to, Getting a, an fro funded that's right. And there's, there's further chicken and egg to it. that has to be solved in the sense that, when you go to funders and you say, why, you know, I have an idea for an fro. We also need to explain what an fro is, right? in a way that both, engages people in creating these futuristic models, which many people want to do, While also having some specificity of, of what we're looking for and what, what, what we think is as possible. So, and then the same on, on the, on the side of, of scientists and engineers and entrepreneurs all over the world, who, you know, have the ideas certainly, but most of those ideas have been optimized to hit, the needs of existing structures. So, so we are, we are trying to, I think, broker between those, And [00:31:00] then start prototyping a few. but the, you know, the immediate thing I think is to make, w Tom Coolio has referred to a catalog, a Sears catalog of moonshots. and so we're trying to make a catalog of, of moonshots that fit the fro category. but that sounds like the perfect name for this podcast, by the way. the cataloging mood child, like, you're kind of kind of cataloging moonshots and ways to get moonshots and yeah, absolutely. Yeah. and so I guess another sort of, thing that I've seen, and I'm not sure, it's almost like for people like a lot of people who like really want. Who like sees something as inevitable and they really want to get it done. In sort of like the current environment we're recording in October, 2020. there's. There's sort of this perception that capital is really cheap. [00:32:00] you know, there's a lot of venture capitalists there. They're pretty aggressive about funding and one could make an argument that, if it's, if it, it really is going to be inevitable and it really is going to start a new industry. Then that is exactly where venture capital funding should come in. And I do see this a lot where people, you know, it's like they have this thing that they really want to see exist and they, you know, come out of the lab and it started a company that's sort of extremely common. so. I guess, like, what almost would you say to someone who you see doing this that you think maybe should do an fro instead? Yeah, that's a great question. I mean, I think it's a complicated question and obviously, you know, we got to see VC also, you know, obviously VC backed, you know, innovation is, is, is one of, if not sort of the key, [00:33:00] Things that is driving technology right now. So, so I'm in no way saying that fro is, are somehow superior to two startups, in any generalized way. So I think that things that can be startups and are good as startups should be startups and people, if you have an idea that could be good for a startup, I think you should go do it. Generally speaking. But, there, there are a few considerations, so yeah. So I think you can divide it into categories where VCs, no, it's not a good idea for startups. And therefore won't talk to you, in cases where VCs don't always know whether or not it's good for a startup or whether there's a way that you could do it as a startup, but it would involve some compromise that is actually better not to make, even potentially for the longterm. economic prospects of, of an area. So things that can happen, would be, if you have something that's basically meant to be a kind of platform technology or which you [00:34:00] need to develop a tool or a platform in order to explore a whole very wide space of potential applications. maybe you have something like a new method of microscopy or something, or a new way to measure proteins in the cell or things like that, that, you know, you could target it to a very particular, if you want product market fit application, where you would be able to make the most money on that and get the most traction, the soonest. Yeah. Sometimes people call this, you know, the, the, the, the sort of Tesla Roadster, equivalent. You want to guys as quickly as you can to the Tesla Roadster. And I think generally, what people are doing with, with that kind of model, where you take people that have science, to offer, and you say what's the closest fastest you can get, to a Tesla Roadster that lets you it lets you build, get, get revenue and start, start being financially sustainable and start building a team, to go further. generally that's really good. and generally we need more scientists to learn how to do that. it'd be supported to do that, but, [00:35:00] sometimes you have things that really are meant to be. either generalized platforms or public goods, public data, or knowledge to underlie an entire field. And if you work to try to take the path, the shortest path to the Roadster, you would end up not producing that platform. You would end up, producing something that is specialized to compete in that lowest hanging fruit regime, but then in the, in, in doing so you would forego the more general larger. Larger thing. And, you know, Alan Kay has, has the set of quotes, that Brett Victor took is linked on his website. and I think Alan K meant something very different actually, when he said this, but he's, he refers to the dynamics of the trillions rather than the billions. Right. and this is something where in, and we can talk about this more. I'd be curious about your thoughts on that, but something like the transistor. You know, you, you could try to do the transistor as a startup. and maybe at the time, you know, the best application for transistors would have been [00:36:00] radios. I don't think like that. I think it was, it was guiding a rockets. Yeah. So you could have, you could have sort of had had a transistors for rockets company and then tried to branch out into, becoming Intel. You know, but really, given the structures we had, then the transistor was allowed to be more of a, a broadly, broadly explored platform. yeah, that, that progressed in a way where we got the trillions version. And I worry sometimes that even some startups that have been funded at least for a seed round kind of stage, and that are claiming that they want to develop a general platform are going to actually struggle a little bit later. when investors, you know, see that, see that they would need to spend way more money to build that thing. then the natural shortest path to a Roadster, or another words the Roadster is, is, somehow illusory. yeah. Yeah, this [00:37:00] is, this is a. Sort of like a regime that I'm really interested in and a, just on the transistor example, I've, I've looked at it. So just the, the history is that it was developed at bell labs, in order to prevent a T and T from being broken up, bell labs had to, under strictly licensed a bunch of their innovations, including the transistor William Shockley went off and, Started, chocolate semiconductor, the traders eight then left and started, Fairchild and then Intel. And, believe that that's roughly the right history. but the, the really interesting thing about that is to ask the question of like, one, what would have happened if, bell labs had exclusive license to the transistor and then to what would have happened if they had like exclusively licensed it to, Shockley semiconductor. And I think I would argue in both of those situations, you don't [00:38:00] end up. Having the world we have today because I fell labs. It probably goes down this path where it's not part of the core product. and so they just sort of like do some vaguely interesting things with it, but are never incentivized to like, you know, invent, like the, the planner processing method or anything. Interesting. yeah. Yeah. And so I guess where I'm. Go. And then like at the same time, the interesting thing is like, so Shockley is more, akin to like doing a startup. Right. And so it's like, what if they had exclusive license to it? And the, what I would argue is actually like that also would've killed it because, you have like, they had notoriously bad management. And so if you have this, this company with. And like the only reason that, the trader could go and start a Fairchild was because they, that was, that was [00:39:00] an open license. So this is actually a very long way of asking the question of, if F borrows are going to have a huge impact, it seems like they should default to. Really being open about what they create from like IP to data. but at the same time, that sort of raises this incentive problem where, people who think that they are working on something incredibly valuable, should want to do a startup. And then. And so there, and then similarly, even if they'd be like that sort of couldn't be a thing, they would want to privatize as much of the output of an fro. and so which. Maybe necessary in order to, to get the funding to make it happen. So I guess like, how are you thinking about that tension? That was a very long winded. Yeah. [00:40:00] Yeah. Well, there's, there's a lot, a lot there, I think, to loop back to you. So, so I think, right, so, so this idea that we've talked a bit about as sort of default openness, so, so things that can be open for maximum impact should be open. there are some exceptions to that. So, so if, And it's also has to do partly with how you're scoping the problem. Right? So, so rather than having an SRO that develops drugs, let's say, because drugs really need to be patentable, right. In order to get through clinical trials, we're talking about much more money than the fro funding, you know, to do the initial discovery of a target or something. Right. So to actually bring that to humans, you know, you need to have the ability to get exclusive IP. for downstream investors and pharma companies that that would get involved in that. so there are some things that need to be patented in order to have to have their impact. but in general, you, you want, I think fro problems to steer themselves to things where indeed. it can be maximally open and maybe, maybe you, you provide [00:41:00] a system that can be used to, to, or underlie the discovery of a whole new sets of classes of drugs and so on. But you're not so much focused on the drugs themselves. Now, that being said, right. if I invest in an SRO, and I've enabled this thing, right. It kind of would make sense for the effort, you know, maybe three of the people of, of, of, of 15 in the fro will then go and start a company afterwards that then capitalizes on this and actually develops those drugs or what have you, or it takes it to the next stage. And gosh, it would really make sense if I had funded in fro. that's, those people would like to take me as a sort of first, first, first refusal to get a good deal on, on investing in this startup, for example. Right. so I think there are indirect network-based, or potentially even legal based, structure, structure based ways to both incentivize the investors and, But it's, it's a weaker, admittedly weaker, incentive financially than, [00:42:00] than, than the full capture of, of, of something. But then, but then there's, I think this gets back to the previous discussion. So which is sort of the trillions rather than the billions. So if you have something where maybe there are 10 different applications of it, Right in 10 different fields. you know, maybe, maybe we have a better way to measure proteins and based on this better way to measure proteins, we can do things in oncology and we can do things in Alzheimer's and we can do things in a bunch of different directions. We can do things in diagnostics and pandemic surveillance, and so many fields that one startup, It would be hard even to design, to start, if that could capture all of that value just as it would have been hard to design sort of transistor incorporated. Right. Right. given that, I think there's, there's a lot of reason to. To do an fro and then explore the space of applications. Use it as a means to explore a full space in which you'll then get [00:43:00] 10 startups. so if I'm the investor, I might like to be involved in all 10 of the new industry, right. And the way to do that would be to create a platform with which I can explore, but then I have a longer time horizon. Cause I have to first build the thing. Then I have to explore the application space and only then. do I get to invest in a specific verticals, right? Yeah. I think the, the two sort of tricky questions that I, I wonder about what that is one. So you mentioned like, Oh, there's 15 people in an fro, three of them go off to start a startup. What about those other 12 people? Like, I, I assume that they might be a little bit frustrated if, if that happens, Yeah, because like, like they, they did, they did help generate that value in it. It sort of gets into two questions of like capturing, like sort of kicking back, value generated by research in general, but like, yeah, it could, it could, it could be all 15 people, you know, we saw something [00:44:00] similar with open AI, you know, in a way, for example, converting, you know, into, into a, for profit or at least a big arm of it being, being the for-profit, and keeping all the people. Right. So you, you, you, you can imagine, just blanket converting. but yeah, I think, I think it's sort of, In the nature of it, that these are supposed to be things that open up such wide spaces that there's, there's sort of enough for everyone, but no, no, no one person necessarily one startup would completely capture. And I think that's true for clinic Comix too, for example. Right. So if you had really high throughput clinical, connectomics just, just to keep going on this example, that's a great example of perfect. It's a good thing as a good example. It's not. Depending on the details, whether this is exactly the first fro or not. I think it's totally, totally other issue, but, but. Connectomics there's potentially applications for AI and you know, how, how the neurocircuits work, and sort of fundamental, funding. Mental is a brain architecture and intelligence. although there's a bunch of ranges of the sort of uncertainty of exactly what that's going to be. So it's hard to sort of [00:45:00] know it until you see the data. There's also potentially applications for something like drug screening, where you could put a bunch of different, Kind of some CRISPR molecules or drug perturbations on, on a, on a brain and then look at what each one does to their, the synopsis or, and look at that in a, in a brain region specific way and sort of have ultra high, but connect to them based drug screening. Neither of those are things you can start a start up until you have connected. Right. working. but so anyway, so maybe three people would start an AI company and maybe those would be the very risk tolerant ones. and then three would start at, you know, a crisper drug company and, and, and, three would just do, do fundamental neuroscience with it and, take those capabilities and, and, and go, go back into the university system or so on and yeah. And start using that. Yeah. And the, the sort of the other related to. like creating value with it. there's, there's a little like uncut discomfort that like even I have [00:46:00] with, say like philanthropic or government funding, then going to fund a thing that proceeds to make a couple of people very wealthy. Which like, and like, there's very much arguments on both sides, right. Where it's like, it'll generate a lot of good for the world. and, and all and, and such. so, so like, I guess what would you say? I guess like, as a, as a, like, if I were a very wealthy philanthropist and I'm like, do it, like, you know, it's like, I'm just giving away money so that these people can. Yeah, the company is a complicated thing. Right? How much, how many further rich people, you know, did the Rockefeller foundation, you know, investing in the basics of molecular biology or things like that ended up generating? I mean, I think that, I think you, I think in some way the government does want to end up is they want the widely distributed benefit. And I think everything that should be an SRO should have widely distributed benefits. It shouldn't just [00:47:00] be a kind of, A startup that just, just enhances one, one person. It should be something that really contributes very broadly to economic growth and understanding of the universe and all that. But it's almost inevitable. I think that, if you create a new industry, you're gonna, you know, you're gonna, you're gonna feel it going to be some more written about rich successful people in that industry. And they're probably going to be some of the people that were involved. Early and thinking about it for the longest and waiting for the right time to really enter it. And so, yeah, that's a really good point. I guess the, then the question would be like, how do you know, like, like what are, what are sort of a, the sniff test you use to think about whether something would have broadly distributed benefits? That's a great question. Cause it's like connect to them. It seems like fairly clear cut or, or generating sort of like a massive data set that you then open up. Feels very [00:48:00] clear. Cut. it's. We we've talked before about that, like fro is, could like scale up a process or build a proof of concept of, of a technology. and it, it seems like that it's less clear cut how you can be sure that those are going to, like if they succeed. Yeah. I mean, there are a few different frames on it, but I mean, I think one is, FRS could develop technologies that allow you to really reduce the cost of having some. Downstream set of capabilities. so, you know, if, just to give you an example, right? If, if we had, much lower costs, gene therapies available, right? So, so sometimes when drug prices are high, you know, this is basically it's recouping these very large R and D costs and then there's competition and, and, and profit and everything involved. you know, there was the marching squarely situation and, you know, there's a bunch of, sort of. What was that? there was, remember the details, but there [00:49:00] was some instance within which, a financially controlling entity to sort of arbitrarily bumped drug prices way high, right. A particular drug. and then w was, you know, was regarded as an evil person then, and maybe that's right. but anyway, there are some places I think, within the biomedical system where you can genuinely reduce costs for everyone. Right. and it's not simply that I, you know, I make this drug and I captured a bunch of value on this drug, but you know, it's really, it should be available to everyone and I'm just copying there. There's genuine possibility to reduce costs. So if I could reduce the cost of, of the actual manufacturing of. The viruses that you use for gene therapy, that's a, that's a process innovation. that would be, you could order as a magnitude drop the cost of gene therapy. If you could figure out what's going on, in the aging process and what are the real levers on a single, you know, biological interventions that would prevent multiple age related diseases that [00:50:00] would massively drop the cost. Right? So those, those are things where, Maybe even in some ways it would be threatening, to some of, some of the pharma companies, you know, that, that work on specific age related diseases, right? Because you're going to have something that, that replaced, but this is, this is what, you know, things that are broad productivity improvements. And I think economists and people very broadly agree that, that the science and technology innovations, For the most part. although sometimes they can be used to in a way that sort of, only benefits, a very small number of people that generally speaking there's a lot you can do, with technology that will be extremely broadly shared in terms of benefit, right? Yeah. Yeah. I mean, I, I do actually, like I agree with that. I'm, I'm just, I'm trying to represent as much skepticism as, as possible. Definitely. I know you agree with that. And actually, another thing that I have no idea about which I'm really interested in is as you're going and sort of creating this, [00:51:00] this moonshot catalog. how do you tell the difference between people who have these really big ideas who are like hardcore legit? but like maybe a little bit crazy. And then people who are just crackpots. Yeah, well, I don't claim to be able to do it in every field. and, and I think there's a reason why I've, I'm not trying to do a quantum gravity, fro you know, both, both, because I don't know that that's, you know, I think that's maybe better matched for just individual. Totally. Open-ended Sunday, you know, fun, brilliant people for 30 or 40 year long period to just do whatever they want. Right. Yeah. For quantum gravity, rather than directed, you know, research, but, But also because there's a class of problem that I think requires a sort of Einsteinian type breakthrough in fr fro is, are not, not perfect for that in terms of finding people. I mean, I, I find that, there's a lot of pent up need for, this is that's my preliminary feeling. and you can see there's a [00:52:00] question of prioritizing, which are the most important, but there's a huge number of. Process innovations or system building innovations that are needed across many, many fields. And you don't need to necessarily have things that even sound that crazy. There are some that just kind of just make sense, you know, are, are very simple. You know, we here, here in our lab, we have this measurement technology, but we, you know, we can only have the throughput of one cell, you know, every, every few weeks. And if we could build the system, we could get a throughput of, you know, A hundred thousand cells, you know, every month or something. Right. there are some, there's some sort of ones that are pretty obvious, or where there's an obvious inefficiency. In kind of, how things are structured. Like every, every company and lab that's that's modeling fusion reactors, and then also within the fusion reactor, each individual component of it, like the neutrons in the wall versus the Plaza and the core, those are basically modeled with different. Codes many of which are many [00:53:00] decades old. So there's sort of an obvious opportunity to sort of make like a CAD software for fusion, for example, you know, that the, the, it doesn't, it's not actually crazy. It's actually just really basic stuff. In some cases, I think they're ones where we'll need more roadmapping and more bringing people together to really workshop the idea, to really have people that are more expert than me say, critique each other and see what's. Really going on in the fields. and I also rely on a lot of outside experts. if I have someone comes with an idea, you know, for, for energy, you know, and I'm talking to people that are like former RPE program managers or things like that, that, that know more of the questions. so I think we can, we can, we can do a certain amount of, of due diligence on ideas and. and then there are some that are, that are really far out. you know, we both have an interest in atomically precise manufacturing, and that that's when, where we don't know the path I think, forward. and so that's maybe a pre fro that's something where you [00:54:00] need a roadmapping approach, but it's maybe not quite ready to, to just immediately do an fro. Yeah, no, that's, you sort of hit on a really interesting point, which is that. when we think of moonshots, it's generally like this big, exciting thing, but perhaps some of the most valuable is will actually sound incredibly boring, but the things that they'll unlock will be. Extremely exciting. yeah, I think that's true. And, and you have to distinguish there's there's boring. Right? So, so I think there's, there's some decoupling of exactly how much innovation is required and exactly how important something is. And also just how much brute force is required. So I think in general, our system might under weight, the importance of brute force. And somewhat overweight the importance of sort of creative, individual breakthrough thinking. at the same time, there are problems where I think we are bottlenecked by thinking I'm like really how to do something, not just to [00:55:00] connect them of brain, but how do you actually do activity map of entire brain? You actually need to get a bunch of physicists together and stuff to really figure out what's, you know, there's a level of thinking that is not very non-obvious similarly for like truly next gen fabrication. You really, really, really need to do the technology roadmapping approach. And that's a little different than the fro. And in some cases there may be a, as we discussed, I think in the past, there was sort of a, a continuum potentially between DARPA type programs or programs that would start within the existing systems and try to catalyze the emergence of ideas and discoveries. And then fro is, which are a bit, a bit more cut and dry. And in some cases, even you could think of it as boring. but just very important.  how do we prevent Faros from becoming a political football? because you see this all the time where, you know, a Senator will say, well, like I'll sponsor this bill, as long as we mandate that. 50% of the work has to happen in my particular state or [00:56:00] district. and, and I imagine that that would be counterproductive towards the goals of . so do you, do you have any sense of like how to, how to get around that probably much easier in philanthropic setting than governments? Although I think I'm overall, I'm, I'm sort of optimistic that, if. If the goals are made very clear, the goal is disruptive, you know, multiplicative improvements in scientific fields. that's the primary goal. It needs to be managed well. so it's not either about the individual peoples, if you want academic politics and also that it doesn't, doesn't become about sort of, you know, districts, congressional districts, or all sorts of other things. I think there's a certain amount of complexity, but the other, the other thing is. I think there's really amazing things to be done in all sorts of places and by all sorts of people that are not necessarily identified as, as the biggest egos or the largest cities also, although certainly there are hubs that [00:57:00] matter. yeah. Cool. I think so. I think those are all like the actual questions I have. Is there anything you want to talk about that we have not touched on? Yeah, that's a good question. I mean, how does this fit into two things that you're thinking about, in terms of your overall analysis of the research system, then, do you think this, what is this leave unsolved as well? if, even if we can get some big philanthropic and government, donors. Yeah. So, so there are sort of two things that I. see it not covering. And so the, the first that you you've sort of touched on is that there are, some problems that still like don't fit into academia, but are not quite at the point where they're ready to be at fro. And so, they need, the, the like mindset of the fro without. Having this sort of, cut and dryness [00:58:00] that you need to sort of plunk down, like have the confidence to plunk down $50 million. so, so we need sort of a, a, what I would see as a sustainable, way of. Getting to the point of fro type projects. And as you know, I'm spending a lot of time with that. and then sort of a, the other thing that I've realized is that when, when people, we sort of have these discussions that are like research is broken, I think what we're actually talking about is, is sort of two really separate phenomenon. So, what we've been talking about, like Efros, Are really sort of sitting in like the Valley of death where it's like helping bridge that. but I think that at the same time, there there's like what I would call like the, the Einstein wouldn't get in any funding problem, which is, as you alluded to there, there are some of these things, like some of the [00:59:00] problems with research that we talk about are just about, The sort of conformity and specialization of really idea based exploratory, like completely uncertain research. And that's also really important, but I, I think it's what we don't do is, is, is sort of like separate those two things out and say like, these are both fall under the category of research, but are in fact. Extremely different processes. They require very different solutions. Yeah. Actually let me, let me, since you mentioned that, and since we are here together on the podcast, I agree with that and I, I have some things to say about that as well. So, so I think that the fro is indeed only address, or are designed to address this issue of sort of system building. problems that have a sort of catalytic nature and are a particular kind of pre-commercial stage. Right? So in some ways, [01:00:00] even though I'm so excited about borrows and how much they can unlock, because I think that this is one of two or three categories that has been, you know, under emphasized by current systems or has systems currently have struggled with it. there are these others. So, so I think that. The, the supporting the next Einstein and people that may have also have just be cognitively socially in any other number of ways, just different and weird and not good at writing grants. You know, not good at competing. Maybe not even good at graduating undergrad. Yeah. You know, I'm running a lab who are, are brilliant and because the system now. Has proliferate in terms of the number of scientists. it's very competitive and, and there is a, there's a lot of need to sort of filter people based on credentials. So there's this sort of credential there's people that don't fit with perfectly with credentials or with a sort of monoculture of who is able to get NSF grants and go through the university system and [01:01:00] get the PhD and all those different Alexey goosey has this nice blog post is oriented toward biomedical, but saying basically that in order to get through the system, you need to do 10 or 15 things simultaneously. Well, and also be lucky. And maybe we want to be looking for some people that are only able to do three of those things about, but are orders of magnitude better than others, then there's people even who have done well with those things, but still don't have the funding or sort of sustained ability, to, to pursue their own individual ideas over decades. even if they do get tenure or something, because the grant system is based on peer review and is, is sort of filtering out really new ideas, for whatever reason, There's kind of the broader issue that Michael Nielsen has talked about, which is sort of the idea that too much funding is centralized in a single organizational model. So particularly the NIH, the NIH grant is kind of hegemonic as, as, as a structure and as a peer review mechanism. then I think we need more [01:02:00] DARPA stuff. We probably need more darker agencies for other problems. Even though I've, I've sort of said that I think Rose can solve some problems that DARPA DARPA will struggle with. Likewise, DARPA walls solve problems that fro may struggle with. particularly if there's a very widely distributed expertise across the world that you need to bring together in a, some transient, interesting way, for a little bit more discovery oriented, perhaps in Faros and less deliverable oriented or team oriented. And then there's even bigger things we need, you know, like we need to be able to create, you know, a bell labs for energy, you know, or sort of something even bigger than fro. so yeah, I think the thing that you're, you're getting at that I is, is sort of simple, but under done is actually analyzing like what the activity is and what. How to best support it. Yep. Which is instead of just saying [01:03:00] like, ah, there's some research let's give some money to the research and then magical things will happen actually saying like, okay, like, like how does this work? Like what, and then what can we do for these, these specific situation? Yes. I think as you've identified. Like there's both on the one hand, there's the tendency to micromanage research and say, research has to do this, this with this equipment and this timescale it's entirely, this is sort of subject to milestone. And on the other hand is research is this magical thing. We have no idea. but just. Let other scientists, peer review each other, and just sort of give as much money to it as we can. and then we see what happens. Right. And I think neither of those, is a, is a good design philosophy, right? Yeah. Yeah. And I think it involves people like thinking it's it's uncomfortable, but like, like thinking and learning about. How, how did you think then understanding how it could, how it could be different? [01:04:00] How it's not a it's it's a system. Kevin has felt set, said it said it well. And so in some ways it's been designed, but really our scientific systems are something that has evolved into large degree. No one has designed it. It's not. Something that's designed to be optimal is it's a, it's a emergent property of many different people's incentives. And, if we actually try to apply more design thinking, I think, I think that can be good as long as we're not over overconfident in saying that there's one model for everyone. Yeah. I think that the trick to, sort of fixing. Emergent systems is to like, basically like do little experiments, poking at them. And that's, that's very much what I see getting fro is going okay. It's like, you're not saying, Oh, we should like dismantle the NSF and have it all be . Okay. Let's do a couple of these. See what happens. That's right. It's I think it's inherently a small perturbation and it it's. And I [01:05:00] think DARPA, by the way is a similar thing. It's sort of dark. You wouldn't need DARPA. If everything else was already sort of efficient, right. Given that things are not perfectly efficient, Darko has all these, all these sort of this niche that it fills. I think similarly Faros, they can only exist. if you also have a huge university system and you also have companies that that doesn't make sense, otherwise it's, it's a perturbation, but as we, I think it's a perturbation in which you unlock a pretty big pressure stream sort of behind it when you open it up. So. Excellent. Well, I think that's, that's actually a great place to close. I guess the last question would be, Like, if people are interested in, in Faros, especially like funding or running one, what is the best way for them to reach you? Well, they can, they can talk to me or they can talk to you. my email has, is prominently listed on my website. Twitter is great. and that, yeah, I really interested in, people that have a kind of specificity [01:06:00] of, of, of what they want of, you know, here here's, here's what I would do, very specifically, but I'm also interested in talking to people that, See problems with the current systems and want to do something and want to learn about, other highly specific fro ideas that others might have, and how to enable those.  

Michael Filler and Matthew Realff discuss Fundamental Manufacturing Process innovations. We explore what they are, dig into historical examples, and consider how we might enable more of them to happen. Michael and Matthew are both professors at Georgia Tech and Michael also hosts an excellent podcast about nanotechnology called Nanovation. Our conversation centers around their paper Fundamental Manufacturing Process Innovation Changes the World. If you’re in front of a screen while you’re listening to this, you might want to pull up the paper to look at the pictures. Key Takeaways Sometimes you need to go down to go back up The interplay between processes and paradigms is fascinating We need to spend more time hanging out in the valley of death Links Fundamental Manufacturing Process Innovation Changes the World(Medium)(SSRN) Michael on Twitter Matthew Realff's Website Michael Filler's Website Nanovation Podcast Topics - The need for the innovator to be near the process - Continuous to discrete shifts - Defining paradigms outlines what progress looks like - Easy to pay attention to artifacts, hard to pay attention - Hard to recreate processes - The 1000x rule of process innovations - Quality vs price improvements - Process innovation as a discipline - Need to take a performance hit to switch paradigms - How to enable more fundamental manufacturing process innovations Transcript [00:00:00] this conversation, I talked to Michael filler and Matthew Ralph about fundamental manufacturing process innovations. We explore what they are, dig into historical examples and consider how we might enable more of them to happen. Michael and Matthew are both professors at Georgia tech and Michael also hosts an excellent podcast about nanotechnology called innovation. Our conversation centered around their paper called fundamental [00:01:00] manufacturing process. Innovation changes the world, which I've looked to in the show notes and highly recommend the fact that they posted it on medium. In addition to more traditional methods, give you a hint that they think a bit outside the normal academic box. However, I actually recommend the PDF version on SSRN, which is not behind a paywall only because it has great pictures for each process that I found super helpful. If you're in front of a screen, while you're listening to this, I suspect that having them handy, it might enhance the conversation. And here we go. the, the place that I'd love to start is, to sort of give everybody a, get them used to both of your voices and sort of assign a personality, a personality to each of you. so if each of you would say a bit about yourselves, and the. The, the sort of key bit that I've loved you to say is to, to focus on something that you believe that many people in your discipline would sort [00:02:00] of cock an eyebrow at because clearly by publishing this piece on medi you sort of identify yourself as not run of the mill professors.   Oh boy. Okay. So we're going to start juicy, real juicy. So I guess I'll go since I'm speaking, this is Mike filler speaking. Great to be here. so I've been a professor of chemical engineering at Georgia tech for a little over 10 years now. my research group works in nanoscale materials and device synthesis and scale up. So for say electronics applications, Yeah. I mean, this article, which we'll talk about emerged from, you know, can I say a frustration that I had around electronics really is where it started for me, at least, that. We have all this focus on new materials or new device physics or new circuit. And I know your listeners are probably thinking about morphic computing or quantum computing, and these are all very cool things, but it seemed to me [00:03:00] that we were entirely missing the process piece. The, how do we build computers? and, and, and circuitry. And, and so that's where this started for me was, starting to realize if we're not dealing with the process piece, that we're, we're missing a huge chunk of it. And I think one of the things is that people, people miss that where within working within the context of something developed 50 or 60 years ago, in many cases, and it's it's was really hidden to a lot of people. And so that, that was where I came at this. Great. All right. So, yeah, so I'm, also a professor of chemical and biomolecular engineering at Georgia tech. my background is actually in process systems engineering. And, if you go back to the late 1960s, early 1970s, actually frankly, before I was a much more than in shorts, there was a, that was a real push towards. The role of process systems engineering in [00:04:00] chemical engineering in it really arose with the, with the advent of computing and the way that computing could be used to help in chemical engineering. And then slowly over time, the, the role of process systems engineering has become, I think, marginalized within the chemical engineering community, it's gone much over towards. What I call science and engineering science in a way from the process systems piece of it. And so, you know, as Mike would, would berate me with the, with his travails over, over what he was trying to do with nano integration and nanotechnology, I realized that what he was doing was describing a lot of the same frustrations I felt with the way that process systems engineering was being marginalized and pushed to the edges of chemical engineering with the. Focus more around fundamental discoveries rather than actually how we translate those fundamental discoveries, into, functioning, processes that then lead to outcomes that affect society. So for me, it, it, it [00:05:00] was a, it was a combination of, talking to Mike and then my own frustrations around how my own field was somewhat marginalized within the context of chemical engineering. Got it. And, sort of to, to anchor everybody and, and start us off. could you just explain what a fundamental manufacturing process innovation it's. So the way we think of fundamental process innovation or manufacturing process innovation is actually rethinking how the steps in a process are organized and connected together. And so that has become the paradigm which we have. we have set for fundamental manufacturing process innovation, and these innovations come in in different categories that enable us to put these processes together. And one of the examples of which for example, is. I'm factoring taking something that has been done together at one process step and separating it into two different steps that occur maybe at different [00:06:00] times or in different places. And by so doing, we actually enable us to make, a tremendous change in the way that that process operates. So it's really around. The strategy for organizing and executing the manufacturing steps and using a set of schema is to sort of understand how over history we have been able to do that. Do you want to add to that mic? Yeah. I want to take a step back outside of manufacturing. So one of the examples we give at the outset of the piece is not in manufacturing, but in shopping something that every single person listening to this can wrap their mind around, I think. and I still love the example cause it just kind of. I miss it every single day. and this is all pre COVID thinking of course, but the idea that say a hundred years ago, and a lot or Western societies, you would go to let's call it the general store. and you'd walk in, go up to the counter. And, if I have a list maybe, and you'd handle lists to the purveyor, and they would go [00:07:00] in the back rows of shelves and they'd pull off what was on your list and they'd bring it out to you, you pay for it and you go on your Merry way. And then, you know, several decades ago, this started to change, probably half century my ex ex ex. Exactly sure. The timing, but, to, to a model, where instead of a single shop keeper, having to interface with many individual, shoppers, it was now many shoppers who did the traversing of those aisles themselves, right? This is at least in Western society is what we are familiar with today as the grocery store or the target or the Walmart. And what you do is you. Trade one thing for another in doing that right. Instead of, the person, the, the purveyor, getting things for you, which from a customer's perspective is very nice. Right? you, you, you no longer have that, right. You're being told. Okay. He used to, yeah, he or she used to get it for you now. You're going to go and traverse the ALS yourself. But you do get something in return as the [00:08:00] shopper. And that is a lower costs because now one store at the same time can be, open to many, many people stopping shopping simultaneously. So, selection goes up, costs go down and there's a benefit for the customer, and the shopkeeper. So this is an example of a process innovation it's the it's still shopping, but it, it takes the old process paradigm and inserts a new one. Excellent. And so you, in your paper, you illustrate eight major historical, fundamental process innovations. And I would love to sort of frame the conversation by walking through them so that, a just because they're great history and B, so that everybody can sort of be anchored on the very concrete, examples while at the same time, I'll, I'll sort of poke at, The, the more sort of abstract questions and ideas around this. so the, the first, [00:09:00] the first one you talked about is the shift from the new Komen to the watt steam production process. So like, what was that? And, and why was that important? it was important because, what it did was it changed fundamentally how we could make power. So the newcomer engine had, the condensation of steam in the same vessel, as, as the, as what was being the vacuum was being pulled to enable the, Pulling of water up from the coal mines in Britain, turns out it's actually 10 mines rather than coal mines, where this was first developed. And what, what did was to factor that's one of a fundamental process schema factor, the two pieces so that the vacuum pulling and the condensation happened in different vessels. And as a result of that, he was able to increase the efficiency of the steam engine by, an order of magnitude. and, and through other innovations that then followed from that. The steam engine became [00:10:00] significantly more efficient. Now, what did that do? Well, the first thing it did was is it meant that you could pump water out of deeper mines, so you could actually now get coal out of deeper mines and so you can increase coal production significantly. The other thing it did, of course, it meant that for the same amount of power, the engine could actually get quite a bit smaller. In fact, it could get small enough that he could actually move itself on rails. And so what that also then enabled was. Stevenson and essentially the invention of railways without the steam engine. You wouldn't have railways with railways. Now, suddenly you can bring the coal, which you've now enabled yourself to dig out of Deepa mines. You can now bring that to manufacturing sentence. So there's a whole follow on set of innovations. And in fact, a complete reorganization it's called the industrial revolution. That is, that is based on these kinds of process innovations. And this was one of the most central ones, right? To that actual outcome was the idea of factoring these students. [00:11:00] Two steps leading to much greater efficiency in the way that a steam engine could be used. And, and that, there's actually two pieces that I think are fascinating about that. And one is, this phenomenon that you see over and over again, where what I would sort of call a continuous efficiency increases, right? Where it's. It was, it was like a fairly steady, increase of efficiency. But then, because as you point out, it eventually got efficient enough that it could power, a rail car that all of a sudden made this like discrete difference in what the process was actually capable. and I feel like you see this in school, many of the examples that you give, and I like, I just love that. And then the other piece. That I believe is the case. Is that, what was, what was, new Cummins apprentice, right? That I'm not sure about that actually. I mean, I think he was familiar with new [00:12:00] comes work. but I don't know if he was actually his apprentice or not in that particular context. W w and the reason that I ask is that, like, do you think that what would have been able to. Create this, this process innovation, if he hadn't been like, sort of actively working with the new Komen engine in the first place. No, I think the answer is, is that without that he, he, you know, you have that you had to have a starting point. And I think he understood, once, once he sold the starting point that, yeah, that was a, there was a way in which he could make this more efficient. the other thing about the, the, the efficiency and the scanning of efficiency is what we see in a lot of these fundamental process innovations is that there is a step change, but not only that, it shows how then off to that fundamental process innovation has happened. That, that can be this continuous increase, right? So there is, it unlocks an enormous potential to suddenly change the game in terms of the efficiency. So, [00:13:00] so the point being that say the original engine was maybe less than, than 3%. Maybe one, 2% efficient. And what, what did with the sort of next version was increased that by an order of magnitude, and then suddenly with that innovation now by better manufacturing, higher pressure vessels, et cetera, you could actually then go into an even higher level of, of efficiency. Not only that, but it drove the development of the sort of discipline of thermodynamics. Now you have to analyze the engines on their efficiencies and understand what could lead to greater efficiency in the future. And so, and you know, entirely scientific discipline was built on top of the, of innovations that were occurring in heat engines. Yeah. Well, I think there's an important point here in the efficiency discussion, right? And Matthew and I have chatted about this. A fair amount is that you kind of have the efficiency piece and as you're pointing out, Ben, it's really critical. Look it up for some threshold with a lot of these, but efficiency is kind of zero to a hundred, [00:14:00] right? And then you have the whole cost throughput piece. And as we show in the piece, you have many orders of magnitude possible gains on that side of the equation. and some of it goes hand in hand with efficiency, but I sometimes think that that is there's an overemphasis, often on efficiency.   you gotta get through the threshold and then recognize that the driving down of costs or increasing of throughput can happen, you know, a million X, you know, as, as for example, the planar process of integrated circuit shows it's more than a million X decrease in cost over time. Yeah. And, and this, this idea is that, that you point out about almost sort of like the process innovation, defining a paradigm that then sort of sets the pack for things is, is a theme that we'll like, let's, let's almost like poke it that as we, as we go through through everything else. and, before we move on, I guess the last piece, [00:15:00] sort of going back to. like Watts familiarity with the process in the first place. And sort of tying it back to to today is, I guess what, what's your take on sort of like the, the, the familiarity that the people who are working on cross as possible process innovations have with the processes now, Let's see, I probably phrased that a little bit weird, but, I guess my concern is that there's, there's more of a separation between the people that we expect to do the innovating and the people who are working on the processes. So, so yeah, this is a really critical point. I mean, what we have done in the modern innovation enterprise right, is we've split, so-called fundamental research with applied research. and, these examples, many, the ones that we give are really squarely between the two and they need both [00:16:00] to function. And so this is, for this kind of innovation or real. I think a real issue with the current way, things are set up, because it requires some knowledge of the science that's kind of emerging. It requires some knowledge of some engineering, and it's a matter of integrating these things. And it's not, so much, I think what the prevailing view of the world is, which is fundamental innovation gets developed and leads to some specific technology. It happens between the two. and so that's, that is, that is, That is a theme I think, and these innovations and it's something that I think today is harder to do. we could talk for a long time about why it's harder to do, but it's harder to do today. Cool. Well, we'll, we'll we'll. Circle back on that, as, as we get sort of closer to the present. so can I say one more thing? This is such a good example, but everyone knows the, the watt engine and we are very careful to call it, the watt, what do we call it? We call it the [00:17:00] walk process, right? We call it the what process, what process for energy generation or something like that. But yeah, we focus on the process and I think this is one of the reasons why these kinds of manufacturing innovations are missed all the time is that you focus on the engine, the physical thing that carries out the process and you're missing that. Oh, actually, what, what did was he factored these two steps? It's still a machine like new Coleman's machine, but in the end, what made it so powerful was the underlying process that. It carried out. And I think that that is one of the reasons why these manufacturing innovations are missed in manufacturing versus in other areas where process is talked about much more frequently. So I wanted to make sure, well, actually, as long as we're on that topic, I want to, sort of the talk like the. call out the sort of obsession with novelty in academia, where like, [00:18:00] if like, it's, it's really important to call out the, the process innovation. Because if you look at it just as like steam power, then you could sit, like you could sit a lot, like what's novel, like near your dinner, your power from steam, new colon generated power from steam. and so, so we, like, we need to. Really sort of pay attention to what's going on on the inside and like how that really different, even though on the outside, it does not look that different. For sure. And, and I think the point that we arrived at there is, is, is when we went back into deep history and asked ourselves, well, what do we call the ages of the past? And we call them things like the INH. We don't call it the smelting age. Right. Right, right. We could, we could call it by the process, but we don't, we call it by the thing that was made. you know, we don't, we don't talk, we talk about Flint's and we talk about Flint arrows. We don't talk about the ways in [00:19:00] which those flints were shaped into arrowheads, the flaking and the, and the, and the. But essentially those kinds of processes, which we don't even know in many cases how to reproduce and they lose that knowledge for, for many, many years. In fact centuries, the one example we use in the paper is that a Roman concrete, you know, we were able to, to look at Roman buildings, but we were not able to reproduce them because we had lost the, the recipe. We lost the recipe for making, concrete, with the, with the sort of dissipation of the Roman empire. And so in fact, we couldn't reproduce these buildings, so we could look at them, but we couldn't reproduce them because we had lost the process. Well, I think that that's so key to point out because it's almost what, like similar to the, the streetlight effect where, it's, it's so much easier to look at and point out and talk about the, the artifact. but it's, it's, not as legible what work went into making and even, even now, like, even [00:20:00] now, when you like, literally when everybody's writing everything down, it's still, there are so many little things that go into these processes, that are sort of illegible. and I think that it's. Easy to forget about that and think like, Oh, well, you know, someone wrote it up. Therefore we know everything that can be known about it. Yeah. History is kind of similar, right? The history. Yeah. We, we, we look back on history and we don't see the generator of the history. Yeah. So it's, it's often very hard to get our true handle on what it was that led to certain phenomenon. We, we, we look back and we start to come up with theories. and I mean maybe sometimes they're right. Sometimes they're wrong. We don't have, we have some ways of knowing and other areas. We have no way of knowing because it's, what happened is lost to time. Yeah. Sorry. This is kind of very similar in terms of the fleeting nature of processes. Yeah. And, and, and the fact that it's not easy, I think it should be born out [00:21:00] by anybody who's ever tried to read the materials and methods, sections of academic papers, because you will discover that very rarely do the researchers actually document the materials and methods in sufficient detail to actually reproduce them. There's a, there's a, there's something that they do in the lab that they just forget to write down. That's actually absolutely critical to make the, the, the, the material process work. you'll just discover that they, Oh yeah, we soaked it in methanol for 60 minutes. Oh, I'm sorry. We left that out. you know, there's, there's there are, there are easy to leave out these steps that turn out to be crucial, but they're not the final artifact that's being exhibited in the paper. Yeah. Yeah, there's this, there's this, sorry, there's this, this kind of discussion in, today in science about irreproducibility and we have this reproduction crisis and okay. Maybe we can be doing a better job, but I think a lot of it it's just, as Matthew's describing it's stuff that is not obvious you, as the experimenter are doing the experiment. You, even, if you wrote [00:22:00] down absolutely everything you thought you did. There are things you didn't even realize you were doing that were central to the process and it gets lost. And that, that to me is likely the main source of a lot of these, these issues. Yeah. I wonder what would happen if we actually had a system where you just videoed, literally everything that someone did in a process and then, like captured every key stroke on their computer and it would be it. Yeah, but , I wonder, I wonder whether it would just be completely, unintelligible or  whether there'd be something useful that came out of it. Just for the sake of time. I love, yeah, let's move on the second of eight. so, the, the, the second process you talk about is, the, the, the foreigner process for continuous papermaking, which I did not know anything about before I read this. so yeah, like what, what was that, why was it important? So, so here is it's a lot like, what,   Gutenberg good with the press. but, [00:23:00] paper prior to this innovation was Preston single sheets and dried as single sheets. basically a fully integrated process on one sheet of paper. And, what, continuous papermaking did was it took each of those steps and separated them into individual components. So that's a factoring schema, as we describe in the paper, where you first throw down the slurry of pulp. Right. And then, there's a section where you let the water drain. you consolidate the Pope down into something that's like a sheet, and then you push that sheet through rollers. and then you dry it, but each of those steps are different, right? The pulp deposition, the rolling and the drying are separated in space and time now. Whereas before they were more or less in the same space. And so that, that factoring allows you to scale up by orders and orders of magnitude, that production rate of paper. And so we talk a lot about Gutenberg's press, being central to mass literacy and it clearly [00:24:00] was. But, and, and we're not the first people to point this out, Tim Harford, who I like a lot who writes for the financial times and his own books, has talked about this where, you need to have the continuous paper. Manufacturing piece so that you could get those books to so many more people. And it was really both of those together that, that led to that. The other point I was going to make about that is, is it also revealed that we, that we were going to that as soon as we were able to, you know, produce, paper at large rates, we needed some sort of raw material that could also be produced. At large rates. And so this idea that you are going to continue to use rags as the, as the input, suddenly became difficult. And so people had to scout around for other forms of fiber that you could use. And that's really what led to the whole, you know, creation of, of the pulping industry that, that takes what. Well on the face of it, a tree doesn't exactly. Look like paper, takes a tree and turns it into something that you can make a make paper out of. [00:25:00] So again, it's this upstream and downstream it's the, the downstream effect is, is. The societal mass literacy, the upstream effect is, is the, is the creation of a, of an entire industry around, you know, turning trees into, into pulp. and so some people might disagree with doing that, but, but the bottom line is, is that's what enabled, the, those two pieces to be driven was the creation of the, of the, of, of papermaking in the, in the middle of that. Yeah. And something that. So, did you have a sense of how people were thinking about papermaking? Oh, for, before for generic came up with process that is like, did, did they realize that it should be possible to make paper more efficiently? Or was it just like, just that's the way it wants? because I feel like so many of these process innovations. [00:26:00] There are people just sort of accept whatever level of whatever process we have. And we're like, Oh, like that's the way it is. Yeah. Maybe we can make it a little better until something new comes along. One of the things we were careful to do in the piece. And I'll be honest because we're not historians is to, to try to stay away a little bit from like the, the, the driving forces. Right. And kind of what people were thinking. I'm really focused on the mechanisms. And that's one of the things, you know, I've really enjoyed learning from people who are in the, the progress studies community, that emerging community. in general, I find that they really know a lot about history and that's great. and we really wanted to make sure we could pay attention to mechanism at the, at the actual innovation level. and so I guess I'm saying that as a long winded answer to say, I don't know how they thought about it. but, you know, but I think that there's kind of been a shift over time. you know, Matthew was sending me, show me something from scientific American recently. [00:27:00] They just, what was their anniversary? Matthew? A 175th. I can't remember what that is in Latin, but, but it's, it's a very long and complicated word. Yeah. But  DECA. Yes, exactly. Quickie and no versary. Yes. It's something like that. I buy, if I pumped up, I could go get my issue and they have it in there, but, but it is, it's quite a complicated word. That's all I remember. And they have a article in there talking about the shift in how people speak, spoke about science and engineering. And, h hundred years ago, there was this kind of more engineering processing, which that was far more common. And then around at the time of world war two, it kind of shifted, be more about science and the emphasis on science. At least as far as that magazine goes, but I think the magazine is probably fairly representative of the endeavor as a whole. And so, yeah, that's, that's kind of fascinating. You're saying, did they appreciate, whether the process could be [00:28:00] better? And my gut feeling is they maybe in, in the 18 hundreds, they appreciated that it could be better, more. Did they have an appreciation for how much better that's that's probably dubious. Right? I think most of these, if you went back and asked the original innovator. Did you know, you were setting us on a pathway or a trajectory that led to, you know, the world, as we know it today, I think they'd probably be like, wow, no, I did not expect that. I just was trying to make an extra buck. Yeah. But I think it's like, it's actually almost like a powerful, admonition people to sort of like, keep in mind the different schemas that you lay out and just to like walk around the world. Saying like, Oh, like, could this, could this apply here? and it almost like gives you a bit of humility that it might be possible that like these could always happen. that's for us, that's kind of [00:29:00] emerging from doing this and we're, we're continuing to work on, on, on next pieces basically is a kind of a thousand X heuristic. Whereas you have a two D technology today and you ask yourself, can I do it a thousand X cheaper or a thousand X faster? with the way we do it today? if the answer is yes. Okay, great. And you're really competent that if the answer is no, it may be time for a process innovation. Maybe to us a thousand X is, is sufficiently beyond someone,   you know, giving you the pop out answer. Of course, we've made progress in the last 10 years and I expect more progress. Well, that's kind of a cop out answer. A thousand X is quite a bit faster or quite a bit a higher throughput. So that's, I think that's a good metric for anyone working on any technology. and I think COVID COVID is a great example of what we've been experiencing in the last, however many months. It feels like two years, and you know, we needed rapid vaccine [00:30:00] manufacturing. We needed rapid testing, basically a thousand X faster. And we didn't really have that capability in hand and people have done tremendous work right in the, in the intervening months to try and get us a lot closer. I know Matthew has done some work on this. but when the whole thing started, we hadn't really thought about it so much yet. How could we speed up this a thousand X? And so for us, it's a pretty good heuristic is that, is I like that a lot. That is a very powerful heuristic. and it's also like it's, it's aggressively ambitious, which really, really does speak to me. cool. And so, let's, let's talk about the, the Bessemer process for steel manufacturing, which, His age is really cool. everybody listening, go check out the pictures. so, so what is that and why was it important? So again, I think it was important because what it led to obviously was a, was a, a better steel and, steel that you could make. Again, as Mike has pointed out, you could [00:31:00] make,   the steel significantly faster than the existing processes. and what it came down to was was, was a recognition that actually to remove the impurities from the steel, you, you could blow air through the steel. That that would cause a reaction that would cause the steel to heat up. Whereas if you think about blowing edge generally, if you blow on things, it makes things colder. So this idea that you would blow air through something to make it hotter was was, was obviously a, you know, something you do in bellows and had been at. Had been thought about in terms of bellows, but actually literally blowing the air through the steel was, was not something that had been done and, and combined with that idea was also this idea that by removing all the impurities and making essentially something that was, that was pure. And then adding back dosing back impurities after you've purified. So that you had control over the composition instead of attempting to stop right at the moment when you had exactly the [00:32:00] right amount of carbon, for example, in the steel, that, that was then another powerful idea that came about. So, so the Bessemer process really. Had a profound impact, both in terms of, again, how much steel you could make in a given amount of time, because it increased the rate by this heating, and then also the control of quality by this site, this very counterintuitive idea of removing all the impurities and then adding something back in order to get to the, to the final product that you wanted. That led then to, to much stronger steels than had been capable of being produced previously and much higher quality control too. I mean, that was a key piece of that. And so actually on that point, you, you, you, you note that the, the best word process led to, three order magnitude, three orders of magnitude increase in, in steel production. And, I'm not, this is something that I, I always wonder about with the, these process innovations that both make it cheaper and [00:33:00] increase the quality, Do you have a sense of whether the order of magnitude increase was primarily due to sort of like moving down the supply demand curve, where there was just like people, you know, because the see was cheaper, they would consume more of it or was it primarily driven by, by new applications of the higher quality steel? obviously it was both, but it's interesting to think about like, which of those. Ends up being, I think the high quality in this case was a, was a very critical factor in the, in the, in the equation poly, because one of the things that opened up was is it opened up the idea of making steel rights, as opposed to what was made from iron rails and steel rails were able to bear a huge, a significant amount, more weight. And because of the fact that they could bear more weight. Now, suddenly again, you could increase the distances and volumes of which trade could happen. And so this, this was one of the reasons why, for example, you could spread [00:34:00] all the way across the United States because you could connect the resource rich West to the, population rich East. with, you know, now a much more powerful, communications network driven by, you know, the steel rails that you were able to produce. So I think that a lot of it was, was, was, you know, bound up with this idea that suddenly now this new application came, came about, that you could do much as the steam engine sort of. When you were able to move the steam engine with its fuel, you now actually could even start that whole process going. So, so again, it's this knock on effect, here, follow up on that and just make the connection for everyone that the efficiency threshold we talked about with watt is very similar to the strength threshold Matthew's talking about with steel. Right. And cross that threshold to a new material, a new strength threshold, but then it was really this driving up production, driving down costs by orders of magnitude. And yeah, we, we got better [00:35:00] higher stress, but you're not going to change the strength of something by a million times. Right. Right. So again, it's, it's kind of these two columns, the efficiency or performance column, and then the manufacturing scale column. Right. And, and going on to the next process in the, in that, in that, in our list, the calorie cracking process, again, you have that same, juxtaposition. You have the fact that by factoring the catalyst regeneration from the production of the fuel, you enabled yourself now to have a continuous process. which enabled you to increase the throughput in terms of the barrels of oil that you could, you could bring through this process, you enabled it to be increased significantly, but also this innovation was happening at a, at a time period where aviation in war was a significant factor and the quality of the fuel that you actually produced. Out of the, out of the catalytic cracking process was higher than the quality of fuel you [00:36:00] produced just by distilling off a certain fraction of the, of the crude oil. And so what you were able to do essentially was, was have a higher performance aircraft engine that was quite significant in terms of its power to, to wait. A ratio in terms of what it could deliver. And so that gave a, you know, allied aircraft, actually a significant boost in performance by having this fuel available to them. And again, provided a significant driving force to scale up the process, which again, went up by a factor of at least a thousand, over the course of, two or three years. Yeah. It's these numbers like whatever, would it be? Say these numbers it's still sort of crazy because it feels like. So many things, focus on like getting, you know, like 10% more efficiency. whereas like, like truly getting to a thousand thousand Xs is like mind boggling. So, I believe this was the case for catalog cracking, and I know that it's the case for many process innovations, [00:37:00] where, at first the, the innovation actually makes the process less efficient like wall while you sort of are figuring out how to get everything working. And then, once you do that, then it makes the whole thing skyrocket. and so I, I guess, The question is like, do you have a sense of how people sort of got past got out of these, like these local equilibria where, you know, if you went to someone you're like, Hey, I want to think less efficiently so that eventually it will become more efficient. so like how, how these, these things even got through. I'm not sure I have any great answers except perseverance. I mean, I think a lot of this stuff comes down to, to the inventor, really, you know, from their experience from their early work on, innovation recognizing in themselves and in their work, that there is the potential, even if right now it's not quite there. you know, [00:38:00] Bessemer was the same thing where, you know, you first,   licensed the patent to people and they could reproduce what he did. So the separation of full separation of impurities came later, so that people could reproduce it. So that was a reproducibility problem in the beginning, not so much a strength problem. and, yeah, I don't know. I think a lot of this just comes down to the person, saying I see it just like any of today's, you know, visionaries we talk about in the innovation space and then just keep hammering on it. Yeah, right. I mean, there's counterfactuals, right? So sorry, Matthew. I mean, it was just, we can't, we don't know the ones where the person didn't hammer on it and it never came to fruition. So it's hard to know. Right. I'm going to string together, you know, a few thousand laptop batteries and stick them on the bottom of, of a, of a car. And that is going to create a company called Tesla. Right. so, so, so the answer is, is, is it's very hard to predict, obviously a and B the T's about a lot of it is about [00:39:00] perseverance and certainly Elon Musk will we'll talk at length about the fact that he, he. He's thinks his quality is perseverance. And that it's, that that's, that's very important in this context or I'm going to have a rocket that goes up into the air and then eventually pirouettes and lands on a, on a platform floating in the middle of the seat. so these, these are, these are, you know, innovations where, where certainly the, the individual involved has plays a pretty signature. If you can, too, to the perseverance necessary to get it to that stage. But, but it's also important to recognize, right. That it's not perseverance along the existing trajectory. Right. It's stepping aside trying to establish a brand new trajectory and pushing on that. And I think sometimes those, those two are missed a lot. When you use the word perseverance people, miss that. It's it's, it's also this stepping outside of the existing trajectory. Yeah. I I'm, I'm particularly interested in whether we can like. Create Mehta innovations in sort of [00:40:00] roadmapping out what that stepping aside looks like. So instead of just, I'm saying like, okay, we're gonna go this other way. Like really sort of saying, we'd go this other way. And like, this is what it will take to get this too. Do that, that thousand X to hopefully make it easier for, these individuals too. So just convince other people that they're not crazy, when, when they don't maybe have a couple of million dollars to go off and like blow up rockets on an Island. Yeah. It's I think it's, it's, it's hard to figure out. I mean, look at, look at the bottleneck that emerged that Matthew was talking about and continuous paper manufacturing. I, you know, I think I'm pretty sure when they started, developing that process, they didn't expect that to be the next roadblock. Right. but it was, and so, so again, this comes back to the perseverance thing. I think, I think you can try and outline it stuff, but there's going to be roadblocks. And you probably should. Right? Don't just, this is not just serendipitous. I think there's a certain kind of [00:41:00] force that comes with these things that people push on the innovations. but you know, recognizing that there's going to be one new bottlenecks that emerge, but not to let those discourage you and that, you know, this, they think of them as, you know, motivating new science and engineering and, and that's how I view a lot of this stuff. And, and yeah, that's what I would say, Matthew. Yeah. And, and actually on the note of sort of unexpected bottlenecks, I think that that's another key point is that, like so much science and engineering does come out of trying to implement things and then running into bottlenecks that you can't even expect. Right. Like, instead of trying to like, imagine everything through,   cool. So just in it for the sake of time, let's talk about the, the planar process for integrated circuitry, which like arguably, has been the driving force of at least the second half of the 20th century. [00:42:00] Yeah, and I think it's often a missed, right. We talk about the integrated circuit and information technology, and miss the fact that there's this process underlying it, that has enabled us to interconnect. I mean, it's in certain settings, it's hundreds of billions of transistors now. Right. And so, in the early days, everything was discreet. just like everything else, everything was modular and discrete components. Yeah, transistors were all sold as single tracks. I would tell them that way. Yeah, exactly. No, no. Yeah. I'll, I'll take three. And, they, P people have the idea of interconnecting them. We, we were building computers. We recognized how hard it was to take these modular components with the technology of the time and integrate them. the other thing that was happening at the same time was some science. And actually, this is one of the cool things about the planar process was that there was science going on. Where there was a recognition that embedding these electronic devices all the way inside a single crystal, Silicon wafer gave you much better performance. [00:43:00] And so it was kind of the realization that you could jam these things inside the top surface of a wafer. There was also surface passivation, for those who are familiar with this process, that was key to making the devices good once they were embedded, but then once they were inside the wafer, the top surface remained flat. but they were embedded. Right. but the, the technology before that was what they used to call Mesa technology, where the transistors were kind of built on top, like mesas and Utah or Arizona, but putting them in, okay. The wafer left the top surface flat and much easier to interconnect using this development of photo lithography. And then it went from there. and, and so that, that was the key innovation, was this extreme parallelization basically. of embedding, not just a single transistor, but thousands and then millions and billions of transistors. And I want to also point out, you know, The, the, the trajectory that, that set us on as described by Moore's law, [00:44:00] this idea that we, decrease the size, increase the number at a, at a rate that's, gives us Moore's law and, and potentially that's slowing down. that's another one of the features of process innovations in many cases is that they, they eventually will run out of steam. and, I, I think we're starting to see this with the planar process, where it's had a tremendous runway. but we're getting to the point where the underlying assumptions of it may no longer not, they're not going to go away, but that we may benefit from an alternative way of building circuitry. Yeah. The, these processes they're, their effects tend to fall as you point out, tend to follow S-curves. Right. So that's, we're sort of, you see it when you start to like hit the top of that. S-curve that's when you need to think about like these fundamental process innovations. I think we've been at the top of the S curve for a long time, the processing, I mean the prediction of the [00:45:00] end of Moore's law. And I say that in quotes, it has been around for decades and, always been able to get around it. and that's impressive. It's a Testament to the scientists and engineers that work in the industry. But, you know, you can only get so small. yeah, that was an interesting thing here about biases also that, the planar process biased us towards miniaturization, right. biased us. But one of the central tenants of the planar process is perfection at every step. Once you put transistors in the solid wafer and you can't pull them out very easily, or really you can't, if they're defective, You're now in a world where every transistor up to these tens of billions, we're talking about better, be really close to. Perfect. And, so what that drives you towards it incentivizes you to, not change too much about the process and find a trajectory that allows you to still increase performance. And that trajectory was just shrinking thing. Don't change the materials too much. Don't change the [00:46:00] processes by a large amount to shrink stuff. And that was very synergistic, right? That's Moore's law and it's a tremendous success, but it did incentivize us down that pathway. And it's a bias that process innovation set up and that other innovations would set us up to go in a different direction. Yeah. Yeah. That's the, the counterfactuals are fascinating. And, and, and another thing that I think is really interesting about the, the planet process. and, and it happens in other places where,   horny, who, who came up with it happened to have had experience with printing, if I remember correctly. And so you tend to see these, these situations where like someone who has experienced in like a completely different discipline. Just so happens to be interacting with the process and say like, Oh, Hey, perhaps this thing from this other discipline can be applied in this process. and I wonder if there are that, like, do you have an incentive, like sort of better ways to get that to [00:47:00] happen? well I do, which is to create a specific, discipline around, this. So, so I, you know, if I'm going to take a very strong position here, I would say we need, we need a discipline of process studies. where we do try to lead, you know, young minds because ours have too inflexible at this point, across these different kinds of examples and allow them to see the connections between the different processes in different technological domains. And that may be, although that's not a, not a, a pedagogical, certainly that will be this opportunity. They will then connect these ideas in some other manufacturing domain, or even across. for example, service domains, I do see that there is this general principle around process innovation, manufacturing, so potentially, possibly founded on the schema that we've, that we've outlined that could enable people to see these [00:48:00] connections and start to use ideas from one process discipline in another. And so factoring could be sunny appears as we've said, in, in services. And it could appear in other manufacturing domains as well. So, so I would advocate for a borough, sort of a discipline that's built around this, these ideas so that we could lead people to make this more efficient in terms of our discovery. Wait, Mike's refraining. No. I, I, I agree. I think probably the things we're talking about or the discipline Matthew's talking about, I would liken it a lot to the role mathematics plays, right? Mathematics is its own discipline. it's separate, but all of the engineering and sciences use it. and so this is kind of similar and we were very careful, to pick out to process innovations that span the gamut. We really, we think, I think it's hard to argue that any of the eight we picked, were not really impactful. but they, they really [00:49:00] span a whole variety of, of disciplines kind of showing that it really is everywhere, but we don't recognize it as, so as pervasive as something like mathematics. and, I, I don't want to be heard as saying, well, we're as important as mathematics. mathematics has been along around a long time, but it's something akin to that. Right? I think the one place that I think it's different and would need to be adjusted somehow is that there's there isn't a ton. I mean, there are some, but like there isn't a whole lot of feedback loops between. Matt and the, all the other disciplines that math, enables. so the, so like occasionally you'll see like a mathematical problem. That's been inspired by a, a sort of more applied problem. whereas I imagine in some kind of, process innovation discipline, you really do need to have these, like these feedback [00:50:00] loops. Between, the, the discipline and the, and the sort of like the effective disciplines and sort of like setting up those, those feedback loops seems, important and harder. Yeah. Discipline is hard. Yes, absolutely. And I think with mathematics, we may have been doing it for so long that we don't see it. Right. I think, I think, you know, if you think about astronomy, for example, astronomy uses uses mathematics falling objects, is one of the inspirations for a lot of, a lot of mathematics. And so sometimes I think we know that mathematics has become the problems in mathematics have become so embedded with each other in some sense that we don't see that we need to create that, that, that feedback loop. Right. whereas, you know, geometry, for example, is another one, where, whereas in, in process, I agree with you. It's still something that I think is despite us having, you know, used [00:51:00] processes since we were, you know, since we were time in Memorial, right. We haven't really set up that as a formal means of, of analyzing the way we, the way we do things, right? I mean, that's, that's, if you like, it's the science of the way we do things. and that's what we need to, we need to think about and actually put that out. I'm going to argue against myself and, and there's, there's tons of examples of math, being inspired by, by applications where like, look at information theory, right? Like the whole reason that. We have information theories because they wanted to see how much information they could cram in a single copper wire. So, so I will actually rescind that really. Yes, I think so. And I, and I think the other thing there is, is look how impactful, what is the impactful mathematics? It is actually, I mean, in some sense, almost by default, but it is the sorts of things where now, you know, where information theory was obstructed away from the app, from the original idea. And [00:52:00] now has come back to influence a whole range of. Of of applications beyond that. And that's, that's the, the value. And I think that's the same thing with process innovation, right? If we could abstract away find the, find the, the, the core of that as a discipline that could then come back and influence a whole range of, of the way that we do things. Yeah. And, and so, so I do want to be respectful of both of your times. so, what I will do is encourage people, listening to go look, read, like, read the paper, to discover, the, the last three, fundamental process innovations. And the way I'd love to close is, sort of beyond reading this paper, like, how do you think that we could. Get beyond, reading the paper and Vicky about a new discipline. Like what, what are ways to get more of more fundamental process innovations? Well, I think we, we, at least in some, [00:53:00] some amount of our innovation sequence, need to recognize that there are things that happen. Within the Valley of death. So, you know, we talk a lot about the Valley of death as something to cross. first of all, Valley death is very manmade because we've split fundamental science and applied science and processes. An example where the splits are really bad thing. And instead of crossing it, we should look at at it as we want to go into it and hang out in it. Yeah, right. I think this is one of the issues with it. This course is it's all about something bad versus no, it's actually where we need to be. for, for certain innovations. you know, I think you think about the Nobel prize from this last week for CRISPR like that, that is squarely in my mind, that is a discovery. It's a fundamental discovery and it'll be translated that that's kind of the conventional view of things, but there we are not doing ourselves any favors by. By having the scale too [00:54:00] much on the fundamental side and that we should at least rebalance a little bit and force ourselves down into that Valley. Just hang out. Yeah. Love it. Matthew, what do you think. Yes. I think the, the stepping away from some of the things that we take for granted, like electronics manufacturing, and, and considering Mike's question around what would make this a thousand X,   better in some dimension. Is is, is really the way that we can, that we can make progress. And again, your point was very well taken, which is sometimes when we get better at something, we're going to get worse at something else. Right. And, and it could be that we're going to have to accept that we will not have circuitry that behaves as, as, as well, or as fast as it did previously. But now we may have gained in some other dimension. So again, it's about taking the blinkers off and not saying, okay, we have to have these particular metrics [00:55:00] always be improving, but think about how through processes. We may take some other metric and now make that significant it'd be better than it was previously. And then. Hang out and see what happens as Mike said, because by doing so, we may in fact then lead ourselves to improve other areas as well. And that, that could then lead to the kinds of scalings we saw with making steel, making paper or making energy. And so that's what we really need to think about. Here are my key takeaways. Sometimes you need to go down, go back up. The interplay between processes and paradigms is absolutely fascinating. And we don't talk about it enough. And finally, we need to spend more time hanging out in the Valley of death. [00:56:00]