“So my overall interest in technology in general, not just AI, is how it supports human potential. And so for me, that’s defined as people being healthy, happy, and really able to fulfill their purpose and potential.”

– Nichol Bradford

What you will learn

• Exploring the role of AI in enhancing human potential
• The concept of the ‘Human MESH’ for mental and emotional health
• Redefining work and human uniqueness in the AI age
• The importance of soft skills and unique perspectives
• Successful AI implementation through human-centered approaches
• Investing in technology for mental health and performance
• Addressing global challenges with advanced AI

Episode Resources

• Artificial intelligence (AI)
• ChatGPT
• Human MESH
• World of Warcraft
• Blizzard
• SHRM
• Apollo Neuro
• Accenture
• Generative AI
• Predictive model
• Machine learning
• Living Networks by Ross Dawson

Transcript

Ross Dawson: Nichol, it’s awesome to have you on the show.

Nichol Bradford: Thank you, Ross, I’ve been wanting to talk to you for a long time. So when you reached out, I was really thrilled.

Ross: Yeah, oh, it’s very strong alignment with their messages in this, humans in AI and potential. So I’d love to ask you to give me your frame, and describe how you see humans in an AI world.

Nichol: So my overall interest in technology in general, not just AI, is how it supports human potential. And so for me, that’s defined as people being healthy, happy, and really able to fulfill their purpose, to fulfill their potential. 

Specifically, I spent a decade so far looking at technology, specifically as it ties to what I call the ‘Human MESH’. So mental, emotional, social health, and human performance, and how we can leverage technology to support the Human MESH. And so there’s a long line of technologies that have applications there. And I started one of the first communities dedicated to fostering companies in that area. AI is only the most recent entrant into technology that can allow us to heal, grow, and thrive.

Ross: That is awesome. This goes a little bit back to my book Living Networks, which came out in 2002. And at the time, if back in the 90s, everyone, you say, ‘oh, tech, that’s for geeks sitting in basements’, and I’m saying, ‘well, no, that helps us connect to, to be more to think better.’ And other people didn’t quite see it at the time. But I love the mental improvements around mental health, as well as the ability to think and the emotions. And, you know, there’s been some things you know, it’s not a one-way street, as in, there’s some positive and negative potentials from technology, but the positive potential is so, so massive, and so wonderful to see you on that journey.

Nichol: Well, you have been ahead of your time, as well. And so how I followed you was initially seeing your work on just really sort of how to manage the cognitive stress of modern life, and then the way that you have thought about networks and other things. So I’d love to know, what is your definition of human potential?

Ross: So I don’t have a nice acronym today or a structured one, but it’s, it’s who we can be. And this comes back to the becoming, you know, we are aware, you know, it’s not just being versus doing, you know, it’s about becoming, that is what it is to be human is to always be different. 

I often reflect that it’s this paradox, we are the one person from when we are born to when we are teenagers, when we are older, we are one person yet, in fact, we are completely different people, all of the cells are different, the way that we think is different. So we are in the process of letting go of the old and embracing the new, and not enough people are too many people are static in their lives. But we are becoming more and more and I always think of it in terms of how we could be so many people, every one of us could live a hundred wonderfully different rich lives and discover what we could do. And so for example, I’m a bit of a repressed musician at the moment, you know, I think I have a lot of musical potential, but I’ve just been busy doing other things. And I want to come back to that. And there are many other things where I’m sure that there are talents and capabilities I have, and you have ones you were not even aware of. So the potential of everybody is vast. We can make choices around what we do to try to discover, what it is we are best at and that’s in a way the human journey. So when we talk about technology to enable that, I mean, particularly when I see aI think ‘wow’, well, our ability to think about and imagine to enable us to do things being enabled by AI is just mind-boggling. So this is something we got to do.

Nichol: Yeah, I think also, you know, one of the things that I believe AI or the AI Age to make it broader than not  Because there’s so many different types of AI, but the AI age, it’s going to force us to finally answer some of the fundamental questions that humanity has been asking for a very long, like, who are we? Where are we going, like what is what are we doing collectively and individually, and also as AI, as Generative AI starts to reshape work, and starts to eat into the things that we call work, but might not really be work. And we could talk about that in a second. But as it starts to eat into that, and the what’s left part, things that are going to make people stand out are going to be things like really being in your zone of genius, like really being in the thing that puts you on fire that you’re so excited about. So you can bring that kind of energy, I’m really having a unique perspective. 

Yeah, having a truly unique perspective, really having developed your taste. Because in a world where almost anything can be generated, the ability to have taste is going to be very important, especially once everything gets generated, people start to follow curators, and communities with greater dedication, they’re going to be following taste. So it’s like having a unique point of view, having clear taste, and understanding what it takes to develop your taste, which means you have to co-pilot you cannot autopilot and have taste at the same time. 

And then also these things that people have called soft skills, that are about humans solving problems together in a better way. Really being good at those things. So that when the humans are in the room together, they’re able to make really high-value decisions, taste statements, points of view to solve problems, whether that’s the problem of a new market, a new product, or a new competitor. And so that’s sort of like where compensation is going to go to and value is going to go to, and so it’s going to force everyone to, to change. It’s kind of exciting.

Ross: I’m in violent agreement with everything you’re saying. Ever since I was young, I’ve believed that we are all far more unique than we know. And that basically we are all different. But society, we go to school, and we watch TV programs, we all get indoctrinated to be, we kind of look and feel and act or pretty similar, but that society heavily indoctrinates us. That’s my school’s job function is to make us fit in society. And so we’re all you know, as I was talking before, that human potential, I mean, it’s that direction of that potential and who we are is so different. We are so unique. And I absolutely agree that this is a time when our uniqueness has more value than ever before. The stochastic parrots of the generative AI, they are, they are homogenized. Now, they are averaging in a way that everybody says has a great value in that. But to complement the uniqueness of individuals. And so that’s one thing which we need to do for ourselves. And how we structure organizations, I think that’s one of the things that employers need to do is say, ‘All right, well, I’m not looking for a box to put a person in, I’m trying to find a person who can do things which nobody else ever does to complement the these tools.’

Nichol: Yeah. You know, there is, are you familiar with the dead internet point-of-view? So it basically says that today, more than at least half the traffic on the internet is bot traffic. Twitter is filled with bots, and there’s a lot of activity on the public Internet. So they call that the dead internet, that it isn’t as bouncy as we think it is. Because of what so much traffic is and then if you think by this time next year, or by the end of next year, pretty much everything you see online will be generated in whole or in part which means that it’s either that almost everything is synthetic, or close to synthetic. And then it does things like, by the end of next year, if you have an ugly website, you just don’t care. Like you really don’t care, because there’ll be too many tools to have beautiful websites with beautiful images. And so it was Ethan Malek who pointed this out. And I was like, ‘oh, boy’, was that, right now, only a small part of the world’s data is on the public Internet, almost everything is private, everything isn’t, you know, corporate intranets. And, and, you know, personal hard drives, and, you know, or personal drives, even if they’re on the cloud. 

And so, it’s this idea that if everybody in a company is co-piloting, and let’s say half of them are auto-piloting, and if the generators or the LLMs, are really only your mediocre work, your average, then there’s a certain point if a company is not paying attention, then they just become by definition, marginal. And so you could have, instead of the dead internet, you have a dead intra-net, you have a synthetic intra-net, you have a marginal thing, and it sort of forces you to redefine what is work, because right now a heck of a lot of work is someone checking, the weekly report that someone put together about what everybody was doing, and we call that work. 

But that’s actually really great. It’s okay for that to be marginal. That’s like being averagely written doesn’t matter at all, because that’s not the important part. And so we’re gonna have to redefine what we call work, because a lot of what we call work, and what managers use as proxy for work, was just sort of seeing if people were working, versus the stuff that actually really drives the business.

I’ll give you one case, and then I’m going to ask you for a case that you’ve seen, but I was talking to someone who did an implementation with a grocery chain, that was finding that they were losing 80% of their new hires. And so he did a predictive model with them and included their values, and also human-decision factors and, and a bunch of things that he consulted. And so they redid their predictive model for how they sort of looked at applicants. And they were able to in about six months, flip that, that 80% loss into an 80% retention. And then a few months later, they started losing people, again, not the same amount. But they started having a noticeable loss of new talent. And because they targeted high performers. 

So a lot of times people think about when they think about AI, they’re only thinking about corporate jobs or whatever. But like you can have a high performer in a warehouse, you can have a high performing grocery store, at a gas station, like a hardware store, like all of these things. And so they changed how they looked at people to pick high performers. And once they started hiring all those high performers reliably, they started losing them again, because their managers were used to managing bodies. After all, the grocery store used to be or the chain used to be like, ‘Do you have a pulse? Great’, and so they had a bunch of managers whose management skills were all around managing people with pulses, versus managing people who took a lot of pride, who wanted to finish a job, who wanted to solve problems on the floor of a grocery store. We have a second order problem that a lot of people are not tracking, that, you know, that’s in sort of like the definition of work, and leadership and management and all of these things that’s coming right behind once we clean up all this lower level stuff and use AI for it, then we’re going to hit back at just the people part. It’s still about people in the end, so I thought that was a fascinating case.

Ross: I think these are the shifts so it’s not just oh, we plug in AI here. Then you’ve got to the same as you had before, except with a bit of AI in it, you know, it does reconfigure everything. And the thing about the humans plus AI workflows, more broadly than the changes in the structure of the organization, amongst other things, you know, who you need there. I think that’s a, that’s a really nice example for thinking through some of the implications.

Nichol: Yeah, I’d love to hear something that you come across in your travels, a case of an implementation where it went a little bit differently than people expected. And there were like of like some non-obvious thing about because everybody’s at the all the people that you and I are interacting with, they’re at the beginnings of their generative AI implementations, they might have done a bunch of predictive and a bunch of machine learning and that kind of thing, but like, in the front, and workflows, it’s new. And so I’m just curious if there’s anything you’ve seen.

Ross: Just take one example, in a financial advisory firm, where they were starting to use AI for basically supporting marketing efforts. So doing research, all social media posts, and things like that. And I think one level is sort of more productive and effective, you know, these are not core jobs of servicing clients. And that relatively small organization, so they’re able to just take things off the plate and push that, but I think some of the findings from that were simply that some of the slightly higher order capabilities of the system started to be seen by, you know, advisors and so on, and saying, Well, okay, I can get in getting the feel for how they were used in sort of a fairly, very specific contained model, as in, you know, generate LinkedIn posts, for example, or consider how to think about different marketing segments. And then starting to extrapolate some of the use of that, in these contain cases and in terms of how they were thinking about investments and structuring and communicating to clients, and that, which is another use case for where I suppose those that learning from one quite contained specific thing to gain skills to apply that in other parts of the work.

Nichol: Yeah, it’s exciting to see people trying things and coming up with new use cases. What are the things that we’ve really seen? Some of the research that I’ve read with Sherm, or that we’ve done with Sherm is that Society for Human Resource Management. I’m their executive and resident working on human and AI enablement, which is really exciting because they have 340,000 members who take care of 325 million workers and their families every week. So I love that ability or like that vector for the impact of really figuring out like, how do we actually bring AI into organizations in a human-centered way? 

But one of the things that we’ve seen is that in a lot of the early cases, where people are having success, the winning combination is a partnership between the CHRO and the CIO, where the two of them, link arms, and realize that they truly are peanut butter and jelly, that they truly, truly, truly make the difference. Because it allows the HR person to have the kind of fluency that allows them to only need to have a kind of fluency that allows them to be in the conversation, understand the basics, but they don’t have to have two decades in IT to be effective. They’ve got their partner, and then what the, with the CIOs, who they’ve seen every wave from cloud to, like everything, and have a lot of lessons learned on implementation. What they’re saying is that when they work closely with the CHROs to raise the AI flu wouldn’t see, then that process they actually start to get the bubbled up use cases, from the the employees about like how their people within their business and their business model actually want to use it because like, what a manufacturer is going to do is going to be very different than what a services organization is going to do. And what a company and one market in the same category is going to do is different from someone who’s working in Latin America. You know, and so there’s, there’s not going to be a cookie cutter, sort of set of like real workflows, when you get in the nitty gritty for differentiated companies. It’s sort of like I think it was McKinsey did there, you know, make no, it was take, shape, or make as how people are using tools. And they didn’t necessarily say this, because this is sort of a downstream effect. 

But everyone starts out taking, and you know, mixing things up. But if you stay in the taking category, and you don’t shape, like build instances on top and do your own thing. So if you don’t like, build on top of the, you know, the big utilities, really, then you have no differentiation from your competitors, zero, because they can all do the same thing. And so eventually, even people who start out, they have to at least move into shaping, to be able to competitively differentiate, and maybe move into making depending on how specialized something is, not making a foundation model, but making something specific to their business eventually. And it’s not a good idea to run here until you know what you need. So you shouldn’t run to make it, right? But there is like, start out by knowing that you at least have to move to shaping and get smart about figuring out what you might make if your business specifically requires that.

Ross: Yes. So I wouldn’t want to come back to that. And perhaps you can give some high-level advice to leaders and people or organizations that want to learn about you. So you are an executive and resident side of human resource management, you are a keynote speaker, and you communicate. You have to understand this stuff, and you have a mission to help you and your potential. So what would describe more about what it is you are doing to change the world?

Nichol: Like, like, how did that happen?

Ross: That’s about how you got here? Because I’m sure that will be well, that’s another story. What are you doing to change the world? What’s the day by day practice? What are you? How are you? How are you changing things?

Nichol: Well, there’s one missing thing. I also have a, I’m a co-founder of a precede and seed stage venture fund. I have invested in technology, at the access of human potential and technology, specifically around the Human MESH. So like I am an investor in Apollo. Neuro, which uses haptics to reduce stress and anxiety, is clinically validated. I invest in neuro tech that is being used for Alzheimer’s and stuff like that. So how it all sort of ties together as I’ve been in tech for 20 years. The first ten were in consumer-facing technology, specifically games, and the most known for operating World of Warcraft, China, and all the Blizzard properties for China. So I ran the game platform and in the market, and then the second half, was…the net of it is I went on a meditation retreat, I had a powerful experience, and I was like, everyone should have access to this. I’ve always been a believer in science and technology, as a way to make things affordable, accessible, and available. My father was a plumber. So I like humble beginnings. But that gives me a very democratic sense of wanting all of the good things, the things that allow people to heal, grow, and thrive. I have to be widely available so that people can choose. So I don’t believe in forcing the same outcomes. But I do believe in creating opportunities for access. And there isn’t anything like technology that can provide access. And so I think it’s a matter of not necessarily, there’s a lot of people who are very down on tech, and I’m not gonna I’m also, you know, I don’t consider myself a tech optimist, in the sense that I don’t think that there’s some people that think if you just make the tech then magically, humans change. Like, that’s, that’s magical thinking, I think. And some very famous people in the Valley are tech optimists. That’s not me. 

But I do believe that there is a category of technology that can support human, mental, emotional and social health. And I believe if we raise the floor with accessibility there, then we are more likely, as a species, to be better able to decide how we take care of one another. What society do we want to build, what is it we want to create? So I came back from China, and I started a community of founders and investors, and subject matter experts, clinicians, psychiatrists, psychologists, and coaches, who wanted to build these types of technologies. So that was in 2014. And in 2015, I had my first conference, which had about 250 people. in 2019, right before the pandemic, we had over 1000 people. And it was all builders. So it wasn’t a consumer-facing conference, it was the mission or the mandate . If you want to build technology that allows human beings to become, and to have a better chance of becoming, if you feel passionate about building this kind of technology, come to our community. And we’ll connect you and help you find each other and help you find others to build this kind of thing. And last summer, Sherm was looking at putting sort of a finer point on their AI, X activities. And when you talk to most people in AI, most people who have 20 years of tech haven’t spent a lot of time thinking about what it means to be human. And how do you support the Human MESH? And most of the people who spent 20 years thinking about the Human MESH, don’t know that much about technology. 

And so I’m one of the few people that actually like it right in the middle. You know, like I’ve seen, I’ve seen all of the early sensors, you know, I’ve seen all of them, like the early research on, on, what could actually be sent through heart rate variability, how that aligns to, like back when like, now you can get heart rate variability off of like your Logitech camera, can pick up Heart Rate Variability through the whites of your eyes, and through the heat map on your face. But it used to be you had to have this as an aura ring, you had to have a sensor to do it. So I’ve seen that whole arc and have sort of spent the last decade you know, looking at, you know, digging for all the different ways that one could leverage technology to improve the Human MESH. 

So another company I invested in is a company called Hola Biome, which is engineering, pre, and probiotics. And I find that fascinating, because the other thing that is the gift of technologies we’re beginning to understand and to be able to witness we haven’t quite gotten to, to, you know, breaking it down and knowing what to do, but the gut-brain access, you know, all of your serotonin, most of your serotonin is made in your gut. And so if your gut is off like you just can’t be happy. You just can’t. So, there’s, you know, incredible research where people are, you know, where certain researchers like they’ve created these polymers, these filaments, where you can actually Put them in the gut and like actually see the connection to the brain. It’s amazing. So in the next decade, our ability to support those in need to really help people, struggling with mental health to prevent it, we support people through emotional health, too and to really also start to understand how our social health affects our ability to collaborate, and then also to be healthy together. So that’s my whole life. That’s how it all ties together.

Ross: That’s fantastic. Yeah, it’s a few threads there. One is the democratization of power through technology. So I’m old enough to remember when desktop printing was a massive thing, you didn’t need a printing press anymore. And there are all these layers of power to the individual, we all have access to these incredible technologies. It is a power of all of us, you know, and that’s one of the biggest, biggest things with all these technologies, making sure these are all tools that everyone has access to, it’s not just the few wealthy people that have access to it. That’s one of the good things about AI. Generally, this is available. 

But we’re back to you know, you’re framing it is around, it’s human first, human, how do we help humans? Well, we have technologies, we can create more technologies, there’s all these possibilities for how we can be more. And it is fundamentally an attitude. And as you say, it’s sitting at that intersection of this focus on humanity and understanding the science and technology and being able to pull that together is extraordinary, what the most important place to be, given the power of these, you know, the sciences and technologies we have today. 

But don’t round out, I want one of them to distill as much as possible, your vast wisdom and insight to So alright, let’s say we have a leader of an organization who is saying, we have technologies. And this is going to mean that organizations need to become more unique. To your earlier point, we can’t be if we are the same as the other organizations, which are using the same models in the same ways, then we have no differentiation. And that’s not going to take us anywhere. So what’s your advice in this world of the technology we have today rather than the emergence of building a unique, distinctive, high-potential organization shifting from the past?

Nichol: Yeah, I would say that there are two. Two things are sort of, two things really have to change. There’s two big efforts like that. So the first effort is sort of, how does one have a successful AI implementation, whether it’s predictive slash machine learning or generative. And, you know, when you look at the history of predictive and machine learning implementations, They’re notorious for failures. And then business transformation is notorious for failure. So you kind of have a double-double failure opportunity. 

And so it makes it worth looking at what caused success in the successes, what was present. One of the things that was present in the successes was a catalyst, a person or group of people that helped raise the overall firm knowledge, who helped the senior team identify the right projects. And so there’s, there’s pieces of that, but ultimately, companies are going to have to bring these technologies in. And to do it successfully, if history teaches us anything, then it needs to be human-centered. Because that’s how you get the buy-in. The UPS implementation is one of the legendary ones, one of the most successful, and one of the hardest because it was one of the big first predictive implementations. But, you know, when you talk to that guy, it was like, it was all about the people part. The technology part was easy. You know, the hard thing was like getting the drivers to do it. You know, and then helping them understand how even if it was counterintuitive, it actually was better for them. 

You know, for their happiness, for their sanity, for them being finished on time, like the things that matter to them. So it still comes down to being human. So one, it’s like, how do you have a successful implementation in a world where everything’s going to include AI, it’s going to be like electricity. And so that requires a human-centered change management for success. And then the subset of generative AI in that is that it looks like the use cases, for actually like pixelating and redefining the work bubble up. And in order to have that line manager really know, you know, or that product manager or that salesperson for their particular business. Really know what is needed.

So, for that bubble up to happen, and for it to mean anything, because, you know, asking ChatGPT, how to cook asparagus is useless, in a business context, have to have that bubble up, that really is a problem that you have, your people have to have a certain level of fluency. To be able to imagine what they might do with something they have to cross the threshold to where their first question is’, oh, could I use AI for that?’, and to try it, and for you to have the sandbox and governance in place that it’s safe for them, and it doesn’t expose you or your clients in any way. So, one part is actually like, bringing the technology and, and being successful at it. 

The second part speaks to the second-order effects that we talked about with the grocery store chain. On the other hand, it’s like, actually doing something about culture. Like, in cultures, what are those things, it’s like, smoke. Like, if you can see the smoke, then there’s a fire, and you have a problem. But if you tried to touch it, and grab it in your hand, it is like grabbing smoke, so everybody knows it’s important. They know it beats strategy, eats strategy for breakfast, they know all these things, but we don’t know how to do anything with it. But to have a culture, a data culture, a learning culture, an evolution culture, a culture, where people actually become good managers. Like that, they actually know what that means, the ability to unlock someone else’s potential. And so the second part, is culture, and all of the pieces, you know, those two things together is what gets you, that advantage machine that allows you to reset the board, or take advantage of a reset, you know, terrify your incumbents. Whomever it is like it’s, it’s that that’s what a leader has to do, they have to do both. Now, they can start over here. But they have to go over here. But if they have the culture, then this part’s easy. You probably looked at all that Accenture research on innovator cultures who invested in tech and people during the pandemic. And how those same companies are investing in Generative AI. Basically, they have an innovative culture. So easy for them to bring new technologies in. And people trust them when they bring new technologies in because they see them using them to evolve with the people, as opposed to just using them to replace them.

Ross: Yes, yes. Yeah. And I think that’s, you know, the way I put it very, very crudely, leaders can have an attitude that technologies can cut costs, replace people, or they can augment people and do more for them and will give them better tap their potential. People working for a company will pretty much which, what the leaders’ intent is whatever they say, and will flock to and prosper the ones which have that attitude of this technology to make people better and support them. So Nichol, where can people go to find more of your work?

Nichol: So at NicholBradford.com, or also on SHRM,  our AI project. And there is one thing I want to say. And you can include it or not. But really with AI, it’s AI in the nick of time. And the reason why is because of the dramatic decline in global population, that’s going to happen in the neck in a decade from now, like, a decade from now, every, like, most of the western industrialized countries will look like Japan, in 10, to 15 years. 

And so, what all of this, so one AI is actually not very good. Like, you’ve been looking at tech for a long time, you know, it’s not good. Like, it’s not great. It’s great at things, but it’s not, it’s not great enough to actually replace a human being. And so, by 2050, the only countries, there’s only four countries in the world, they’re all in Africa, who won’t have a completely inverted population pyramid. It’s only four countries, every other country in the world is moving towards looking like Japan. And so we actually need amazing AI to allow everyone to really fulfill their potential, and to create and to build and to innovate. Um, because our current economic models, like the whole way we calculate GDP, have an implicit population growth factor in it. It doesn’t say population, but it talks about consumption growth. Well, young people consume expensive things. They consume education, they consume houses, and they do all the things that come with having kids. So with that, being forever changed, and in lots of countries historically have tried to buy their way out of it. They can’t. No one’s been ever, ever, ever able to break out of this who’s tried. What that means is actually,, we need this technology, like, we need AI, we need to be very, very good. So that’s something that I don’t hear many people talking about.

Ross: Yeah. The population is between 2040 and 2070, globally, but as you say, that’s only in a few pockets that are still increasing anyway, certainly one developed country in the world, which currently has more than a replacement fertility rate. So a lot of it is around immigration as well, which is another overlay on all of this. But I think a bit of the broader point is that we have unprecedented challenges, climate, population decline, social challenges, and so these tools, which if we use well, we can help to address these extraordinary challenges to create a prosperous future, which you are working hard to do.

Nichol: Well, I’m so happy to meet you face to face now. And I just love your work. I hope this is the beginning of a collaboration and thought exchange because I love the way you think about things.

Ross: Absolutely. Well, I’m honored to meet you and delighted that you’re out there making a difference, because your attitude is a lot of what’s going to shape a better world using technology. So thank you, Nichol.

Nichol: Thank you.