In this episode I talk to Eli Velasquez about creating startup ecosystems, commercializing research, especially when it's not necessarily venture-backable, and how the US government thinks about startups. Eli is the head of Venture Development at VentureWell - a non profit organization that funds and trains faculty and student innovators to create businesses. VentureWell helps run I-corps, which talked to Errol Arkilic about in Episode 15. Currently, Eli runs all over the world helping create fertile ground of startup ecosystems and in the past he's worked with intellectual property both in industry at Boeing and Academia at Texas Tech. Basically he's working on meta-meta innovation: creating new ways to make places where it's easier for people to create new things. Major Takeaways Too much government aid can turn companies into zombies because their customer becomes the grant-giver instead of money-paying customers. At the end of the day ecosystems happen because people's mindsets change On average bringing a technology to market on average takes more than five years.   Resources Venturewell  

In this episode I talk to Bill Janeway about previous eras of venture capital and startups, how bubbles drive innovation, the role of government in innovation. Bill describes himself as "theorist-practitioner": he did a PhD in Economics, was a successful venture capitalist in the 80's and 90's with the firm Warburg Pincus and is now an affiliated faculty member at Cambridge and the member of several boards. Key Takeaways Bubbles have arguably been the key enabler of infrastructure-heavy technology. Venture capital may be structurally set up to only be useful for computing and biotech. Most technology that venture capital invested in was subsidized at first by the government in one way or another. Resources Doing Capitalism in the Innovation Economy VC: An American History Wikipedia article on Bill NYT Article on Fred Adler from 1981 Bill's Website Bill on Twitter

In this episode I talk to Mark Hammond about how Deep Science Ventures works, why the linear commercialization model leaves a lot on the table, and the idea of venture-focused research. Mark is the founder of Deep Science Ventures, an organization with a fascinating model for launching science-based companies. Mark has many crisply articulated theses about holes in the current system by which research becomes useful innovations and what we might do to fill them. Key Takeaways: There are many places where innovation is slow and incremental because everybody is focused on individual pieces: batteries are a great example here. The perception that deep/frontier/hard tech companies are riskier and take longer to provide returns may in fact be more grounded in popular perception than fact The factors that make translational research so expensive may not be inherent but instead driven by administrative overhead and the fact that much of it is pointed in the wrong direction. Resources Deep Science Ventures Mark on Twitter (@iammarkhammond) Systematised ‘quant’ venture in the sciences. LifeSciVC on biotech returns

Alexey Guzey is an independent researcher focusing on how to systemically increase the rate of biology discoveries and the idea that reviving the patronage system may be a way to do that. We spend most of our time talking about the project he's been working on for the past year but also touch on some of his thinking around connecting with people, which he's written about extensively.  Key Takeaways Most people doing biology research are embedded in a system that incentivizes incremental consensus steps and divides researcher time There are some institutions that stand at least partially outside of that system - Calico and Janelia being two examples Maybe we should be supporting more crackpots Resources Alexey's Essay: Reviving Patronage and Revolutionary Industrial Research Followup: How Life Sciences Actually Work: Findings of a Year-Long Investigation Alexey on Twitter:@alexeyguzey Alexey's Website HHMI Janelia Calico Andrew York Ronin Institute Emergent Ventures Phillip Gibbs - crackpots who turned out to be right

Cindy Wu and Denny Luan are the founders of experiment.com - a platform that allows anybody to request funding for a science project and anybody to fund them. It's fascinating because it stands completely outside of the grant funding and publication system that drives most science today. In this podcast we discuss how the current system prevents the creating of new fields, why science communication may be even more important that science funding, and new models for company governance.  Key Takeaways The incentives built into the grant system make it hard for new fields to emerge Arguably, changing how science is communicated might have the biggest impact on our knowledge creation system. The concept of ownership and governance of companies being two separate axes that need to be considered separately Resources Experiment.com The Science of Science Funding DIY biohackers trying to see infrared with vitamin A Innocentive Public benefit corporation Purpose Trusts Wellcome Trust/Foundation Employee Owned Breweries Topics Consolidation and risk aversion in science Hard to fund research outside of funding buckets Field politics Hard for younger scientists to get funding NIH budget stayed the same, proposals have doubled Government funds what's popular CERN is a consortium of companies doing funding Only real solution is disseminating knowledge DIY biohackers trying to see infrared with vitamin A Digging up dinosaurs No money to prepare dinosaur bones Incentives for science Brewery example of employee owned corporation New models for funding businesses Ownership and Governence Axes Making scientists stakeholders in Danger of masking philanthropy as investment and vice versa Would VCs ever fund something that's not purely for profit New Company structures

Errol Arkilic discusses bridging research and markets, emphasizing bespoke approaches, the commercial value of research, and the importance of assembling complementary teams. He explores the MIST vs. TIMS mental model and the challenges of commercializing research in different contexts.

In this conversation Sam Arbesman and I talk about unlocking cross-disciplinary innovations, long term organizations, combinatorial creativity and much more. As you might expect from someone with Generalist Thinking as a main area of interest, Sam has out-of-the-box insights in a ton of domains and he's amazing at capturing them in tight concepts like "knowledge mining" and "jargon barriers." By day Sam is the Scientist in Residence at Lux Capital. Don't cite me on it but I think he may be the only person with that job title in the world. In the past he's done research in complexity science and history and the two of them combined, written books, and worked in non profits.   Key Takeaways The concept of knowledge mining - recombining existing knowledge to create new knowledge. Unintuitively, Video games may secretly be some of the most powerful cross-disciplinary research labs. There are tactics you can use to generate cross-disciplinary creativity by cultivating a bit of randomness in your life. Resources T-Shaped Individuals Sam on Twitter Sam's Website Small World Networks Complexity Undiscovered Public Knowledge (and a 10-year update) Spore Kongō Gumi - the 1400 year company The Red Queen Hypothesis Other content from Sam: https://fs.blog/samuel-arbesman/ https://25iq.com/2016/03/12/richard-feynman-and-charlie-munger-expert-generalists/   Topics Favorite examples of combinations of ideas via generalists Ref: Small world networks paper T shaped individuals Attempts towards systemic cross-discipline idea sharing Don Swanson - undiscovered public knowledge Jargon Barriers Jefferson West Uwash - topographical map of fields Combinatorial creativity Systems for increasing the rewards for broad thinking vs. specialized thinking Need to define complexity science Computer games as a place that rewards generalist research Meta portfolio for generalist institution Self-sustaining insitutions and criteria for them Reinventing selves Or provide something people always want Japanese construction company that lasted 1500 years IBM original machines The Red Queen Hypothesis wrt Organizations Model that you need massive innovations to sustain growth (look up professor) Does the VC funding research paradigm constrain what can exist? Wired magazine researcher - "everyone loves the big idea that changes the world, but what about the ones that make a difference?" The importance of different approaches to making things exist How do you know if small ideas and tweaks in complex systems have intended effects? Promoting randomness and optionality What are tactics for increasing randomness and optionality? Randomly reminding about books Go to crazy different conferences

In this episode I speak to Matt Clifford about talent investing, how big long term projects can start small, and financial innovations. Matt is the CEO and co-founder of Entrepreneur First. Entrepreneur First, abbreviated as EF, is a fascinating system. It starts with cohorts of around fifty to a hundred ambitious, talented people who want to start companies but might not even have an idea to build around. Key Takeaways The mental model of predictable vs. unpredictable value. The idea that hypothesis testing speed predicts success even in projects where you won't see real results any time soon. The idea of money as a commodity that fuels innovations Background on EF (context for some of the podcast) EF then helps cohort members pair up into teams and get companies off the ground. Matt and Alice Bentinck started EF in 2011 and the history is kind of a crazy story: it started as a non-profit and now has raised a massive fund from LPs. One of the highlights in the story that really put EF on the map was a company named Magic Pony that sold to Twitter for an unconfirmed 150 million dollars eighteen months after starting at EF. There are links to Matt talking more about both the structure of EF and EF's history in the show notes. EF is a fascinating innovation system because it challenges many ideas that have basically become gospel in the startup world - everything from "if someone isn't willing to start a company in a garage with no income they don't have what it takes" to "only founding teams with a long working relationship can succeed." Resources Matt on Twitter (@matthewclifford) Matt's weekly newsletter EF on Wikipedia Magic Pony exit referenced in podcast Matt speaking at Startup Grind about how EF works   Ideas Capital as a resource like any other Adverse selection The best CEO of a deep tech business often doesn't know the best CTO of that business Predictable value vs Unpredictable value Predictable market does not necessarily mean existing markets Basically logic-able innovations Job as founder is to lay out 18 month roadmaps Think of VC as a financial product Providing optionality to the founder Income sharing, with optionality The power of finance innovations  Misalignment of incentive between VCs and entrepreneurs because VCs have a portfolio

In this episode I talk to Evan Miyazono about tackling metaresearch questions, how novel physical phenomena go from "oh that's cool" to devices that harness cutting edge physics, and how we could better incentivize the creators of innovations where traditionally it's hard to capture value, like open-source software and early-stage research. Evan is a research scientist at Protocol Labs where he helps lead their research efforts - coordinating researchers both inside and outside the company. Protocol labs is best known for Filecoin: a blockchain application for distributed storage. At the same time they also have a much larger mission that we get into in the podcast. Before joining Protocol Labs, Evan did his PhD at Caltech where he worked on turning crazy physics into practical devices for cryptography. Key Takeaways There might be ways to demystify both intuition and "big H Hard" research research in order to improve our systems for breakthrough discoveries. It's still super speculative but worth thinking about. Observations about physical phenomena and the world are at the core of many innovations, but the most of the process is driven from the top down by the problem, rather than bottom-up by the solution. On top of that, the process of solving the problem can actually feed back and increase our understanding of the underlying phenomena. Finally, there might also be new legal structures we could put in place to encourage more open-source development and fundamental research by allowing people to access more of the value they create in those activities. Resources Protocol Labs Evan on Twitter A quick talk on Protocol Labs research Metascience Cloud Seeding - From the abstract: "The intent of glaciogenic seeding of orographic clouds is to introduce aerosol into a cloud to alter the natural development of cloud particles and enhance wintertime precipitation in a targeted region. ... Despite numerous experiments spanning several decades, no direct observations of this process exist." SourceCred - a tool to help open source contributors capture the value of their contributions. Evan on Google Scholar if you want to go really deep. Try saying "Coupling of erbium dopants to yttrium orthosilicate photonic crystal cavities for on-chip optical quantum memories" three times fast.

In this episode I talk to William Gunn about the guts of science publishing, changing incentives in science, and the relationship between publishing and funding. William is currently the Director of Scholarly Communication at Elsevier. He joined Elsevier when they acquired Mendeley, which is a platform designed to help researchers share papers and notes about them. Before that he was an academic researcher himself and, for a time, a professional chef. Key Takeaways Science publishers aren't idiots - they realize that the internet is making anything free that can be free and are trying to adjust their business models accordingly. The metrics we use to judge research innovation are starting to shift and interestingly that is speeding up the "speciesation" of fields. Science has shifted more towards "big science" - big teams with big funding doing big experiments. However, there may be room to discover many more things if we put more focus on smaller projects. Resources William on Twitter @MrGunn Mendeley - a platform for sharing science Are Ideas Getting Harder to Find? Diminishing Returns from Science