Advertise on podcast: The Generalist

Jaan Tallinn is a founding engineer of Skype and Kazaa, and a co-founder of the Cambridge Centre for the Study of Existential Risk and Future of Life Institute. For more than fifteen years, he has been among the most consistent voices arguing that advanced AI poses an extinction-level risk to humanity. Jaan became interested in AI risk in 2008 after reading the work of Eliezer Yudkowsky, a founding researcher of the field of AI alignment. In 2009, after a four-hour meeting with Yudkowsky, he made his first donation to what became the Machine Intelligence Research Institute (MIRI). He went on to become an early investor in DeepMind and led the first round into Anthropic – despite believing that AI development, if unchecked, could kill us all. His reasoning: his money displaced VC money rather than bringing Anthropic into existence, and the influence and proceeds that come with it help fund safety work. In this conversation, we explore what Jaan believes the OpenAI/Hugging Face attack revealed about AI motivation and why he was less surprised than most. We discuss his evolving risk estimate, his case for an immediate moratorium on frontier model training, the technical and geopolitical case for hardware-based verification as a foundation for global AI governance, and why he asked Dario Amodei to collect competing term sheets before accepting his own investment. — Transcript: https://www.generalist.com/i/218944380/transcript — Every timestamp (00:00) Intro (02:28) Growing up in Soviet-occupied Estonia (05:54) How Jaan assesses AI risk and why he’s more optimistic now (07:16) AI’s preferences and motivations (12:44) The OpenAI Hugging Face incident (16:12) Meeting with Eliezer Yudkowsky (18:20) Everything as computation (20:22) The Coxon moment (23:05) Who dismisses AI alignment risks (26:17) The AI race, the case for a pause, and safer alternatives (32:47) AI 2027 (33:44) Investing in DeepMind and Anthropic (40:03) The “big blob of compute” and competing approaches to AI (44:42) What effective AI safeguards could look like (48:15) Verifying countries’ compliance with AI safeguards (50:15) AI kill switches and their limits (52:11) Jaan’s love of dance and musical influences (59:50) Jaan’s favorite movies (1:01:41) What Jaan likes about AI — Jaan’s complete reading list Shadows of the Mind: A Search for the Missing Science of Consciousness: https://www.amazon.com/dp/0195106466— Other resources Skype: https://en.wikipedia.org/wiki/SkypeLife is NOT a Journey - Alan Watts: https://www.youtube.com/watch?v=rBpaUICxEhk&t=10sAI Billionaire on Existential Risk: Jaan Tallinn – #112: https://www.manifold1.com/episodes/ai-billionaire-on-existential-risk-jaan-tallinnThe Basic AI Drives: https://selfawaresystems.com/wp-content/uploads/2008/01/ai_drives_final.pdfThe Hugging Face incident and the road ahead: https://openai.com/index/hugging-face-incident-and-the-road-aheadRogue AI didn’t breach Hugging Face, human decisions did: https://thebulletin.org/2026/09/rogue-ai-didnt-breach-hugging-face-human-decisions-didMIRI: https://intelligence.orgJacob Coxon on X, “I resigned from Anthropic today.”: https://x.com/hilbertspaess/status/2097476196791709843AI 2027: https://ai-2027.comGoogle DeepMind: https://deepmind.googleInside the Biggest Feud in Artificial Intelligence: https://www.theatlantic.com/technology/2026/09/openai-v-anthropic-inside-biggest-rivalry-tech/688819The Bitter Lesson: http://www.incompleteideas.net/IncIdeas/BitterLesson.htmlAI 2040: https://ai-2040.comThe Glass Bead Game: https://www.amazon.com/dp/B00DO994WYJaan’s favorite movies: https://jaan.info/favmoviesThe Odyssey: https://www.imdb.com/title/tt33764258On the Edge: https://www.imdb.com/title/tt0221559Limitless: https://www.imdb.com/title/tt1219289— Where to find Jaan Tallinn Website: https://jaan.info — Production and marketing by penname.co. For inquiries about sponsoring the podcast, email [email protected].

Liam Fedus and Dogus Cubuk are the co-founders and co-CEOs of Periodic Labs, a startup building “synthesis superintelligence.” In practice, that means an AI system that closes the loop between hypothesis, experiment, and learning. Liam previously led post-training at OpenAI, where he was one of the creators of ChatGPT. Dogus spent years at Google DeepMind leading a large team of chemists and materials scientists. Together, they’ve brought those two trajectories to bear on a problem that has stymied science for decades: not just discovering new materials, but making them reliably and at scale. In this conversation, we explore the limits of language models, the commercialization bottleneck in materials science, why Periodic chose high-temperature superconductors as its first target, and what Liam learned from watching ChatGPT go from an internal product to one of the most successful products of all time. — Transcript: https://www.generalist.com/i/215855083/transcript — Every timestamp (00:00) Intro (02:05) Periodic’s mission (03:14) What it takes to build a synthetic superintelligence (04:48) Why “thinkism” falls short (05:46) Why Periodic is pursuing high-temperature superconductors (07:21) How Heike Kamerlingh Onnes discovered superconductivity (09:36) Why synthesizing new materials is so difficult (16:44) Dogus’s path to AI (20:18) Liam’s path to AI (23:28) Lessons from launching ChatGPT (25:24) Why Dogus and Liam chose each other (27:01) How Periodic attracts top talent (29:50) Inside an experimental loop at Periodic (35:10) What AI learns across experiments (40:10) What Periodic builds versus buys (44:37) Balancing diversity and depth across experiments (49:23) The difference in LLM performance on math vs. science (51:26) Why Periodic doesn’t depend on better models (53:20) Periodic’s short-term goals (54:09) The competitive landscape (55:39) Final meditations — Liam and Dogus’s complete reading list The Beginning of Infinity: Explanations That Transform the World (recommended by Liam): https://www.amazon.com/Beginning-Infinity-Explanations-Transform-World/dp/0143121359Subtle Is the Lord: The Science and the Life of Albert Einstein (recommended by Dogus): https://www.amazon.com/Subtle-Lord-Science-Albert-Einstein/dp/0192806726— Other resources Periodic Labs: https://periodic.comBell Labs: https://www.nokia.com/bell-labsQBasic: https://en.wikipedia.org/wiki/QBasicGoogle DeepMind: https://deepmind.googleGoogle Brain: https://en.wikipedia.org/wiki/Google_BrainOpenAI: https://openai.com— Where to find Liam Fedus LinkedIn: https://www.linkedin.com/in/liam-fedus-26547811 X: https://x.com/LiamFedus — Where to find Dogus Cubuk LinkedIn: https://www.linkedin.com/in/ekin-dogus-cubuk-9148b8114 X: https://x.com/ekindogus — Production and marketing by penname.co. For inquiries about sponsoring the podcast, email [email protected].

Scott Morton is the founder and CEO of Revel, a software platform for testing and controlling complex hardware systems. Before founding the company in 2024, Scott spent nearly a decade at SpaceX building control software for Falcon 9 and Starship. That experience helped him recognize a broader problem: while rockets, nuclear systems, supersonic aircraft, and other advanced machines have grown increasingly sophisticated, the software used to test and control them is often fragmented and decades old. Revel was created to close that gap. The company has raised $180 million, was reportedly valued at just over $1 billion, and counts Impulse Space and Radiant Nuclear among its customers. Its platform scales from small benchtop tests to industrial systems with hundreds of thousands of telemetry channels. Revel has also created its own programming language, RevelCode, designed to combine performance and accessibility with the runtime safety required for high-stakes physical systems. In our conversation, we explore: How AI can accelerate high-consequence software teams without replacing rigorous testing and reviewWhy hardware test and industrial-control software still relies on tools created in the 1980s and 1990sWhat Scott took from Elon Musk about betting on teams before you know the answersWhy Revel created its own programming languageThe “runtime safe” philosophy: what it means and why it mattersRevel’s one-to-many model and the hardware frontiers it’s touchingThe strategic case behind Revel’s $150 million Series BHow Revel’s edge showed up in an industrial-control bake-offHow Scott hopes to preserve Revel’s engineering culture as it scales toward 500 peopleRevel’s long-term mission – and what it means for the hardware renaissance— Timestamps (00:00) Intro (02:04) Why no one will vibe-code a nuclear reactor (04:15) Revel’s engineering edge (05:27) What Revel actually does (06:34) Why industrial software has stagnated (07:52) Why previous startups couldn’t crack it (10:26) The limits of building control software in-house at SpaceX (15:20) Lessons from nearly a decade at SpaceX (20:22) Why Revel built RevelCode (24:46) Will Revel open-source the language? (25:25) Scott’s early projects and builder mentality (32:28) Where his drive comes from (33:50) What Scott took from Elon – and what he chose to leave behind (36:02) Scott’s standards as CEO (38:56) Scaling from small tests to industrial systems (43:40) Deploying Revel and the hiring bottleneck (45:23) How Revel finds and assesses talent (46:21) Emerging frontiers in hardware (49:36) Revel’s unusually smooth trajectory (51:05) How AI fits into Revel’s platform (52:42) Revel’s long-term vision (54:18) Final meditations — Follow Scott Morton LinkedIn: https://www.linkedin.com/in/scott-morton-68334a15 X: https://x.com/scottgmorton — Resources and episode mentions: https://www.generalist.com/p/an-ex-spacex-engineer-on-elon-musk — Production and marketing by penname.co. For inquiries about sponsoring the podcast, email [email protected].

Farooq Malik is the co-founder and CEO of Rain, a financial infrastructure company powered by stablecoins. Growing up in an immigrant family, Farooq saw firsthand the friction involved in moving money across borders. Alongside co-founder Charles Yoo-Naut, Farooq spent years building infrastructure before stablecoins became mainstream, betting that tokenized money would eventually become a foundational layer of the global financial system. Today, Rain powers card issuance, payments, and other financial products built on stablecoin rails, helping companies move money faster and operate across markets. In our conversation, we explore: How stablecoins combine the advantages of cash and electronic moneyThe barriers that still make moving money across borders expensive and inefficientThe parallels between being an immigrant and being an entrepreneurHow Farooq and Charles met through On Deck and decided to build togetherWhat Rain gained by building before the market was readyHow Rain earned the trust of early partners who later became customersWhat The Art of War taught Farooq about patienceThe misconception that stablecoins are only for emerging marketsWhy the payments market is big enough for multiple winnersWhy he believes Rain’s infrastructure is well positioned for a future shaped by AI agents— Thank you to the partners who make this possible Brex: The intelligent finance platform. — Timestamps (00:00) Intro (02:46) An overview of Rain and global-first financial infrastructure (06:57) The barriers to moving money and how technology can reduce them (15:34) How stablecoins behave like cash (18:46) The economic opportunity of a more efficient monetary system (22:06) How Farooq’s childhood as an immigrant shaped him (26:00) Farooq’s first entrepreneurial venture (29:11) Lessons from Farooq’s career before founding Rain (36:09) Connecting with Charles through On Deck (39:51) From Sign and Wire to Rain (42:18) Why Rain bet on stablecoins (47:25) How a Rain card works (49:15) How Rain thinks about its business (51:12) Lessons from The Art of War (54:30) Rain’s approach to hiring and management (55:47) Why the US is a stablecoin hub (59:10) Why there’s room for more than Stripe (1:03:39)How Farooq and Charles stay aligned with limited meetings (1:05:54) Rain’s most critical mantras (1:08:56) Why Rain is ready for AI agents (1:12:30) Final meditations — Follow Farooq Malik LinkedIn: https://www.linkedin.com/in/fhmalik X: https://x.com/rooqster Website: https://fhmalik.com — Resources and episode mentions: https://www.generalist.com/p/38x-in-ten-months-inside-one-of-fintechs — Production and marketing by penname.co. For inquiries about sponsoring the podcast, email [email protected].

Eric Nguyen is the co-founder and CEO of Radical Numerics, an AI research lab that has raised $50 million to train models directly on biological data. Before starting the company, Eric helped develop Evo and Evo 2, large-scale genome language models trained on unlabeled DNA sequences. Radical Numerics is now building models that can connect information across DNA, RNA, proteins, epigenetics, and other parts of biology, rather than treating each as a separate problem. Researchers have already used Evo to generate viable bacteriophage genomes, and Eric says Radical Numerics’ newer model, Omnii, matched key findings from two years of Alzheimer’s wet-lab research in a matter of days. He also believes these tools could make it easier to create dangerous pathogens, which is why the company is working on both biological design and biodefense. In our conversation, we explore: What AI models can learn by treating DNA as a languageWhy reading scientific papers is not the same as learning directly from biological dataHow Eric’s unusually free-range childhood shaped the way he follows his curiosityWhy biology may have more useful data than researchers know how to useHow Radical Numerics plans to connect information across DNA, RNA, proteins, and other biological systemsWhere the company sees early opportunities in drug discovery, diagnostics, synthetic biology, and biodefenseWhy testing AI-generated biology in the lab is still slow and difficultHow models that design biological systems could also help detect dangerous or manipulated pathogensHow to make powerful biology models safer without eliminating the capabilities that make them valuable— Thank you to the partners who make this possible Ahrefs Brand Radar: Find your brand in AI results. Brex: The intelligent finance platform. Guru: The AI source of truth for work. — Timestamps (00:00) Intro (03:35) An overview of Radical Numerics (06:35) From protein models to modeling all of biology (11:08) Why they started with DNA (15:04) The process of mapping DNA as a language (19:47) What’s unknown, and how we learn from novelty (26:24) The limits of language models in biology (31:15) Eric’s free-range upbringing and path to his PhD program (41:20) Applying long-context models to DNA and meeting his co-founders (46:36) Biology’s untapped data opportunity (49:02) Why biology needs multimodal AI (55:30) How better general LLMs benefit Radical Numerics (57:19) The challenges of biological verification (1:02:05) Making biology more concrete (1:04:51) Radical Numerics’ strategy and early use cases (1:07:26) Balancing safety with capable AI models (1:15:47) What success in biodefense looks like (1:18:09) Final meditations — Follow Eric Nguyen LinkedIn: https://www.linkedin.com/in/nguyenstanford X: https://x.com/exnx Website: https://erictnguyen.com — Resources and episode mentions: https://www.generalist.com/p/ai-got-good-at-language-now-its-learning — Production and marketing by penname.co. For inquiries about sponsoring the podcast, email [email protected].

Matan Grinberg is the co-founder and CEO of Factory, an AI company valued at $1.5 billion that helps enterprises like Nvidia, Morgan Stanley, and Adobe automate software development through “Droids,” intelligent agents designed to streamline software engineering. Before Factory, Matan spent more than a decade in theoretical physics, studying string theory at Princeton and UC Berkeley. His work now centers on a different kind of complex system: how software gets built in an era of increasingly capable AI agents, open models, and shifting compute economics. In our conversation, we explore: How Emmy Noether’s theorem continues to shape Matan’s approach to technology, business, and AIWhy Matan believes there will always be more problems to solve, even as AI becomes more capableThe resource allocation problem facing CEOs as they balance headcount, compute, and token budgetsWhy Factory is betting on model independence and Matan’s take on the SpaceX-Cursor dealWhy Matan pushes back on conflating open models with “Chinese models” and wants a stronger open-model ecosystemThe identity crisis that followed Matan’s decision to leave physicsLessons from Factory’s first few years, including learning to push back and identify gaps in his own knowledgeFactory’s culture, values, and Matan’s partnership with co-founder Eno Reyes— Thank you to the partners who make this possible .tech domains: An identity for builders at their core. Brex: The intelligent finance platform. Persona: Trusted identity verification for any use case. — Transcript: https://www.generalist.com/p/the-token-budget-problem — Timestamps (00:00) Intro (03:50) Noether’s theorem explained (06:45) How the search for what’s conserved informs Matan’s work (10:53) Why there will always be more problems to solve (11:58) The resource allocation problem of the AI era (15:54) Factory’s mission: bringing autonomy to software engineering (18:28) How Factory decides what to build next (20:10) Why Factory abstracts away model choice (22:07) How Factory wins enterprise customers (23:15) Matan’s take on the SpaceX-Cursor deal (27:48) Why open-weight models matter (29:19) Anthropic’s Fable 5 release and the debate over AI guardrails (35:33) How Matan got into string theory (38:21) Working with Juan Maldacena (41:53) Startup founders vs. theoretical physicists (46:15) Rethinking physics and redefining his identity (51:29) Discovering AI and code generation (52:53) The origins of Factory (55:52) Lessons from Factory’s first few years (59:58) Learning to push back and finding the holes in his knowledge (1:03:17) Factory’s culture and values (1:08:11) Matan’s predictions for the future of AI and Factory (1:10:49) Final meditations — Follow Matan Grinberg LinkedIn: https://www.linkedin.com/in/matan-grinberg X: https://x.com/matanSF Website: https://factory.ai — Resources and episode mentions: https://www.generalist.com/p/the-token-budget-problem⁠ — Production and marketing by penname.co. For inquiries about sponsoring the podcast, email [email protected].

Yash Patil is the 23-year-old founder and CEO of Applied Compute, a $1.3 billion company helping businesses train custom AI models on their own data: smaller, cheaper, and purpose-built for the work they actually do. Before founding the company, Yash dropped out of Stanford and spent two years at OpenAI working on post-training infrastructure and Codex. He left with one core conviction: every company that runs its critical workflows on someone else’s model is building on shifting sand. Applied Compute is his answer to that problem, already serving customers including DoorDash, Cognition, and Mercor. In our conversation, we explore: Why “own or be owned” is becoming existential for any company that relies on frontier AI modelsWhat it was like inside OpenAI the weekend the board fired, and then reinstated, its CEOWhy post-training is where competitive advantage is now being built, and what reinforcement learning with verifiable rewards actually isWhy evals have become the new production environment, and why companies will never share them with frontier providersHow a specialized model built for DoorDash outperformed frontier models on a narrow, high-value taskWhy cost, not capability, is now the primary driver pushing companies toward custom modelsWhy Yash believes AI’s transformation of the economy will unfold over decades, and why near-term fears about mass job displacement are misplaced— Thank you to the partners who make this possible Brex: The intelligent finance platform. Guru: The AI source of truth for work. Persona: Trusted identity verification for any use case. — Transcript: https://www.generalist.com/p/own-or-be-owned-why-every-company — Timestamps (00:00) Introduction (03:50) Fable 5 and the case for owning your own models (09:22) Why Applied Compute is betting on custom AI models (12:30) Yash's early influences and first projects (17:42) His brief time building at Stanford (19:29) Leaving Stanford for OpenAI (25:58) Inside OpenAI during Sam Altman's firing (28:18) What Yash admires about Sam Altman (29:43) Teaching models to reason (35:39) The core insight behind Applied Compute (39:40) How Applied Compute works with its customers (45:55) Why model training never ends (48:56) Why not every task needs a frontier model (51:25) The culture and people of Applied Compute (54:50) Applied Compute's training infrastructure (58:43) The coming compute crunch and other predictions (1:03:48) Final meditations — Follow Yash Patil X: https://x.com/ypatil125 Website: https://yashpatil.me LinkedIn: https://www.linkedin.com/in/yash-s-patil — Resources and episode mentions: https://www.generalist.com/p/own-or-be-owned-why-every-company⁠ — Production and marketing by penname.co. For inquiries about sponsoring the podcast, email [email protected].

Bryon Hargis is the co-founder and CEO of Castelion, a defense startup building low-cost hypersonic missiles designed to be manufactured at scale. Before founding Castelion, Bryon spent more than a decade at Johns Hopkins Applied Physics Laboratory and nearly six years at SpaceX, where he worked on national security space programs and saw firsthand how iterative engineering and manufacturing speed could reshape aerospace. Castelion’s first missile, Blackbeard, is slated for integration on the Navy’s F/A-18 Super Hornet in roughly a year. — In our conversation, we explore: Why Bryon believes building missiles is paradoxically essential to maintaining peaceThe game theory behind warfare and why tit-for-tat strategies require credible middle-ground responsesHow China’s 2021 hypersonic test revealed not just a capability gap but a manufacturing and cost advantageWhy traditional aerospace processes—optimized for low risk and high cost—can’t compete with rapid iterationWhat Bryon learned in his first week at SpaceX (after 12 years in traditional aerospace)Why building a carrier-based, air-launched hypersonic missile as a first product was the hard but right choiceHow focusing on manufacturability and cost over maximum capability can produce more effective deterrenceWhy the person who adapts faster in warfare always wins, and how that shapes Castelion’s philosophy— Thank you to the partners who make this possible .tech domains: An identity for builders at their core. Ahrefs Brand Radar: Find your brand in AI results. Persona: Trusted identity verification for any use case. — Timestamps (00:00) Intro (04:01) Why America needs hypersonic missiles (07:13) China’s edge in hypersonics (12:05) The missing middle ground in deterrence (18:05) Preventing warhead ambiguity (19:40) How hypersonics differ from ballistic missiles (25:05) The economics of defensive vs. offensive systems (28:21) How SpaceX differs from traditional aerospace (37:40) Why Bryon chose to build in defense over space (42:42) Key factors that drove Castelion’s success (48:28) Designing Blackbeard, Castelion’s first hypersonic missile (1:01:06) The importance of lower costs and quicker manufacturing (1:10:04) Book recommendations — Follow Bryon Hargis LinkedIn: https://www.linkedin.com/in/hargsb X: https://x.com/hargsb — Resources and episode mentions: https://www.generalist.com/p/what-america-is-missing-between-sanctions — Production and marketing by penname.co. For inquiries about sponsoring the podcast, email [email protected].

Davide Asnaghi is the co-founder and CEO of Diode, a Brooklyn-based startup using AI to design and manufacture circuit boards in the United States. Before Diode, Davide worked on Apple’s Special Projects Group and spent time in Hong Kong and Shenzhen studying Asia’s electronics manufacturing ecosystem. That experience convinced him that PCB design, despite powering everything from smartphones and satellites to medical devices and autonomous systems, remained one of the most overlooked layers of the tech stack. Since its founding just two years ago, Diode has landed Physical Intelligence and Saronic as customers and partnered with Anthropic to help Claude become a better electrical engineer. The company’s ultimate ambition: to make hardware as nimble as software. In our conversation, we explore: Why the West outsourced PCB manufacturing to Asia in the 2000s and why bringing it back matters for American competitivenessWhat Shenzhen’s manufacturing culture does better than Silicon Valley (and what the U.S. can learn from it)How Diode’s models can one-shot much of schematic design and compress hardware timelines from months to weeksThe three-week YC pivot that transformed Diode from a design validation tool into a full-stack manufacturerWhy circuit boards are the “forgotten middle child” between silicon and softwareHow Diode partners with Anthropic to make LLMs better electrical engineersWhat it takes to build a hardware company in 2025—and why the talent bar must stay incredibly highHow Italian, American, and Chinese cultures shaped Davide’s approach to entrepreneurship and manufacturing— Thank you to the partners who make this possible .tech domains: An identity for builders at their core. Guru: The AI source of truth for work. Brex: The intelligent finance platform. — Transcript: https://www.generalist.com/p/our-goal-is-to-build-an-electrical-engineer — Timestamps (00:00) Intro (04:15) Why Davide calls himself a copper merchant (05:53) Diode’s mission to rebuild PCB manufacturing in the U.S. (07:58) What success looks like (09:00) Growing up in northern Italy and spending a year in Minnesota (13:14) Why Italy produces fewer venture-backed founders (15:30) Why Hong Kong accelerated Davide’s learning (19:09) Silicon Valley vs. Shenzhen (22:05) What Davide learned in Apple’s Special Projects Team (24:11) Why Davide left Apple after two years (26:54) Meeting his co-founder, Lenny (29:32) How Davide uncovered the need for better PCB design and manufacturing (33:23) PCB manufacturing in Asia, and Diode’s approach (41:29) The YC pivot that changed Diode’s business (44:39) Inside Diode’s customer journey (48:10) Where the value is in electronics manufacturing, and Davide’s AGI thesis (51:30) What separates a working board from a great one (55:32) Where Diode fits in the electronics stack (59:55) Diode’s early near-death moment and long-term vision (1:02:30) Diode’s exceptionally high bar for hiring (1:04:48) Where Davide gets his best ideas (1:07:00) Final meditations — Follow Davide Asnaghi LinkedIn: https://www.linkedin.com/in/d-asnaghi X: https://x.com/davideasnaghi GitHub: https://hexdae.github.io — Resources and episode mentions: https://www.generalist.com/p/our-goal-is-to-build-an-electrical-engineer⁠ — Production and marketing by penname.co. For inquiries about sponsoring the podcast, email [email protected].

Cyan Banister has built one of the most distinctive early-stage track records of the last fifteen years, with early bets on companies like Uber, SpaceX, DeepMind, Niantic, and Postmates. Today, she is co-founder and general partner at Long Journey Ventures, where she backs what she calls “magical weirdos.” Banister describes herself as a professional daydreamer, running constant thought experiments and paying close attention to signals others ignore. In this episode, she explains how that mindset translates into investing, and why many of her best opportunities have come from observation, curiosity, and a willingness to look in unlikely places. In our conversation, we explore: Cyan’s philosophy of treating life as a series of experimentsThe strange, profound experiences that led her to question and ultimately move beyond her atheismHow scanning Wi-Fi networks in a Four Seasons café led her to Flock Safety, last valued at $8.4 billionLong Journey Ventures’ “Biz, Tizz, and Rizz” framework for identifying exceptional founders and why the trifecta is rareHow AI will enable the age of the polymathWhy she believes brain-computer interfaces are closer than most people thinkWhy she says Pokémon Go was “the closest we ever came to world peace”Why she lives part-time in a retirement community and her vision for a more connected future— Thank you to the partners who make this possible .tech domains: An identity for builders at their core. Brex: The intelligent finance platform. Persona: Trusted identity verification for any use case. — Transcript: https://www.generalist.com/p/investing-like-a-mystic-cyan-banister — Timestamps (00:00) Intro (03:51) Never playing the game you appear to be playing (07:18) Practicing childlike wonder as a daily discipline (10:08) Questioning belief after her stroke (13:30) Cyan’s metaphysical experiments (23:24) Non-local consciousness and creativity (27:22) Investing with extreme openness to signals (29:05) The importance of timing in investing (32:26) Meeting Travis Kalanick (34:19) Finding Flock Safety through a chance encounter (38:23) The summer of Pokémon Go (what worked and what didn’t) (39:55) Human nature and what makes something "stick" (42:15) Brain-computer interfaces and AI’s accelerating effect (52:53) “Biz, Tiz, Riz:” her framework for evaluating founders (59:20) Why Cyan lives in a retirement community part-time (1:03:50) A unique way of finding books that speak to you (1:08:44) Final meditations — Follow Cyan Banister: LinkedIn: https://www.linkedin.com/in/cyanb X: https://x.com/cyantist Newsletter: https://uglyduckling.substack.com Website: https://cyanbanister.com — Resources and episode mentions: https://www.generalist.com/p/investing-like-a-mystic-cyan-banister — Production and marketing by penname.co. For inquiries about sponsoring the podcast, email [email protected].

For decades, drug discovery has shifted away from nature and toward biology-first approaches. Viswa Colluru believes that shift was a catastrophic mistake. His company, Enveda Biosciences, has raised over $500 million to build a “search engine for nature’s chemistry.” The mission is personal: he grew up around his father’s pharmacy in India and later lost his mother to a treatable cancer whose medicine his family couldn’t afford. Many life-changing medicines, including morphine, aspirin, and metformin, originated in nature, but there has never been a reliable, scalable way to systematically explore its chemistry. Colluru founded Enveda in 2019 with $55,000 of his own savings to change that. The company has since identified 18 drug candidates, with three now in clinical trials. In our conversation, we explore: Why the pharmaceutical industry abandoned nature (and why that was a massive mistake)How Enveda built a system to decode unknown molecules in natureThe deeply personal story of his mother’s battle with leukemia and how it shaped his life’s workWhy old ideas, from immunotherapy to natural products, often hold the most latent potentialHow Enveda developed 18 drug candidates for about $1 million each instead of $10-15 millionEnveda’s three leading drug candidates targeting eczema, obesity, and ulcerative colitisWhy first-in-class medicines capture the vast majority of returns in pharmaWhat competitive table tennis taught him about building companies— Thank you to the partners who make this possible Brex: The intelligent finance platform. Ahrefs Brand Radar: Find your brand in AI results. Persona: Trusted identity verification for any use case. — Timestamps (00:00) Introduction to Viswa Colluru (03:57) His father’s pharmacy and early exposure to Western and Ayurvedic medicine (07:06) Early pull toward technology (09:29) His mother’s leukemia diagnosis (14:24) Studying Biotechnology (16:07) Graduate school (17:55) Studying immunotherapy when it was unfashionable (24:23) Innovation vs. novelty (27:24) Lessons from table tennis (32:05) Joining Recursion (37:10) Learning urgency and courage (40:42) What launched Enveda (45:40) The limits of reductionist drug discovery (49:53) Chemistry-first approach (52:17) Raising $225K and investing $55K personally (56:04) Initial studies and targets (1:04:30) Three categories of leading drugs: Eczema, obesity, ulcerative colitis (1:13:27) Why GLP-1s are not the whole answer (1:18:27) Enveda’s long-term vision (1:21:31) Book recommendation — Follow Viswa Colluru LinkedIn: https://www.linkedin.com/in/viswacolluru X: https://x.com/viswacolluru — Resources and episode mentions: https://www.generalist.com/p/the-future-of-drug-discovery — Production and marketing by penname.co. For inquiries about sponsoring the podcast, email [email protected].

Tudor Achim is the co-founder and CEO of Harmonic, a startup working to solve one of AI’s hardest problems: mathematical reasoning. In July 2024, Harmonic achieved gold-medal-level performance on International Math Olympiad problems alongside systems from OpenAI and Google DeepMind—but with a key difference: every proof Harmonic submitted was formally verified. Tudor's path to Harmonic wound through competitive piano, computational biology, and autonomous driving. He studied at Carnegie Mellon's music preparatory school, worked on machine learning at Quora, briefly pursued a PhD before dropping out, and then co-founded an autonomous driving company, Helm.ai. Harmonic's core product, Aristotle, uses reinforcement learning and the programming language Lean 4 to solve problems and verify solutions. In our conversation, we explore: Why Tudor believes math is the fundamental toolkit to understand the worldHow Harmonic uses hallucinations as a feature, not a bugHow Aristotle works and the applications beyond pure mathematicsThe reinforcement learning process that lets Harmonic generate synthetic training data and solve problems humans have never attemptedWhy Tudor believes AI could surpass human mathematicians on specific tasks within 2–3 yearsWhy the future of mathematics looks more like GitHub than academic journalsThe alternating pattern between intellect leaps and data leaps throughout scientific historyHow studying piano under an extraordinary teacher taught Tudor discipline and the value of sticking with hard problems— Thank you to the partners who make this possible Brex: The intelligent finance platform. Guru: The AI source of truth for work. Rippling: Stop wasting time on admin tasks, build your startup faster. — Transcript: https://www.generalist.com/p/how-a-20-person-startup-won-gold — Timestamps (00:00) Intro (03:34) From competitive piano to computer science (06:28) The mathematical foundations of music (and why Tudor keeps them separate) (08:24) Can AI ever create art with true intent? (09:51) Early obsessions (12:52) Defining intelligence (14:49) Discovering machine learning’s potential at Quora (17:30) Why Tudor chose computational biology for his PhD (19:19) The decision to drop out and build Helm.ai (22:55) The two breakthroughs that made mathematical AI possible in 2023 (25:28) The importance of Lean 4 (28:21) How Tudor and Vlad Tenev discovered they shared the same impossible dream (32:35) Why formal verification became the core conviction (34:21) The timeline for AI surpassing human mathematicians (35:25) An overview of Aristotle: the world’s first always-correct mathematical agent (38:12) Why Tudor says hallucinations are the engine of creativity (39:30) The translation challenge from natural language to formal proof (40:40) Reinforcement learning (42:10) Why Aristotle is both faster and cheaper than alternatives (43:34) Tradeoffs and use cases (45:34) Math in AI now and what’s next (47:38) Tying with OpenAI and DeepMind at the International Math Olympiad (49:08) Democratizing AI and correctness (53:13) Tudor’s 2030 thesis (56:02) History’s alternating rhythm of thinking and measuring (57:53) What Tudor has been wrong about (58:52) What Tudor’s best at (1:00:18) Final meditations — Follow Tudor Achim LinkedIn: https://www.linkedin.com/in/tudorachim X: https://x.com/tachim/with_replies — Resources and episode mentions: https://www.generalist.com/p/how-a-20-person-startup-won-gold⁠ — Production and marketing by penname.co. For inquiries about sponsoring the podcast, email [email protected].

Anil Varanasi, co-founder and CEO of Meter, is building a new kind of networking company for the AI era. Alongside his brother Sunil, he has helped raise more than $250 million to challenge incumbents like Cisco with a vertically integrated approach spanning hardware, software, deployment, and ongoing operations, all delivered through a utility-style model. His view is that networking has remained largely unchanged for decades, even as it has become foundational to everything from AI workloads to real-world infrastructure. Meter’s ambition is not just to improve existing networks, but to make them autonomous over time. Before starting the company, Anil and Sunil were deeply involved in filmmaking, a background that still shapes their philosophy of building with cathedral-level craft across every layer of the stack. Together we explore: The “burden of knowledge” and why progress is getting harder across fieldsWhy most companies over-index on technology and ignore business model innovationThe three ways companies create advantage: technology, delivery, and business modelHow Meter’s trade-in model borrows from the automotive industryWhy networking should function like electricity or water—not hardwareLessons from Japanese vending machine logistics for infrastructure deploymentThe hidden coordination problem behind vertically integrated companiesWhy Anil believes “common knowledge” is often wrongHow COVID forced Meter to abandon geographic constraints and scale nationallyThe case for fully autonomous networks in a world of exploding demand— Thank you to the partners who make this possible .tech domains: An identity for builders at their core. Granola: The app that might actually make you love meetings. Brex: The intelligent finance platform. — Transcript: https://www.generalist.com/p/the-case-for-autonomous-networks — Timestamps (00:00) Introduction to Anil Varanasi and Meter (03:52) The burden of knowledge and slowing innovation (08:18) Losing creativity vs gaining expertise (10:25) What Meter actually does (13:26) Early life, immigration, and upbringing (15:47) Parental influence (20:03) Film, storytelling, and creative influence (22:55) Why Anil didn’t pursue filmmaking (25:44) Parallels between company building and filmmaking (27:00) Early programming and building (28:05) George Mason and understanding systems (29:59) The dynamic of working with his brother as a co-founder (34:03) His first business and lessons learned (or lack thereof) (35:15) Lessons from successful companies (38:16) Japanese vending machines and logistics insight (41:10) Scrapping 18 months of work (42:40) Conviction and long-term company building (46:02) COVID shock and near-death moment (49:59) Building hardware like a cathedral (52:25) Rethinking the networking business model (57:06) Build vs buy and transaction costs (59:39) Networking as infrastructure and utility (01:01:30) The case for autonomous networks (01:03:25) Hiring, talent, and what actually matters (01:06:15) Big unanswered questions (sleep, science) (01:07:28) Rethinking education (01:09:02) Infinite games and long-term thinking — Follow Anil Varanasi LinkedIn: https://www.linkedin.com/in/anilcv X: https://x.com/acv Website: https://anilv.com — Resources and episode mentions: https://www.generalist.com/p/the-case-for-autonomous-networks⁠ — Production and marketing by penname.co. For inquiries about sponsoring the podcast, email [email protected].

Much of the fear around AI centers on misalignment – the idea that powerful systems might act against human interests. Vincent Weisser worries about something different: what happens if advanced AI systems are perfectly aligned with the interests of a small group of institutions? That concern led him to co-found Prime Intellect, a startup building open infrastructure for training and deploying advanced AI models. Before Prime Intellect, Weisser helped organize Vitalik Buterin’s Zuzalu experiment and worked in decentralized science, where he helped unlock roughly $40 million in funding for unconventional research. Today, he’s applying that same open ethos to AI, working to ensure the tools that shape superintelligence remain broadly accessible rather than concentrated in the hands of a few. — In our conversation, we explore: Why Vincent believes multiple superintelligences are safer than oneThe intellectual influences that shaped Vincent’s thinking about intelligence and progress, including David Deutsch and Nick BostromPrime Intellect’s evolution from distributed compute infrastructure to frontier model training and reinforcement learning toolsWhy Vincent believes open and decentralized science could accelerate discoveryThe Zuzalu experiment and what it suggests about the future of scientific communitiesThe role of aesthetics and craft in building technologyWhy Europe might have a cultural advantage in a post-superintelligence worldVincent’s predictions for the next five years of AI— Thank you to the partners who make this possible Granola: The app that might actually make you love meetings. Brex: The intelligent finance platform. Rippling: Stop wasting time on admin tasks, build your startup faster. — Transcript: https://www.generalist.com/p/why-one-superintelligence-is-more — Timestamps (00:00) Introduction to Vincent Weisser (03:28) The book behind Prime Intellect’s name (07:35) The case for suffering (09:35) An overview of Prime Intellect (13:03) Why open source models matter (21:18) Vincent’s intellectual influences (25:17) Early years in the startup scene (31:48) Funding science outside traditional institutions (41:22) The past 6 months of AI progress (43:45) Deciding to build Prime Intellect (46:55) Why GPUs were the right starting point (51:39) Training models on Prime Intellect (59:48) Why beauty matters (1:03:48) The Zuzalu experiment (1:06:27) Prime Intellect’s AGI Easter egg (1:11:13) Predictions for the next five years (1:15:09) Final meditations — Follow Vincent Weisser LinkedIn: https://linkedin.com/in/vincentweisser X: https://x.com/vincentweisser Goodreads: https://www.goodreads.com/user/show/69248416-vincent-weisser Website: https://primeintellect.ai — Resources and episode mentions: https://www.generalist.com/p/why-one-superintelligence-is-more⁠ — Production and marketing by penname.co. For inquiries about sponsoring the podcast, email [email protected].

Karol Hausman is the co-founder and CEO of Physical Intelligence, a robotics company building a general-purpose “AI brain for the physical world.” The company has raised more than $1 billion in funding to develop foundation models that allow robots to operate across many machines, environments, and tasks rather than being programmed for a single purpose. The core thesis: the same scaling dynamics that transformed language models may also unlock robotic intelligence. But only if you resist every commercial pressure pushing you toward specialization. The central challenge isn’t mechanical design. It’s intelligence: how robots learn, generalize, and interact with a physical world that is far harder to simulate than it is to describe. Before launching Physical Intelligence, Karol worked at Google Brain and Stanford University, studying robot learning alongside researchers Sergey Levine and Chelsea Finn, who later became his co-founders. In our conversation, we explore: How growing up in a small town in Poland and watching Star Wars sparked Karol’s fascination with robotsThe moment a lecture from Sergey Levine convinced him to abandon his PhD research direction and pivot fully to deep learningWhy robotics has historically lagged behind breakthroughs in language modelsThe case for building a general “AI brain” for the physical world rather than a single specialized robotThe role of real-world data in training robots, the limits of simulation, and how deployment could create a powerful data flywheelThe return of reinforcement learning and the parallels between human learning and robot trainingThe unique challenges of physical intelligence and why robots must operate with far higher reliability than language models— Thank you to the partners who make this possible Brex: The intelligent finance platform. Granola: The app that might actually make you love meetings. — Transcript: https://www.generalist.com/p/karol-hausman-physical-intelligence — Timestamps (00:00) Intro (04:05) Karol’s early fascination with robots (07:38) How Karol relates to Fei-Fei Li’s biography (08:52) What inspired Karol to build better robots (11:19) Philosophical influences (15:33) Parallels between The Inner Game of Tennis and robotics (18:21) Karol’s entry point to robotics and PhD program (25:49) Combining robotics with LLMs: The Taylor Swift demo (30:48) The 1970s SHRDLU AI experiment (32:33) Founding Physical Intelligence (35:13) How Lachy Groom got involved (39:40) How research shapes what Physical Intelligence builds (45:22) The importance of real-world data (49:07) The return of reinforcement learning in robotics (53:31) The risk of commercializing too early (55:47) Finding the right partners for the business (57:13) Open research questions (1:00:00) NVIDIA’s simulation engines (1:01:57) The surprising speed of progress (1:04:16) Reliability in robotics (1:07:31) Compensating for missing senses (1:12:28) Book recommendation — Follow Karol Hausman LinkedIn: https://www.linkedin.com/in/karolhausman X: https://x.com/hausman_k — Resources and episode mentions: https://www.generalist.com/p/karol-hausman-physical-intelligence — Production and marketing by penname.co. For inquiries about sponsoring the podcast, email [email protected].