The Generalist

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1. A portrait of eight billion people

   3 november 2026 · 1 uur 10 min

   Afspelen
   Before Joon Sung Park and I had ever met, he simulated the conversation. What questions would Mario, or a person like Mario, ask him? What would I find most interesting to dig into? I do not know how closely I adhered to my facsimile, though I wish I did. Lees meer

   Joon has an advantage in rehearsing these encounters, and thousands of others. He is the Co-founder and CEO of Simile, an applied AI company building a foundation model of human behavior. That involves creating digital twins of real people – like a writer or podcaster – and simulating how they might react to certain circumstances. As you might imagine, that’s an attractive proposition for many companies: CVS Health, for instance, can test a new pricing policy against thousands of simulated patients. A bank can model how customers will react before announcing a fee change. Joon’s ambitions stretch far beyond these cases, though, and Simile has raised more than $300 million, most recently at a $2 billion valuation, to pursue them.

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   Every timestamp

   00:00 Intro

   02:11 What Simile is

   03:14 Learning to predict human behavior

   04:33 From Schelling’s agent-based models to AI simulations

   07:40 What drew Joon to simulations and how the idea took shape

   10:32 Joon’s sci-fi inspirations

   14:20 The ethical questions raised by simulation

   15:34 How Joon got into art

   17:58 Art that reveals deeper meaning

   22:48 The artist’s framing and its role in simulation

   27:04 A Simile customer journey

   30:46 How Simile gathers data

   37:55 Gaining trust

   42:59 Public vs. hidden data and how Simile collects it

   45:55 Use cases for simulation, from legal strategy to finding a partner

   48:29 SimReddit

   51:06 Using simulation internally at Simile

   52:43 Surprising results from an EV simulation

   54:27 Simile’s “siblings”

   56:00 Simulation for investing

   58:13 Winning in a simulated world

   1:00:58 Joon’s hiring philosophy and his own contradictory superpowers

   1:06:22 Final meditations

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   Resources

   • Simile: https://www.simile.com

   • Sc…

2. Pete Florence: When Robots Improvise

   20 oktober 2026 · 1 uur 6 min

   Afspelen
   Pete Florence and I began by agreeing that his company has an excellent name. Generalist builds AI for robots, including models that can begin learning a new task from a single demonstration. We talked about dexterity, the strange business of gathering physical experience for a machine, and what we might build if robots could do much more of the work. Lees meer

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   Every timestamp

   00:00 Intro

   02:24 GEN-1.5 and one-shot learning

   08:55 What makes a task difficult

   10:53 Dexterity in robotics

   14:53 Matt Mason’s definition of a hand

   17:34 Giving robots a sense of touch

   20:30 Growing up in a family of technologists and scientists

   22:46 What drew Pete into robotics

   27:38 Generalist’s founding thesis and what sets it apart

   32:17 Protecting simplicity

   35:01 How Generalist’s data collection has evolved

   35:30 Generalist’s scaling philosophy

   39:53 Generalist’s build-versus-buy philosophy

   42:15 Balancing general intelligence with commercial deployment

   44:24 Why Pete is betting on dexterity

   47:33 The durability of the robotics application layer

   49:09 Open-weight robotics models and data flywheels

   50:52 Geopolitics, safety, and alignment

   52:42 The parallels between teaching humans and robots

   1:00:05 The timeline for robotics progress and adoption

   1:03:37 Book recommendations

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   Pete’s complete reading list

   • The Score Takes Care of Itself: My Philosophy of Leadership: https://www.amazon.com/Score-Takes-Care-Itself-Philosophy/dp/1591843472

   • Wooden on Leadership: How to Create a Winning Organization: https://www.amazon.com/Wooden-Leadership-Create-Winning-Organization/dp/0071453393

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   Other resources

   • Generalist: https://generalistai.com

   • GEN-1.5: https://generalistai.com/blog/gen-1.5

   • Proprioception: https://en.wikipedia.org/wiki/Proprioception

   • Cutaneous receptor: https://en.wikipedia.org/wiki/Cutaneous_receptor

   • Fukushima nuclear accident: https://en.wikipedia.org/wiki/Fukushima_nuclear_accident

   • The bitter lesson: https://www.cs.utexas.edu/~eunsol/co…

3. Jaan Tallinn would like us to survive

   8 oktober 2026 · 1 uur 7 min

   Afspelen
   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. Lees meer

   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.

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   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…

4. Periodic Labs: A chatbot walks into a laboratory

   29 september 2026 · 1 uur

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   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. Lees meer

   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.

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   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

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   Liam and Dogus’s complete reading list

   • The Beginning of Infinity: Explanations That Transform the World (recommended by Liam): https://…

5. An Ex-SpaceX Engineer on Elon Musk, Starship, and Building a $1B-Valued Startup (Scott Morton, Founder & CEO of Revel)

   8 september 2026 · 57 min

   Afspelen
   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. Lees meer

   In our conversation, we explore:

   • How AI can accelerate high-consequence software teams without replacing rigorous testing and review

   • Why hardware test and industrial-control software still relies on tools created in the 1980s and 1990s

   • What Scott took from Elon Musk about betting on teams before you know the answers

   • Why Revel created its own programming language

   • The “runtime safe” philosophy: what it means and why it matters

   • Revel’s one-to-many model and the hardware frontiers it’s touching

   • The strategic case behind Revel’s $150 million Series B

   • How Revel’s edge showed up in an industrial-control bake-off

   • How Scott hopes to preserve Revel’s engineering culture as it scales toward 500 people

   • Revel’s long-term mission – and what it means for the hardware renaissance

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   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:…

6. 38x in Ten Months: Inside One of Fintech’s Fastest-Growing Infrastructure Companies (Farooq Malik, CEO of Rain)

   18 augustus 2026 · 1 uur 15 min

   Afspelen
   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. Lees meer

   In our conversation, we explore:

   • How stablecoins combine the advantages of cash and electronic money

   • The barriers that still make moving money across borders expensive and inefficient

   • The parallels between being an immigrant and being an entrepreneur

   • How Farooq and Charles met through On Deck and decided to build together

   • What Rain gained by building before the market was ready

   • How Rain earned the trust of early partners who later became customers

   • What The Art of War taught Rain about patience

   • The misconception that stablecoins are only for emerging markets

   • Why the payments market is big enough for multiple winners

   • Why Rain believes its infrastructure is well positioned for a future shaped by AI agents

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   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 in 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 at On Deck

   (39:51) From Sign and Wire to Rain

   (42:18) Why Rain bet o…

7. AI Got Good at Language. Now It’s Learning the Language of Life. (Eric Nguyen, Co-Founder and CEO of Radical Numerics)

   4 augustus 2026 · 1 uur 22 min

   Afspelen
   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. Lees meer

   In our conversation, we explore:

   • What AI models can learn by treating DNA as a language

   • Why reading scientific papers is not the same as learning directly from biological data

   • How Eric’s unusually free-range childhood shaped the way he follows his curiosity

   • Why biology may have more useful data than researchers know how to use

   • How Radical Numerics plans to connect information across DNA, RNA, proteins, and other biological systems

   • Where the company sees early opportunities in drug discovery, diagnostics, synthetic biology, and biodefense

   • Why testing AI-generated biology in the lab is still slow and difficult

   • How models that design biological systems could also help detect dangerous or manipulated pathogens

   • How to make powerful biology models safer without eliminating the capabilities that make them valuable

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   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…

8. The Token Budget Problem Nobody Is Talking About (Matan Grinberg, Co-Founder & CEO of Factory)

   30 juni 2026 · 1 uur 17 min

   Afspelen
   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. Lees meer

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   In our conversation, we explore:

   • How Emmy Noether’s theorem continues to shape Matan’s approach to technology, business, and AI

   • Why Matan believes there will always be more problems to solve, even as AI becomes more capable

   • The resource allocation problem facing CEOs as they balance headcount, compute, and token budgets

   • Why Factory is betting on model independence and Matan’s take on the SpaceX-Cursor deal

   • Why Matan pushes back on conflating open models with “Chinese models” and wants a stronger open-model ecosystem

   • The identity crisis that followed Matan’s decision to leave physics

   • Lessons from Factory’s first few years, including learning to push back and identify gaps in his own knowledge

   • Factory’s culture, values, and Matan’s partnership with co-founder Eno Reyes

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   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.

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   Transcript: https://www.generalist.com/p/the-token-budget-problem

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   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 b…

9. Own or Be Owned: Why Every Company Needs Its Own AI Model (Yash Patil, Co-Founder & CEO of Applied Compute)

   23 juni 2026 · 1 uur 8 min

   Afspelen
   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. Lees meer

   In our conversation, we explore:

   • Why “own or be owned” is becoming existential for any company that relies on frontier AI models

   • What it was like inside OpenAI the weekend the board fired, and then reinstated, its CEO

   • Why post-training is where competitive advantage is now being built, and what reinforcement learning with verifiable rewards actually is

   • Why evals have become the new production environment, and why companies will never share them with frontier providers

   • How a specialized model built for DoorDash outperformed frontier models on a narrow, high-value task

   • Why cost, not capability, is now the primary driver pushing companies toward custom models

   • Why Yash believes AI’s transformation of the economy will unfold over decades, and why near-term fears about mass job displacement are misplaced

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   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

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   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

   …

10. What America Is Missing Between Sanctions and Nuclear War (Bryon Hargis, Co-Founder & CEO of Castelion)

    2 juni 2026 · 1 uur 16 min

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    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. Lees meer

    In our conversation, we explore:

    • Why Bryon believes building missiles is paradoxically essential to maintaining peace

    • The game theory behind warfare and why tit-for-tat strategies require credible middle-ground responses

    • How China’s 2021 hypersonic test revealed not just a capability gap but a manufacturing and cost advantage

    • Why traditional aerospace processes—optimized for low risk and high cost—can’t compete with rapid iteration

    • What 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 choice

    • How focusing on manufacturability and cost over maximum capability can produce more effective deterrence

    • Why 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
    …

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