The Generalist

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.

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
Schelling’s model of segregation: https://en.wikipedia.org/wiki/Schelling%27s_model_of_segregation
Wicked problem: https://en.wikipedia.org/wiki/Wicked_problem
On the Opportunities and Risks of Foundation Models: https://arxiv.org/abs/2108.07258
Psychohistory: https://asimov.fandom.com/wiki/Psychohistory
Generative Agents: Interactive Simulacra of Human Behavior: https://arxiv.org/abs/2304.03442
Fresh Air: https://www.npr.org/programs/fresh-air
Wiz: https://www.wiz.io
The Rehearsal on HBO Max: https://www.hbomax.com/show/609d8b4c-f0a6-4a5d-b9d3-bb0f2e207efb
Remainder: https://www.amazon.com/Remainder-Tom-McCarthy/dp/0307278352
The 36 Questions That Lead to Love: https://www.nytimes.com/2015/01/09/style/no-37-big-wedding-or-small.html
Social Simulacra: Creating Populated Prototypes for Social Computing Systems: https://dl.acm.org/doi/fullHtml/10.1145/3526113.3545616
Moltbook: https://www.moltbook.com/
Triptych: https://www.francis-bacon.com/artworks/paintings/triptych-1
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Where to find Joon Sung Park
LinkedIn: https://www.linkedin.com/in/joonspark
Website: https://www.joonsungpark.com
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Production and marketing by penname.co. For inquiries about sponsoring the podcast, email jordan@penname.co.

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.
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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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Transcript: https://www.generalist.com/i/220756708/transcript
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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/courses/data/bitter_lesson.pdf
GEN-1: https://generalistai.com/blog/gen-1
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Where to find Pete Florence
LinkedIn: https://www.linkedin.com/in/peteflorence
X: https://x.com/peteflorence
Website: https://www.peteflorence.com
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Production and marketing by penname.co. For inquiries about sponsoring the podcast, email jordan@penname.co.

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.
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Transcript: https://www.generalist.com/i/218944380/transcript
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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 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
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Jaan’s complete reading list
Shadows of the Mind: A Search for the Missing Science of Consciousness: https://www.amazon.com/dp/0195106466
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Other resources
Skype: https://en.wikipedia.org/wiki/Skype
Life is NOT a Journey - Alan Watts: https://www.youtube.com/watch?v=rBpaUICxEhk&t=10s
AI Billionaire on Existential Risk: Jaan Tallinn – #112: https://www.manifold1.com/episodes/ai-billionaire-on-existential-risk-jaan-tallinn
The Basic AI Drives: https://selfawaresystems.com/wp-content/uploads/2008/01/ai_drives_final.pdf
The Hugging Face incident and the road ahead: https://openai.com/index/hugging-face-incident-and-the-road-ahead
Rogue AI didn’t breach Hugging Face, human decisions did: https://thebulletin.org/2026/09/rogue-ai-didnt-breach-hugging-face-human-decisions-did
MIRI: https://intelligence.org
Jacob Coxon on X, “I resigned from Anthropic today.”: https://x.com/hilbertspaess/status/2097476196791709843
AI 2027: https://ai-2027.com
Google DeepMind: https://deepmind.google
Inside the Biggest Feud in Artificial Intelligence: https://www.theatlantic.com/technology/2026/09/openai-v-anthropic-inside-biggest-rivalry-tech/688819
The Bitter Lesson: http://www.incompleteideas.net/IncIdeas/BitterLesson.html
AI 2040: https://ai-2040.com
The Glass Bead Game: https://www.amazon.com/dp/B00DO994WY
Jaan’s favorite movies: https://jaan.info/favmovies
The Odyssey: https://www.imdb.com/title/tt33764258
On the Edge: https://www.imdb.com/title/tt0221559
Limitless: https://www.imdb.com/title/tt1219289
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Where to find Jaan Tallinn
Website: https://jaan.info
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Production and marketing by penname.co. For inquiries about sponsoring the podcast, email jordan@penname.co.

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.
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Transcript: https://www.generalist.com/i/215855083/transcript
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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://www.amazon.com/Beginning-Infinity-Explanations-Transform-World/dp/0143121359
Subtle 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
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Other resources
Periodic Labs: https://periodic.com
Bell Labs: https://www.nokia.com/bell-labs
QBasic: https://en.wikipedia.org/wiki/QBasic
Google DeepMind: https://deepmind.google
Google Brain: https://en.wikipedia.org/wiki/Google_Brain
OpenAI: https://openai.com
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Where to find Liam Fedus
LinkedIn: https://www.linkedin.com/in/liam-fedus-26547811
X: https://x.com/LiamFedus

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Where to find Dogus Cubuk
LinkedIn: https://www.linkedin.com/in/ekin-dogus-cubuk-9148b8114
X: https://x.com/ekindogus
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Production and marketing by penname.co. For inquiries about sponsoring the podcast, email jordan@penname.co.

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 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
(07:52) 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
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Follow Scott Morton
LinkedIn: https://www.linkedin.com/in/scott-morton-68334a15
X: https://x.com/scottgmorton
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Resources and episode mentions: https://www.generalist.com/p/an-ex-spacex-engineer-on-elon-musk
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Production and marketing by penname.co. For inquiries about sponsoring the podcast, email jordan@penname.co.