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/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
—
Where to find Jaan Tallinn
Website: https://jaan.info
—
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.
—
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/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
—
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
—
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 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
—
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 jordan@penname.co.

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 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 Farooq about patience
The misconception that stablecoins are only for emerging markets
Why the payments market is big enough for multiple winners
Why 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 jordan@penname.co.

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 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
—
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 jordan@penname.co.