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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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.