The Generalist

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.

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
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Thank you to the partners who make this possible
Brex: The intelligent finance platform.
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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
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Follow Farooq Malik
LinkedIn: https://www.linkedin.com/in/fhmalik
X: https://x.com/rooqster
Website: https://fhmalik.com
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Resources and episode mentions: https://www.generalist.com/p/38x-in-ten-months-inside-one-of-fintechs
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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
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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.
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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
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Follow Eric Nguyen
LinkedIn: https://www.linkedin.com/in/nguyenstanford
X: https://x.com/exnx
Website: https://erictnguyen.com
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Resources and episode mentions: https://www.generalist.com/p/ai-got-good-at-language-now-its-learning
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Production and marketing by penname.co. For inquiries about sponsoring the podcast, email jordan@penname.co.

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 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 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
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Follow Matan Grinberg
LinkedIn: https://www.linkedin.com/in/matan-grinberg
X: https://x.com/matanSF
Website: https://factory.ai
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Resources and episode mentions: https://www.generalist.com/p/the-token-budget-problem⁠
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Production and marketing by penname.co. For inquiries about sponsoring the podcast, email jordan@penname.co.

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 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.
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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
(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
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Follow Yash Patil
X: https://x.com/ypatil125
Website: https://yashpatil.me
LinkedIn: https://www.linkedin.com/in/yash-s-patil
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Resources and episode mentions: https://www.generalist.com/p/own-or-be-owned-why-every-company⁠
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Production and marketing by penname.co. For inquiries about sponsoring the podcast, email jordan@penname.co.