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.—Episode Transcript: https://www.generalist.com/i/222804125/transcript—Every timestamp00:00 Intro02:11 What Simile is03:14 Learning to predict human behavior04:33 From Schelling’s agent-based models to AI simulations07:40 What drew Joon to simulations and how the idea took shape10:32 Joon’s sci-fi inspirations14:20 The ethical questions raised by simulation15:34 How Joon got into art17:58 Art that reveals deeper meaning22:48 The artist’s framing and its role in simulation27:04 A Simile customer journey30:46 How Simile gathers data37:55 Gaining trust42:59 Public vs. hidden data and how Simile collects it45:55 Use cases for simulation, from legal strategy to finding a partner48:29 SimReddit51:06 Using simulation internally at Simile52:43 Surprising results from an EV simulation54:27 Simile’s “siblings”56:00 Simulation for investing58:13 Winning in a simulated world1:00:58 Joon’s hiring philosophy and his own contradictory superpowers1:06:22 Final meditations—ResourcesSimile: https://www.simile.comSchelling’s model of segregation: https://en.wikipedia.org/wiki/Schelling%27s_model_of_segregationWicked problem: https://en.wikipedia.org/wiki/Wicked_problemOn the Opportunities and Risks of Foundation Models: https://arxiv.org/abs/2108.07258Psychohistory: https://asimov.fandom.com/wiki/PsychohistoryGenerative Agents: Interactive Simulacra of Human Behavior: https://arxiv.org/abs/2304.03442Fresh Air: https://www.npr.org/programs/fresh-airWiz: https://www.wiz.ioThe Rehearsal on HBO Max: https://www.hbomax.com/show/609d8b4c-f0a6-4a5d-b9d3-bb0f2e207efbRemainder: https://www.amazon.com/Remainder-Tom-McCarthy/dp/0307278352The 36 Questions That Lead to Love: https://www.nytimes.com/2015/01/09/style/no-37-big-wedding-or-small.htmlSocial Simulacra: Creating Populated Prototypes for Social Computing Systems: https://dl.acm.org/doi/fullHtml/10.1145/3526113.3545616Moltbook: https://www.moltbook.com/Triptych: https://www.francis-bacon.com/artworks/paintings/triptych-1—Where to find Joon Sung ParkLinkedIn: https://www.linkedin.com/in/joonsparkWebsite: https://www.joonsungpark.com—Production and marketing by penname.co. For inquiries about sponsoring the podcast, email [email protected].

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.—Every timestamp00:00 Intro02:24 GEN-1.5 and one-shot learning08:55 What makes a task difficult10:53 Dexterity in robotics14:53 Matt Mason’s definition of a hand17:34 Giving robots a sense of touch20:30 Growing up in a family of technologists and scientists22:46 What drew Pete into robotics27:38 Generalist’s founding thesis and what sets it apart32:17 Protecting simplicity35:01 How Generalist’s data collection has evolved35:30 Generalist’s scaling philosophy39:53 Generalist’s build-versus-buy philosophy 42:15 Balancing general intelligence with commercial deployment44:24 Why Pete is betting on dexterity47:33 The durability of the robotics application layer49:09 Open-weight robotics models and data flywheels50:52 Geopolitics, safety, and alignment52:42 The parallels between teaching humans and robots1:00:05 The timeline for robotics progress and adoption1:03:37 Book recommendations—Transcript: https://www.generalist.com/i/220756708/transcript—Pete’s complete reading listThe Score Takes Care of Itself: My Philosophy of Leadership: https://www.amazon.com/Score-Takes-Care-Itself-Philosophy/dp/1591843472Wooden on Leadership: How to Create a Winning Organization: https://www.amazon.com/Wooden-Leadership-Create-Winning-Organization/dp/0071453393—Other resourcesGeneralist: https://generalistai.comGEN-1.5: https://generalistai.com/blog/gen-1.5Proprioception: https://en.wikipedia.org/wiki/ProprioceptionCutaneous receptor: https://en.wikipedia.org/wiki/Cutaneous_receptorFukushima nuclear accident: https://en.wikipedia.org/wiki/Fukushima_nuclear_accidentThe bitter lesson: https://www.cs.utexas.edu/~eunsol/courses/data/bitter_lesson.pdfGEN-1: https://generalistai.com/blog/gen-1—Where to find Pete FlorenceLinkedIn: https://www.linkedin.com/in/peteflorenceX: https://x.com/peteflorenceWebsite: https://www.peteflorence.com—Production and marketing by penname.co. For inquiries about sponsoring the podcast, email [email protected].

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 listShadows of the Mind: A Search for the Missing Science of Consciousness: https://www.amazon.com/dp/0195106466—Other resourcesSkype: https://en.wikipedia.org/wiki/SkypeLife is NOT a Journey - Alan Watts: https://www.youtube.com/watch?v=rBpaUICxEhk&t=10sAI Billionaire on Existential Risk: Jaan Tallinn – #112: https://www.manifold1.com/episodes/ai-billionaire-on-existential-risk-jaan-tallinnThe Basic AI Drives: https://selfawaresystems.com/wp-content/uploads/2008/01/ai_drives_final.pdfThe Hugging Face incident and the road ahead: https://openai.com/index/hugging-face-incident-and-the-road-aheadRogue AI didn’t breach Hugging Face, human decisions did: https://thebulletin.org/2026/09/rogue-ai-didnt-breach-hugging-face-human-decisions-didMIRI: https://intelligence.orgJacob Coxon on X, “I resigned from Anthropic today.”: https://x.com/hilbertspaess/status/2097476196791709843AI 2027: https://ai-2027.comGoogle DeepMind: https://deepmind.googleInside the Biggest Feud in Artificial Intelligence: https://www.theatlantic.com/technology/2026/09/openai-v-anthropic-inside-biggest-rivalry-tech/688819The Bitter Lesson: http://www.incompleteideas.net/IncIdeas/BitterLesson.htmlAI 2040: https://ai-2040.comThe Glass Bead Game: https://www.amazon.com/dp/B00DO994WYJaan’s favorite movies: https://jaan.info/favmoviesThe Odyssey: https://www.imdb.com/title/tt33764258On the Edge: https://www.imdb.com/title/tt0221559Limitless: https://www.imdb.com/title/tt1219289—Where to find Jaan TallinnWebsite: https://jaan.info—Production and marketing by penname.co. For inquiries about sponsoring the podcast, email [email protected].

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 listThe Beginning of Infinity: Explanations That Transform the World (recommended by Liam): https://www.amazon.com/Beginning-Infinity-Explanations-Transform-World/dp/0143121359Subtle 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 resourcesPeriodic Labs: https://periodic.comBell Labs: https://www.nokia.com/bell-labsQBasic: https://en.wikipedia.org/wiki/QBasicGoogle DeepMind: https://deepmind.googleGoogle Brain: https://en.wikipedia.org/wiki/Google_BrainOpenAI: https://openai.com—Where to find Liam FedusLinkedIn: https://www.linkedin.com/in/liam-fedus-26547811X: https://x.com/LiamFedus—Where to find Dogus CubukLinkedIn: https://www.linkedin.com/in/ekin-dogus-cubuk-9148b8114X: https://x.com/ekindogus—Production and marketing by penname.co. For inquiries about sponsoring the podcast, email [email protected].

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 reviewWhy hardware test and industrial-control software still relies on tools created in the 1980s and 1990sWhat Scott took from Elon Musk about betting on teams before you know the answersWhy Revel created its own programming languageThe “runtime safe” philosophy: what it means and why it mattersRevel’s one-to-many model and the hardware frontiers it’s touchingThe strategic case behind Revel’s $150 million Series BHow Revel’s edge showed up in an industrial-control bake-offHow Scott hopes to preserve Revel’s engineering culture as it scales toward 500 peopleRevel’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 MortonLinkedIn: https://www.linkedin.com/in/scott-morton-68334a15X: 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 [email protected].

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 moneyThe barriers that still make moving money across borders expensive and inefficientThe parallels between being an immigrant and being an entrepreneurHow Farooq and Charles met through On Deck and decided to build togetherWhat Rain gained by building before the market was readyHow Rain earned the trust of early partners who later became customersWhat The Art of War taught Farooq about patienceThe misconception that stablecoins are only for emerging marketsWhy the payments market is big enough for multiple winnersWhy he believes Rain’s infrastructure is well positioned for a future shaped by AI agents—Thank you to the partners who make this possibleBrex: 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 MalikLinkedIn: https://www.linkedin.com/in/fhmalikX: https://x.com/rooqsterWebsite: 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 [email protected].

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 languageWhy reading scientific papers is not the same as learning directly from biological dataHow Eric’s unusually free-range childhood shaped the way he follows his curiosityWhy biology may have more useful data than researchers know how to useHow Radical Numerics plans to connect information across DNA, RNA, proteins, and other biological systemsWhere the company sees early opportunities in drug discovery, diagnostics, synthetic biology, and biodefenseWhy testing AI-generated biology in the lab is still slow and difficultHow models that design biological systems could also help detect dangerous or manipulated pathogensHow to make powerful biology models safer without eliminating the capabilities that make them valuable—Thank you to the partners who make this possibleAhrefs 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 NguyenLinkedIn: https://www.linkedin.com/in/nguyenstanfordX: https://x.com/exnxWebsite: 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 [email protected].

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 AIWhy Matan believes there will always be more problems to solve, even as AI becomes more capableThe resource allocation problem facing CEOs as they balance headcount, compute, and token budgetsWhy Factory is betting on model independence and Matan’s take on the SpaceX-Cursor dealWhy Matan pushes back on conflating open models with “Chinese models” and wants a stronger open-model ecosystemThe identity crisis that followed Matan’s decision to leave physicsLessons from Factory’s first few years, including learning to push back and identify gaps in his own knowledgeFactory’s culture, values, and Matan’s partnership with co-founder Eno Reyes—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.—Transcript: https://www.generalist.com/p/the-token-budget-problem—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—Follow Matan GrinbergLinkedIn: https://www.linkedin.com/in/matan-grinbergX: https://x.com/matanSFWebsite: https://factory.ai—Resources and episode mentions: https://www.generalist.com/p/the-token-budget-problem⁠—Production and marketing by penname.co. For inquiries about sponsoring the podcast, email [email protected].

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 modelsWhat it was like inside OpenAI the weekend the board fired, and then reinstated, its CEOWhy post-training is where competitive advantage is now being built, and what reinforcement learning with verifiable rewards actually isWhy evals have become the new production environment, and why companies will never share them with frontier providersHow a specialized model built for DoorDash outperformed frontier models on a narrow, high-value taskWhy cost, not capability, is now the primary driver pushing companies toward custom modelsWhy Yash believes AI’s transformation of the economy will unfold over decades, and why near-term fears about mass job displacement are misplaced—Thank you to the partners who make this possibleBrex: 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—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—Follow Yash PatilX: https://x.com/ypatil125Website: https://yashpatil.meLinkedIn: https://www.linkedin.com/in/yash-s-patil—Resources and episode mentions: https://www.generalist.com/p/own-or-be-owned-why-every-company⁠—Production and marketing by penname.co. For inquiries about sponsoring the podcast, email [email protected].

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.—In our conversation, we explore:Why Bryon believes building missiles is paradoxically essential to maintaining peaceThe game theory behind warfare and why tit-for-tat strategies require credible middle-ground responsesHow China’s 2021 hypersonic test revealed not just a capability gap but a manufacturing and cost advantageWhy traditional aerospace processes—optimized for low risk and high cost—can’t compete with rapid iterationWhat 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 choiceHow focusing on manufacturability and cost over maximum capability can produce more effective deterrenceWhy 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(37:40) Why Bryon chose to build in defense over space(42:42) Key factors that drove Castelion’s success(48:28) Designing Blackbeard, Castelion’s first hypersonic missile(1:01:06) The importance of lower costs and quicker manufacturing(1:10:04) Book recommendations—Follow Bryon HargisLinkedIn: https://www.linkedin.com/in/hargsbX: https://x.com/hargsb—Resources and episode mentions: https://www.generalist.com/p/what-america-is-missing-between-sanctions—Production and marketing by penname.co. For inquiries about sponsoring the podcast, email [email protected].