No Priors: AI, Machine Learning, Tech, & Startups

Andrej Karpathy: "The science of manipulating the brains isn't fully developed yet." He explains that context windows are a cheap way to manipulate AI, but fine-tuning or customizing a model without losing its capabilities is a lot more tricky than most people realize.

Glenn Fogel, CEO of Booking Holdings, tells @eladgil why he doesn't believe in moats: "Today we have a competitive advantage, absolutely, but that's gonna go away tomorrow.”

Most marketplaces fear AI will replace them. Glenn Fogel, CEO of Booking Holdings, tells Elad Gil why the opposite is true for them. AI is helping them make travel easier for customers and more valuable for partners, at the same time.

When Glenn Fogel joined Priceline in 2000, the business was worth a few hundred million dollars. One week later, the Nasdaq peaked, eventually sending its stock down to a dollar a share. But over 25 years later, Booking Holdings has scaled over 1000x into an over $100 billion dollar global travel behemoth. Elad Gil is joined by Booking Holdings CEO Glenn Fogel to discuss his career, from law school and Wall Street to working at Priceline through the dot-com crash, and to helping grow the business into a multifaceted, dynamic travel marketplace in the AI era. Glenn explains how leveraging AI and agents such as Priceline’s ‘Penny’ makes travel planning and customer service better, while emphasizing the importance of preserving some human support for some users. He also talks about Booking’s strategy of reinvesting over $700 million into AI and other technologies while still offering stock buybacks and dividends, the durability of their scale and complexities of dealing with a large portfolio physical properties across the world, and why upskilling is so important for employees amid concerns about AI-driven job displacement.      Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @bookingcom | @priceline Chapters: 00:00 – Cold Open 00:05 – Glenn Fogel Introduction 00:41 – Glenn’s Early Career 06:49 – Lessons from the Early Internet 09:24 – Deciding Factors for Exiting 10:56 – Travel Through the Lens of AI 13:30 – Agentic Travel Planning  18:59 – Agents, Token Economics, and ROI 22:46 – Booking’s Capital Investment Philosophy 25:23 – Scale as Durable Asset 29:40 – Purpose and Choosing Wisely 33:18 – AI’s Impact on Jobs 36:38 – Upskilling in the AI Era 38:36 – Public Perception of AI 40:24 – Conclusion

Satya Nadella thinks the next great startup might not be an app or a model. It might be a new university. His case for reinventing how we learn:

Priscilla Chan says rare diseases get ignored because they're too niche to make big bets on. She explains why decentralizing the tools changes that: "If you put the tools in that person's hands, they're gonna be able to make progress in a way if you had to focus your efforts and make big bets, you probably wouldn't.”

Valar Atomics Founder Isaiah Taylor on why he's building nuclear reactors with a SpaceX mindset: "Harder iteration, harder execution, building the simplest and safest reactor that allows it to scale.”

The first AI chip ever powered by a nuclear reactor just went live. Isaiah Taylor, Founder of Valar Atomics, on connecting an NVIDIA Blackwell chip directly to their reactor, and the strangest rule in their merch store.

While the rest of the nuclear industry still relies on simulations and paper designs, Valar Atomics is busy splitting atoms. In fact, they just powered an NVIDIA Blackwell chip directly with a live nuclear reactor in order to power the world’s first nuclear powered website. Sarah Guo joins Valar Atomics founder and CEO Isaiah Taylor on-site at their Utah nuclear facility to talk about how Valar is shifting nuclear energy from the theoretical to the practical by building and perfecting reactors via hardware iteration. Isaiah discusses why the US stopped building nuclear reactors in the 1970s, and how Valar utilized a little-known pathway via the Department of Energy, revived by a Trump administration executive order, to successfully develop and run their advanced reactor. He also shares Valar’s strategy for vertical integration, their venture-backed approach to financing, their giga-site plans, and why he believes cheap, abundant atomic energy has the power to vastly improve the quality of human life. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @isaiah_p_taylor | @valaratomics Chapters: 00:00 – Cold Open 00:57 – Isaiah Taylor Introduction 01:30 - Valar’s Mission and Origin 04:24 - Why Nuclear Development Stalled 07:18 - Reviving Nuclear through DoE and Executive Order 10:59 - Control Room Tour 16:17 - Misunderstandings About Nuclear 20:07 - Issues with Reliability 22:14 - Nuclear is a Hardware Execution Problem 24:32 - Timeline to Scale Production 26:32 - Introducing Ward 250 30:42 - Speed Through Simplicity 33:33 - AI Drives Nuclear Demand 35:02 - Running a Reactor with NVIDIA Blackwell 36:27 - Valar’s Nuclear Conviction 40:16 - Verticalization as Path to Scale 43:58 - Valar’s Control Skid 48:00 - Venture-Backed Nuclear 50:51 - Gigasite Strategy 53:11 - CEO Tick Rate 55:37 - Abundant Energy and Hyper-Techno Industrialism 1:01:27 – Conclusion

OpenAI Research Scientist Noam Brown on why the AI release cycle has outpaced our ability to evaluate AI models, and what that means for the labs shipping them.

“We’re in a world were the capability of the model is a function of how much money you put into it. At what budget should you evaluate these models?” OpenAI Researcher Noam Brown why the AI industry’s traditional benchmark grids are broken.

When a new AI model drops, it’s judged based on a static benchmark grid that doesn’t account for how long the model is allowed to think. How then should we measure a model’s true capability? OpenAI research scientist Noam Brown returns to talk with Sarah Guo about his latest essay on why the AI industry’s traditional benchmark grids are broken, and how large-scale test-time compute is fundamentally changing how models are evaluated. Noam explains how, if properly scaffolded, today’s models can reason for weeks or even months on complex tasks. He also discusses real-world implications of test-time compute, from building poker solver bots to disproving legendary math conjectures. Together, they also unpack the large gaps in current AI safety frameworks, explore the bottlenecks for recursive self-improvement, and look ahead at the future of multi-agent collaboration and global knowledge sharing. Read more: - Implications of Large-Scale Test-Time Compute⁠: https://x.com/polynoamial/status/2064210146558136827?s=20 Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @polynoamial | @OpenAI Chapters: 00:00 – Cold Open 00:43 – Noam Brown Introduction 01:23 – Why Benchmarks Are Broken 04:19 – Compute Budgets and Projections 05:34 – How Long Should Models Think? 06:47 – Benchmark-Maxxing 08:34 – Using Poker Bots as Evals 11:26 – Safety Evals When Model Capability Scales With Budget  14:41 – Release Cycle vs. Agent Runtime  17:06 – Latent Model Capability  20:59 – Limits on Recursive Self-Improvement 27:09 – Large-Scale Multi-Agent Coordination  29:11 – Competition at the Frontier  31:51 – Breaking the Benchmark Grid Equilibrium  33:29 – Why Benchmarks Should be Evaluated by Cost 36:18 – Conclusion

The biggest threat to a fast-growing company isn't competition. It's becoming risk-averse. Andrewd Feldman on what he protects most as Cerebras scales: "We would much rather fail in pursuit of the extraordinary than succeed in the ordinary."

Manufacturing in the US is no longer optional. Intel CEO Lip-Bu Tan on why supply chain resilience is now a national priority. No major chip company can afford to depend on one or two players in one or two geographies. "More and more people are going to realize making in the United States is critical."

Intel CEO Lip-Bu Tan on working with Elon Musk: "He's one of the best, if not the best, entrepreneurs in this century." "He and I share the same view that semiconductor infrastructure has not caught up with AI growth. We need the capacity and productivity. We need to drive efficiency. That's something we both feel is missing."

At 66 years old, instead of heading towards retirement, former Cadence CEO and legendary investor Lip Bu Tan decided to take on the hardest job in tech: turning Intel around. Elad Gil and Sarah Guo sit down with Intel CEO Lip Bu Tan to talk about why he took the job and what “saving” Intel actually looks like. Tan explains how his experience in startup culture informed his decisions to drive Intel’s culture towards faster decisions, focus on customer satisfaction, and engineer accountability. He also discusses his strategy to strengthen Intel’s balance sheet by welcoming investments from Jensen Huang’s Nvidia, Softbank, and the US government. Tan also shares his product roadmap that centers the CPU for agentic AI and inference, the collaboration with Elon Musk on Terafab, his investing framework for semiconductors, and his views on how AI is reshaping design and operations at, as he puts it, a ‘legacy spreadsheet’ tech company. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @LipBuTan1 | @intel Chapters: 00:00 – Cold Open 01:01 – Lip Bu Tan Introduction 01:24 – Why Lip Bu Took the Reins at Intel 03:00 – Fixing Culture 04:08 – Intel’s 10-Year Vision 07:57 – Working with Elon Musk on Terafab 09:59 – Shifting Supply Chain for Semiconductors 15:34 – Limits to Scaling and Packaging 18:30 – Physical Limits to Engineering and Design 20:33 – Challenges in Semiconductor Investing 26:29 – Lessons from Cadence 28:02 – Scaling and Investment Decisions 32:03 – Rethinking Teams in AI Era 34:31 – Industrial Policy and Funding 37:25 – What Investors Misunderstand About Intel 41:10 – Where Compute Will Live 44:59 – Conclusion

Mark Zuckerberg said it out loud: He doesn't believe in a centralized future where a small number of institutions are advancing all of the stuff. "A positive future is one where you build a technology as a tool, put it in individuals' hands, and that's how society makes progress." He's not philosophizing; that's his actual $500M thesis with Biohub. New No Priors episode is live.

Satya Nadella on where Microsoft's ambition goes next: The real test now isn't doing the old work faster. As he puts it, "true ambition is about making the impossible possible." What most companies are missing is a new conceptual model for what they can even build.

Enterprises won't share agent behavioral data with Anthropic or OpenAI. They know these "data-hungry companies will train on it." Meanwhile, the problem of models developing their own semi-conscious perspective on what should happen is seemingly very hard to tackle today, even for the large vendors. Maxim Bar Kogan of Onyx Security on why that problem isn't getting solved anytime soon.

You get sick. Something feels off. You go to PubMed, find a paper, scroll through the methods, and ask yourself: am I even represented in this study? Priscilla Chan says that's the state of medicine right now. We're making educated guesses. Her goal with Biohub: treat every patient as an individual, understand the exact mechanism, and actually intervene. New episode of No Priors with Mark Zuckerberg, Priscilla Chan, and Alex Rives live now.

Satya Nadella on why AI's energy bill is only justified if it actually delivers value: "If you do have a token economy that drives productivity, that drives economic growth, that drives broad spread participation, better health outcomes, then I think we'll be in a great place."

Mark Zuckerberg wanted to cure, prevent, and manage all diseases by the end of the century. He and Priscilla then had a series of meetings where Nobel Prize-winning scientists laughed at them. Now Zuckerberg says, "I thought that by the end of the century was a stretch. Now I think it's too conservative."

Biohub started with an ambitious goal of curing, preventing, and managing all disease by the end of the century. A decade later, thanks to the convergence of frontier AI and biological data, that goal may have been too conservative. In this episode, Elad Gil and Sarah Guo sit down with Biohub co-founders Mark Zuckerberg and Priscilla Chan, alongside Biohub Head of Science Alex Rives. Together, they discuss Biohub’s $500 million virtual biology initiative, which integrates frontier AI with wet-lab work to build predictive world models of cells, proteins, and systems. They also talk about their newly announced open-source engine for digital protein and antibody design, ESMFold2; why Biohub is a nonprofit rather than a venture-backed startup; and how hierarchical simulations will soon allow doctors to treat patients at an individual, mechanistic level. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @Biohub | @finkd | @alexrives | @ChanZuckerberg Chapters: 00:00 – Cold Open 01:02 - Mark Zuckerberg, Priscilla Chan, and Alex Rives Introduction 01:26 – Why Biohub and Their Mission 08:27 – Integrating Frontier AI and Frontier Biology 09:45 – Micro to Macro Biological Modeling 14:22 – Mechanistic Interpretiability 16:58 – Why Biohub is a Non-Profit 21:41 – Understanding How Biology Works 24:23 – Timeline for Curing All Diseases 26:25 – Translating Research to Patient Impact 28:04 – Launch of ESMFold2 32:13 – Tackling Off-Target Effects and Edge Cases 38:39 – Putting the Tech in Individual Hands 41:06 – Talent at Biohub 44:25 – What’s Next After ESMFold2 46:10 – Connecting ESMFold2 to Agentic Systems 46:51 – The Virtual Cell 49:33 – Defining Success for Biohub 51:52 – Biohub Strategy Update 56:20 – Conclusion

Satya Nadella thinks AI has finally made it possible to put the value of human knowledge on a balance sheet. Capture how your people and your agents actually work together, and that knowledge becomes something you can own. A real asset.

What does it mean for a business to truly operate at the AI frontier? In a special crossover episode at Microsoft Build, Sarah Guo and Elad Gil team up with Latent Space host “swyx” to talk with Microsoft Chairman and CEO Satya Nadella about the future of AI platforms, software development, and the tech ecosystem. Satya reflects on the latest breakthroughs from Microsoft Build, the strategic shift toward multi-model harnesses, and why private evaluations (evals) are now a company’s most important intellectual property. They also discuss how autonomous AI agents are reshaping the role of software engineers, the durability of SaaS business models, and why showing communities the ROI on data centers is so critical. Plus, Satya shares his thoughts on the economic and societal impacts of the token economy, as well as the future of AI-driven education startups. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @satyanadella | @Microsoft | @latentspacepod | @swyx Chapters: 00:00 – Satya Nadella Introduction 01:48 – Reflections from Microsoft Build 03:12 – Microsoft’s AI Training Strategy 05:48 – Complexity of Real-World Deployment of AI 07:33 – Augmenting Human Capital 09:37 – Harnesses for Enterprise 11:49 – Developer Value 15:09 – Can Everybody Operate at the Frontier with Their Frontier Intelligence? 15:51 – Modern Definition of IP 17:38 – Future of Vendor vs. Enterprise Agents 21:48 – Near-Term Predictions on Model Pricing 24:02 – Durability of SaaS 25:58 – What Satya’s Building 28:18 – Future of Engineering Roles 30:54 – How Microsoft Can Be More Ambitious 34:36 – Data Centers and Community Impact 38:01 – AI’s Impact on Society 39:52 - AI and Education 42:28 – Conclusion

Why do the world's fastest-growing AI companies never switch providers? Cursor, Abridge, Clay, and Open Evidence all run on Baseten, and none of them have ever left. Because there's a difference between selling GPUs and selling inference. One is a commodity; the other is a moat. And whoever controls the compute controls who gets to run AI at all. Tuhin Srivastava on why infrastructure is the real AI power play.

Tell Claude Code to delete and recreate your database, that's great, but Claude Code decides on its own to delete and recreate your database? That's a disaster. Same action, completely different context, and your existing security tools can't tell the difference. Maxim Bar Kogan of Onyx Security on building controls that actually understand what agentic AI is doing.

We are now closer than ever before to living in a world where AI agents are smart enough to run our power grids and manage water supplies. How do we keep them from going rogue? Sarah Guo sits down with Maxim Bar Kogan, founder and CEO of Onyx Securities, to explore the complexities of supervising and securing autonomous agents at the enterprise level. Maxim explains Onyx’s product as an AI control plane, which oversees the permissions and flexible contexts of agents while balancing latency, cost, and reliability. He also discusses how current controls have insufficient context to monitor agent intent, tradeoffs for gradual model rollout, the need for vendor-independent oversight, and Israel’s growing AI and security talent ecosystem. Plus, why Maxim is all-in on AGI. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @maximbarkogan Chapters: 00:00 – Cold Open 00:45 – Maxim Bar Kogan Introduction 01:10 – AutoGPT and Betting on Agent Actions 05:17 – What Onyx Product Does 07:47 – State of Deployment in Large Enterprises 09:58 – Securing Agents 12:45 – Why Proxies Don’t Work 14:11 – Why Onyx Trains Its Own Models 18:38 – Onyx’s Talent Culture 21:24 – Mechanistic Interpretability 23:35 – How Onyx Builds Customer Trust 25:10 – Mitigating Risk at the Foundational Level 27:45 – Phased Rollout of Glasswing and Daybreak 29:11 – Large Enterprise Holdouts 30:46 – Onyx and the Larger AI Security Space 32:36 – Should Labs Address Model Trust and Governance? 36:56 – What Needs to Happen in Security 39:14 – Why Maxim is AGI-Pilled 41:15 – Conclusion

Netflix used to mail DVDs. When the internet got faster, they became a movie studio. That's what Andrew Feldman thinks fast AI will do; it won't just replace what exists, it will create entirely new business models nobody can see yet.

The fastest AI chip in the world had zero customers for 8 years. Not because the chip wasn't good enough, but because AI wasn't useful enough yet. As Cerebras CEO Andrew Feldman puts it: "Once you use something every day in your work, it can't be slow." The moment AI became necessary, speed became everything, and Cerebras was already there with the right product.