No Priors: Artificial Intelligence | Technology | Startups

Travel Through the Lens of AI with with Booking.com CEO Glenn Fogel

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

How Nuclear Will Unlock Energy Abundance with Valar Atomics Founder Isaiah Taylor

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 reactor site in Utah 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

Really Big Test-Time Compute in AI Changes Benchmarks, Safety and Research with OpenAI Research Scientist Noam Brown

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 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

Re-engineering the Semiconductor Supply Chain with Intel CEO Lip-Bu Tan

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

Biohub: The Future of Biology is Open-Source with Co-Founders Mark Zuckerberg, Priscilla Chan, and Head of Science Alex Rives

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