No Priors: Artificial Intelligence | Technology | Startups

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.

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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

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.

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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

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

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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

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.        

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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 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.  

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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