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

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.

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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

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.

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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

Companies in Silicon Valley from Nvidia to AMD are racing to fuel the AI revolution with postage stamp-sized AI chips. Meanwhile, a chip the size of a dinner plate just fueled a $63 billion IPO for Cerebras. Elad Gil and Sarah Guo sit down with Cerebras founder and CEO Andrew Feldman to discuss the company’s journey to making one of the largest tech go-publics in history. Andrew details the multi-year journey of pioneering wafer-scale AI computing, including surviving a brutal period of being ahead of market demand. He also explains the engineering breakthroughs that led to delivering inference speeds at 20x that of standard GPUs. Andrew then shares how a remarkable $20 billion deal with OpenAI came together in only four weeks. Plus, Andrew’s thoughts on why architecting the future of AI requires the fortitude to be a “professional David” against the Goliaths of tech.

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Chapters:

00:00 – Cold Open

00:36 – Andrew Feldman Introduction

01:19 – Cerebras’ Evolution

02:48 – Wafer-Scale Bet Pays Off

06:38 – Challenges and Breakthroughs

08:37 – Crossing the Market Chasm

10:38 – Scaling Software and Hardware

12:03 – Relevance of AI-Generated Coding

13:31 – Leadership and Hiring Culture

17:16 – When to Quit vs. Persist

19:40 – Why Cerebras Went Public

22:57 – The OpenAI Deal

25:54 – Open Source and Post-Trained Workloads

27:37 – How Speed Opens Up New Business

30:33 – Conclusion

Securing AI dominance requires more than just semiconductors; it demands a complete overhaul of how the West manages everything that goes into them, from rare earth minerals to actuators. Enter: Pax Silica. Sarah Guo and Elad Gil sit down with US Under Secretary of State for Economic Affairs Jacob Helberg to discuss the launch and expansion of Pax Silica, a 14-country economic security coalition designed to secure the entire AI supply chain. Jacob talks about the creation of a forward-deployed industrial base in the Philippines, where 4,000 acres will be developed into an “economic security zone.” He also compares and contrasts Pax Silica with China’s Belt and Road initiative, explains how the US plans to reindustrialize through automation and robotics, and explores how the Trump administration envisions making these policies durable across future presidencies. Plus, we hear why Jacob believes America to be a “global underdog.”

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Chapters:

00:00 – Cold Open

00:41 – Jacob Helberg Introduction

01:02 – Pax Silica’s Mission

03:51 – Investing in AI Chip Supply Chains

05:43 – Comparing Pax Silica to China’s Belt and Road Initiative

12:38 – Pax Silica’s Value Proposition

14:38 – US vs. Partnered Manufacturing

19:10 – Rare Earth Mineral Pricing

22:16 – Role of Venture Capital in Pax Silica

24:50 – Near vs. Long-Term Priorities

27:09 – Making AI Policy Durable

28:09 – How Policies Impact Entrepreneurs

31:00 – Trump’s Entrepreneurial Administration

33:00 – Why America is a Global Underdog

38:00 – Conclusion

The world’s first AI-take-private just proved that AI can revolutionize the real economy. Long Lake Management co-founder and CEO Alexander Taubman joins Elad Gil to discuss his firm’s agreement to acquire the legacy platform American Express Global Business Travel (Amex GBT) in a deal valued at $6.3 billion. Alexander explains the mechanics of AI-driven roll-ups, and why Long Lake chooses to acquire and transform businesses rather than simply selling them software. He also talks about how Long Lake’s horizontal AI platform, Nexus, automates workflows across diverse verticals, and how automation through AI not only powers growth for their portfolio companies, but results in both satisfied customers and employees. Plus, they explore Alexander’s vision of Amex GBT as a multi-decade compounding machine. 

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Chapters:

00:00 – Alexander Taubman Introduction

00:30 – Long Lake’s Nexus Platform

03:35 – Retention and Talent Flywheel

05:01 – Acquisition vs. Offering Software

06:57 – Building Long Lake’s Founding Team

10:37 – Taking American Express Global Business Travel Private

13:36 – Taking Berkshire Hathaway’s Approach to Management

16:37 – How AI Strategy Makes Long Lake Stand Out 

19:32 – AI Makes Services Scale

22:00 – Conclusion

Baseten CEO and co-founder Tuhin Srivastava sits down with Sarah Guo and Elad Gil to discuss the rapid growth of AI inference demand, Baseten’s 30x growth, and why inference is becoming the strategic “last market.” Tuhin Srivastava argues the application layer will persist because companies with unique user signals can encode value into workflows and post-train specialized models, citing examples like Abridge and support workflows. The conversation covers GPU capacity constraints, Baseten’s multi-cloud fabric across 18 clouds and 90 clusters, long-term contracting dynamics, the importance of the software layer for stickiness, evolving workloads, multichip possibilities, and operational lessons at scale.

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Chapters:

00:31 Baseten growth

01:55 Why the app layer wins

05:57 Serving frontier customers

07:55 Open source model mix

09:21 Chinese models and geopolitics

13:07 Custom inference dominates

14:22 Post training acquisition

17:10 When to invest in custom models

18:35 Supply crunch and data centerse

22:25 Longer GPU Contracts

24:09 What Makes a Winner

26:07 Multi Chip Future

28:19 Runtime Roadmap

31:08 Scaling Edge Cases

33:48 Hiring and Leadership

36:44 Operations Pager Culture

38:19 Efficiency Drives Demand

40:41 Concierge Everything Future

42:34 Conclusion