No Priors: Artificial Intelligence | Technology | Startups

From data center orchestrators to AGI and robotics, CPUs remain the heart of modern computing. Arm CEO Rene Haas joins Elad Gil and Sarah Guo to explore how Arm is positioned at the epicenter of AI-driven demands for compute. Rene explains Arm’s position in the chip supply chain, and how Arm transitioned from an IP licensing model to producing physical chips like the Arm AGI CPU for Meta. He also discusses bottlenecks in hardware supply chains, SoftBank’s ecosystem and capital strategy, why US semiconductor manufacturing independence is critical, the future of robotics, and why CPUs remain crucial for executing AI workloads.

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Chapters:

00:00 – Cold Open Trailer

00:49 – Rene Haas Introduction

01:14 – Arm and Chip Supply Chain

02:37 – Shift from IP to Manufacturing CPUs

04:23 – CPU IP and Customers

06:55 – AI Adoption at Arm

10:15 – Changes in Chip Time to Market

13:27 – Data Center Buildout Bottleneck

15:13 – Softbank Leverage and Capital Strategy

17:43 – Softbank Portfolio Overview

20:13 – Robotics Opportunities for Arm

24:49 – US Manufacturing Protectionism

28:59 – Data Center Backlash

32:30 – Arm Outlook

33:31 – CPU Opportunity

37:06 – Conclusion

Google’s purchase of Spirit Airlines’ data out of bankruptcy signaled a shift in how the tech world values real-world datasets. Although compute and models get much of the attention, in this landscape, it’s data that is a company’s protective moat. Eon CEO / Co-Founder Ofir Ehrlich and President / Co-Founder Gonen Stein join Elad Gil to talk about how Eon is redefining cloud backup into a secure data foundation designed to power and protect enterprise AI. Ofir and Gonen discuss why historical enterprise data is in demand by AI labs, and how Eon facilitates access to scattered and locked data across business units through providing the mapping, classification, and access controls needed to connect it into AI workflows. They also explore how traditional ransomware defenses must now protect against rogue AI agents with legitimate system permissions, concerns around the influx of autonomous agents and non-human identities, and the implications for the breakneck speed of AI adoption compared to the slowness of the cloud era. 

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Chapters:

00:00 – Cold Open Trailer

00:59 – Ofir Ehrlich and Gonen Stein Introduction

01:27 – What Eon Does

02:41 – Data as Moat

06:43 – Training Agents with Good Data

09:39 – Data is the New Oil 

15:00 – Autonomous Security Threats

18:15 – How Agents Change the Enterprise Stack

22:11 – Re-imagining Data Infrastructure

27:52 – Cloud vs. AI Era Shift

30:26 – How AI is Changing Companies

34:31 – Conclusion

Max Hodak, co-founder and CEO of Science Corporation, joins Sarah Guo to discuss the future of vision, brain-computer interfaces, and the human experience. Max explains how Science’s PRIMA retinal implant could restore functional vision for people who have lost their sight, and why treating the brain as a computational system could unlock new approaches to medicine.

They explore the broader potential of neural devices, from restoring lost capabilities to expanding human potential, as well as deeper questions around identity, consciousness, and whether the human experience can persist as our biological hardware changes.

Max also shares Science’s long-term vision for reducing the fragility of the human condition by repairing, replacing, and ultimately upgrading parts of ourselves. Finally, he discusses the surprising parallels between AI models and biological brains, and why AI may offer a powerful new lens for understanding intelligence.

Chapters:

00:00 – Cold Open Trailer

01:40 – Max Hodak Introduction

02:00 – Science Corporation Overview and Origin

02:53 – A Revolutionary Solve for Blindness 

06:32 – Scope of Timeline and Engineer Cost

09:10 – Clinic Trial Process

09:45 - The Response from Clinicians

12:21 – Broader Biotech Landscape

14:59 – Brain’s Relationship to Senses

17:35 – The Study of Consciousness 

19:50 – Investments in Brain Computer Interface

22:10 – Fertile Ways to Study Neuroscience

24:46 – Biotech Expansion for Science Corporation

27:50 – What Success Looks Like in Neuroscience and Tech

29:06 - Goals Within Human Preservation vs. Adaptation 

30:25 – Conclusion

In a world of infinite gaming and entertainment possibilities, how does a centuries-old game stay so popular? Chess.com co-founder and CEO Erik Allebest joins Sarah Guo to explain how the evolution of technology has kept people coming back to chess, even when machines can beat us at the game. Erik talks about how the desire to build a MySpace-like community for chess led to the purchase of a domain name from a bankruptcy sale back in 2005, and scaled into a community with 10 million daily active users and 250 million total registered members. He also discusses the growth of the cultural relevance of chess, how investments from private equity firms General Atlantic and CVC helped grow and strengthen their platform, and how Chess.com is leveraging AI both within the business itself and to make a better product for its community.

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Chapters:

00:00 – Cold Open Trailer

01:05 – Erik Allebest Introduction

01:48 – Chess.com Today

02:57 – Buying and Scaling Chess.com 

06:29 – Competition and Growth

11:52 – Chess and Cultural Relevance

14:32 – Private Equity Investment

19:31 – Playing Games Amid Evolving Tech

25:09 – Tech, Skill Distribution, and Expertise

28:40 – Chess and Cheating

31:20 – What Makes Chess Special

33:17 – Chess.com Future Vision

34:54 – Founder Advice

36:48 – AGI/ASI Predictions

40:02 – AI Investments at Chess.com 

42:13 – How AI May Change Product at Chess.com

43:27 – Poker Rating Algorithms

46:07 – Conclusion

Is the tech industry moving too quickly, or are founders letting fear of AI labs stunt their ambitions? Sarah and Elad explore the current landscape of artificial intelligence, venture capital, and startup dynamics. They discuss the realities of building multi-trillion-dollar companies, shifting market sizes and outcome-based pricing models, and how founders are reacting to the rise of major AI labs. They also talk about what the framework for startup exits should look like, the potential for researcher burnouts in the next eighteen months as ASI looms on the horizon, bottlenecks for compute, and the impact of regulatory capture and shifting ecosystems from California to Texas.

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Chapters:

00:00 – Cold Open Trailer

00:31 – Episode Introduction

01:44 – The Next Trillion-Dollar Company

03:12 – Tech Waves as Punctuated Equilibria 

04:42 – TAM vs. Revenue Reality

07:14 – Market Size vs. Speed

10:32 – When Founders Should Sell

14:04 – Financing and Time Cost

17:57 – RSI and the Looming Promise of ASI

21:49 – Compute Power Laws

28:12 – Regulations and Disruption

33:06 – Beyond Transformers

34:26 – Tradeoffs - Safety vs. Progress

39:11 – Conclusion

When your AC fails in a heatwave, you don’t want a busy signal; you need a solution. Netic founder and CEO Melisa Tokmak joins host Elad Gil to explain how Netic’s autonomous AI platform acts as an intermediary between companies and customers, deploying agents to instantly handle essential services, from emergency home repairs to hospitality to pet care. Melisa describes the complexity of these real-world workloads, which have traditionally relied on large human support teams, and how over 70% of Netic’s customers interact first with AI. She also talks about the reasoning behind building a scalable product company rather than an AI roll-up, why she believes robotics will not catch up in these industries in the near future, why she doesn’t view large frontier labs as competitive threats, and how private equity’s playbook has shifted toward measurable ROI in the AI-era. Plus, why Melisa is optimistic about the impact AI will have on education.

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Chapters:

00:00 – Melisa Tokmak Introduction

00:32 – What Netic Builds

03:53 – Automating Workflows for Essential Services

06:26 – Building a Service vs. AI Roll-Up

10:38 – AI for the Real World Timeline

12:56 – Can Big Labs Compete?

15:35 – Modern Founder Mindset

19:09 – Screening for Agency

22:25 – Five Year Vision

23:53 – Selling to Slow Industries

27:23 – How Private Equity Approached AI

31:14 – What Excites Melisa About the Future of AI

34:27 – Conclusion

DoorDash is not just a delivery company. From its inception, co-founders Andy Fang and Stanley Tang operated it as a robotics and autonomy company. Andy and Stanley join Sarah Guo to explain how autonomous tech and AI are reshaping consumer habits, commerce, and delivery. Andy and Stanley talk about the rollout of Ask DoorDash, a natural-language interface that’s driving both restaurant discovery and larger grocery orders. They also discuss Dot, their in-house autonomous delivery robot that has operated in Phoenix for over two years, and how it highlights the operational and hardware challenges they have faced and solved in autonomous tech. Andy and Stanley also speak about the “first and last 100 feet problem” in autonomous delivery, why multimodal strategies are the key to success, scaling autonomy and operations, and why they believe that more Dashers, not fewer, are the future of DoorDash. 

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Chapters:

00:00 – Andy Fang and Stanley Tang Introduction

00:34 – Agentic Commerce and Behavioral Changes

03:52 – Next Steps for Ask DoorDash

06:54 – Investing in Robotics and Autonomy

16:31 – Building Autonomous Tech in the Physical World

21:20 – Dot: DoorDash’s Autonomous Delivery Robot

22:08 – Collecting Realistic Data

25:48 – Why Work at DoorDash

28:04 – Challenges in Scaling Up Autonomy

39:30 – Productivity Benchmarks

44:56 – Future of Agentic Commerce

49:10 – Conclusion

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