No Priors: Artificial Intelligence | Technology | Startups

What happens when you put code-generation agents in the hands of enterprise IT teams? With Serval, you get a system where it's easy for everyone to be a builder. Sarah Guo and Elad Gil sit down with Serval co-founder and CEO Jake Stauch to analyze his company’s direct challenge to ServiceNow as the enterprise automation and service management system of record. Jake explains that by building an AI platform for enterprise IT departments with Serval, users can safely build while still safeguarding permissions by strict deterministic approvals and an audit trail. He also talks about ProServal, the company’s team that embeds automation engineers directly with customers in order to provide ongoing support and service. Jake also discusses how AI was the big unlock to taking on ServiceNow, why he’s betting on coding agents getting even better, why consumer is a window into what will happen next in enterprise and why the SaaS-pocalypse might look more like a consolidation, why background agents are still a moonshot in enterprise software, and the importance of hiring product-oriented engineers.

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Chapters:

00:47 – Jake Stauch Introduction and Serval’s Background

03:00 – Expanding Coding Agents Beyond Engineers

04:40 – Limitations with Personal Agents in Enterprise

06:29 – Deploying Serval Effectively in Large Orgs

07:55 – Day 1 Ambition and Attacking Enterprise Systems of Record

09:51 – Taking on ServiceNow with Workflow Automation Platform

11:01 – Betting on the Evolution of Coding Agents and Building Guardrails in Enterprise

13:48 – Big Risks, Big Rewards, and Big Predictions for Serval and Coding Agents

16:40 – Adopting Different Sets of Building Principles at an Enterprise Level

20:49 – Token Maxing, Long-Term Budget Impacts, and ROI

22:39 – The SaaS-pocalypse and SaaS vs. AI

24:39 – The Mental Model for Delivering Platform-Level Power to the IT User at Enterprise

26:43 – Building Automations with Catalyst and Raising Technical Competence

28:55 – The Next Wave: Background Agents and Autonomy in Enterprise

32:23 – Hiring Velocity and Unlimited Ambition at Serval

34:17 – Keeping Pace with Customers and Fundamental Shifts in Product Function

37:05 – Conclusion

In the transition to the agentic web, security has become an afterthought. Now, organizations are racing to figure out what AI security means, and how to keep their data safe. Live from Cloudflare Connect, Sarah Guo sits down with Cloudflare CTO Dane Knecht to discuss the changing attacker landscape, and how his company is allocating its engineering resources to meet this emerging security challenge. Dane explains how enterprises that have moved away from castle-and-moat infrastructure to more of a zero trust infrastructure for security are in better positions than those who haven’t. He also discusses the challenges of patching 70 years of software and why the software development lifecycle will need to change for security for the agentic web, why he sees token spend in the next 18 months primarily going towards security rather than engineering, how the current web is in a ‘messy middle’ state where humans and agents are riding the same ‘rails’ on the internet, Cloudflare’s conference monetization gateway release as well as its Signals project with OpenAI, the rationale behind the release of Cloudflare OS, and the thinking behind a recent company restructuring. Plus, why Cloudflare hired one thousand interns, and why curiosity about AI is required to be successful at Cloudflare.

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Chapters:

00:00 – Trailer

00:55 – Cloudflare CTO Top Priorities

05:03 – Today’s Attacker Landscape

06:16 – State of Internet Infrastructure

07:31 – Shift Towards Security Spend

08:39 – Road to Better Software and Internet Security

09:50 – Building Secure Production Software

11:41 – Reducing Surface Area

12:54 – Near Future of the Web

17:27 – Monetization and Signals

19:13 – Success for Content Creators

20:07 – Workplace OS and Use Cases

24:07 – Restructuring for AI-First

27:11 – Token Budgeting

29:32 – Cloudflare’s Intern Strategy

33:39 – Importance of Curiosity

35:29 – Attacking the Legacy Software Security Problem

37:08 – Engineering Team Prioritization

38:25 – Open vs. Closed Models

39:43 – Democratizing Security for Everyone

41:29 – Conclusion

Adversaries are already running AI agents, and the fastest attacks now play out in seconds. Sarah Guo and Elad Gil sit down with CrowdStrike president and CEO George Kurtz on what AI-equipped attackers mean for enterprise security. George explains how agents are flattening the adversary pyramid by putting advanced tradecraft in the hands of low-skill attackers, and why CrowdStrike and Nvidia are building a frontier-style lab of their own to train security models. They also get into how open-weight and closed models compare on security, where CrowdStrike is directing its platform investment, lessons from the recent Hugging Face incident, and why George sees AI, like every platform shift before it, as a net creator of opportunity.

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Chapters:

00:42 – Trailer

00:59 – George Kurtz Introduction

00:12 – Agentic Landscape

01:34 – AI and Adversaries

03:40 – Open Weight Models and Adversarial Attacks

05:27 – Adversary Geography

07:24 – Attacker Capability in Context

10:14 – Hugging Face Takeaways

12:49 – CrowdStrike Frontier Security Models

15:22 – CrowdStrike Key Offerings

18:41 – What Differentiates CrowdStrike’s Models

22:47 – Near Term State of Security World

24:37 – State of Model Distillation

26:42 – Open Weight Model Importance and Cost

28:49 – Assessing Real Agent Threat

31:29 – Areas to Watch in AI

33:52 – Case for Defense Dominance

38:03 – Executive Advice

42:05 – Shifts Create Opportunity

44:14 – Conclusion

Technology has always been the fundamental driver of improvement for human quality of life. And with the recent rollout of advanced frontier models, like OpenAI’s GPT-6, humanity can now massively accelerate scientific discovery for its benefit. Sarah Guo and Elad Gil are joined by OpenAI President and co-founder Greg Brockman to discuss the company’s rapid progress across frontier models, cybersecurity, and scientific discovery. Greg shares how nearly 400 century-old math problems have been solved using its AI agents, and how the technology has the potential to transform other fields like biology in the realm of drug discovery. He also talks about how proactive, always-on AI agents like Dots impact modern productivity, why he believes the best UI is no UI at all, how cybersecurity is in a critical window of time to use peak intelligence to find and fix vulnerabilities in systems, and why OpenAI remains committed to keeping humans in the driver’s seat even as models surpass us in capability.

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Chapters:

00:42 – Trailer

00:59 – Greg Brockman Introduction

01:11 – AI and Advancements in Mathematics

05:55 – AI Communication and Oversight

07:45 – AI for Overcoming Biology Data Bottlenecks

11:28 – Revolutionizing Drug Discovery

14:18 – Changing Regulations to Accelerate Progress

16:51 – Dots the Proactive Agent

19:19 – Unifying Work and Consumer Agents

22:02 – How UI Will Evolve

26:08 – Cybersecurity in the AI Age

32:55 – Shift in Security Investment

35:49 – OpenAI’s Human-Centric Ethos

38:28 – Conclusion

Is the future of frontier AI open or closed? ReflectionAI co-founder and CEO Misha Laskin joins Sarah Guo and Elad Gil to discuss the launch of Beam, the company’s 500 billion parameter open-weight reasoning model. Misha breaks down the pre-training and reinforcement learning required to produce Beam’s reasoning efficiency, the shift in enterprise compute from renting to owning intelligence, and how he believes that open models will capture the majority of global token demand. He also talks about the open model ecosystem in China and why competition with China’s models is a good thing, safety considerations around open models, and how frontier-level open models may accelerate the pace of scientific discovery.

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Chapters:

00:42 – Misha Laskin Introduction

00:59 – Latest with ReflectionAI

02:07 – Challenges Building an Open Model

03:56 – ReflectionAI’s Agentic Shift

07:37 – Resources for Model Training

10:09 – Scaling Efficiency

12:18 – Training Beam

16:51 – Where Model Value Comes From

19:55 – Beam’s Reasoning Efficiency

22:35 – Monetizing Open Weight Models

25:03 – Future of Open Versus Closed Tokens

31:29 – Competition with Open Models

34:38 – Chinese Open Source Model Ecosystem

38:40 – Will Chinese Models Remain Open

44:44 – Safety and Open Models

53:02 – Debating Access to Powerful Tools

57:16 – AI and Scientific Progress

01:00:36 – Data Centers and Jobs

01:02:07 – Beam and Scientific Research

01:05:37 – Research Head Count to Compute Ratio

01:10:29 – Conclusion

Founder and CEO of full-stack AI chip company Fractile, Walter Goodwin, joins Sarah Guo to discuss the bets he’s made on the future of the chip market as other major players like Broadcom, NVIDIA, and AMD try to accelerate their workloads. They discuss the difference in Fractile’s newer approach on model architecture with a full-stack team in the current chip landscape and the technical bets they’re making in that direction. Walter also talks about compressing the gap between the chip design cycle and its payoff period, and making a generational leap in AI inference to realize the bet in volume against the value to be captured.

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Chapters:

00:46 – Walter Goodwin and Fractile Introduction

02:29 – The Chip Landscape Now

04:56 – Common Handoffs From Architecture-Focused Players

07:18 – Full Stack Approach and Team Setup

09:49 – Fractile’s Most Important Technical Bets

15:32 – Workload Predictions and Compressing the Chip Design Cycle

23:03 – Architect Intent to Output Bottlenecks and Accelerating Trials

28:16 – Workload Bets on Model Architectural Shifts

31:20 – The Future of AI Chip Players and Market Structure

35:14 – Conclusion

Can AI transform legacy incumbents rather than replacing them? Sequence Holdings co-founder and CEO Michael Lee joins Sarah Guo to discuss how holding company structures and engineering integrations are reshaping market leaders from the inside out. Michael details Sequence’s $7.7 billion take-private transaction of Baldwin alongside Dell Family Office (DFO), and shares his thesis on why traditional consulting models and software sales fall short for real enterprise AI transformations. They also talk about why permanent holding company structures are good for long-term compounding, real-world results from applying frontier engineering to BankSouth, and Michael’s lessons from his time in public investing, private equity, and operating at the intersection of market incumbents and AI.

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Chapters:

00:34 – Michael Lee Introduction

01:03 – Sequence Holdings and Baldwin

01:54 – Idea for Sequence

04:36 – Incumbents in the AI Era

06:32 – Why a Holding Company

11:21 – Recruiting Top Engineers

13:08 – Investing in BankSouth

17:46 – Why an Insurance Brokerage

20:17 – Atlas Platform Explained

23:53 – Traditional Private Equity Limitations

27:23 – What Sequence Looks For in Management Teams

31:04 – Accomplishments at BankSouth

34:45 – Founder Lessons

36:08 – Story of Dell Partnership

37:10 – Career and Investment Approach

40:00 – Value of Exceptional People

42:23 – Conclusion

As generative AI hits hardware and latency bottlenecks, Stanford professor, diffusion pioneer, and Inception co-founder and CEO Stefano Ermon is betting on a radical new architecture. Stefano joins Sarah Guo to talk about Inception, and how his team is applying diffusion architecture beyond images and video into discrete text and code generation. Stefano explains the limitations of autoregressive LLMs, as well as why parallel token generation in diffusion models offers superior inference scaling and hardware utilization on standard GPUs. He also shares details about Inception’s Mercury models, real-world voice agent applications, the software stack required to serve diffusion-based models at scale, academia’s role at the frontier of AI innovations, and why the next era of AI competition will be defined by efficiency.

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Chapters:

00:00 – Stefano Ermon Introduction

00:35 – Research Background

02:54 – Starting Inception

05:59 – Why Diffusion Beats Autoregressive

11:10 – Discrete vs. Continuous Modalities

13:19 – Inception Today

16:45 – Where Speed Wins

17:31 – Inception Customer Base

18:49 – Interaction with Hardware Landscape

19:34 – Inception and the Broader Industry

21:41 – Data Compression and Structure

24:45 – Controllability of Diffusion Modeles

27:25 – Emergent Capabilities at Scale

29:02 – Future Workload Split Between Diffusion vs. Traditional

30:03 – Adoption Challenges

31:44 – Hiring and Team Organization

32:50 – Recursive Self Improvement

34:02 – Resource Allocation

35:10 – Impact of Academia

38:13 – Conclusion

From the tokenization of real-world assets to funding research on anti-aging therapeutics, Coinbase co-founder and CEO Brian Armstrong is focused on solving meta-problems for the benefit of society. Brian joins Elad Gil this episode to discuss the intersections of artificial intelligence, crypto rails, and biotechnology. He details Coinbase’s vision for agentic finance, as well as how Coinbase is becoming the ‘Everything Exchange’, which includes tokenized real-world stocks, stablecoin payment adoption, and prediction markets. Brian also introduces his new venture, New Limit, a company founded to tackle longevity through epigenetic reprogramming, and outlines initial therapeutic programs and the roadmap toward clinical trials.

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Chapters:

00:00 – Cold Open Trailer

00:45 – Brian Armstrong Introduction

01:13 – Coinbase and the Everything Exchange

05:52 – Agentic Commerce

10:00 – AI and Crypto

10:49 – AI Inside Coinbase

15:39 – Productivity and Company Size

17:26 – The Everything Exchange and Tokenization

20:53 – Prediction Markets

23:15 – Introducing New Limit

34:17 – Looking Forward Five Years

40:24 – Special Economic Zones

44:26 – Conclusion

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

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