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.  Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @Eon_io_ | @OfirEhrlich 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. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @chesscom | @erikallebest 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. Apply for Embed - Conviction’s Catalyst for AI-Native Startups Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil  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. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @netic_AI | @melisatokmak 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.  Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @stanleytang | @andyfang | @DoorDash 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.      Sign up for new podcasts every week. Email feedback to [email protected] 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

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 [email protected] 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

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 [email protected] 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

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 [email protected] 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