No Priors: Artificial Intelligence | Technology | Startups

What happens when you apply the scaling laws of large language models to the physical work of atoms? Elad Gil sits down with Liam Fedus, co-founder at Periodic Labs, which is pioneering an AI foundation lab for atoms. Liam discusses how he pivoted from dark matter physics research to the front lines of artificial intelligence, including stints at Google Brain and working on ChatGPT at OpenAI. He talks about how Periodic is connecting massive language models to the physical world to overcome data bottlenecks in material science. Liam also shares how they use language models as an orchestration layer operating alongside specialized neural nets to run closed-loop physical experiments. They also explore the future of AGI and ASI, as well as the role of robotics in lab automation. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @LiamFedus | @periodiclabs Chapters: 00:00 – Cold Open 00:05 – Liam Fedus Introduction 00:39 – Liam’s Background at Google Brain, OpenAI 05:14 – From ChatGPT to Materials and Atoms 06:34 – Training Data in the Physical World 09:52 – Generalization Across Domains 11:31 – Models as an Orchestration Layer 12:48 – Commercialization and Business Model 16:10 – How Periodic’s Success May Shape the Future 17:45 – Multidisciplinary Scaling 19:41 – Capital and Compute 21:12 – Hiring at Periodic 21:44 – Thoughts on AGI and ASI 23:30 – Timeline for Machine-Directed Self-Improvement 25:39 – Automation and Data Generation 27:59 – Why Liam is Excited About the Future of Robotics 29:25 – Conclusion

What happens when AI agents can design experiments, collect data, and improve — without a human in the loop? Andrej Karpathy joins Sarah Guo on the state of models, the future of engineering and education, thinking about impact on jobs, and his project AutoResearch: where agents close the loop on a piece of AI research (experimentation, training, and optimization, autonomously). 00:00 Andrej Karpathy Introduction 02:55 What Capability Limits Remain? 06:15 What Mastery of Coding Agents Looks Like 11:16 Second Order Effects of Natural Language Coding 15:51 Why AutoResearch 22:45 Relevant Skills in the AI Era 28:25 Model Speciation 32:30 Building More Collaboration Surfaces for Humans and AI 37:28 Analysis of Jobs Market Data 48:25 Open vs. Closed Source Models 53:51 Autonomous Robotics 1:00:59 MicroGPT and Agentic Education 1:05:40 Conclusion

Notion isn’t designing AI agents that just use tools. Their agents can autonomously build their own integrations, as well as write the code needed to finish a task. Sarah Guo sits down with Notion Co-Founder Simon Last to explore Notion’s rapid evolution from a simple writing assistant to a sophisticated platform for custom AI agents. Simon discusses the technical hurdles of indexing disparate data from sources like Slack and Google Drive, as well as the internal shift toward using coding agents to build Notion itself. Plus, Simon elaborates on what he sees as a fundamental transition in productivity: moving from a tool where humans do the work, to one where humans manage a swarm of agents. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @simonlast | @NotionHQ Chapters: 00:00 – Cold Open 00:05 – Simon Last Introduction 00:26 – Genesis of Notion AI 04:10 – Challenge of Semantic Indexing and Retrieval 07:16 – The Six-Month Rewrite Cycle 08:12 – Notion’s Coding Agent Era 09:44 – Impact on Team Dynamics 12:49 – Launching Custom Agents 15:39 – Notion as the ‘Switzerland’ for Models 17:33 – Designing APIs for Agent Customers 20:09 – Simon’s Personal Agentic Workflows 24:48 – Notion: Tool for Work is Now A Tool for Agents 27:28 – How Building Has Changed for Simon 29:00 – Conclusion

By the end of 2026, AI capital expenditure is projected to hit nearly $700 billion. The question isn’t who has the best model, but who has the most creative financing to build out AI infrastructure and beyond. Sarah Guo is joined by Neil Tiwari, Managing Director at Magnetar Capital, a financial innovator helping the AI industry scale from billions to trillions of dollars in CapEx. Neil explains some of the debt structures used to finance massive GPU clusters, who is taking the risk, and how the industry is maturing. Sarah and Neil also discuss how power distribution, energy storage, and physical materials like steel are the bottlenecks of the AI industry. Plus, Neil gives his take on the future of inference-optimized clouds, and why the market shift away from software and into infrastructure might be an overreaction. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil Chapters: 00:00 – Cold Open 00:05 – Neil Tiwari Introduction 00:26 – Magnetar’s Story 01:28 – Why CoreWeave Helped Magnetar Win 06:15 – Scaling CapEx Efficiently 09:02 – Debunking GPU Collateral Risk 11:42 – How Deal Structures Evolve 13:01 – What Bottlenecks Buildout 15:28 – Circular Financing Critiques 17:35 – The Shift from Training to Inference Workloads 23:10 – AI Factories 24:12 – Constraints of the Current Power Grid 28:27 – Sovereign Compute Buildouts 29:54 – Physical AI Capital Needs 32:48 – The Capital Rotation Away from SaaS 36:04 – Conclusion

In this episode of No Priors, Sarah and Elad dive into the evolving landscape of software, exploring how AI is transforming the traditional SaaS model. They discuss whether SaaS as we know it is coming to an end, what new business and sales strategies are emerging, and how AI is reshaping the way software is built, sold, and scaled. The conversation also examines whether or not these shifts are a good thing for both big and small companies, and how coders and software experts are reacting to abrupt AI transitions. They also dig into how AI is reshaping sales, automating workflows, and enabling more predictive customer strategies. Beyond individual companies, they examine how tech giants are increasingly dominating the S&P 500, and what this concentration of power means for the future of startups, innovation, and the broader entrepreneurial ecosystem. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | Chapters: 00:00 – Cold Open 00:35 – The SaaS-polcalypse discussion 4:55 – AI Change Management in Large vs. Small Companies 05:43 – “Is Software Eating the World?” 08:38 – Addressing the Unsolved Problems 14:00 – The Noise of the Last Month vs. Excitement 21:32 – What Proportion of GDP is Tech? 23:20 – Market Cap Shifts 25:02 – As a Company, When Should You Sell? 29:05 – Multi-Product Bundle Defense 30:45 – Conclusion

Autonomous vehicle technology has moved past human-coded rules and into an era of neural networks and custom computer chips. And to solve the most difficult driving scenarios, electric vehicle company Rivian abandoned its original technology platform to build a vertically integrated data stack. Sarah Guo sits down with Rivian Founder and CEO RJ Scaringe to explore the seismic shift in the automotive industry toward AI-driven, software-defined vehicles . RJ discusses the move away from function or domain-based architecture for vehicle electronic systems to software-defined architecture, which allows for dynamic, monthly updates to features in Rivian’s vehicles. RJ also talks about the upcoming launch of Rivian’s R2 model, which aims to be a distinct, affordable, mass-market alternative to the Tesla Model Y. Plus, RJ shares his vision for a future where vehicles don’t just drive us, but inspire personal freedom and exploration. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @RJScaringe | @Rivian Chapters: 00:00 – Cold Open00:35 – RJ Scaringe Introduction0:58 – Rivian’s Autonomy Evolution05:19 – Why Rivian’s Tech is Vertically Integrated10:06 – Levels of Autonomous Driving Technologies14:00 – Importance of a Software-Defined Architecture19:28 – Differentiating Autonomous Vehicle Models23:20 – R2: The First Mass Market Autonomous Vehicle25:02 – Do Americans Want EVs?29:05 – How Our Relationship to Vehicles is Evolving30:45 – Conclusion

From “virtual doppelgängers” to “real-time dreaming,” online gaming platform Roblox is using AI technology to build the “Holodeck” envisioned in science fiction decades ago. Sarah Guo and Elad Gil sit down with Roblox CEO Dave Baszucki at Roblox headquarters to explore the intersection of AI, physics simulation, and the future of human connection. Dave discusses the evolution of the 4D creation tool in Roblox, a high-fidelity simulation that enables thousands of people to interact in real-time with photo-realistic graphics and acoustic physics. Dave reveals how Roblox is leveraging 13 billion hours of monthly user data to train native AI models that go beyond simple LLMs, enabling NPCs that can navigate and play games with human-like intuition. He also talks about how immersive communication will change video conferencing, how Roblox searches for unlikely talent outside of traditional elite universities, and how he balances rapid weekly iterations with keeping a “long view” on Roblox’s vision. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @DavidBaszucki | @Roblox Chapters: 00:00 – Cold Open 00:36 – Dave Baszucki Introduction 01:16 – Realizing Robolox’s 20-Year Vision 05:29 – Using 4D Immersive Simulations in Virtual Interactions 08:22 – Physics Engine vs. Photorealism 11:50 – Storing Roblox History as Vector Data 14:00 – Training NPCs - Moving Beyond LLMs 18:05 – The Future of the Game Designer 19:54 – Video Latent World Models 23:53 – Social Simulation - AI Companions and Virtual Relationships 27:26 – Why Asset Costs Haven’t Changed the Gaming Industry 29:52 – AI Coding in Roblox Studio 31:36 – The Roblox Creator Economy 33:57 – Long-Term Conviction vs. Weekly Iteration 37:50 – Dave’s Hiring Philosophy for Roblox 43:44 – Conclusion

What if we could pause biological time to wait for a cure for a disease? Thanks to innovations and research in reversible cryopreservation, this possibility is no longer just science fiction. Sarah Guo sits down with Laura Deming, CEO and co-founder of biotech startup Until, to dive deep into the growing field of reversible cryopreservation. Laura talks about how her time as a Thiel Fellow as well as her founding of the Longevity Fund fueled her obsession with solving the “social blindspot” of aging. Laura details how her new startup, Until, seeks to build tools that allow for “pressing pause” on biological time, starting with human organs with the hopes of scaling up to full body medical hibernation. Together, they also discuss why ice is the enemy of tissue, using engineering tools to help solve biological problems, and how this technology may revolutionize organ transplantation by removing time as a variable. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @LauraDeming | @untillabs Chapters: 00:00 – Cold Open 01:08 – Laura Deming Introduction 01:53 – Why Laura Focused on Cryo Preservation and Longevity 06:20 – Bringing on Co-Founder Hunter Davis 07:55 – Until’s Goal 10:10 – Other Use Cases for Cryo Technology 12:22 – Scientific Challenges in Cryo Tech 15:36 – Using Engineering Principles to Solve Biological Problems 20:18 – Scaling Up Cryo Preservation 21:48 – Leading and Recruiting at Until 25:02 – Why Hasn’t Cryo Tech Been Worked On More? 27:14 – Making Time Not a Variable in Organ Transplants 29:06 – Changing How the Molecular World is Depicted 30:47 – Conclusion