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.

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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.

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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.

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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.

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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