No Priors: Artificial Intelligence | Technology | Startups

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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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.

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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.

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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

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.

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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. 

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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