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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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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Chapters:

00:00 – Cold Open
00:35 – RJ Scaringe Introduction
0:58 – Rivian’s Autonomy Evolution
05:19 – Why Rivian’s Tech is Vertically Integrated
10:06 – Levels of Autonomous Driving Technologies
14:00 – Importance of a Software-Defined Architecture
19:28 – Differentiating Autonomous Vehicle Models
23:20 – R2: The First Mass Market Autonomous Vehicle
25:02 – Do Americans Want EVs?
29:05 – How Our Relationship to Vehicles is Evolving
30:45 – 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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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. 

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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

Why are there only a handful of companies in the world with over $10 billion in pure-play software revenue? CJ Desai believes the reason is that products are replaceable, but platforms are forever. For No Priors’ very first live from MongoDB.local SF, Sarah Guo is joined by CJ Desai, CEO and President of software developer MongoDB, to discuss the shifting landscape of enterprise software. CJ discusses whether AI will erode the value of software, and what truly constitutes a “moat” in the age of generative AI. CJ also talks about why AI adoption with Fortune 500-sized companies is still lagging, the importance of customer relationships, and why the “bear thesis” on SaaS may be overblown. 

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Chapters:

00:00 – Cold Open

00:58 – CJ Desai Introduction

01:38 – The AI Stack and the Future of Software

04:18 – Why Platforms, Not Products, Are Sticky

09:59 – Vibe Coding and the Threat of On-Demand Apps

12:15 – Paths to Success for Software Vendor Incumbents

14:24 – How CJ Chose MongoDB

18:55 – Debunking the SaaS Bear Thesis

22:07 – Fortune 500 Perspectives on AI Value

24:24 – Can AI Native Startups Replace Systems of Record?

28:10 – The Importance of Customer Relationships

31:46 – Managing Through Massive Technology Transitions

36:37 – Conclusion

Today’s arms race looks a little different from those of the past. Under the Trump administration, the US Department of War (DoW) is deploying generative AI to millions of employees in order to maintain a strategic edge over our global adversaries. Sarah Guo and Elad Gil sit down with Emil Michael, the Under Secretary of War for Research and Engineering of the United States, to discuss the radical technological transformation of the US military. Emil outlines the architecture and launch of GenAI.mil, a DoW internal AI platform powered by Gemini and Grok that reached over one million unique users in its first 30 days. He also highlights critical technology priorities for national security, including hypersonics, direct energy, and autonomous drone swarms. Together, they also explore the urgent need to rebuild the American defense industrial base and end dependency on foreign supply chains for critical materials, as well as how Emil is recruiting the next generation of “fixer-builder” workers to serve their country in government. 

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Chapters:

00:00 – Cold Open

00:00 – Emil Michael Introduction

00:58 – Emil’s Role at the Department of War

05:22 – Innovation Priorities for the DoW

08:27 – Shift Toward Autonomous Defense Technologies

10:41 – Identifying Common Needs Across the DoW

12:02 – Architecting GenAI.mil

13:48 – Applied AI Initiatives at the DoW

15:57 – The Future of Warfare

17:55 – Recruiting for DoW

19:33 – Arsenal of Freedom Tour

22:25 – Opportunities for Entrepreneurs at DoW

25:49 – Speeding Up and Scaling DoW Initiatives

28:37 – Innovation in Defense Tech

30:00 – Change Management in Government

32:09 – Rebuilding the Defense Industrial Base

37:27 – Initiatives and Opportunities at the Office of Strategic Capital

41:41 – Lessons from Emil’s Government Experience

44:30 – Conclusion

Even if ChatGPT never existed, the tech giant NVIDIA would still be winning. The end of Moore’s Law—says NVIDIA President, Founder, and CEO Jensen Huang—makes the shift to accelerated computing inevitable, regardless of any talk of an AI “bubble.” Sarah Guo and Elad Gil are joined by Jensen Huang for a wide-ranging discussion on the state of artificial intelligence as we begin 2026. Jensen reflects on the biggest surprises of 2025, including the rapid improvements in reasoning, as well as the profitability of inference tokens. He also talks about why AI will increase productivity without necessarily taking away jobs, and how physical AI and robotics can help to solve labor shortages. Finally, Jensen shares his 2026 outlook, including why he’s optimistic about US-China relations, why open source remains essential for keeping the US competitive, and which sectors are due for their “ChatGPT moment.” 

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Chapters:

00:00 – Jensen Huang Introduction

00:17 – Biggest AI Surprises of 2025

04:12 – AI and Jobs: New Infrastructure and Demand for Skilled Labor

09:03 – Task vs. Purpose Framework in Labor

12:31 – Solving Labor Shortages with Robotics

15:14 – The Layer Cake of AI Technology

18:39 – The Importance of Open Source

21:52 – The Myth of “God AI” and Monolithic Models

23:54 – Addressing the “Doomer” Narrative and Regulation

29:25 – The Plummeting Cost of Compute and Tokenomics

35:09 – The Return to Research

37:49 – Future of Coding and Software Engineering

43:20 – The Industries Due For Their “ChatGPT” Moments

46:00 – The Evolution of Self-Driving Cars and Robotics

54:06 – Energy Demand and Growth for AI

58:49 – 2026 Outlook: US-China Relations and Geopolitics

1:04:43 – Is There An AI Bubble?

1:16:20 – Conclusion

Pundits are screaming about the so-called “AI bubble.” But historically slow-to-adopt industries like medicine and law are actually embracing AI at an unprecedented speed. Sarah Guo and Elad Gil look ahead to 2026, breaking down the major trends that will define the next era of AI technologies. They explore the future of AI foundational models, predicting breakthroughs in solving complex scientific problems. They share competing views on the timeline for robotics and self-driving cars, debating whether startups have a chance for survival or if incumbents will dominate. Elad and Sarah also discuss the return of tech IPOs and M&As, forecast a new wave of AI consumer agent software, and explore why consumer product innovation has been slower than expected. Finally, the two offer bold non-AI predictions for the new year, including the acceleration of defense tech startups and the second-order underrated impacts of GLP-1 drugs on biohacking.

Plus, stick around to hear predictions on what’s next for AI in 2026 from some of tech’s biggest names and industry leaders. We hear from Jensen Huang (Founder/CEO NVIDIA), Arvind Jain (Founder/CEO, Glean), Winston Weinberg (Founder/CEO, Harvey), Scott Wu (Founder/CEO, Cognition), Raiza Martin (Founder/CEO Huxe), Zach Ziegler (Founder/CTO, Open Evidence), Aaron Levie (Founder/CEO, Box), Misha Laskin (Founder/CEO, ReflectionAI), Noam Brown (Research Scientist, OpenAI), Joshua Meier (Founder/CEO Chai Discovery), Bryan Johnson (Living Man, Don't Die), Sholto Douglas (Member of the Technical Staff, Anthropic), Ben & Asher Spector (Stanford PhDs) and Dylan Patel (Founder/CEO SemiAnalysis).

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Chapters:

00:00 – Introduction

02:43 – AI Predictions for 2026

04:40 – Adoption of AI in Professional Fields

07:17 – Robotics and Self-Driving Cars

08:25 – Robotics: Incumbents vs. Startups

13:59 – Future of IPOs and M&A in AI

16:42 – Challenges in Consumer AI Innovation

21:08 – Funding of Neo Labs, RL Research

26:28 – Predictions for 2026 Beyond AI

26:44 – The Future of Defense and Technology

28:23 – Biohacking and Peptide Therapies

30:37 – 2026 Prediction from AI Industry Leaders

40:46 – Conclusion

Imagine learning chess from a grand master, or negotiating tactics from an expert FBI hostage negotiator. ElevenLabs’ voice AI technology is making that unlock possible. Sarah Guo sits down with Mati Staniszewski, co-founder of ElevenLabs, to explore how the three-year old company is transforming how humans interact with technology through voice. Mati talks about the technical challenges of building foundational audio models, the strategic thinking between conducting research and deploying products in tandem, and why voice is the ultimate interface for everything from computers to robots to immersive media. They also discuss how the coming revolution of AI personal tutors will shift agentic AI from reactive to proactive support, break down language barriers globally, and even provide the framework for agentic government services.

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Chapters:

00:00 – Mati Staniszewski Introduction

00:46 – 11 Labs: Growth and Scale

02:46 – Voice Technology and Applications

06:52 – Research and Product Development

12:36 – Voice Quality and Customer Preferences

17:54 – Agent Platform and Use Cases

23:21 – Choosing the Right Technology Partner

26:43 – The Role of Foundation Models

29:58 – Open Source Models and Future Trends

32:37 – Research and Development Focus

36:53 – Future of AI Companions and Education

41:37 – Conclusion

In just over three years, Harvey has not only scaled to nearly one thousand customers, including Walmart, PwC, and other giants of the Fortune 500, but fundamentally transformed how legal work is delivered. Sarah Guo and Elad Gil are joined by Harvey’s co-founder and president Gabe Pereyra to discuss why the future of legal AI isn’t only about individual productivity, but also about putting together complex client matters to make law firms more profitable. They also talk about how Harvey analyzes complex tasks like fund formation or M&A and deploys agents to handle research and drafting, the strategic reasoning behind enabling law firms rather than competing with them, and why AI won’t replace partners but will change law firm leverage models and training for associates.

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Chapters:

00:00 – Gabe Pereyra Introduction

00:09 – Introduction to Harvey

02:04 – Expanding Harvey’s Reach

03:22 – Understanding Legal Workflows

06:20 – Agentic AI Applications in Law

09:06 – The Future Evolution of Law Firms

13:36 – RL in Law

19:46 – Deploying Harvey and Customization

23:46 – Adoption and Customer Success

25:28– Why Harvey Isn’t Building a Law Firm

27:25 – Challenges and Opportunities in Legal Tech

29:26 – Building a Company During the Rise of Gen AI

37:24 – Hiring at Harvey

40:19 – Future Predictions

44:17 – Conclusion