More than fifty years ago, the modern idea of the standard enterprise software was birthed at SAP. Now, after managing companies through technological shifts from the mainframe to mobile, SAP is at the forefront of closing the AI adoption gap for their customers. SAP Chief Technology Officer Philipp Herzig joins Sarah Guo to talk about how SAP has remained a durable end-to-end “operating system” for its more than 400,000 customers from finance to supply chain. Philipp argues that the AI transition in businesses should focus on customer outcomes, UI changes, business processes, and the data layer. He also explains the challenges in enterprise AI adoption, including security, scaling, and data fragmentation, as well as the importance of evals and verifiability. They also discuss SAP’s suite of AI products, limitations of predictive tabular models, how SAP is shifting its pricing models in the AI era, and Philipp’s interest in quantum computing optimization.

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Chapters:

00:00 – Cold Open

00:42 – Philipp Herzig Introduction

01:18 – What SAP Does

02:51 – Why SAP Endures

06:53 – CTO Priorities and AI Push

12:14 – Scaling AI in Enterprise

17:06 – Verifiability and Agent Mining

20:42 – Tool Calling vs. Computer Use

22:11 – Domains Where Agents Deliver Value

24:58 – Limitations of Predictive Tabular Models

29:07 – Barriers to Enterprise Adoption

31:54 – How AI Will ‘Uplevels’ Work

34:03 – How AI Changes SAP’s Pricing Model

36:41 – What Makes a Winner in the AI Era

38:53 – Day in the Life of a CTO

40:08 – Customer Challenges

42:36 – Business Problem of Quantum Computing

46:21 – Conclusion

Few teens are business owners, but by age 16, Bill McDermott had purchased and was running a local deli. Now he runs leading global technology powerhouse ServiceNow, a company that is defining how the world’s largest organizations transform for the digital age. Sarah Guo sits down with ServiceNow CEO Bill McDermott to discuss his journey from child entrepreneur to CEO, and how he navigates his role as a leader in the age of AI. Bill argues that human connection is still a vital part of being a successful leader, and as such, AI must be used to serve people rather than substitute for ambition. He breaks down the mechanics of hyper-growth, and the art of staying customer-centric at a global scale. They also discuss the future of enterprise software, how generative AI is fundamentally reshaping the labor market, and what founders need to know about building a resilient company culture that survives economic and technological shifts.

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Chapters:

00:00 – Cold Open

00:50 – Bill McDermott Introduction

01:14 – Lesson from Buying a Deli

07:35 – Leadership in the AI Era

09:41 – How Bill Got Hired at Xerox

15:47 – Can Agency Be Taught?

18:40 – Seeing Change as Opportunity

25:18 – ServiceNow as an AI Control Tower

30:30 – Which SaaS Gets Disrupted?

32:22 – Defining a Platform Business

36:25 – Does AI Decrease Implementation Time?

39:06 – Agents Will Reshape the Workforce

40:59 – Success Signals at ServiceNow

44:07 – Enterprise Attitudes About AI

48:41 – How AI Has Changed Customer Conversations

50:48 – Bill’s Curiosity Beyond ServiceNow

52:29 – Day in the Life of a CEO

57:27 – Conclusion

AI agents can already collaborate, but they lack a trustworthy medium in which to store value and execute contracts. Enter Circle’s Arc Blockchain, an economic “operating system” designed for a world where machines drive the real economy. Circle co-founder and CEO Jeremy Allaire joins Elad Gil to dive into the future of programmable money and the agentic economy. Jeremy explains why traditional banking fails to support the needs of AI agents, and how stablecoins like USDC facilitate an internet-native economy. They also discuss the tokenization of real-world assets, the move toward full-reserve banking, and Jeremy’s predictions for double-digit GDP growth as AI and blockchain reach their “broadband moment.” 

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Chapters:

00:00 – Cold Open

00:05 – Jeremy Allaire Introduction

00:21 – Origin Story of Circle

02:11 – Rethinking the Financial System

05:26 – The Role of Stablecoins

09:52 – Use Cases for USDC

11:30 – Programmable Money 

12:25 – Blockchain as Operating System

14:37 – The Agentic Economy

17:45 – Arc Blockchain Use Cases

27:00 – Scaling Models and Privacy Tech

30:45 – Securitization of Other Assets Under the Blockchain

34:16 – Prediction Markets

35:09 – Incremental Revenue Through GPU Usage

37:19 – Jeremy’s 10 Year Future Vision

41:12 – AI and GDP

44:00 – Conclusion

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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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