We are now closer than ever before to living in a world where AI agents are smart enough to run our power grids and manage water supplies. How do we keep them from going rogue? Sarah Guo sits down with Maxim Bar Kogan, founder and CEO of Onyx Securities, to explore the complexities of supervising and securing autonomous agents at the enterprise level. Maxim explains Onyx’s product as an AI control plane, which oversees the permissions and flexible contexts of agents while balancing latency, cost, and reliability. He also discusses how current controls have insufficient context to monitor agent intent, tradeoffs for gradual model rollout, the need for vendor-independent oversight, and Israel’s growing AI and security talent ecosystem. Plus, why Maxim is all-in on AGI.

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Chapters:

00:00 – Cold Open

00:45 – Maxim Bar Kogan Introduction

01:10 – AutoGPT and Betting on Agent Actions

05:17 – What Onyx Product Does

07:47 – State of Deployment in Large Enterprises

09:58 – Securing Agents

12:45 – Why Proxies Don’t Work

14:11 – Why Onyx Trains Its Own Models

18:38 – Onyx’s Talent Culture

21:24 – Mechanistic Interpretability

23:35 – How Onyx Builds Customer Trust

25:10 – Mitigating Risk at the Foundational Level

27:45 – Phased Rollout of Glasswing and Daybreak

29:11 – Large Enterprise Holdouts

30:46 – Onyx and the Larger AI Security Space

32:36 – Should Labs Address Model Trust and Governance? 

36:56 – What Needs to Happen in Security

39:14 – Why Maxim is AGI-Pilled

41:15 – Conclusion

Companies in Silicon Valley from Nvidia to AMD are racing to fuel the AI revolution with postage stamp-sized AI chips. Meanwhile, a chip the size of a dinner plate just fueled a $63 billion IPO for Cerebras. Elad Gil and Sarah Guo sit down with Cerebras founder and CEO Andrew Feldman to discuss the company’s journey to making one of the largest tech go-publics in history. Andrew details the multi-year journey of pioneering wafer-scale AI computing, including surviving a brutal period of being ahead of market demand. He also explains the engineering breakthroughs that led to delivering inference speeds at 20x that of standard GPUs. Andrew then shares how a remarkable $20 billion deal with OpenAI came together in only four weeks. Plus, Andrew’s thoughts on why architecting the future of AI requires the fortitude to be a “professional David” against the Goliaths of tech.

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Chapters:

00:00 – Cold Open

00:36 – Andrew Feldman Introduction

01:19 – Cerebras’ Evolution

02:48 – Wafer-Scale Bet Pays Off

06:38 – Challenges and Breakthroughs

08:37 – Crossing the Market Chasm

10:38 – Scaling Software and Hardware

12:03 – Relevance of AI-Generated Coding

13:31 – Leadership and Hiring Culture

17:16 – When to Quit vs. Persist

19:40 – Why Cerebras Went Public

22:57 – The OpenAI Deal

25:54 – Open Source and Post-Trained Workloads

27:37 – How Speed Opens Up New Business

30:33 – Conclusion

Securing AI dominance requires more than just semiconductors; it demands a complete overhaul of how the West manages everything that goes into them, from rare earth minerals to actuators. Enter: Pax Silica. Sarah Guo and Elad Gil sit down with US Under Secretary of State for Economic Affairs Jacob Helberg to discuss the launch and expansion of Pax Silica, a 14-country economic security coalition designed to secure the entire AI supply chain. Jacob talks about the creation of a forward-deployed industrial base in the Philippines, where 4,000 acres will be developed into an “economic security zone.” He also compares and contrasts Pax Silica with China’s Belt and Road initiative, explains how the US plans to reindustrialize through automation and robotics, and explores how the Trump administration envisions making these policies durable across future presidencies. Plus, we hear why Jacob believes America to be a “global underdog.”

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Chapters:

00:00 – Cold Open

00:41 – Jacob Helberg Introduction

01:02 – Pax Silica’s Mission

03:51 – Investing in AI Chip Supply Chains

05:43 – Comparing Pax Silica to China’s Belt and Road Initiative

12:38 – Pax Silica’s Value Proposition

14:38 – US vs. Partnered Manufacturing

19:10 – Rare Earth Mineral Pricing

22:16 – Role of Venture Capital in Pax Silica

24:50 – Near vs. Long-Term Priorities

27:09 – Making AI Policy Durable

28:09 – How Policies Impact Entrepreneurs

31:00 – Trump’s Entrepreneurial Administration

33:00 – Why America is a Global Underdog

38:00 – Conclusion

The world’s first AI-take-private just proved that AI can revolutionize the real economy. Long Lake Management co-founder and CEO Alexander Taubman joins Elad Gil to discuss his firm’s agreement to acquire the legacy platform American Express Global Business Travel (Amex GBT) in a deal valued at $6.3 billion. Alexander explains the mechanics of AI-driven roll-ups, and why Long Lake chooses to acquire and transform businesses rather than simply selling them software. He also talks about how Long Lake’s horizontal AI platform, Nexus, automates workflows across diverse verticals, and how automation through AI not only powers growth for their portfolio companies, but results in both satisfied customers and employees. Plus, they explore Alexander’s vision of Amex GBT as a multi-decade compounding machine. 

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Chapters:

00:00 – Alexander Taubman Introduction

00:30 – Long Lake’s Nexus Platform

03:35 – Retention and Talent Flywheel

05:01 – Acquisition vs. Offering Software

06:57 – Building Long Lake’s Founding Team

10:37 – Taking American Express Global Business Travel Private

13:36 – Taking Berkshire Hathaway’s Approach to Management

16:37 – How AI Strategy Makes Long Lake Stand Out 

19:32 – AI Makes Services Scale

22:00 – Conclusion

Baseten CEO and co-founder Tuhin Srivastava sits down with Sarah Guo and Elad Gil to discuss the rapid growth of AI inference demand, Baseten’s 30x growth, and why inference is becoming the strategic “last market.” Tuhin Srivastava argues the application layer will persist because companies with unique user signals can encode value into workflows and post-train specialized models, citing examples like Abridge and support workflows. The conversation covers GPU capacity constraints, Baseten’s multi-cloud fabric across 18 clouds and 90 clusters, long-term contracting dynamics, the importance of the software layer for stickiness, evolving workloads, multichip possibilities, and operational lessons at scale.

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Chapters:

00:31 Baseten growth

01:55 Why the app layer wins

05:57 Serving frontier customers

07:55 Open source model mix

09:21 Chinese models and geopolitics

13:07 Custom inference dominates

14:22 Post training acquisition

17:10 When to invest in custom models

18:35 Supply crunch and data centerse

22:25 Longer GPU Contracts

24:09 What Makes a Winner

26:07 Multi Chip Future

28:19 Runtime Roadmap

31:08 Scaling Edge Cases

33:48 Hiring and Leadership

36:44 Operations Pager Culture

38:19 Efficiency Drives Demand

40:41 Concierge Everything Future

42:34 Conclusion

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