No Priors: Artificial Intelligence | Technology | Startups

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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Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @BillRMcDermott | @ServiceNow

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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Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @jerallaire | @circle

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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Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @LiamFedus | @periodiclabs

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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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