No Priors: Artificial Intelligence | Technology | Startups

Founder and CEO of full-stack AI chip company Fractile, Walter Goodwin, joins Sarah Guo to discuss the bets he’s made on the future of the chip market as other major players like Broadcom, NVIDIA, and AMD try to accelerate their workloads. They discuss the difference in Fractile’s newer approach on model architecture with a full-stack team in the current chip landscape and the technical bets they’re making in that direction. Walter also talks about compressing the gap between the chip design cycle and its payoff period, and making a generational leap in AI inference to realize the bet in volume against the value to be captured.

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Chapters:

00:46 – Walter Goodwin and Fractile Introduction

02:29 – The Chip Landscape Now

04:56 – Common Handoffs From Architecture-Focused Players

07:18 – Full Stack Approach and Team Setup

09:49 – Fractile’s Most Important Technical Bets

15:32 – Workload Predictions and Compressing the Chip Design Cycle

23:03 – Architect Intent to Output Bottlenecks and Accelerating Trials

28:16 – Workload Bets on Model Architectural Shifts

31:20 – The Future of AI Chip Players and Market Structure

35:14 – Conclusion

Can AI transform legacy incumbents rather than replacing them? Sequence Holdings co-founder and CEO Michael Lee joins Sarah Guo to discuss how holding company structures and engineering integrations are reshaping market leaders from the inside out. Michael details Sequence’s $7.7 billion take-private transaction of Baldwin alongside Dell Family Office (DFO), and shares his thesis on why traditional consulting models and software sales fall short for real enterprise AI transformations. They also talk about why permanent holding company structures are good for long-term compounding, real-world results from applying frontier engineering to BankSouth, and Michael’s lessons from his time in public investing, private equity, and operating at the intersection of market incumbents and AI.

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Chapters:

00:34 – Michael Lee Introduction

01:03 – Sequence Holdings and Baldwin

01:54 – Idea for Sequence

04:36 – Incumbents in the AI Era

06:32 – Why a Holding Company

11:21 – Recruiting Top Engineers

13:08 – Investing in BankSouth

17:46 – Why an Insurance Brokerage

20:17 – Atlas Platform Explained

23:53 – Traditional Private Equity Limitations

27:23 – What Sequence Looks For in Management Teams

31:04 – Accomplishments at BankSouth

34:45 – Founder Lessons

36:08 – Story of Dell Partnership

37:10 – Career and Investment Approach

40:00 – Value of Exceptional People

42:23 – Conclusion

As generative AI hits hardware and latency bottlenecks, Stanford professor, diffusion pioneer, and Inception co-founder and CEO Stefano Ermon is betting on a radical new architecture. Stefano joins Sarah Guo to talk about Inception, and how his team is applying diffusion architecture beyond images and video into discrete text and code generation. Stefano explains the limitations of autoregressive LLMs, as well as why parallel token generation in diffusion models offers superior inference scaling and hardware utilization on standard GPUs. He also shares details about Inception’s Mercury models, real-world voice agent applications, the software stack required to serve diffusion-based models at scale, academia’s role at the frontier of AI innovations, and why the next era of AI competition will be defined by efficiency.

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Chapters:

00:00 – Stefano Ermon Introduction

00:35 – Research Background

02:54 – Starting Inception

05:59 – Why Diffusion Beats Autoregressive

11:10 – Discrete vs. Continuous Modalities

13:19 – Inception Today

16:45 – Where Speed Wins

17:31 – Inception Customer Base

18:49 – Interaction with Hardware Landscape

19:34 – Inception and the Broader Industry

21:41 – Data Compression and Structure

24:45 – Controllability of Diffusion Modeles

27:25 – Emergent Capabilities at Scale

29:02 – Future Workload Split Between Diffusion vs. Traditional

30:03 – Adoption Challenges

31:44 – Hiring and Team Organization

32:50 – Recursive Self Improvement

34:02 – Resource Allocation

35:10 – Impact of Academia

38:13 – Conclusion

From the tokenization of real-world assets to funding research on anti-aging therapeutics, Coinbase co-founder and CEO Brian Armstrong is focused on solving meta-problems for the benefit of society. Brian joins Elad Gil this episode to discuss the intersections of artificial intelligence, crypto rails, and biotechnology. He details Coinbase’s vision for agentic finance, as well as how Coinbase is becoming the ‘Everything Exchange’, which includes tokenized real-world stocks, stablecoin payment adoption, and prediction markets. Brian also introduces his new venture, New Limit, a company founded to tackle longevity through epigenetic reprogramming, and outlines initial therapeutic programs and the roadmap toward clinical trials.

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Chapters:

00:00 – Cold Open Trailer

00:45 – Brian Armstrong Introduction

01:13 – Coinbase and the Everything Exchange

05:52 – Agentic Commerce

10:00 – AI and Crypto

10:49 – AI Inside Coinbase

15:39 – Productivity and Company Size

17:26 – The Everything Exchange and Tokenization

20:53 – Prediction Markets

23:15 – Introducing New Limit

34:17 – Looking Forward Five Years

40:24 – Special Economic Zones

44:26 – Conclusion

From data center orchestrators to AGI and robotics, CPUs remain the heart of modern computing. Arm CEO Rene Haas joins Elad Gil and Sarah Guo to explore how Arm is positioned at the epicenter of AI-driven demands for compute. Rene explains Arm’s position in the chip supply chain, and how Arm transitioned from an IP licensing model to producing physical chips like the Arm AGI CPU for Meta. He also discusses bottlenecks in hardware supply chains, SoftBank’s ecosystem and capital strategy, why US semiconductor manufacturing independence is critical, the future of robotics, and why CPUs remain crucial for executing AI workloads.

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Chapters:

00:00 – Cold Open Trailer

00:49 – Rene Haas Introduction

01:14 – Arm and Chip Supply Chain

02:37 – Shift from IP to Manufacturing CPUs

04:23 – CPU IP and Customers

06:55 – AI Adoption at Arm

10:15 – Changes in Chip Time to Market

13:27 – Data Center Buildout Bottleneck

15:13 – Softbank Leverage and Capital Strategy

17:43 – Softbank Portfolio Overview

20:13 – Robotics Opportunities for Arm

24:49 – US Manufacturing Protectionism

28:59 – Data Center Backlash

32:30 – Arm Outlook

33:31 – CPU Opportunity

37:06 – Conclusion