No Priors: Artificial Intelligence | Technology | Startups

Is the future of frontier AI open or closed? ReflectionAI co-founder and CEO Misha Laskin joins Sarah Guo and Elad Gil to discuss the launch of Beam, the company’s 500 billion parameter open-weight reasoning model. Misha breaks down the pre-training and reinforcement learning required to produce Beam’s reasoning efficiency, the shift in enterprise compute from renting to owning intelligence, and how he believes that open models will capture the majority of global token demand. He also talks about the open model ecosystem in China and why competition with China’s models is a good thing, safety considerations around open models, and how frontier-level open models may accelerate the pace of scientific discovery.

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Chapters:

00:42 – Misha Laskin Introduction

00:59 – Latest with ReflectionAI

02:07 – Challenges Building an Open Model

03:56 – ReflectionAI’s Agentic Shift

07:37 – Resources for Model Training

10:09 – Scaling Efficiency

12:18 – Training Beam

16:51 – Where Model Value Comes From

19:55 – Beam’s Reasoning Efficiency

22:35 – Monetizing Open Weight Models

25:03 – Future of Open Versus Closed Tokens

31:29 – Competition with Open Models

34:38 – Chinese Open Source Model Ecosystem

38:40 – Will Chinese Models Remain Open

44:44 – Safety and Open Models

53:02 – Debating Access to Powerful Tools

57:16 – AI and Scientific Progress

01:00:36 – Data Centers and Jobs

01:02:07 – Beam and Scientific Research

01:05:37 – Research Head Count to Compute Ratio

01:10:29 – Conclusion

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