Revolutionizing Cybersecurity and AI-Driven Science with OpenAI President Greg Brockman

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.   Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @MishaLaskin | @reflection_ai Chapters: 00:00 – Misha Laskin Introduction 00:22 – Latest with ReflectionAI 01:30 – Challenges Building an Open Model 03:19 – ReflectionAI’s Agentic Shift 07:00 – Resources for Model Training 09:32 – Scaling Efficiency 11:43 – Training Beam 16:14 – Where Model Value Comes From 19:18 – Beam’s Reasoning Efficiency 21:58 – Monetizing Open Weight Models 24:28 – Future of Open Versus Closed Tokens 30:54 – Competition with Open Models 34:01 – Chinese Open Source Model Ecosystem 38:03 – Will Chinese Models Remain Open 44:07 – Safety and Open Models 52:25 – Debating Access to Powerful Tools 56:39 – AI and Scientific Progress 59:59 – Data Centers and Jobs 01:01:32 – Beam and Scientific Research 01:05:03 – Research Head Count to Compute Ratio 01:10:18 – 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.  Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @goodwin_ml | @fractile_ai 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. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @mjlee_2014 | @seqholdings 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.  Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @StefanoErmon | @_inception_ai 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.   Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @brian_armstrong | @Coinbase 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. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @renehaas237 | @Arm 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