No Priors: Artificial Intelligence | Technology | Startups

• Chips, Neoclouds, and the Quest for AI Dominance with SemiAnalysis Founder and CEO Dylan Patel

  What would it take to challenge Nvidia? SemiAnalysis Founder and CEO Dylan Patel joins Sarah Guo to answer this and other topical questions around the current state of AI infrastructure. Together, they explore why Dylan loves Android products, predictions around OpenAI’s open source model, and what the landscape of neoclouds looks like. They also discuss Dylan’s thoughts on bottlenecks for expanding AI infrastructure and exporting American AI technologies. Plus, we find out what question Dylan would ask Mark Zuckerberg.  Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @dylan522p | @SemiAnalysis_ Chapters: 00:00 – Dylan Patel Introduction 00:31 – Dylan’s Love for Android Products 02:10 – Predictions About OpenAI’s Open Source Model 06:50 – Implications of an American Open Source Model for the Application Ecosystem 10:48 – Evolution of Neoclouds 17:26 – What It Would Take to Challenge Nvidia 27:43 – What Would an Nvidia Challenger Look Like? 28:18 – Understanding Operational and Power Constraints for Data Centers 34:48 – Dylan’s View on the American Stack 43:01 – What Dylan Would Ask Mark Zuckerberg 44:22 – Poker and AI Entrepreneurship 46:51 – Conclusion

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• The Shifting Value of Content in the AI Age with Cloudflare CEO Matthew Prince

  Cloudflare has spent nearly fifteen years making the Internet faster, more reliable, and more secure. So now that AI systems are changing the way we interact with the Internet, Cloudflare wants to help level the playing field for content creators. Sarah Guo and Elad Gil sit down with Matthew Prince, co-founder and CEO of Cloudflare to discuss the evolution of the internet from search to AI, including Cloudflare’s role in facilitating that shift. Matthew talks about how AI assistants are changing the shape of the Internet, the problems Google created by making traffic the arbiter of content value, and how he sees Cloudflare’s part in facilitating the new content marketplace for the mutual benefit of creators and AI companies. Plus, a look towards how agentic infrastructure may unfold in the near future. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @eastdakota | @Cloudflare Chapters: 00:00 – Matthew Prince Introduction 00:37 – Cloudflare’s Role in Securing the Internet 02:08 – The Road to Cloudflare’s Dominance 03:20 – The Internet’s Shift from Search to AI 06:34 – Role of Agents and Content on the New Web 09:44 – Reshaping the Content Market Online 13:05 – De-emphasizing Traffic as a Proxy for Value 18:04 – Will We Run Out of Quality Human-Generated Content? 20:01 – Scaling the Value of Content in the AI Age 22:32 – Cloudflare’s Approach to Inference 24:55 – How Cloudflare Responds to Market Demand 26:04 – Open vs. Closed Models 27:21 – Path to the New Marketplace for Content 30:58 – Advice for Content Creators 32:47 – Exploring the Timeline for Running Models Locally 40:07 – The Future of Agentic Infrastructure 44:52 – Conclusion

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• America’s Plan to Dominate the Full AI Stack with Sriram Krishnan

  Sriram Krishnan was never interested in policy. But after seeing a gap in AI knowledge at senior levels of government, he decided to lend his expertise to the tech-friendly Trump administration. Senior White House Policy Advisor on AI Sriram Krishnan joins Elad Gil and Sarah Guo to talk about America’s AI Action Plan, a recent executive order that outlines how America can win the AI race and maintain its AI supremacy. Sriram discusses why winning the AI race is important and what that looks like, as well as the core goals of the Action Plan that he helped to author. Together, they explore how AI is the latest iteration of American cultural exportation and soft power, the bottlenecks in upgrading America’s energy infrastructure, and the importance of America owning the “full stack” from GPUs and models to agents and software. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @skrishnan47 | @sriramk Chapters: 00:00 – Sriram Krishnan Introduction 01:00 – Sriram’s Role in Government 03:43 – Impetus for the America AI Action Plan 06:14 – What Winning the AI Race Looks Like 10:36 – Algorithms and Cultural Bias 12:26 – Main Tenets of the America AI Action Plan 19:13 – Infrastructure and Energy Needs for AI 22:56 – Manufacturing, Supply Chains, and AI 24:52 – Ensuring American Dominance in Robotics 26:30 – Translating Policy to Industry and the Economy 29:30 – Should the US Be a Technocracy? 32:33 – Understanding the Argument Against Open Source Models 36:07 – Conclusion

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• The Power of Quality Human Data with SurgeAI Founder and CEO Edwin Chen

  In the generative AI revolution, quality data is a valuable commodity. But not all data is created equally. Sarah Guo and Elad Gil sit down with SurgeAI founder and CEO Edwin Chen to discuss the meaning and importance of quality human data. Edwin talks about why he bootstrapped Surge instead of raising venture funds, the importance of scalable oversight in producing quality data, and the work Surge is doing to standardize human evals. Plus, we get Edwin’s take on what Meta’s investment into Scale AI means for Surge, as well as whether or not he thinks an underdog can catch up with OpenAI, Anthropic, and other dominant industry players. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @echen | @HelloSurgeAI Chapters: 00:00 – Edwin Chen Introduction 00:41 – Overview of SurgeAI 02:28 – Why SurgeAI Bootstrapped Instead of Raising Funds 07:59 – Explaining SurgeAI’s Product 09:39 – Differentiating SurgeAI from Competitors  11:27 – Measuring the Quality of SurgeAI’s Output 12:25 – Role of Scalable Oversight at SurgeAI 14:02 – Challenges of Building Rich RL Environments 16:39 – Predicting Future Needs for Training AI Models 17:29 – Role of Humans in Data Generation 21:27 – Importance of Human Evaluation for Quality Data 22:51 – SurgeAI’s Work Toward Standardization of Human Evals 23:37 – What the Meta/ScaleAI Deal Means for SurgeAI 24:35 – Edwin’s Underdog Pick to Catch Up to Big AI Companies 24:50 – The Future Frontier Model Landscape 26:25 – Future Directions for SurgeAI 29:29 – What Does High Quality Data Mean? 32:26 – Conclusion

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• Asimov: Building An Omniscient RL Oracle with ReflectionAI’s Misha Laskin

  Superintelligence, at least in an academic sense, has already been achieved. But Misha Laskin thinks that the next step towards artificial superintelligence, or ASI, should look both more user and problem-focused. ReflectionAI co-founder and CEO Misha Laskin joins Sarah Guo to introduce Asimov, their new code comprehension agent built on reinforcement learning (RL). Misha talks about creating tools and designing AI agents based on customer needs, and how that influences eval development and the scope of the agent’s memory. The two also discuss the challenges in solving scaling for RL, the future of ASI, and the implications for Google’s “non-acquisition” of Windsurf.  Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @MishaLaskin | @reflection_ai Chapters: 00:00 – Misha Laskin Introduction 00:44 – Superintelligence vs. Super Intelligent Autonomous Systems 03:26 – Misha’s Journey from Physics to AI 07:48 – Asimov Product Release 11:52 – What Differentiates Asimov from Other Agents 16:15 – Asimov’s Eval Philosophy 21:52 – The Types of Queries Where Asimov Shines 24:35 – Designing a Team-Wide Memory for Asimov 28:38 – Leveraging Pre-Trained Models 32:47 – The Challenges of Solving Scaling in RL 37:21 – Training Agents in Copycat Software Environments 38:25 – When Will We See ASI?  44:27 – Thoughts on Windsurf’s Non-Acquisition 48:10 – Exploring Non-RL Datasets 55:12 – Tackling Problems Beyond Engineering and Coding 57:54 – Where We’re At in Deploying ASI in Different Fields 01:02:30 – Conclusion

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