No Priors: Artificial Intelligence | Technology | Startups

In the transition to the agentic web, security has become an afterthought. Now, organizations are racing to figure out what AI security means, and how to keep their data safe. Live from Cloudflare Connect, Sarah Guo sits down with Cloudflare CTO Dane Knecht to discuss the changing attacker landscape, and how his company is allocating its engineering resources to meet this emerging security challenge. Dane explains how enterprises that have moved away from castle-and-moat infrastructure to more of a zero trust infrastructure for security are in better positions than those who haven’t. He also discusses the challenges of patching 70 years of software and why the software development lifecycle will need to change for security for the agentic web, why he sees token spend in the next 18 months primarily going towards security rather than engineering, how the current web is in a ‘messy middle’ state where humans and agents are riding the same ‘rails’ on the internet, Cloudflare’s conference monetization gateway release as well as its Signals project with OpenAI, the rationale behind the release of Cloudflare OS, and the thinking behind a recent company restructuring. Plus, why Cloudflare hired one thousand interns, and why curiosity about AI is required to be successful at Cloudflare.  Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @Cloudflare | @dok2001 Chapters: 00:00 – Trailer 00:55 – Cloudflare CTO Top Priorities 05:03 – Today’s Attacker Landscape 06:16 – State of Internet Infrastructure 07:31 – Shift Towards Security Spend 08:39 – Road to Better Software and Internet Security 09:50 – Building Secure Production Software 11:41 – Reducing Surface Area 12:54 – Near Future of the Web 17:27 – Monetization and Signals  19:13 – Success for Content Creators  20:07 – Workplace OS and Use Cases 24:07 – Restructuring for AI-First 27:11 – Token Budgeting 29:32 – Cloudflare’s Intern Strategy 33:39 – Importance of Curiosity 35:29 – Attacking the Legacy Software Security Problem 37:08 – Engineering Team Prioritization 38:25 – Open vs. Closed Models 39:43 – Democratizing Security for Everyone 41:29 – Conclusion

Technology has always been the fundamental driver of improvement for human quality of life. And with the recent rollout of advanced frontier models, like OpenAI’s GPT-6, humanity can now massively accelerate scientific discovery for its benefit. Sarah Guo and Elad Gil are joined by OpenAI President and co-founder Greg Brockman to discuss the company’s rapid progress across frontier models, cybersecurity, and scientific discovery. Greg shares how nearly 400 century-old math problems have been solved using its AI agents, and how the technology has the potential to transform other fields like biology in the realm of drug discovery. He also talks about how proactive, always-on AI agents like Dots impact modern productivity, why he believes the best UI is no UI at all, how cybersecurity is in a critical window of time to use peak intelligence to find and fix vulnerabilities in systems, and why OpenAI remains committed to keeping humans in the driver’s seat even as models surpass us in capability. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @gdb | @OpenAI Chapters: 00:42 – Trailer 00:59 – Greg Brockman Introduction 01:11 – AI and Advancements in Mathematics 05:55 – AI Communication and Oversight 07:45 – AI for Overcoming Biology Data Bottlenecks 11:28 – Revolutionizing Drug Discovery 14:18 – Changing Regulations to Accelerate Progress 16:51 – Dots the Proactive Agent 19:19 – Unifying Work and Consumer Agents 22:02 – How UI Will Evolve 26:08 – Cybersecurity in the AI Age 32:55 – Shift in Security Investment 35:49 – OpenAI’s Human-Centric Ethos 38:28 – 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 [email protected] 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

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 [email protected] 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