Person
Matt Turck
OpenAI’s Compute Chief: We Can’t Build Fast Enough | Sachin Katti
Sachin Katti says OpenAI is racing to build compute faster than the physical world can supply power, chips, cooling, and labor. He frames AI data centers as giant liquid-cooled factories turning electrons into tokens, with custom silicon, new power infrastructure, and networking all becoming strategic bottlenecks.
Stripe's AI Chief: How AI Agents Will Buy, Sell, and Pay
Stripe’s Emily Sands says agentic commerce is moving from theory to production, with protocols, wallets, and real-time billing infrastructure already being built. She also argues that token theft, first-party abuse, and deployment friction are emerging as the key constraints on the AI economy.
Inside Nemotron & NVIDIA’s AI Lab | Bryan Catanzaro
Bryan Catanzaro explains why NVIDIA builds and gives away open AI models: to understand future systems better and strengthen the open ecosystem. He also details Nemotron’s engineering choices, from 4-bit pretraining and hybrid architectures to long context, multi-token prediction, and MoE-heavy systems design.
Cloudflare CEO: The Internet's Business Model Is Dead
Matthew Prince argues Cloudflare is seeing the internet tip from human-driven to bot-driven traffic, and that this will break the ad-supported web economy. He also explains how Cloudflare’s edge network, Workers, AI Gateway, and internal AI tools are being rebuilt for an agentic internet.
The GPU Myth: State of AI Compute 2026 | Stephen Balaban
Stephen Balaban argues GPU compute is not becoming a commodity; instead, AI infrastructure is becoming more vertically integrated around land, power, cooling, networking, software, and financing. He also traces Lambda’s evolution from an early facial-recognition startup into a near-$1B cloud business and lays out a long-term vision where AI becomes the software layer itself.
OpenAI's Dan Roberts: Why AI Can Now Make Discoveries
Dan Roberts argues that modern reinforcement learning and test-time compute are turning language models into systems that can reason, explore, and sometimes make genuine scientific discoveries. He uses OpenAI’s recent math work, RLHF, and physics-inspired thinking to explain why AI progress looks smooth, why scale alone is not enough, and why future models may help drive science itself.