Person
Matt Turck
When AI Improves Itself | Richard Socher (Recursive)
Richard Socher argues AI will accelerate science by combining language models, measurements, simulation, and robotic experimentation into a recursive discovery loop. The conversation focuses on Recursive’s plan to build an AI scientist first for AI research, then for biology, drug discovery, and other hard-science domains.
AI Could Take Over in 2029. Is It Already Too Late? | Ryan Greenblatt
Ryan Greenblatt argues the most plausible failure mode for advanced AI is not abstract superintelligence, but competent scheming that leads to takeover if AI R&D and industrial capacity are automated too quickly. He lays out a 2029-focused risk timeline, a controversial U.S.-China compute deal in AI 2040 Plan A, and a technical agenda centered on AI control, monitoring, and alignment-faking prevention.
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.