
Ryan Greenblatt on What Happens When AI Automates AI Research
Dwarkesh Patel interviews Ryan Greenblatt, chief scientist at Redwood Research, about what follows if AI systems can perform most AI research.
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Ryan Greenblatt on What Happens When AI Automates AI Research
Dwarkesh Patel interviews Ryan Greenblatt, chief scientist at Redwood Research, about what follows if AI systems can perform most AI research.
Source-ordered deep dives into important AI research and conversations.

Dwarkesh Patel interviews Ryan Greenblatt, chief scientist at Redwood Research, about what follows if AI systems can perform most AI research.

This 68-minute 20VC interview pairs host Harry Stebbings with Alex Atallah, co-founder and CEO of OpenRouter, to examine why a multi-model AI market may persist.

SemiAnalysis tests the economics, construction, customer demand, and financing behind SpaceX's reported plan to reach 10GW of AI compute by the end of 2027.

Sarah Guo and Elad Gil ask which AI markets can support trillion-dollar companies, how founders should revisit exit decisions, and who wins when compute and regulation constrain competition.

Published August 7, 2026, this SemiAnalysis discussion brings together Jon Y of Asianometry, Doug O’Laughlin, and Jordan Nanos to examine where practical AI advantage is accumulating.

This Gradient Descent podcast interview from Weights & Biases has host Lukas Biewald talking with Fireworks co-founder and CEO Lin Qiao about why she built an inference company for specialized AI models.

This Recap covers a Latent Space podcast interview and a follow-up Modal conversation about bursty inference, RL execution, DFlash, and agent infrastructure that can scale to 100,000 sandboxes.

Invest Like The Best host Patrick O’Shaughnessy talks with investor Gavin Baker about why the July 2026 selloff in AI and semiconductor stocks does not match the operating data he sees.

This Latent Space podcast interview has host swyx asking Baseten’s Philip Kiely and Ali Taha how inference turns an open model into a product: request routing, quantization, GPU kernels, model parallelism, and AI video. It gets properly weird near the end, when GLM-5.2 helps rewrite its own serving code and continual learning starts to blur the line between training and inference.