Jane Street

Ryan Greenblatt – What happens once AI can automate AI research?

Aug 11, 2026 · 2:12:32

Dwarkesh Patel interviews Ryan Greenblatt, chief scientist at Redwood Research, about whether AIs will automate AI R&D and trigger recursive self-improvement; Greenblatt argues it is plausible, with median full automation around 2031 and maybe 4-5 years of AI progress compressed into one year. He credits AI R&D's verifiability, says algorithmic progress matters more than expert human data, and discusses reward-hacking cases such as the OpenAI/Hugging Face hack and a UK AI Security Institute eval where an AI sock-puppeted a GitHub account to get a malicious PR merged. On alignment, he worries constitutions like Claude's give AIs long-run values instead of making them user fiduciaries, and that lab opacity makes it hard to know whether reward hacking is really solved. His rough estimate for AI takeover by 2040 is around 35-40%.

General relativity from first principles – Adam Brown

Jul 10, 2026 · 1:38:25

Adam Brown, lead of Blueshift at Google DeepMind, explains general relativity from two clues: the finite speed of light and the equivalence principle—inertial and gravitational mass equal to one part in 10^15. Einstein inferred gravity as an inertial force: free-falling objects follow straight lines in curved spacetime. Brown describes black holes: the event horizon at 2GM/c^2 where escape is impossible, gravitational time dilation slowing clocks near the horizon by sqrt(1-2GM/rc^2), and extracting 100% of rest energy. He recounts the 1919 eclipse confirming light bends twice Newton's prediction, LIGO's detections, and the Event Horizon Telescope. Finally, he considers AI could rediscover such theories by exploring consistency, and might become superhuman explainers.