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

Patel opens by naming his own position: “historically, I’ve been quite skeptical that this kind of thing happens, but you seem to think that it might be plausible, and so I wanted to hear the case for it.” What follows is two hours of him pressing on recursive self-improvement from the outside while Greenblatt builds it up from the inside — AI research being unusually verifiable, unusually well-optimised-for by the labs, and therefore the most likely place a feedback loop starts. Greenblatt’s median is roughly four or five years of AI progress compressed into one, which he is careful to note requires overcoming enormous diminishing returns rather than assuming them away.

The episode acquired a second life a few weeks later. While it was being recorded, Greenblatt was in the middle of the six-day sprint assembling the METR and Redwood investigation into the OpenAI/Hugging Face incident — and so already knew the counterexamples to several of the objections Patel was raising, but was bound by confidentiality and said nothing. Patel noted the irony afterwards. The conversation is a record of a careful skeptic and a careful worrier working through the argument with the evidence still sealed.