What changes when AI can automate AI research?

Dwarkesh Patel and Redwood Research chief scientist Ryan Greenblatt debate what happens if AI systems can do most AI research themselves. The crux is whether research tasks are verifiable and iterative enough for AI agents to improve models faster than human researchers, or whether scarce compute, expert judgment, and hard-to-measure research taste keep progress bottlenecked. They also examine who increasingly capable systems should be aligned to, and whether reward hacking, collusion, and deceptive behavior could scale into loss-of-control risks. It matters because the pace and shape of AI R&D automation would affect both how quickly capabilities advance and how much time institutions have to build reliable oversight.