John Schulman on where human judgment still matters as AI self-improves

Thinking Machines shared a Dwarkesh Patel podcast episode with John Schulman discussing where human judgment remains essential as models improve and self-improve.

Schulman highlights teaching models to handle messy real-world tasks, applying taste to what works in the long run, and specifying what people actually want. The episode also covers recursive self-improvement, long-horizon RL, and the sim-to-real gap.