Dwarkesh Patel laid out the frontier labs' core bet, learning on the job via RLVR across thousands of environments.

His June 26 episode argues that training on millions of verifiable tasks across thousands of diverse RL environments is the path the labs believe approximates AGI (Dwarkesh Patel). He doesn't sell it. He probes the cracks: whether RLVR alone generalizes, the unsolved problem of getting on-the-job learning back into the weights, grindability versus verifiability. Strategic listening if you're deciding how much to bet on agentic RL pipelines versus just riding in-context learning. I lean toward in-context for now, but I want to be wrong slowly, not fast.