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Back to AI researchers debate how close we are to recursive self-improvement, with John Schulman & Beren Millidge.

Why Do AI Labs Still Train Models on Finance?

John Schulman says a model good enough at learning in context would not need training on finance. It could read the books on the fly and work in the appropriate jurisdiction. Labs might still bake those domain skills into the weights with reinforcement learning so the model is more efficient at runtime. In practice, model providers go domain by domain and strengthen the highest-value domains and the most common skills. He counts that coverage as one reason the models have gotten so much better.

Dwarkesh Patel had asked why a lab would bake in PowerPoint if a model can learn on the job the way a person does. Before Schulman answered, Patel offered two candidates: the models are not there yet, so labs amortize the skills now, and deployed revenue is poured back into a model built for recursive self-improvement.

Grokipedia·Dwarkesh Patel

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AI researchers debate how close we are to recursive self-improvement ·

Dwarkesh Podcast

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