Dwarkesh: Lack of continual learning prevents LLMs from replacing human labor
Dwarkesh Patel · Dwarkesh Patel: AI Continuous Improvement, Intelligence Explosion, Memory, Frontier Lab Competition · Jun 18, 2025 · at 2:21
Dwarkesh Patel discusses why architectural limitations, specifically session-based memory resets, prevent LLMs from acting like human workers.
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“I think a big bottleneck these models have is their inability to learn on the job, to have continual learning. Their entire memory is extinguished at the end of a session. There's a bunch of reasons why I think this actually makes it really hard to get human-like labor out of them.”
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