Sutton Rebuts LLM Scaling, Says Raw Compute Alone Cannot Yield Intelligence

1 reports archived1 independent sourcesupdated 11/8/2026, 19:44:41

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Turing Award winner Richard Sutton, author of the influential 2019 essay "The Bitter Lesson," argues in a new post that raw compute alone is insufficient for intelligence and that AI science needs clever new methods and is far from finished. He has previously criticized the limits of LLM scaling, called synthetic data a "big mistake," and called for agents with world models that continuously learn from their own experience.

LATEST/Turing Award winner Richard Sutton, author of the influential 2019 essay "The Bitter Lesson," argues in a new post that raw compute alone is insufficient for intelligence and that AI science needs clever new methods and is far from finished. He has previously criticized the limits of LLM scaling, called synthetic data a "big mistake," and called for agents with world models that continuously learn from their own experience.

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The DecoderT2Nov 8, 19:44·72 pts
Sutton Rebuts LLM Scaling, Says Raw Compute Alone Cannot Yield Intelligence
- Sutton argues compute scaling is not sufficient for intelligence and new methods are still needed
- He calls synthetic data a "big mistake" and favors agents with world models that learn continuously from experience
- Human expertise as a bottleneck blocks true scaling, opposing the mainstream scaling narrative
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