Ethan Mollick argues small accuracy gains compound on long tasks, favoring frontier models
Overview
Ethan Mollick says on X that a paper has convinced him frontier AI models cannot easily be replaced by cheaper ones.
He argues that small gains in accuracy and judgment compound over long-horizon tasks, producing large differences in outcomes. He recommends using the best models as orchestrators. The post links to no specific paper or data, and the report's supporting evidence field is empty.
Written by AI from the articles below · updated Oct 15, 10:37 PM ET
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Ethan Mollick@emollickXMollick says small accuracy gains compound over long tasks, favoring frontier modelsAIEthan Mollick says a paper convinces him frontier models cannot easily be replaced by cheaper ones. He argues small improvements in accuracy and judgment compound over long-horizon tasks, producing large gains in outcomes, and recommends using the best models as orchestrators.

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