Ethan Mollick Argues for Frontier Models as Orchestrators on Long-Horizon Tasks

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Ethan Mollick argued that the best frontier models should serve as orchestrators for long-horizon tasks, citing a paper as support for his skepticism that cheaper models can easily replace them. He said small improvements in accuracy and judgment compound over extended tasks, producing large gains in outcomes.

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Latest development10/16 10:17 (UTC+8)

This paper shows why I am skeptical of the idea that frontier models can be easily replaced by cheaper ones (and why you should use the best models as orchestrators): Small improvements in accuracy & judgement compound over long-horizon tasks, creating huge gains in outcomes.Ethan Mollick · X

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10/16

10:17
Ethan Mollick · X
This paper shows why I am skeptical of the idea that frontier models can be easily replaced by cheaper ones (and why you should use the best models as orchestrators): Small improvements in accuracy & judgement compound over long-horizon tasks, creating huge gains in outcomes.
This paper shows why I am skeptical of the idea that frontier models can be easily replaced by cheaper ones (and why you should use the best models as orchestrators): Small improvements in accuracy & judgement compound over long-horizon tasks, creating huge gains in outcomes.

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