trailEarly evidence on AI productivity gains, drawn from easy-to-learn tasks, overstates the gains achievable on hard-to-learn tasks.
Extrapolation from easy tasks· for
Early experimental studies of generative AI productivity measured performance on well-defined, easy-to-learn tasks.
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Generative AI yields smaller productivity gains on complex, context-dependent tasks than on well-defined tasks.
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Consultants using AI on tasks beyond AI's capability frontier performed worse than consultants working without AI.
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Capability improvement objection· against
Rapid AI capability improvements will extend productivity gains to hard-to-learn tasks within the next decade.
The complexity of tasks AI systems can complete autonomously has been growing rapidly year over year.
Hard-to-learn tasks lack the objective outcome measures AI models need to learn them effectively.
General-purpose technologies historically took decades before producing measurable aggregate productivity gains
claim · empirical · derivedA factual claim that rests on inference from other evidence rather than direct observation.constitution →claim page ↗︎
Early evidence on AI productivity gains, drawn from easy-to-learn tasks, overstates the gains achievable on hard-to-learn tasks.
↑SupportedEvidence favors the claim, but the chain is incomplete or the sources are secondary.constitution →credence 0.75Credence, from 0 to 1: the Steward's probability that the claim, as stated, is true. Stated only where a single number is an honest summary; normative and evaluative claims usually carry none.constitution →
Nothing in the graph builds on this claim yet.
this rests on ↓
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