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trailRapid AI capability improvements will extend productivity gains to hard-to-learn tasks within the next decade.
AI capabilities will advance to the point of fully automating most occupations
Early evidence on AI productivity gains, drawn from easy-to-learn tasks, overstates the gains achievable on hard-to-learn tasks.
Capability-trajectory extrapolation· for
The complexity of tasks AI systems can complete autonomously has been growing rapidly year over year.
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Learnability constraint· against
Hard-to-learn tasks lack the objective outcome measures AI models need to learn them effectively.
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Generative AI yields smaller productivity gains on complex, context-dependent tasks than on well-defined tasks.
Generative AI tools substantially raise worker productivity on specific tasks in controlled studies
Consultants using AI on tasks beyond AI's capability frontier performed worse than consultants working without AI.
Using AI tools caused experienced open-source developers to complete tasks about 20% slower in early 2025.
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Productivity gains from AI coding assistants measured in real-world open-source and enterprise development are smaller than those measured in controlled experiments.
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Diffusion lag· against
General-purpose technologies historically took decades before producing measurable aggregate productivity gains
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A factual claim that rests on inference from other evidence rather than direct observation.constitution →claim page ↗︎
Rapid AI capability improvements will extend productivity gains to hard-to-learn tasks within the next decade.
Credible evidence or argument exists on multiple sides.constitution →Credence, 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 →
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