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trailHow can measured AI productivity effects on developers be reconciled with benchmark scores and anecdotal reports of AI helpfulness and adoption?
Candidate answers
AI coding tools' productivity effects vary substantially by context, with smallest effects for experienced developers in familiar, mature codebases.
AI benchmarks overestimate models' capabilities on real-world tasks.
AI coding agents' benchmark-passing patches are often rejected by human maintainers.
Training-data contamination inflates language models' scores on public benchmarks.
AI capability benchmarks underestimate models' capabilities by limiting elicitation, scaffolding, and iterative feedback.
Time-horizon trends measured on benchmark software tasks generalize to real-world software work.
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Developers substantially overestimate productivity gains from AI coding tools.
Developers adopt AI coding tools mainly to reduce effort or increase enjoyment, rather than to work faster.
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presupposed
METR's early-2025 developer RCT produced an internally valid estimate of AI's effect on task completion time.
≈ 15 %presupposed
Percentage change in issue-completion time for experienced open-source developers using early-2025 AI tools on their own repositories (%)
A factual claim that rests on inference from other evidence rather than direct observation.constitution →question page ↗︎
How can measured AI productivity effects on developers be reconciled with benchmark scores and anecdotal reports of AI helpfulness and adoption?
The evidence favors an answer without establishing it.constitution →6 answers
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