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trailMETR's 2025 AI slowdown finding generalizes to experienced open-source developers beyond its 16 participants.
Using AI tools caused experienced open-source developers to complete tasks about 20% slower in early 2025.
Statistical robustness of the trial design· for
Issue-level randomization made METR's 246 completed issues, not its 16 developers, the effective sample for estimating the AI slowdown.
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METR's measured AI slowdown remained statistically significant when accounting for developer-level clustering.
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METR's measured AI slowdown is robust across alternative statistical estimators and empirical strategies
METR's measured AI slowdown estimate was similar across alternative outcome estimators, including a naive ratio estimator
Differential issue dropout and attrition do not explain METR's measured AI slowdown
In METR's 2025 randomized controlled trial, using AI tools increased developers' task completion time by 19%
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Setting-specificity of the participants and mechanisms· against
METR's study participants were more deeply familiar with the repositories they worked on than typical experienced open-source developers.
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High repository familiarity and repository size and maturity contributed to the slowdown METR measured.
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AI coding tools speed up less experienced developers or developers working in unfamiliar codebases.
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METR's 2025 AI slowdown finding generalizes to experienced open-source developers beyond its 16 participants.
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