METR is an AI research nonprofit, and this July 2025 study measures something usually argued from anecdote: whether AI tools actually speed up experienced developers working in their own repositories.

The design is what gives it weight. Sixteen experienced open-source developers contributed 246 real issues from projects they already maintain, and each issue was randomly assigned to permit or forbid early-2025 AI tools. Developers took 19% longer on the issues where AI was allowed. The more uncomfortable finding sits right beside it: they had forecast a 24% speedup going in, and after doing the work still believed they had been sped up by 20%. The slowdown was real and invisible to the people living it.

The page is built for reading rather than skimming, with sections for motivation, methodology, the core result chart and a factor analysis working through candidate explanations. Joel Becker, Nate Rush, Beth Barnes and David Rein are credited, and arXiv and citation links sit at the top. Be clear about the scope before you quote it: sixteen developers on mature codebases is one setting, and METR presents the number as a snapshot rather than a verdict on AI coding generally.

Treat it as the landmark early result, not the current one. A yellow callout at the top of the page states plainly that the results are out of date and points to a February 2026 continuation measuring late-2025 models.