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METR · February 24, 2026

Developer-productivity studies: AI adoption can bias participation and task selection

analysis Model EvaluationAI Engineering
Why it matters

METR explains why its follow-up developer experiment did not provide a reliable estimate of current AI productivity gains. Developers increasingly avoided participation or withheld tasks they did not want to perform without AI; lower compensation added another selection concern. Concurrent agent use also complicated time accounting. The raw results suggested possible speedups but had wide uncertainty and omitted important users and tasks, prompting changes to the study design.

My takeaway: Track who declines participation, which tasks are withheld, and whether assigned work is completed. Separate active human time from parallel agent elapsed time, and compare output quality as well as duration. Treat self-reported gains and selected experimental samples as different evidence.
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