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Patent-lawyer trial finds AI productivity gains but uneven durable learning

The results separate immediate AI-assisted output gains from durable skill formation: experienced professionals may learn more from the tool even when junior users obtain larger assisted-performance gains. The working paper is not peer reviewed, the sample is narrow, and Google funded the experiment; one coauthor is a Google employee.

Coverage window: 2026-09-01–2026-09-14 · publication dates shown on each item
01 / Research

Patent-lawyer trial finds AI productivity gains but uneven durable learning

The results separate immediate AI-assisted output gains from durable skill formation: experienced professionals may learn more from the tool even when junior users obtain larger assisted-performance gains. The working paper is not peer reviewed, the sample is narrow, and Google funded the experiment; one coauthor is a Google employee.

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National Bureau of Economic Research Sep 07, 2026

Patent-lawyer trial finds AI productivity gains but uneven durable learning

In a preregistered three-month randomized trial with 133 patent lawyers at 11 U.S. firms, access to a custom drafting assistant improved blinded expert ratings of benchmark work by 0.34 standard deviations after 10 days and 0.38 after 90 days. In an unaided redlining task after three months, treated lawyers scored 0.32 standard deviations above controls, but that durable gain was concentrated among senior lawyers; junior lawyers showed no average gain and more dispersed outcomes.

  • AI access improved blinded ratings of patent-drafting benchmark work by 0.34 standard deviations after 10 days and 0.38 after 90 days in the preregistered trial.
  • On an unaided redlining task after three months, the treated group scored 0.32 standard deviations above controls, with the reported gain concentrated among senior lawyers.
Why it mattersThe results separate immediate AI-assisted output gains from durable skill formation: experienced professionals may learn more from the tool even when junior users obtain larger assisted-performance gains. The working paper is not peer reviewed, the sample is narrow, and Google funded the experiment; one coauthor is a Google employee.
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