更好的工作成果总是意味着更好的工作者吗？

David Autor, Technology & Society Visiting Fellow, and Tanya Rodchenko, Principal, AI & Economy In a three-month randomized controlled trial with practicing patent attorneys, we found that, while routinely using AI lifts work quality across the board, its effects on on-the-job learning depend on seniority: senior lawyers who used AI for 90 days demonstrate stronger judgment than those who didn’t, whereas junior lawyers show no average skill gain, their scores splitting into more strong scores and more low scores. Judgment is what sets senior professionals apart from their junior peers. Building judgment requires thousands of hours learning on the job, usually through tedious but formative engagement with relatively routine work, often under guided supervision by experienced workers. But AI is already changing how on-the-job learning works. On the one hand, empirical studies in radiology , business problem-solving , job-seeker writing , and legal education show that when AI tools are thoughtfully embedded into training workflows, less-experienced workers can improve their independent performance. On the other hand, recent experiments with software engineers , management consultants , high school students , and clinicians demonstrate that while AI serves as a temporary exoskeleton that boosts immediate output, those gains often fail to persist once the tool is removed. Understanding how AI can erode or enhance expertise requires three elements rarely found together: (1) sustained AI usage embedded in ordinary professional work over months rather than hours, (2) a credible evaluation of unassisted judgment when AI is unavailable, and (3) blinded grading by domain experts. In “ Does AI Assistance Enhance or Erode Expertise? Evidence from a Three-Month Field Experiment in Patent Drafting ”, published by the National Bureau of Economic Research , we describe a field experiment used to capture changes in both short-term productivity and longer-term skill building that occur as a result of AI usage in the highly expertise-intensive profession of intellectual property law. In our experiment, we randomized access to a then-unreleased Google Labs AI patent writing assistant tool, now part of Gemini Notebook , among 133 lawyers at eleven intellectual property law firms that are engaged in regular, non-exclusive business with Google. Two-thirds of lawyers at each firm (“treatment” group) were given early access to the tool, while the remainder ("control" group) were given some training on how to use AI but were withheld from using the tool until after the three-month study period. After 10 days of AI assistance, we sent all participating lawyers a packet of inventor materials for a hypothetical invention and asked them to draft a patent. We did the same after 90 days, with a different set of simulated inventor materials. We saw the expected AI boost in performance on the drafting tasks at both time periods. At 10 days, AI tool access raised drafting scores (graded on a rubric with Likert scales for five different aspects of quality by independent legal experts) by 0.34 standard deviations, equivalent to a 10-point climb in percentile ranking among control group scores; by 90 days, this improvement increased to 0.38 SD, implying an 11-percentile point increase. These improvements occurred with remarkable consistency across all quality dimensions. Tellingly, better performance came from lower frequency of poor scores and higher frequency of good scores, and no change in the frequency of excellent scores. Formally, scores among lawyers allocated AI access moved from the bottom to the lower-middle and middle quintiles, but there were no noticeable changes in the number of exceptional scores. This pattern was particularly clear for junior lawyers, whose improvements in quality were also matched by significant time savings (18 minutes faster than the control group’s average speed of 124 minutes on the 10-day task, for example). But patent