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Using Claude to assess the survey responses, we rated the extent of people’s self-reported productivity gains from AI on a 1–7 scale, where 1 is “less productive,” 2 is “no change,” and each subsequent level denotes a larger gain. Responses that scored 7 included testimonials like, “It used to take months to make the website I [made] in 4-5 days”; Claude gave a 5 to statements like, “What might have taken four hours was accomplished in half the time,” and a 2 to ones like, “Personally, I had AI help me fix code on a website. But it took multiple passes to get the result I was after.”3 Overall,

The human sciences are shifting: for the first time, core research tasks can be handed off to machines. AI chatbots increasingly contribute to scientific research, including in the most prestigious publications and in the social sciences. This has spurred optimism that AI could boost research productivity—while also stoking fears about overloaded peer review and a deluge of academic AI slop. But while turn-taking AI chatbots have primarily been used for writing assistance, coding agents could restructure social science research more radically. Agentic coding platforms like Claude Code and Code

If workers are able to accelerate a subset of their occupational tasks with AI, the tasks where AI provides less speedup may come to represent a larger and thus more important share of those occupations’ work. For example, AI might help a home inspector prepare reports, but if the inspector still has to spend the same amount of time physically traveling to the property to perform the inspection in person, this could make inspections a greater fraction of the job overall. The figure below illustrates this for a few occupations. For software developers, AI speeds up the process of software devel