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Anthropic studies how scientists use Claude and how much research time AI could take

2 articles2 sourcessince Nov 5Last article 2h ago ·

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Anthropic analyzed 207,049 consumer-plan Claude and Claude Code sessions from August to September 2026 to map how researchers use Claude across scientific fields.

Data analysis made up about one-third of Claude sessions and nearly half of Claude Code sessions, while idea generation and data gathering appeared less often than researchers say they matter.

The report estimates that current capabilities could double research output in some fields, and up to 15-fold in fully automated non-physical work. It says physical bottlenecks limit the bench sciences. A follow-up post from Ethan Mollick cites Anthropic saying that, if adopted widely, AI could take on 27–52% of the researcher time needed to produce a new study, with a median of 38% across fields.

Written by AI from the articles below · updated Nov 6, 7:43 PM ET

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The articles in this story. Times are ET.

Nov 6
  1. Ethan MollickX
    Anthropic study estimates AI could take 27–52% of research time by field

    AIEthan Mollick shares new Anthropic data on AI's impact on scientific work across fields. Anthropic says that with current capabilities, if adopted widely, AI could take on 27–52% of the researcher time needed to produce a new study, with a median of 38% across fields. Charts in the post show Claude and Claude Code use relative to researcher share for each scientific field.

    Image from @emollick's post
Nov 5
  1. Anthropic ResearchOfficialPick
    Anthropic studies how researchers use Claude across scientific fields and tasks

    AIAnthropic analyzed 207,049 consumer-plan Claude and Claude Code sessions from August to September 2026 to map scientific use. Data analysis made up about one-third of Claude sessions and nearly half of Claude Code sessions, while idea generation and data gathering were used less than researchers say they matter. The report estimates that current capabilities could double research output in some fields, and up to 15-fold in fully automated non-physical work, with physical bottlenecks limiting the bench sciences.

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