The mit/wharton github study of 100k developers
the mit/wharton github study of 100k developers: ai assistance lifted coding activity up to 180%, but shipped releases rose only 30%. the bottleneck was never keystrokes — it's the review, merge, and deployment pipeline.
measuring lines written is now measuring the wrong thing.
Context
NBER working paper w35275 by Mert Demirer, Leon Musolff and Liyuan Yang uses data on more than 500,000 GitHub developers combined with AI usage telemetry in a matched event study of autocomplete, interactive coding agents and autonomous agents. Quartz, 12 June 2026, reports more than 100,000 developers, with code submitted for review up about 40% for autocomplete, 140% for conversational assistants and 180% for autonomous agents, and releases up 20%.
The population differs across sources: 500,000 plus in the paper's abstract and 100,000 plus in Quartz, and the note follows the secondary figure. The 180% is Quartz's autonomous-agent figure for code submitted, not general coding activity. The Quartz release figure is 20%, and no source read gives 30%, so the 30% is unverified. The exact release effect in the paper was not read, and the MIT and Wharton affiliations were not confirmed from the PDF text. Measuring lines is the wrong thing is the author's opinion.
Related work
Watch next
- The paper's release-effect table.
Sources
Provenance
The note above is reproduced unedited from the original post, first published on Threads on 18 September 2026 at 12:21 IST. Sources are the papers and datasets the note draws on.
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