NBER study of 500,000+ GitHub developers
Autonomous coding agents raise commits 240% but releases only 30%
Confirmed
The takeaway
NBER working paper 35275, “Writing Code vs. Shipping Code” by Mert Demirer, Leon Musolff and Liyuan Yang (May 2026, revised September 2026), matches AI-usage telemetry with GitHub activity for more than 500,000 developers.
Status
- Claim
Confirmed
- Our reporting
- High confidence
- Importance
- 3 of 5
- Last verified
- 10 October 2026
Your AI and this story
- GPT-6 AstraAfter its cutoff
- Claude Opus 5.5After its cutoff
- Gemini 3.8 FlashAfter its cutoff
- Grok 4.7After its cutoff
None of these four assistants can know about it: it happened after all their cutoffs.
Key facts
- Data: more than 500,000 GitHub developers combined with their AI-usage telemetry; matched event-study design (revised September 2026 version)
- Cumulative effect on commits: autocomplete +30%, interactive coding agents +180%, autonomous coding agents +240%
- Attenuation: the 240% effect on commits falls to +80% in number of projects and +30% in releases
- Estimated elasticity of substitution between AI and human effort: 0.23 (NBER page)
- Earlier version (May 2026, reported by secondary sites): >100,000 developers, effects of 40%/140%/180% on coding activity falling to 50% for projects and 30% for releases; autonomous agents producing 17.3x more lines of code but 1.3x more releases. These figures come from secondary summaries, not the current paper
What happened
The authors follow developers before and after they adopt successive generations of AI coding tools and measure output at each step of the software pipeline, from commits to projects to releases. Each tool generation raises raw coding activity more than the last, but most of the gain disappears before software ships, which the paper reads as review, integration and release work becoming the bottleneck.
Why it matters
It is one of the largest field studies of coding agents, and it gives numbers for a common complaint of 2026: agents write far more code, but output measured as finished software rises much less. It sits alongside other null or small macro effects of AI on work.
Note: Ars Technica’s article was not readable (blocked); the figures come from the NBER abstract page.
Sources
3 sources from 3 sites. Numbers match the chips in the text.
3 sources: 1 primary, 2 press
Primary
- NBER Working Paper 35275: Writing Code vs. Shipping Code: Productivity Effects Across Generations of AI Coding Toolsnber.org, paper
Press
Changes
- Filed