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Writing Code vs. Shipping Code: Productivity Effects Across Generations of AI Coding Tools
Mert Demirer,
Leon Musolff
& Liyuan Yang
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Working Paper 35275
DOI 10.3386/w35275
Issue Date May 2026
How do the productivity effects of AI evolve across successive generations of tools, and to what extent do task-level gains ultimately translate into final output? We study these questions in the context of software development, using data on more than 100,000 GitHub developers combined with their AI usage telemetry. In a matched event study design, we find that autocomplete, interactive coding agents, and autonomous coding agents each significantly increase coding activity (“commits”), with respective cumulative effects of 40%, 140%, and 180%. These gains, however, attenuate sharply across the production hierarchy: the 180% cumulative effect falls to 50% for the number of projects, and to 30% for actual releases. This pattern is consistent with the weak-link hypothesis: the strong productivity gains from AI are attenuated by human bottlenecks in the production chain, with an estimated elasticity of substitution of 0.25 between AI and human effort, which indicates strong complementarities. We further confirm these results across four major app marketplaces, finding a moderate increase in the number of new apps but no increase in total usage. Large task-level AI productivity gains have therefore translated only partially into shipped and used software thus far.
Acknowledgements and Disclosures
We thank Joel Becker, Alexander Bick, Adam Blandin, David P. Byrne, Tom Cunningham, Jan De Loecker, Anders Humlum, Sanjog Misra, Frank Nagle, Sida Peng, Nate Rush, Suproteem Sarkar, Max Schnidman, and Chad Syverson. This paper benefited from comments by seminar participants at BIG.AI@MIT, the Chicago AI Workshop, the RAP Symposium at Harvard & MIT, the PSU AI and the Economy Initiative, the St. Louis Fed, the Government Office of the Czech Republic, Wharton’s AI and the Future of Work Conference, and the Columbia MAD conference. This project was funded in part by the Mack Institute For Innovation Management at Wharton and the Chicago AI Incubator. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
Mert Demirer
Mert Demirer previously held a postdoctoral research position at Microsoft and now works as a paid research consultant for the company.
Leon Musolff
Leon Musolff previously held a postdoctoral research position at Microsoft and now works as a paid research consultant for the company.
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Mert Demirer, Leon Musolff, and Liyuan Yang, "Writing Code vs. Shipping Code: Productivity Effects Across Generations of AI Coding Tools," NBER Working Paper 35275 (2026), https://doi.org/10.3386/w35275.
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