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The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers

Kevin Zheyuan Cui et al.

Management Science2026https://doi.org/10.1287/mnsc.2025.00535article
FT50UTD24AJG 4*ABDC A*
Weight
0.37

Abstract

This study evaluates the effect of generative artificial intelligence (AI) on software developer productivity via randomized controlled trials at Microsoft, Accenture, and an anonymous Fortune 100 company. These field experiments, run by the companies as part of their ordinary course of business, provided a random subset of developers with access to an AI-based coding assistant suggesting intelligent code completions. Although each experiment is noisy and results vary across experiments, when data are combined across three experiments and 4,867 developers, our analysis reveals a 26.08% increase (standard error: 10.3%) in completed tasks among developers using the AI tool. Notably, less experienced developers had higher adoption rates and greater productivity gains. This paper was accepted by Jean-Pierre Dube, marketing. Funding: M. Demirer and T. Salz thank the MIT GenAI Initiative for funding. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2025.00535 .

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https://doi.org/https://doi.org/10.1287/mnsc.2025.00535

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@article{kevin2026,
  title        = {{The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers}},
  author       = {Kevin Zheyuan Cui et al.},
  journal      = {Management Science},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1287/mnsc.2025.00535},
}

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.