# [2507.09089] Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity
Abstract page for arXiv paper 2507.09089: Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity

# Computer Science > Artificial Intelligence

[Submitted on 12 Jul 2025 (v1), last revised 25 Jul 2025 (this version, v2)]

# Title:Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity

Authors: Joel Becker, Nate Rush, Elizabeth Barnes, David Rein
View a PDF of the paper titled Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity, by Joel Becker and 3 other authors
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> Abstract:Despite widespread adoption, the impact of AI tools on software development in the wild remains understudied. We conduct a randomized controlled trial (RCT) to understand how AI tools at the February-June 2025 frontier affect the productivity of experienced open-source developers. 16 developers with moderate AI experience complete 246 tasks in mature projects on which they have an average of 5 years of prior experience. Each task is randomly assigned to allow or disallow usage of early 2025 AI tools. When AI tools are allowed, developers primarily use Cursor Pro, a popular code editor, and Claude 3.5/3.7 Sonnet. Before starting tasks, developers forecast that allowing AI will reduce completion time by 24%. After completing the study, developers estimate that allowing AI reduced completion time by 20%. Surprisingly, we find that allowing AI actually increases completion time by 19%--AI tooling slowed developers down. This slowdown also contradicts predictions from experts in economics (39% shorter) and ML (38% shorter). To understand this result, we collect and evaluate evidence for 20 properties of our setting that a priori could contribute to the observed slowdown effect--for example, the size and quality standards of projects, or prior developer experience with AI tooling. Although the influence of experimental artifacts cannot be entirely ruled out, the robustness of the slowdown effect across our analyses suggests it is unlikely to primarily be a function of our experimental design.

Comments: | 51 pages, 8 tables, 22 figures
Subjects: | Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Software Engineering (cs.SE)
ACM classes: | I.2
Cite as: | arXiv:2507.09089 [cs.AI]
| (or arXiv:2507.09089v2 [cs.AI] for this version)
| https://doi.org/10.48550/arXiv.2507.09089 arXiv-issued DOI via DataCite

## Submission history

From: Nate Rush [view email]
[v1] Sat, 12 Jul 2025 00:16:33 UTC (15,206 KB)
[v2] Fri, 25 Jul 2025 00:43:07 UTC (5,596 KB)
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