AI-Assisted Programming Decreases the Productivity of Experienced Developers by Increasing the Technical Debt and Maintenance Burden

Feiyang Xu, Poonacha K. Medappa, Murat M. Tunc, Martijn Vroegindeweij and Jan C. Fransoo

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Abstract: GenAI solutions like GitHub Copilot have been shown to increase the productivity of software developers. Yet prior work remains unclear on the quality of code produced and the challenges of maintaining it in software projects. If quality declines as volume grows, technical debt accumulates as experienced developers face increased workloads reviewing and reworking code from less-experienced contributors. We analyze developer activity in Open Source Software (OSS) projects following the introduction of GitHub Copilot. We find that productivity indeed increases. However, the increase in productivity is primarily driven by less-experienced (peripheral) developers. We also find that code written after the adoption of AI requires more rework to satisfy repository standards, indicating a potential increase in technical debt. Importantly, the added rework burden falls on the more experienced (core) developers, who review 6.5% more code after Copilot's introduction, but show a 19% drop in their original code productivity. More broadly, this finding raises caution that productivity gains of AI may mask the growing burden of maintenance on a shrinking pool of experts, together with increased technical debt for the projects. The results highlight a fundamental tension in AI-assisted software development between short-term productivity gains and long-term system sustainability.

Date: 2025-10, Revised 2026-01
New Economics Papers: this item is included in nep-eff and nep-ppm
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