Generative AI 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, 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. 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.

翻译：GitHub Copilot等生成式AI解决方案已被证实能够提升软件开发者的生产力。然而，先前研究对于所生成代码的质量及其在软件项目中的维护挑战仍不明确。若代码质量随数量增长而下降，经验丰富的开发者将面临更大的工作量来审阅和重构来自经验不足贡献者的代码。我们分析了GitHub Copilot引入后开源软件（OSS）项目中的开发者活动。研究发现生产力确实有所提升，但这种提升主要来自经验较少（边缘）的开发者。同时发现，采用AI后编写的代码需要更多的返工。重要的是，额外的返工负担落在了经验更丰富（核心）的开发者身上——他们在Copilot引入后需审阅的代码量增加了6.5%，但其原始代码生产力却下降了19%。更广泛而言，这一发现警示我们：AI带来的生产力收益可能掩盖了日益增长的维护负担正由日益缩减的专家群体承担的现实。