研究：AI 编码智能体生成更多代码，但并未带来更多软件产出
AI coding agents generate more code, but not more software
Harvard 研究者 Fiona Chen 和 James Stratton 基于 Jellyfish 的数据分析发现，AI 编码工具的编码阶段效率提升被生产流程下游瓶颈吸收，企业软件产出和就业未见明显变化。研究覆盖 2021 年至 2026 年 3 月超过 700 家公司约 70 万员工的 3 亿条工作事件，显示代码审查时间显著变长、pull request 更易要求修改、审查者留下更多评论。
Anyone who has even tangentially associated with computer programming knows that modern AI coding assistants and agents can be incredibly efficient at generating huge amounts of functional code. But coders making use of those tools also know better than to trust the accuracy of that code, meaning substantial effort needs to be spent reviewing any AI-generated output.
A recent study of actual coding practices across hundreds of firms finds that human code review forms a significant "bottleneck" for the overall efficiency of AI coding tools, resulting in "little evidence that firms increase software output or reduce employment" by using them. Any efficiency increased during the actual coding phase, the study authors find, is "absorbed by downstream constraints in the production process"; as "the code review process significantly increases in length, pull requests are more likely to require revisions, and reviewers leave more comments."
Cut once, measure twice
To come to these conclusions, Harvard University researchers Fiona Chen and James Stratton made use of aggregated analytics data from Jellyfish, which measures the granular output of engineering teams. That data encompasses 300 million individual "work events" (e.g., commits and pull requests) and issue management software data across more than 700,000 employees at over 700 relevant software development firms from 2021 through March of 2026.
来源：Ars Technica：AI · arstechnica.com