研究发现 AI 编码智能体生成更多代码，却未带来更多软件产出
AI coding agents generate more code, but not more software
哈佛大学研究人员分析多家软件公司的工程数据后发现，使用 AI 编码工具并未带来明显的软件产出增长或就业减少。研究数据涵盖 2021 年至 2026 年 3 月的 3 亿条工作事件，涉及 700 多家公司、超过 70 万名员工；代码审查耗时增加、拉取请求更常需要修改，审查者也留下更多评论。
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