研究：AI 编码智能体生成更多代码，但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 Fiona Chen 和 James Stratton 基于 Jellyfish 的工程团队分析数据（覆盖 2021 年至 2026 年 3 月、700 多家软件开发企业逾 70 万名员工的 3 亿条工作事件）发现，人类代码审查成为 AI 编码工具整体效率的显著瓶颈，几乎没有证据表明企业因此提升了软件产出或减少了用工。
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