Harvard 研究发现：AI 编码智能体生成代码多，但软件产出未增加
AI coding agents generate more code, but not more software
哈佛大学研究发现，尽管 AI 编码智能体能高效生成大量代码，但人类代码审查成为瓶颈，导致软件产出和就业率未显著增加。研究基于 Jellyfish 平台的数据，涵盖 2021 年至 2026 年 3 月超过 700 家软件开发公司 700 万员工的工作事件。
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