任何哪怕只是略微涉足计算机编程的人都知道，现代 AI 编程助手和代理能极其高效地生成大量功能性代码。
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.
但使用这些工具的程序员也深知不应盲目信任这些代码的准确性，这意味着必须投入大量精力来审查任何 AI 生成的输出。
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.
一项针对数百家企业实际编程实践的最新研究发现，人工代码审查构成了 AI 编程工具整体效率的重要“瓶颈”，导致“几乎没有证据表明企业通过使用它们能增加软件产出或减少雇佣”。
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.
研究作者发现，实际编码阶段带来的任何效率提升都被“生产流程中的下游约束所吸收”；因为“代码审查过程显著变长，拉取请求更可能需要修改，审查者留下的评论也更多”。
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."
为了得出这些结论，哈佛大学研究人员 Fiona Chen 和 James Stratton 利用了来自 Jellyfish 的聚合分析数据，该数据衡量工程团队的细粒度产出。
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.
这些数据涵盖了 2021 年至 2026 年 3 月期间，700 多家相关软件开发企业中 70 万多名员工的 3 亿个单独“工作事件”（如提交和拉取请求）以及问题管理软件数据。
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.