AI 編碼智能體產生更多程式碼，企業軟件產出卻未見增加
AI coding agents generate more code, but not more software
哈佛大學研究人員發現，AI 編碼工具雖提高編碼效率，人工程式碼審查卻形成瓶頸，企業增加軟件產出或減少僱用的證據有限。Fiona Chen 和 James Stratton 使用 Jellyfish 彙總分析資料，涵蓋 2021 年至 2026 年 3 月 700 多家軟件開發公司、超過 700,000 名員工及 300 million 個工作事件，並包括問題管理軟件資料。
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.
來源：Ars Technica · AI · arstechnica.com