做软件开发或管理团队的读者会看到 AI 编码的真实瓶颈——写代码快不等于交付快，建议点开看看如何优化流程而非只堆工具。
MIT 一项新研究发现，AI 编码工具虽大幅提升代码提交量，但实际软件发布量增长有限。自主 AI 编码代理使提交量增加 180%，但发布量仅增 30%。研究指出，软件生产存在薄弱环节，人类仍需负责审查、连接、测试、打包和交付工作。市场数据显示，新应用数量增加，但总使用量未提升，表明用户并未更多采纳新软件。研究估计 AI 与人类工作的替代弹性仅为 0.25，即 AI 效能大幅提升时，仅能替代少量人类工作。
New MIT study. Code volume surges by 300%, but ou…
New MIT study. Code volume surges by 300%, but output increases by only 30%: The AI dividend meets an awkward reality
Autonomous AI coding agents raised commits by 180%, but releases rose only 30%.
The paper’s main idea is that software production has weak links, so faster code writing does not help as much when humans still need to review, connect, test, package, and ship the work.
The authors also check app marketplaces and find more new apps, but no increase in total usage, which means more software appeared without clear evidence that users adopted more software.
The marketplace evidence points the same way: more new apps appeared, but total usage did not rise.
The authors compare more than 100,000 GitHub developers before and after they start using 3 generations of AI coding tools, from autocomplete to more independent coding agents.
Autocomplete raised commits by 40%, interactive coding agents raised them by 140%, and autonomous coding agents raised them by 180%.
The 180% commit gain shrank to 50% for the number of projects and 30% for actual releases.
The estimated "elasticity of substitution" is 0.25 i.e. for every big improvement in AI’s usefulness, only a small amount of human work can be replaced.
Because AI can write code faster, but humans are still needed to decide what to build, check if the code works, connect it with the rest of the product, fix messy edge cases, and actually ship it.
---
papers .ssrn.com/sol3/papers.cfm?abstract_id=6859839