Rohan Paul@rohanpaul_ai · X麻省理工新研究追踪超10万GitHub开发者使用三代AI编码工具（自动补全、交互式agent、自主agent）的生产漏斗。自主AI agent使代码提交数提升180%，但实际发布仅增30%。代码量激增近300%，经人工审核后收益降至150%，最终发布仅增约30%。研究估算替代弹性为0.25，即AI能力大幅提升时仅能替代少量人类工作。应用市场同样显示新应用数量增加，但总使用量未升。瓶颈在于人类仍需负责审查、测试、打包和发布等环节，AI加速的局部任务并未转化为同等产出增长。
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.
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papers .ssrn.com/sol3/papers.cfm？abstract_id=6859839