arXiv cs.CY· Ina Catrinescu·· 12 小时前AI 评分38
论文探讨 AI 转型中的能力转移：初级岗位需求先降、专家培养周期从五年延至九年
The Capability Transfer: Cohort Reversal, Codified Expertise, and the Adaptability Tax of AI Transformation
AI 导读
一篇工作论文将 Caosun 与 Aral（2026）的单工人模型扩展为含初级与高级两个队列的企业模型，研究编码化工作被自动化后专家形成管道的变化。基准校准下，初级需求较 2022 年水平最低下降约五分之一后大部分回升，高级人才存量下降四分之一、后期减半，内部培养专家的时间从约五年升至九年。作者指出两个关键参数尚未测量，结果属情景而非预测，并提出三项可追踪指标。
正文
Abstract:Most analyses of AI and employment ask which jobs are exposed. This paper asks what happens to the pipeline that produces experts. It extends the model of Caosun and Aral (2026) from a single worker to a firm with two cohorts, juniors and seniors, and studies how expertise formation changes when codified work is automated. Because junior work is the most codified, demand for juniors falls first; the stock of seniors falls later but further, as the pipeline stops replacing the experts who retire. In the baseline calibration, junior demand bottoms out about a fifth below its 2022 level before recovering most of that ground, while the senior stock falls by a quarter and later halves. Because expertise forms through unassisted practice on work that AI cannot do, the time needed to develop an expert from within rises from about five years to nine. The costs fall on a different party from the one that decides on adoption: a manager with a three-year horizon sets AI use to maximise near-term output, cutting the wage bill by 21 percent while the capacity of the firm on work AI cannot do falls by two-fifths and its output on that work goes unserved once the pipeline runs thin. Two key parameters are not yet measured, so the results are scenarios rather than forecasts. The paper proposes three indicators organisations can track: the unaided practice juniors get on work AI cannot do, senior time spent on such work, and the years it takes to develop an expert.
| Comments: | 29 pages, 6 tables, 6 figures. Working paper. Also available at SSRN: this https URL. JEL: J24, O33, M54 |
| Subjects: | General Economics (econ.GN); Computers and Society (cs.CY) |
| ACM classes: | K.4.3 |
| Cite as: | arXiv:2610.33102 [econ.GN] |
| (or arXiv:2610.33102v1 [econ.GN] for this version) | |
| https://doi.org/10.48550/arXiv.2610.33102 arXiv-issued DOI via DataCite |
Submission history
From: Ina Catrinescu [view email]
[v1]
Thu, 22 Oct 2026 15:08:46 UTC (1,337 KB)
来源：arXiv cs.CY · arxiv.org