提出数学模型解释AI如何与人互补而非替代，揭示技能分工对效率的关键影响。
A Mathematical Framework for AI-Human Integration in Work
- 将技能拆分为决策与执行两级，匹配人与AI的互补优势
- 发现低技能者用AI后效率提升更明显，存在成功率突变点
- 适合研究人机协作、职业设计或组织效能的学者与管理者
生成式AI（GenAI）工具的快速兴起引发了关于其在工作场景中是补充还是取代人类员工的讨论。本文提出一个数学框架，用于建模工作、员工及人岗匹配，首次将技能分解为决策级和操作级子技能，以体现人类与GenAI的互补性。分析表明，子技能能力变化会引发任务成功率的显著跃迁，并确立了具备互补子技能的员工组合优于单一员工的充分条件。该模型可解释‘生产力压缩’现象——即低技能者使用GenAI后获益更大。通过O*NET和Big-Bench Lite数据验证，结合子技能划分方法，实现理论与现实数据的良好对齐。结果表明，GenAI应被视作技能增强工具，而非替代品。
原文摘要 · Abstract (English)
The rapid rise of Generative AI (GenAI) tools has sparked debate over their role in complementing or replacing human workers across job contexts. We present a mathematical framework that models jobs, workers, and worker-job fit, introducing a novel decomposition of skills into decision-level and action-level subskills to reflect the complementary strengths of humans and GenAI. We analyze how changes in subskill abilities affect job success, identifying conditions for sharp transitions in success probability. We also establish sufficient conditions under which combining workers with complementary subskills significantly outperforms relying on a single worker. This explains phenomena such as productivity compression, where GenAI assistance yields larger gains for lower-skilled workers. We demonstrate the framework' s practicality using data from O*NET and Big-Bench Lite, aligning real-world data with our model via subskill-division methods. Our results highlight when and how GenAI complements human skills, rather than replacing them.
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