AI 中文总结
研究人类员工何时被人工智能取代，提出HAT分析模型，编码人类技能与人工智能能力的经济不对称，推导多因素对人机替代的影响，得出替代原则，揭示其引发的组织变化及中层管理与高技能工人的脆弱性，统一相关理论。
AI 中文摘要
人工智能正在迅速改变组织，引发了一个基本的组织和经济问题：人类员工何时会被人工智能取代？我们提出了一个分析模型来研究层级组织中的人类-人工智能任务分配（HAT）。HAT模型的一个核心特征是正式编码了人类技能获取与人工智能能力扩展之间的经济不对称性。该模型使我们能够推导风险调整成本、技能、组织深度、部署规模、战略适应和风险如何共同决定人类-人工智能替代在何时、何地、为何以及在何种结构条件下发生。一个关键结果是人类-人工智能替代原则，它基于形式不对称假设提供了人工智能取代人类劳动的精确条件。在此基础上，我们表明人工智能的采用会导致劳动力的突然转变，形成人类-人工智能混合组织，包括风险异质性维持人类和人工智能角色而无需最低人类比例约束的情况，以及具有更宽控制跨度的更扁平管理层级。HAT模型确定了中层管理角色对自动化具有更高脆弱性的结构条件，并表明高技能工人的脆弱性取决于由组织深度、基线成本和风险差异塑造的技能阈值。更广泛地说，本文将自动化经济学、组织设计、人工智能治理和劳动力规划联系成一个统一的人工智能驱动的组织转型理论。
英文摘要
Artificial Intelligence (AI) is rapidly transforming organizations, raising a fundamental organizational and economic question: when will a human employee be replaced by AI? We present an analytical model for studying Human--AI Task Allocation (HAT) in hierarchical organizations. A central feature of the HAT model is that it formally encodes the economic asymmetry between human skill acquisition and AI capability scaling. The HAT model allows us to derive how risk-adjusted costs, skills, organizational depth, deployment scale, strategic adaptation, and risk jointly determine when, where, why, and under what structural conditions human--AI replacement occurs. A key result is the Human--AI Substitution Principle, which provides a precise condition --- grounded in the formal asymmetry assumption --- under which AI replaces human labor. Building on this result, we show that AI adoption can produce abrupt workforce transitions, hybrid human--AI organizations, including cases where risk heterogeneity sustains human and AI roles without requiring a minimum-human-fraction constraint, and flatter managerial hierarchies with wider spans of control. The HAT model identifies structural conditions under which middle-management roles exhibit elevated vulnerability to automation, and shows that the vulnerability of highly skilled workers depends on a skill threshold shaped by organizational depth, baseline costs, and risk differentials. More broadly, the paper connects automation economics, organizational design, AI governance, and workforce planning into a unified theory of AI-driven organizational transformation.
