提出新指标评估智能代理对职业的替代风险，发现超九成高技能岗位面临显著威胁
Agentic AI and Occupational Displacement: A Multi-Regional Task Exposure Analysis of Emerging Labor Market Disruption
- 基于任务数据构建可计算的代理任务暴露分数（ATE），融合能力、覆盖与采纳速度
- 2030年前五大科技区93.2%高信息密度职业将面临中等以上替代风险
- 信用分析师、法官等岗位风险达0.43-0.47，同时催生17类人机协同新岗位
本文拓展了Acemoglu-Restrepo的任务暴露框架，以评估自主型人工智能系统对劳动力市场的影响：这类系统能独立完成整个职业工作流，而非单一子任务。不同于以往仅替代具体环节的自动化技术，代理型AI具备多步推理、工具调用与自主决策能力，显著扩大了职业替代风险。我们提出代理任务暴露（ATE）评分，通过算法从O*NET任务数据中计算得出，不依赖回归估计，综合考虑AI能力得分、工作流覆盖因子及逻辑采纳速率。在2025-2030年期间，对美国五大科技区域（西雅图-塔科马、旧金山湾区、奥斯汀、纽约、波士顿）的六个信息密集型职业类别（金融、法律、医疗、医疗支持、销售、行政/文秘）共236个职业进行分析，发现93.2%的职业将在一级区域达到中等风险阈值（ATE ≥ 0.35），其中信用分析师、法官和可持续发展专家的评分高达0.43-0.47。同时识别出17个新兴职业类别受益于再安置效应，集中于人机协作、AI治理与领域特定的AI运维角色。研究结果对劳动力转型政策、区域经济规划及劳动力市场调整的时间动态具有重要意义。
原文摘要 · Abstract (English)
This paper extends the Acemoglu-Restrepo task exposure framework to address the labor market effects of agentic artificial intelligence systems: autonomous AI agents capable of completing entire occupational workflows rather than discrete tasks. Unlike prior automation technologies that substitute for individual subtasks, agentic AI systems execute end-to-end workflows involving multi-step reasoning, tool invocation, and autonomous decision-making, substantially expanding occupational displacement risk beyond what existing task-level analyses capture. We introduce the Agentic Task Exposure (ATE) score, a composite measure computed algorithmically from O*NET task data using calibrated adoption parameters--not a regression estimate--incorporating AI capability scores, workflow coverage factors, and logistic adoption velocity. Applying the ATE framework across five major US technology regions (Seattle-Tacoma, San Francisco Bay Area, Austin, New York, and Boston) over a 2025-2030 horizon, we find that 93.2% of the 236 analyzed occupations across six information-intensive SOC groups (financial, legal, healthcare, healthcare support, sales, and administrative/clerical) cross the moderate-risk threshold (ATE >= 0.35) in Tier 1 regions by 2030, with credit analysts, judges, and sustainability specialists reaching ATE scores of 0.43-0.47. We simultaneously identify seventeen emerging occupational categories benefiting from reinstatement effects, concentrated in human-AI collaboration, AI governance, and domain-specific AI operations roles. Our findings carry implications for workforce transition policy, regional economic planning, and the temporal dynamics of labor market adjustment
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。