Problem
Research questions and friction points this paper is trying to address.
Agentic AI
Occupational Displacement
Task Exposure
Labor Market Disruption
Workflow Automation
Agentic AI and Occupational Displacement: A Multi-Regional Task Exposure Analysis of Emerging Labor Market Disruption
🤖 AI Summary
This study addresses the limitations of existing task-level automation analyses in accurately assessing the labor market displacement risks posed by agentic AI systems capable of end-to-end workflow execution. Extending the Acemoglu–Restrepo task exposure framework, the authors propose the first Agentic Task Exposure (ATE) scoring system tailored to agentic AI, integrating dimensions of AI capability, workflow coverage, and adoption velocity. Leveraging O*NET task data, they algorithmically construct the ATE metric, moving beyond conventional assumptions that focus solely on substitutability at the subtask level. Their analysis forecasts that by 2030, 93.2% of information-intensive occupations across the five major U.S. tech hubs will face moderate to high displacement risk, while also identifying 17 emerging occupational categories poised to benefit from human–AI collaboration and AI governance.
📝 Abstract
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
Innovation
Methods, ideas, or system contributions that make the work stand out.
Agentic AI
Agentic Task Exposure (ATE)
end-to-end workflow automation
occupational displacement
AI adoption velocity
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