Search papers, labs, and topics across Lattice.
This paper introduces the Agentic Task Exposure (ATE) score, a novel metric for quantifying occupational displacement risk due to agentic AI systems capable of end-to-end workflow automation. The ATE score is algorithmically derived from O*NET data, incorporating AI capability, workflow coverage, and adoption velocity, and applied to six information-intensive SOC groups across five US tech regions. Results indicate that over 93% of occupations in Tier 1 regions face moderate-to-high displacement risk by 2030, while also identifying emerging roles in human-AI collaboration and AI governance.
Agentic AI isn't just automating tasks; it's poised to displace entire occupations, with over 93% of jobs in major tech hubs facing moderate-to-high risk by 2030.
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
