Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Agentic AI and Occupational Displacement: A Multi-Regional Task Exposure Analysis of Emerging Labor Market Disruption

Ravish Gupta, Saket Kumar

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

Agentic AI and Occupational Displacement: A Multi-Regional Task Exposure Analysis of Emerging Labor Market Disruption

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

Paper Structure

This paper contains 29 sections, 3 equations, 2 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Task-Based Frameworks for Automation Analysis
  4. Limitations of Existing Frameworks for Agentic AI
  5. Agentic AI: Definitions and Capabilities
  6. Theoretical Framework: Agentic Task Exposure
  7. The Acemoglu--Restrepo Foundation
  8. The Agentic Task Exposure Score
  9. Adoption Velocity and Temporal Projection
  10. Data and Methodology
  11. Occupational Task Data
  12. AI Capability Scores
  13. Workflow Coverage Estimation
  14. Regional Employment and Adoption Data
  15. Remote Work and the Erosion of Geographic Adoption Buffers
  16. ...and 14 more sections

Figures (2)

  • Figure 1: ATE computation pipeline. O*NET task statements and ratings feed into task weights ($w_{o,t}$), AI capability scores (CAP), and workflow coverage factors (COV). Regional adoption data parameterize the S-curve velocity function $V(t, r, \tau)$. The four components are multiplied and summed across tasks to produce occupation-level ATE scores, which are then classified by displacement risk threshold.
  • Figure 2: Distribution of 2027 ATE scores across all 236 occupations for three representative regions. The dashed red line marks the moderate-risk threshold ($\mathrm{ATE} \geq 0.35$). In the SF Bay Area (Tier 1), 70.8% of occupations exceed this threshold; in Seattle (Tier 2), 0%; in New York (Tier 3), 0%. The rightward shift from Tier 3 to Tier 1 reflects the adoption velocity differential parameterized by the S-curve model.