What question did this study set out to answer?

This research aims to explore the implications of occupational exposure to Agentic AI and its categorization under the ITEA framework.

May 10, 2026Open Access

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Mapping algorithmic expropriation: occupational exposure to Agentic AI under the multidimensional ITEA framework v3.0

AVAlberto García-Lluis ValenciaUniversidad Rey Juan Carlos

Key Points

This research aims to explore the implications of occupational exposure to Agentic AI and its categorization under the ITEA framework.
Applied ITEA version 2.2 framework to 1,016 occupations from O*NET 30.2 with robust analysis on 878 occupations.
Developed the Algorithmic Expropriation Index based on 47,810 O*NET tasks and validated against existing metrics like AIOE.
Documented six key findings related to the metrics and implications of Agentic AI exposure in various occupational sectors.
Convergent validity of ITEA with AEI is robust (r = 0.720, p < 10⁻¹⁴⁸).
External validation against AIOE reached r = 0.807 (p < 10⁻¹⁷⁷) over 769 occupations, surpassing previous benchmarks.
Identified sectoral concentration in Computer, Financial Operations, Life Sciences, and Healthcare professions, indicating high exposure levels.

Abstract

Abstract The empirical literature on occupational exposure to artificial intelligence (Eloundou et al. , 2024; Brynjolfsson, Li and Raymond, 2025; Massenkoff and McCrory, 2026) has converged on metrics calibrated to the generative AI regime, where workers retain workflow control. However, the institutional consensus crystallised in 2025–2026 (Bain Technology Report 2025; McKinsey State of AI 2025; IBM Institute for Business Value, 2025; Microsoft Ignite 2025) identifies Agentic AI as a structurally distinct regime. We argue that this transition is not an incremental extension of capability but a qualitative shift in the mechanism of intellectual capital transfer: under generative AI, worker contribution is diffuse; under Agentic AI, it crystallises into proprietary structural capital. We introduce the term algorithmic expropriation to denote this uncompensated transfer, and we apply the ITEA v2. 2 Framework —a multidimensional system of eight indicators applied to 1, 016 occupations from O*NET 30. 2 (February 2026), with effective analysis on 878 occupations with complete data— to map the exposed population. We operationalise this concept through the Algorithmic Expropriation Index (AEI), built independently on 47, 810 O*NET tasks, and convergently validated against the canonical literature benchmark, the AI Occupational Exposure index (AIOE) of Felten, Raj and Seamans (2021). Version 2. 2 incorporates the thirteen improvements articulated in the internal evaluation report (PMJ Report Nº 010, April 2026), organised in four blocks: (a) editorial corrections, (b) bibliographic insertions anchored in Acemoglu and Restrepo (2022) and Webb (2020), (c) external convergent validation against AIOE and policy quantification of the top 20, and (d) formal derivation of the Insider–Outsider inversion. The central novelty of v2. 2 over v2. 1 is item (c): we report r (OAEI v2. 1, AIOE) = 0. 807 over 769 SOC 6-digit common occupations (p (F + T) ·r of Lindbeck-Snower (1988), shows that under Agentic regime with orchestration cost cₐgent → 0 the condition holds unconditionally and qualification ceases to protect. This articulation closes the conceptual residual identified in previous evaluations.

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Cite This Study

Alberto García-Lluis Valencia (Fri,) studied this question.

synapsesocial.com/papers/6a0021b7c8f74e3340f9c98d https://doi.org/https://doi.org/10.5281/zenodo.20081628

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