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Measuring AI's Economic Reach: A Multi-Dimensional Task Taxonomy

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  • Daniel Parshall

  • Andrea Lopez-Luzuriaga

Abstract

Existing frameworks for measuring AI's labor market exposure decompose imperfectly across distinct dimensions: whether AI can perform a task, whether deployment is physically feasible, and whether institutions permit it. We propose CDR, a three-axis ordinal taxonomy that separates these dimensions into Cognitive complexity (C0-C4), Deployment difficulty (D0–D4), and Regulatory restrictions (R0-R4), extending Autor's (2003) routine/non-routine x cognitive/manual framework into a finer-grained classification space suitable for measuring AI exposure. Applying CDR to the full O*NET task universe (23,850 task-activity pairs across 923 occupations, classified via multi-model LLM consensus: Claude Sonnet 4.6, GPT-5-mini, Gemini 3 Flash, validated against flagship models), we find that 40.2% of U.S. economy-weighted labor time falls in tasks that are within current AI cognitive reach (C

Suggested Citation

  • Daniel Parshall & Andrea Lopez-Luzuriaga, 2026. "Measuring AI's Economic Reach: A Multi-Dimensional Task Taxonomy," Working Papers 2026-005, The George Washington University, The Center for Economic Research.
    • Handle: RePEc:gwc:wpaper:2026-005
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    JEL classification:

    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply

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