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

Daniel Parshall () and Andrea Lopez-Luzuriaga ()

No 2026-005, Working Papers from The George Washington University, The Center for Economic Research

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

Keywords: AI; Task Exposure; Labor Time Allocation. (search for similar items in EconPapers)
JEL-codes: C49 J21 J22 O33 (search for similar items in EconPapers)
Pages: 50 pages
Date: 2026-03
New Economics Papers: this item is included in nep-ain, nep-lma and nep-neu
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