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Helping People Choose Careers in the Age of AI

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  • Jennifer L. Steele
  • Isabella Cruz

Abstract

How should people choose careers when artificial intelligence (AI) is rapidly transforming the nature of work? We first compare six recent projections of occupational exposure to task automation with AI, examining their methods and assumptions. We then propose a new empirical model of occupational AI exposure based on 2025 query data from Anthropic and OpenAI. We find marked heterogeneity in model predictions, though models published since 2020 show positive relationships among AI exposure, salaries, and occupational complexity. To reduce uncertainty due to heterogeneous assumptions about task automation potential, we average the projections from five models, including our own. Using these averages, we report on likely tradeoffs between salaries and AI exposure across interest categories, O*NET Job Zones, and job fields. Jobs in healthcare practice show the strongest balance of higher pay with lower AI exposure. Among jobs making high use of Anthropic's Claude, those that use it as a complement rather than a substitute for human work are modestly higher-paying, though whether this pattern holds will depend on usage norms adopted in each field.

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  • Jennifer L. Steele & Isabella Cruz, 2026. "Helping People Choose Careers in the Age of AI," Papers 2607.15506, arXiv.org.
    • Handle: RePEc:arx:papers:2607.15506
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    References listed on IDEAS

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    1. James Bessen, 2019. "Automation and jobs: when technology boosts employment," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 34(100), pages 589-626.
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