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Complement or substitute? How AI increases the demand for human skills

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  • Elina Makela
  • Fabian Stephany

Abstract

This paper examines whether artificial intelligence (AI) acts as a substitute or complement to human labour, drawing on 12 million online job vacancies from the United States spanning 2018-2023. We adopt a two-pronged approach: first, analysing "internal effects" within roles explicitly requiring AI, and second, investigating "external effects" that arise when industries, occupations, and regions experience increases in AI demand. Our focus centres on whether complementary skills-such as digital literacy, teamwork, resilience, agility, or analytical thinking-become more prevalent and valuable as AI adoption grows. Results show that AI-focused roles are nearly twice as likely to require skills like resilience, agility, or analytical thinking compared to non-AI roles. Furthermore, these skills command a significant wage premium; data scientists, for instance, are offered 5-10% higher salaries if they also possess resilience or ethics capabilities. We observe positive spillover effects: a doubling of AI-specific demand across industries correlates with a 5% increase in demand for complementary skills, even outside AI-related roles. Conversely, tasks vulnerable to AI substitution, such as basic data skills or translation, exhibit modest declines in demand. However, the external effect is clearly net positive: Complementary effects are up to 1.7x larger than substitution effects. These results are consistent across economies, including the United Kingdom and Australia. Our findings highlight the necessity of reskilling workers in areas where human expertise remains increasingly valuable and ensuring workers can effectively complement and leverage emerging AI technologies.

Suggested Citation

  • Elina Makela & Fabian Stephany, 2024. "Complement or substitute? How AI increases the demand for human skills," Papers 2412.19754, arXiv.org, revised Feb 2025.
  • Handle: RePEc:arx:papers:2412.19754
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    References listed on IDEAS

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    1. Daron Acemoglu & Pascual Restrepo, 2018. "Low-Skill and High-Skill Automation," Journal of Human Capital, University of Chicago Press, vol. 12(2), pages 204-232.
    2. Brad Hershbein & Lisa B. Kahn, 2018. "Do Recessions Accelerate Routine-Biased Technological Change? Evidence from Vacancy Postings," American Economic Review, American Economic Association, vol. 108(7), pages 1737-1772, July.
    3. Daron Acemoglu & Pascual Restrepo, 2020. "Robots and Jobs: Evidence from US Labor Markets," Journal of Political Economy, University of Chicago Press, vol. 128(6), pages 2188-2244.
    4. Acemoglu, Daron & Autor, David, 2011. "Skills, Tasks and Technologies: Implications for Employment and Earnings," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 4, chapter 12, pages 1043-1171, Elsevier.
    5. Morgan R. Frank & David Autor & James E. Bessen & Erik Brynjolfsson & Manuel Cebrian & David J. Deming & Maryann Feldman & Matthew Groh & José Lobo & Esteban Moro & Dashun Wang & Hyejin Youn & Iyad Ra, 2019. "Toward understanding the impact of artificial intelligence on labor," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 116(14), pages 6531-6539, April.
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    Cited by:

    1. David Marguerit, 2025. "Augmenting or Automating Labor? The Effect of AI Development on New Work, Employment, and Wages," Papers 2503.19159, arXiv.org.

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