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Does Training in AI Affect PhD Students' Careers? Evidence from France

Author

Listed:
  • Pierre Boutros

    (Université Côte d'Azur, CNRS, GREDEG, France)

  • Eliana Diodati

    (University of Torino, Italy)

  • Michele Pezzoni

    (Université Côte d'Azur, CNRS, GREDEG, France
    Observatoire des Sciences et Techniques, HCERES, Paris, France)

  • Fabiana Visentin

    (UNU-MERIT, Maastricht University, the Netherlands)

Abstract

The rise of Artificial Intelligence (AI) urges us to better understand its impact on the labor market. This paper is the first to analyze the supply of individuals with AI training facing the labor market. We estimate the relationship between AI training and individuals' careers for 35,492 French PhD students in STEM who graduated between 2010 and 2018. To assess the unbiased effect of AI training, we compare the careers of PhD students trained in AI with those of a control sample of similar students with no AI training. We find that AI training is not associated with a higher probability of pursuing a research career after graduation. However, among students who have AI training during the PhD and pursue a research career after graduation, we observe a path dependence in continuing to publish on AI topics and a higher impact of their research. We also observe disciplinary heterogeneity. In Computer Science, AI-trained students are less likely to end up in private research organizations after graduation compared to their non-AI counterparts, while in disciplines other than Computer Science, AI training stimulates patenting activity and mobility abroad after graduation.

Suggested Citation

  • Pierre Boutros & Eliana Diodati & Michele Pezzoni & Fabiana Visentin, 2025. "Does Training in AI Affect PhD Students' Careers? Evidence from France," GREDEG Working Papers 2025-29, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    • Handle: RePEc:gre:wpaper:2025-29
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    References listed on IDEAS

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    1. Dror Shvadron & Hansen Zhang & Lee Fleming & Daniel P. Gross, 2025. "Funding the U.S. Scientific Training Ecosystem: New Data, Methods, and Evidence," NBER Working Papers 33944, National Bureau of Economic Research, Inc.
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    3. Sauermann, Henry & Roach, Michael, 2014. "Not all scientists pay to be scientists: PhDs’ preferences for publishing in industrial employment," Research Policy, Elsevier, vol. 43(1), pages 32-47.
    4. Shibayama, Sotaro, 2019. "Sustainable development of science and scientists: Academic training in life science labs," Research Policy, Elsevier, vol. 48(3), pages 676-692.
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    Full references (including those not matched with items on IDEAS)

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    Keywords

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    JEL classification:

    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General

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