AI-Accelerated Occupational Decline and the Mobility Trap

Abstract

Economic transformation challenges workers beyond job creation or loss by reshaping their ability to move across occupations. This paper examines whether AI-driven occupational restructuring, accelerated after 2018 by transformer-based technologies, creates a “mobility trap” for workers in declining, high-AI-exposed occupations. Using BLS administrative data and CPS worker transitions, we analyze who moves, where they move, and with what outcomes. We find that while these workers are more mobile, they are 5.2 times more likely to move into another declining occupation than a growing one. Nearly 70 percent experience downward or lateral mobility, suggesting that AI-driven restructuring entrenches disadvantage rather than enabling upward mobility.

The authors' own abstract, copied from semanticscholar.org.

How to cite it

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Song, X., Brand, J. E., Yang, S. X., & Lachanski, M. (2026). AI-Accelerated Occupational Decline and the Mobility Trap. AEA Papers and Proceedings. https://doi.org/10.1257/pandp.20261004

Song, Xi, et al. "AI-Accelerated Occupational Decline and the Mobility Trap." AEA Papers and Proceedings, 2026, https://doi.org/10.1257/pandp.20261004.

@article{song2026aiaccelerated,
  title = {AI-Accelerated Occupational Decline and the Mobility Trap},
  author = {Xi Song and Jennie E. Brand and Sukie Xiuqi Yang and Michael Lachanski},
  journal = {AEA Papers and Proceedings},
  year = {2026},
  url = {https://doi.org/10.1257/pandp.20261004},
}

What it is

Xi Song, Jennie E. Brand, Sukie Xiuqi Yang and Michael Lachanski examine how AI can speed the decline of some occupations and make it harder for affected workers to move into better jobs (AEA Papers and Proceedings, May 2026).

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