Trapped Workers: A Network Analysis of Worker Mobility in the AI Economy
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FIG Fellows: Daniel Plaisance, Melissa Pumphrey, Aleister Montfort Ibieta
Authors: Daniel Plaisance, Melissa Pumphrey, Aleister Montfort Ibieta, Liat Krawczyk
Abstract:
Americans from all walks of life are increasingly anxious about the impact of artificial intelligence on job security. Nearly two-thirds expect AI to lead to fewer jobs over the next 20 years, while only 5% expect it to create more opportunities.1 That anxiety has produced a surge of research forecasting which jobs are most exposed to AI impact. While useful, such analysis provides an incomplete picture of the risk of worker displacement. A worker whose job is threatened by automation faces a fundamentally different risk depending on whether the labor market offers a viable path to a more resilient career, or whether every realistic next step leads to occupations that are equally threatened. Recent research identifies the erosion of occupations that have historically served as critical stepping stones for upward mobility. 2
This brief further examines that concern by introducing two concepts that together present a more nuanced picture of which workers are most likely to face sustained job loss due to AI and where policy interventions should focus:
Occupational clusters, the structured networks of jobs through which workers move throughout their careers.
Trapped workers, those for whom vulnerability is compounded because AI threatens both their current role and their most realistic next job.
Our research lays out those concepts and examines the important question: When workers leave an AI-impacted job, do they land somewhere safer or move to a new, similarly risky role?
