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When evaluating AI’s impact on jobs, much of the analysis starts with “Which roles have the most tasks AI can do?” and then jumps to who will be displaced. That’s one part of the narrative, but it’s incomplete. That’s because exposure measures show which jobs have tasks AI can replace or augment, but they don’t tell us who has the resources and options to transition if job loss occurs. This report adds the missing dimension: adaptive capacity, an index of how well workers could weather displacement, based on four factors: 1) Net liquid wealth (financial buffer) savings to absorb shocks; 2) Skill transferability (mobility potential) how portable skills are, weighted to growing roles; 3) Local labor market density (option availability) nearby employers and roles; 4) Age profile (transition friction) older workforces face higher costs and lower reemployment odds. This challenges the view that “the most AI-exposed roles will automatically experience the greatest harm.” A few themes from the analysis: Resilience: 26.5M of 37.1M in the top exposure quartile have above-median adaptive capacity; Vulnerability: 6.1M workers (4.2%) sit in the high exposure/low adaptive capacity zone, largely clerical/administrative roles and disproportionately women (~86%); Geography: these vulnerable roles are more common in smaller metros and college towns. This broader lens helps talent and workforce leaders see where transitions are most likely to be hardest, so they can better prioritize mobility pathways, transferable-skill building, and targeted support.
