Background Rapid advances in general-purpose artificial intelligence are compressing automation timelines: around 40–50% of tasks in advanced economies are technically automatable, and a quarter of total hours could be migrated to machines before 2030. Fears of large-scale displacement from artificial-intelligence (AI) adoption have prompted calls for either mass reskilling or unconditional cash transfers. Another option, the emergence of a “supervisory economy” in which humans specialise in overseeing AI systems, remains empirically under-examined. Methods Using a balanced panel of 12 economies observed annually from 2014 to 2023, we construct a sector-weighted AI-exposure index and match it to labour-force data on unemployment, supervisory employment, public transfers, R effective resilience likely hinges on coupling oversight capacity with targeted technical upskilling and productivity-enhancing growth. Policy mixes that rely primarily on cash transfers or untargeted innovation spending risk delaying, rather than mitigating, employment re-equilibration in the age of AI.
Malliaros et al. (Wed,) studied this question.