Quantify automation exposure across occupational categories, identify vulnerable worker populations, and assess adequacy of existing social protection systems to absorb displacement.

Task-based analysis of automation susceptibility across 800 occupational categories using O*NET task content data matched to ML capability benchmarks. Displacement scenario modeling under low, medium, and high adoption trajectories.

40-85 million jobs in OECD nations face high automation risk by 2030. Most at-risk: routine cognitive work (data entry, bookkeeping, paralegal, radiology) and routine physical work (assembly, driving, warehouse picking). Unlike previous waves, this one is hitting white-collar mid-skill workers — the core of the middle class.

Women are disproportionately exposed: 57% of high-risk jobs are held by women. Social protection systems were designed for cyclical unemployment, not structural displacement — 26-week benefit duration is inadequate for workers needing 18-36 months retraining. Only 10% of displaced workers successfully transition to higher-wage roles within 2 years.