Quantify the occupational and demographic exposure to automation-driven labor displacement over the next decade, assess which groups face the highest risk, and examine the adequacy of existing social protection systems to absorb the shock.

Occupation-level automation susceptibility analysis using updated Frey & Osborne methodology applied to O*NET task databases across 40 countries. Cross-referenced with McKinsey Global Institute automation adoption scenarios and Goldman Sachs generative AI labor market study (2024). Demographic breakdown by gender, education level, and geography.

Goldman Sachs (2024) estimates generative AI could automate 26% of tasks in the US and 25% in Europe across all occupation types — not just routine manual work. For the first time, white-collar knowledge work is significantly exposed: legal, financial analysis, and administrative roles face 40-60% task automation potential.

5x more exposed than men due to concentration in administrative and clerical roles. Workers without post-secondary education face 3x higher displacement risk.

Existing social protection systems were designed for episodic unemployment, not structural labor market transformation — average unemployment benefit duration of 26 weeks in OECD nations is wholly inadequate for workers facing permanent occupational displacement.