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The article reports that 2026 unemployment data shows no unusual impact of AI on recent college graduates' employment, contrasting with prior expectations. It analyzes studies from CESifo and Stanford, finding no statistically significant trends in AI-related job displacement.
This paper develops the AI Exposure and Resilience (AI-ER) framework, a two-dimensional assessment model for evaluating how AI affects software businesses by separating exposure to AI pressure from resilience to adapt.
This study proposes the Agentic Adoption Index (AAI) to measure AI delegation in occupations based on 53,000 agent configurations, revealing that adoption patterns differ from pre-AI risk frameworks and are influenced by technical feasibility but not fully explained by it.
An analysis using ILO data maps AI exposure across China's 362 million workers, revealing that clerical support workers face the highest risk (8.5/10) while craft workers, despite being the largest group, have low exposure (2.5/10).
An analysis of UK occupation data reveals that clerical support workers have the highest AI exposure score (8.5/10), ahead of professionals (6.5/10), while trades and elementary occupations are least exposed. The data suggests AI's labour-market impact may first hit administrative roles due to structured workflows.