Research on AI and labor displacement from academics, scientists, economists, AI labs, and policy scholars. All perspectives represented. The outlook gauge reflects the aggregate tone of the research, not editorial balance.
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The three published AI exposure methodologies used in our composite scores. Pinned here for quick reference.
What it measures: AI exposure at the occupation and industry level using an AIOE index that maps AI capabilities to O*NET task descriptions.
What it doesn't: Does not predict actual job losses (displacement) — only task-level exposure. Coverage limited to 774 SOC codes.
What it measures: LLM exposure across all O*NET occupations, scoring both exposure with and without access to complementary tools.
What it doesn't: Does not model timing or magnitude of displacement. Exposure scores are proxies, not predictions.
What it measures: Firm-level and occupation-level exposure to generative AI, using a task-based methodology grounded in O*NET work activities.
What it doesn't: Focused on finance sector; broader generalization requires extrapolation. Does not predict employment outcomes.
Key articles to understand AI's impact on labor — start with these before browsing the full library
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Resources for journalists, researchers, and policymakers
The AI Labor Displacement Dashboard tracks public labor-market data and published AI-exposure research to provide a real-time, evidence-based view of how AI is affecting the US workforce.
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