trailStatic AI exposure scores do not measure what AI labor policy questions actually require
Exposure is potential, not impact· for
LLM task exposure measures technical potential, not realized labor-market impact or job displacement
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US aggregate labor-market data show no relationship between occupational AI exposure and changes in employment or unemployment since ChatGPT's release
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Scores do not travel across time and place· for
AI occupational exposure scores are snapshots of a specific model's capabilities that become outdated as AI capabilities advance
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AI exposure scores built on US occupational data transfer poorly to other countries' labor markets
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Policy analyses citing the 2023 GPTs-are-GPTs occupational exposure scores do not engage later methodological improvements
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Exposure scores carry valid policy-relevant signal· against
Exposure ratings of O*NET occupational tasks by human annotators and GPT-4 reliably estimate which work tasks LLMs could affect
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Observed real-world AI usage across occupational tasks correlates strongly with task-based AI exposure ratings
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Independently constructed AI occupational exposure indices broadly agree on which occupations are most exposed
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assumed
The 2023 GPTs-are-GPTs occupational exposure scores are a central empirical input to future-of-work debates
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Static AI exposure scores do not measure what AI labor policy questions actually require
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