Generative AI and Data Quality: Implications for Productivity, Labor Displacement, and Policy
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ID: 320847
2026
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Abstract
Abstract Generative artificial intelligence (AI) is increasingly consuming and producing huge amounts of data. We propose a social learning model of AI, emphasizing a data-AI feedback loop: data quality affects AI productivity, which influences AI adoption and, consequently, the composition (AI versus human-generated) and quality of future data. Calibrated to evidence on synthetic training loops, the model predicts hump-shaped labor dynamics—short-term displacement that partially reverses as data quality deteriorates. A Grossman–Stiglitz-style externality emerges: AI adopters free-ride on the human-generated actions that supply the novel information on which AI itself relies. In a competitive market, AI should be taxed to correct the data-quality externality; a concentrated AI industry overcorrects, making a subsidy optimal. (JEL O33, D62, D83, J24)
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| Reference Key |
openalex_W7168144916
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|---|---|
| Authors | Zhifeng Cai |
| Journal | review of financial studies |
| Year | 2026 |
| DOI |
10.1093/rfs/hhag064
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| URL | |
| Keywords | Keywords not found |
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