Generative AI and Data Quality: Implications for Productivity, Labor Displacement, and Policy

• 成果类型: Article; Early Access
• 署名作者: Cai, Zhifeng
• 署名单位: Rutgers University System; Rutgers University New Brunswick
• 刊物名称: REVIEW OF FINANCIAL STUDIES
• ISSN/ISSBN: 0893-9454; 1465-7368
• DOI: 10.1093/rfs/hhag064
• 发表日期: 2026-09-09
• 关键词: O33; D62; D83; J24; GROWTH; uncertainty; TECHNOLOGY; automation; tasks

摘要

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.