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

Zhifeng Cai · Review of Financial Studies · 11 Jul 2026

Where this sits in the debate

Will AI actually boost productivity and growth? · its finding: it depends (why studies differ)
Will AI destroy more jobs than it creates? · its finding: it depends (why studies differ)
Should governments act on AI now? · its finding supports the claim (why studies differ)

Direction of the finding as read from the abstract by an AI model (AI-written).

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.

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Leading field journal. A leading journal in economics or a neighbouring field (on the Tinbergen Institute’s A list or near the top of the RePEc ranking), or a top-rated computer-science venue.

Placed by: Tinbergen Institute A list. How sources are ranked.

Published: 2026 (3 months ago)
Citations: 0 (OpenAlex)

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

Abstract

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