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Pricing AI Model Accuracy

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  • Nikhil Kumar

Abstract

This paper examines the market for AI models in which firms compete to provide accurate model predictions and consumers exhibit heterogeneous preferences for model accuracy. We develop a consumer-firm duopoly model to analyze how competition affects firms' incentives to improve model accuracy. Each firm aims to minimize its model's error, but this choice can often be suboptimal. Counterintuitively, we find that in a competitive market, firms that improve overall accuracy do not necessarily improve their profits. Rather, each firm's optimal decision is to invest further on the error dimension where it has a competitive advantage. By decomposing model errors into false positive and false negative rates, firms can reduce errors in each dimension through investments. Firms are strictly better off investing on their superior dimension and strictly worse off with investments on their inferior dimension. Profitable investments adversely affect consumers but increase overall welfare.

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  • Nikhil Kumar, 2025. "Pricing AI Model Accuracy," Papers 2504.13375, arXiv.org.
    • Handle: RePEc:arx:papers:2504.13375
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    File URL: http://arxiv.org/pdf/2504.13375
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    References listed on IDEAS

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    1. Alexander W. Cappelen & Cornelius Cappelen & Bertil Tungodden, 2023. "Second-Best Fairness: The Trade-off between False Positives and False Negatives," American Economic Review, American Economic Association, vol. 113(9), pages 2458-2485, September.
    2. Annie Liang, 2019. "Games of Incomplete Information Played By Statisticians," Papers 1910.07018, arXiv.org, revised Jul 2020.
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