Pricing AI Model Accuracy
This addresses market dynamics for AI model providers and consumers, offering insights into pricing and investment strategies, though it is incremental as it builds on existing economic models.
The paper tackles the problem of how competition affects firms' incentives to improve AI model accuracy in a market with heterogeneous consumer preferences, finding that firms profit by investing only in error dimensions where they have a competitive advantage, not by improving overall accuracy.
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.