Pricing and Competition for Generative AI

TL;DR

This paper analyzes the pricing strategies and competitive dynamics of generative AI models, highlighting how market timing and task similarity influence profitability and market share.

Contribution

It introduces a game-theoretic model for pricing generative AI, revealing how market information and task similarity affect firms' pricing and cost-effectiveness.

Findings

Later entrants can secure cost-effectiveness on at least one task.

First-to-market firms may need to set higher prices to incentivize latecomers.

High task similarity can render first-to-market models cost-ineffective.

Abstract

Compared to classical machine learning (ML) models, generative models offer a new usage paradigm where (i) a single model can be used for many different tasks out-of-the-box; (ii) users interact with this model over a series of natural language prompts; and (iii) the model is ideally evaluated on binary user satisfaction with respect to model outputs. Given these characteristics, we explore the problem of how developers of new generative AI software can release and price their technology. We first develop a comparison of two different models for a specific task with respect to user cost-effectiveness. We then model the pricing problem of generative AI software as a game between two different companies who sequentially release their models before users choose their preferred model for each task. Here, the price optimization problem becomes piecewise continuous where the companies must choose a subset of the tasks on which to be cost-effective and forgo revenue for the remaining tasks. In particular, we reveal the value of market information by showing that a company who deploys later after knowing their competitor's price can always secure cost-effectiveness on at least one task, whereas the company who is the first-to-market must price their model in a way that incentivizes higher prices from the latecomer in order to gain revenue. Most importantly, we find that if the different tasks are sufficiently similar, the first-to-market model may become cost-ineffective on all tasks regardless of how this technology is priced.