Market Concentration Implications of Foundation Models

TL;DR

This paper examines the market structure of foundation AI models, highlighting their tendency towards natural monopoly and discussing regulatory strategies to promote competition, safety, and social welfare.

Contribution

It provides an analysis of foundation model market dynamics and proposes regulatory approaches to address monopoly tendencies and ensure safety and fairness.

Findings

Most capable models tend towards natural monopoly

Regulation should ensure model quality and safety standards

Intense competition is expected for less advanced models

Abstract

We analyze the structure of the market for foundation models, i.e., large AI models such as those that power ChatGPT and that are adaptable to downstream uses, and we examine the implications for competition policy and regulation. We observe that the most capable models will have a tendency towards natural monopoly and may have potentially vast markets. This calls for a two-pronged regulatory response: (i) Antitrust authorities need to ensure the contestability of the market by tackling strategic behavior, in particular by ensuring that monopolies do not propagate vertically to downstream uses, and (ii) given the diminished potential for market discipline, there is a role for regulators to ensure that the most capable models meet sufficient quality standards (including safety, privacy, non-discrimination, reliability and interoperability standards) to maximally contribute to social welfare. Regulators should also ensure a level regulatory playing field between AI and non-AI applications in all sectors of the economy. For models that are behind the frontier, we expect competition to be quite intense, implying a more limited role for competition policy, although a role for regulation remains.