Concerns over competitors using artificial intelligence (AI) pricing tools to fix prices have dominated antitrust discussions in the U.S. and EU in recent times. Recent cases show how algorithmic pricing might enable unlawful coordination. But AI antitrust risks far exceed this current focus and might likely expand into other concerted practices and unilateral conduct.

In a newly published article, authors Lee F. Berger and Robert Klotz, antitrust and competition lawyers in Washington D.C. and Brussels, examine how the growing use of AI – especially in pricing, market targeting, and data-driven decision-making –creates new antitrust risks in both the United States and the European Union.

In the case U.S. v. RealPage, the DOJ alleged that landlords shared nonpublic, competitively sensitive data through a pricing platform that generated rent recommendations, leading to extraordinary conduct remedies and substantial private settlements. By contrast, Gibson v. Cendyn was dismissed because the pricing software relied mainly on public data and did not compel users to follow its recommendations, so no unlawful agreement was found. In the EU, the European Commission’s Horizontal Guidelines signal that shared pricing algorithms might violate Article 101 TFEU even without explicit coordination, though no enforcement decision has yet addressed self-learning AI price collusion.

Beyond price fixing, the authors highlight several emerging AI-driven antitrust risks:

• Market and customer allocation: AI systems used by multiple competitors may implicitly divide geographic markets or customer segments by learning that avoiding overlap maximizes profits, potentially replicating unlawful allocation agreements without direct communication.
• Price discrimination: AI enables highly personalized pricing based on consumer data. While potentially efficient, this raises concerns under EU abuse of dominance rules, the Digital Markets Act, and U.S. unfair competition law, particularly where transparency is lacking or consumers are exploited. Ongoing cases like FTC v. Amazon may shape how U.S. law treats algorithmic conduct that influences market outcomes without overt collusion.
• Predatory pricing: AI could facilitate targeted below-cost pricing aimed only at customers most likely to switch to rivals, driving competitors out while maintaining overall profitability. Although novel, this behavior could fit within traditional U.S. and EU predatory pricing frameworks and may also implicate the Robinson-Patman Act.
• Data-driven monopolization: Control over large, proprietary datasets can entrench dominance by creating barriers to entry and reinforcing network effects. Here, the authors make particular reference to the cases (i) Google Search (Shopping), where the European Commission found that the more users relied on Google’s search engine, the more data it collected, allowing it to improve the quality and relevance of results, attracting even more users in a self-reinforcing cycle, to (ii) Google Android, where network effects operated across multiple sides of the mobile ecosystem resulting in an increase in users, which then motivated more app developers, which in turn attracted more users and device manufacturers and (iii) Amazon Marketplace, in which the European Commission found that Amazon used non-public data from independent sellers to inform its retail decisions and self-preference its offers and logistics services. According to the authors, these cases illustrate how data advantages can strengthen market power, distort competition, and exclude rivals even before clear consumer harm emerges.

The authors conclude that as AI becomes more deeply embedded in business strategies, antitrust risks will expand beyond traditional categories. Regulators and compliance teams should thus proactively assess AI tools and adapt legal frameworks to address new, technology-enabled forms of market abuse.