AI and Algorithmic Pricing: Navigating the 2025 Antitrust Landscape
The rapid advancement of artificial intelligence (AI) has brought algorithmic pricing under intense scrutiny from antitrust enforcers, including federal agencies, state attorneys general, and private plaintiffs. This heightened scrutiny, evident throughout 2024 and continuing in 2025, focuses on the potential for anticompetitive practices arising from the use of AI-driven algorithmic pricing tools.
Litigation and Legal Challenges
Several antitrust lawsuits have emerged in recent years, alleging violations of antitrust laws by companies using algorithmic pricing tools. The following cases highlight the evolving legal challenges:
- In re RealPage Rental Software Antitrust Litigation (M.D. Tenn.): This class action lawsuit, concerning real property rental price recommendations, is currently in the discovery phase.
- United States v. RealPage (M.D.N.C): The US Department of Justice Antitrust Division (DOJ) and ten co-plaintiff states are pursuing this civil case, which also involves real property rental price recommendations. Motions to dismiss are pending.
- RealPage State Attorney General Actions (Arizona, Maryland, and DC): The attorneys general of Arizona, Maryland, and the District of Columbia have initiated independent actions against RealPage concerning real property rental price recommendations under their respective state/district laws. The Arizona and DC actions are in the discovery phase, while Maryland’s case is at an early stage.
- Duffy v. Yardi (W.D. Wa.): This class action litigation, focusing on real property rental price recommendations, is currently in the discovery phase.
- Cornish-Adebiyi v. Caesar’s Entertainment, Inc. (D.N.J.): This class action, concerning hotel rate recommendations, was dismissed for failure to state a claim. Plaintiffs are appealing.
- Gibson v. MGM Resorts International (D. Nev.): Similar to the previous case, this class action regarding hotel rate recommendations was dismissed, and plaintiffs are appealing.
- In re Multiplan Health Insurance Provider Litigation (N.D. Ill.): This case involves healthcare reimbursement rate recommendations, and motions to dismiss are pending.
Government Involvement
In these cases, the DOJ and/or the Federal Trade Commission, under the Biden administration, have filed Statements of Interest supporting the plaintiffs. These statements argue that the shared use of algorithmic pricing tools by competitors could constitute illegal price-fixing under Section 1 of the Sherman Act, suggesting the alleged conduct should be considered per se unlawful rather than subject to the antitrust rule of reason. Courts have delivered varied outcomes regarding the application of the per se rule or rule of reason, and concerning whether an anticompetitive agreement has been sufficiently established.
Legislative Action at Local and Federal Levels
Beyond litigation, several jurisdictions are enacting or considering legislation to address algorithmic pricing concerns:
- Local Laws: San Francisco and Philadelphia passed local laws in 2024 that restrict certain rental revenue management software using nonpublic information.
- State and Local Proposals: Other states and localities are considering similar measures for 2025.
- Federal Legislation: US Senator Amy Klobuchar reintroduced the Preventing Algorithmic Collusion Act in February 2025. While unsuccessful in 2024, the legislation aims to prevent companies from using algorithms to collude on prices. These legislative proposals are designed to address the perceived risks of antitrust law violations.
Compliance Considerations for Businesses
Given the increased scrutiny from antitrust enforcers, state attorneys general, and private plaintiffs, it is prudent for businesses using, or considering adopting, algorithmic pricing tools to monitor developments and implement robust antitrust compliance programs. The following are crucial compliance considerations:
- No Traditional Unlawful Agreements: Agreements between competitors to fix prices, rig bids, or allocate markets are traditionally per se unlawful, and using an algorithm to facilitate it does not change that.
- Make Pricing Decisions Unilaterally: Ensure companies independently and unilaterally make all pricing decisions. Adopt appropriate policies and procedures that align any use of algorithmic tools with this principle.
- Understand Algorithms and Vendors: Gain a deep understanding of the data and techniques used in algorithms and AI models. Exercise caution when working with vendors that also work with competitors.
- Document Procompetitive Benefits: Document the procompetitive benefits of the algorithm, for example, lower prices for consumers or an expansion of the level of output sold, especially if subject to the antitrust rule of reason.
- Train Personnel: Train business personnel on the risks of using pricing algorithms and exchanging sensitive information.
- Evaluate Design Criteria: Data sources and types influences the design and antitrust risk of algorithmic tools. Be mindful of the type of data and how it is shared.
- Monitor Ongoing Deployment:Assess how algorithmic tools are performing periodically.
- Analyze Information-Sharing Agreements: Consider risks associated with such agreements given legal and technological advancements.
- Humans in the Loop: Consider whether processes should include independent human oversight of algorithmic pricing or output recommendations.
- Disclosure Considerations: Understand what other companies your communications concerning algorithmic tools and what tools your company is using.
How to Get Help
This area of the law remains rapidly evolving, with multiple pending cases across multiple jurisdictions. Companies should be proactive in seeking legal counsel to navigate these complex issues. Organizations like Morgan Lewis are advising clients on issues related to AI applications, aiding in the development of adequate controls and internal policies.
