AI and Algorithmic Pricing: 2025 Antitrust Outlook and Compliance Considerations

While algorithmic pricing has been used in many industries for decades, the rapid development of artificial intelligence (AI) technology has led antitrust enforcers—including federal agencies and state attorneys general—legislators, and private plaintiffs to begin actively scrutinizing potential anticompetitive practices related to the use of algorithmic pricing tools, particularly such tools that may involve systems considered to be AI. These developments have continued apace throughout 2024 and into 2025.

There have been multiple civil antitrust complaints filed in federal and state courts in recent years alleging that certain providers of algorithmic pricing tools and their users have violated antitrust laws, including the following:

In the RealPage class action, Yardi, and Cornish-Adebiyi cases noted above, the DOJ and/or Federal Trade Commission under the Biden administration also filed Statements of Interest supporting the plaintiffs and outlining the agency’s opinions on the legal frameworks they believe the courts should apply.

These statements advocated that competitors’ use of the same algorithmic pricing tool can constitute a form of price-fixing in violation of Section 1 of the Sherman Act and that, with respect to the allegations in those cases, the alleged conduct should be deemed per se unlawful rather than subject to the antitrust rule of reason.

As we have discussed elsewhere, courts ruling on motions to dismiss in these cases have so far reached different outcomes on questions such as whether to apply the per se rule or rule of reason, or whether an anticompetitive agreement has been adequately alleged.   

LOCAL AND FEDERAL LEGISLATIVE ACTIVITY

In addition to litigation under existing state and federal laws, several jurisdictions have adopted or are contemplating legislation to directly address algorithmic pricing concerns. San Francisco and Philadelphia both passed local laws in 2024 banning certain rental revenue management software involving the use of nonpublic information. Various other states or localities have considered or are considering similar proposals for 2025.

At the federal level, US Senator Amy Klobuchar and several other Democratic senators introduced the Preventing Algorithmic Collusion Act in February 2024 to bar companies from using algorithms to collude to set higher prices. While this legislation did not advance in the US Congress in 2024, Senator Klobuchar and other senators have reintroduced it in 2025.

COMPLIANCE CONSIDERATIONS AND MONITORING DEVELOPMENTS

With increased scrutiny from federal antitrust enforcers, state attorneys general, and private plaintiffs’ counsel seeking opportunities to litigate these AI issues, it would be prudent for companies using or considering adopting algorithmic pricing tools to monitor the ongoing developments in this space and weigh the benefits of designing and implementing an antitrust compliance program that is attentive to potential antitrust concerns involving AI, algorithms, and information exchange activity in the digital era.

While each company will have to tailor guidance for its own particular circumstances, the following are some high-level considerations from a US federal antitrust law perspective that companies may want to consider to the extent applicable:

• No Traditional Unlawful Agreements: Express agreements between horizontal competitors to fix prices or output, rig bids, or allocate markets are traditionally treated as per se unlawful. The use of an algorithm to monitor or enforce such an agreement does not change that.
• Make Pricing Decisions Unilaterally: Companies should act independently and unilaterally in making their own ultimate pricing decisions. Consider adopting appropriate policies and procedures to align any use of algorithmic tools with this principle.
• Understand Your Algorithms and Vendors: Court decisions and legislative initiatives have focused particular attention on pricing recommendation algorithms that use confidential, nonpublic information from multiple businesses. Work with your vendors or engineers to understand the data and techniques used in algorithms or in the training of AI models. Companies should exercise caution in working with a vendor to develop or implement pricing tools if the same vendor also works with other companies that could be deemed competitors. Similarly, exercise caution if working directly with other companies in the industry to develop AI applications that may not be pricing tools but could still be deemed to facilitate other types of information exchange.
• Document Procompetitive Benefits: Court rulings in algorithmic pricing antitrust cases have to date reached differing conclusions as to the appropriate standard for analyzing claims about price recommendation algorithms under antitrust law. Because courts applying the antitrust rule of reason will consider procompetitive benefits, make sure that the applicable procompetitive benefits of the algorithm, such as lowering prices for consumers or expanding the level of output sold, are well documented.
• Train Your Business Personnel: Train business personnel appropriately on the risks of using pricing algorithms and exchanging competitively sensitive information with intermediaries that may then use or distribute that data.
• Evaluate Design Criteria: Consider how different types and sources of data and information affect the overall design and antitrust risk associated with an algorithmic tool. While publicly sourced data is generally lower risk, confidential information shared among competitors can have a larger range of risk based on the type of information and how it is shared.
• Monitor Ongoing Deployment: It may be beneficial to periodically assess how your algorithmic tools are performing.
• Analyze Information-Sharing Agreements: Consider the risks associated with agreements that involve disclosing competitively sensitive information in light of the most recent legal and technological developments.
• Humans in the Loop?: Consider whether processes that include independent human oversight or assessment regarding algorithmic pricing or output recommendations are appropriate.
• Disclosure Considerations: Another factor that may be relevant as a compliance consideration is understanding which other companies you communicate to about your algorithmic tools or that otherwise know what tools your company is using.

HOW WE CAN HELP

This area of the law is actively developing with several pending cases across multiple jurisdictions. Morgan Lewis is counseling clients on these and the many other issues surrounding AI applications, including the design and implementation of adequate controls, contracting, public policy advocacy, and internal policies and practices. Morgan Lewis lawyers are closely monitoring relevant developments and are available to discuss any specific questions clients may have.