DOJ Antitrust official lays out criminal enforcement playbook for algorithmic pricing cases

DOJ maintains view that algorithmic pricing can constitute price fixing

Glad reiterated what has been a throughline at DOJ across administrations with respect to antitrust enforcement of algorithmic pricing: agreements among competitors to fix prices, allocate markets, or rig bids violate the antitrust laws, regardless of whether the agreement is facilitated through algorithmic pricing tools or more typical communications. While this view has been the DOJ position since the Biden administration, the Division has yet to bring a criminal antitrust case involving an algorithmic pricing tool. In his May 14, 2026 remarks, Glad made clear that, despite the fact that DOJ’s antitrust enforcement efforts in this area have, to date, been limited to civil enforcement (such as the case it settled against RealPage in 2025), “that does not reflect a view that algorithmic conduct is beyond the reach of criminal antitrust enforcement.” On the contrary, according to Glad, regardless of how an alleged agreement to fix prices, rig bids, or allocate markets has been effectuated, if such an agreement can be proved beyond a reasonable doubt, “criminal charges are on the table.”

Where will DOJ draw the line between bringing civil or criminal antitrust charges in an algorithmic pricing case?

Glad described the current state of civil algorithmic pricing jurisprudence, which, for the most part, has been limited to cases involving software platforms that aggregate competitor data and return pricing recommendations to competitors. Glad called DOJ’s allegations against RealPage¹ that the company violated the U.S. antitrust laws by using data from competing landlords in an algorithm that generates pricing recommendations for rental properties a “paradigm case” in this area. Glad said that, rather than banning the software or algorithmic pricing generally, the consent decree entered into by DOJ and RealPage is intended to target the ingestion of real-time non-public competitor data and the “granular reporting of outputs back to competitors.”

Glad warned that the RealPage settlement is not a signal that DOJ considers algorithmic pricing conduct to be beyond the reach of criminal enforcement. To the contrary, Glad reiterated that an agreement to violate the antitrust laws is per se illegal regardless of what system was used “to replace independent decision-making with shared competitive intelligence.” Glad also cited ongoing private civil litigation that is currently making its way through the courts and shaping algorithmic pricing antitrust jurisprudence. According to Glad, in considering whether an alleged conspiracy is subject to the rule of reason or per se rule, the courts are focused on the nature of the agreement among competitors. Glad asserted that the per se rule— and potential criminal enforcement—may be warranted when competitors understand that their sensitive non-public data will be used to set prices for competitors and continue to use the software with that understanding.

DOJ purportedly well-equipped to tackle criminal algorithmic pricing cases

Glad also emphasized that the Division is armed with the tools necessary to investigate cartels “across mediums,” noting in particular that the Procurement Collusion Strike Force (PCSF) is especially primed to recognize “red flags” when potentially illegal conduct becomes automated. According to Glad, this automation may come in the form of public purchasers’ increased reliance on e-procurement platforms, dynamic pricing modules, machine-assisted bid evaluation, and automated reverse auctions. It also may occur in contractors’ use of bid-preparation software, pricing tools and market-intelligence products that use third-party data. Glad believes these automated tools leave a robust paper trail for antitrust enforcers to detect conduct that violates the antitrust laws. Glad emphasized that these same issues also occur in the non-public procurement context.

According to Glad, the Division’s new Whistleblower Rewards Program is another valuable tool for identifying algorithmic pricing conduct that violates the antitrust laws. Specifically, Glad pointed out that a company’s use of algorithmic pricing tools is often visible to a broad set of employees in a variety of different business units of both the competing firms using the tool and the company that developed and markets the software.²

Glad also discussed how the Division intends to address situations where large language models or other artificial intelligence tools are used by companies to generate pricing recommendations. According to Glad, where a pricing system depends on competitors’ confidential inputs to function, the Division will investigate whether such an arrangement constitutes anticompetitive coordination. In particular, when pricing model training inputs contain confidential economic data, and competing users of the model are aware of that fact,³ enforcers can prove a “meeting of the minds” for the purpose of establishing a per se illegal agreement under Section 1 of the Sherman Act. Reiterating a theme central to DOJ’s understanding of how the antitrust laws apply to algorithmic pricing tools, Glad said that “the form of the hub [in an anticompetitive hub-and-spoke arrangement] does not change its essence”, and that “[w]here competitors have agreed—through architecture, through information sharing, or through follow-the-algorithm understandings—to eliminate competition among themselves, the per se rule applies.”

Compliance best practices in the “algorithmic era”

Glad offered actionable guidance on how a company’s compliance program can sufficiently address AI and algorithmic pricing-related practices. The Division’s Evaluation of Corporate Compliance Programs in Criminal Antitrust Investigations—updated in November 2024—directs DOJ to look at whether the company’s risk assessment program addresses the company’s use of new technologies (including AI and algorithmic revenue management software) and whether compliance personnel are involved in the deployment of any new technology. To ensure a robust compliance program, Glad advised that relevant company personnel know what pricing and procurement tools the company uses, whether these tools rely on non-public competitor data, how these outputs are deployed, and whether the tool facilitates coordination with competitors. This is especially important for companies deploying AI or algorithmic tools in “competitively sensitive areas”. In addition, Glad warned that for companies that operate in a concentrated industry, “repeated interactions with competitors matter.” Employees that participate in trade associations, for example, should be trained about what discussions with competitors are permissible.

Takeaways

While it comes as no surprise that DOJ intends to continue to pursue antitrust enforcement against algorithmic pricing conduct, Glad’s May 16 remarks shed light on just how high a priority algorithmic pricing enforcement is for the Trump 2.0 Antitrust Division. Glad is unambiguous in declaring that such conduct may be per se illegal under the antitrust laws in cases where an agreement is “so plainly anticompetitive that no elaborate market analysis is required to condemn” it. In these cases, Glad states unequivocally that the government can and will bring criminal antitrust charges regardless of whether the agreement was facilitated by an algorithm or by typical anticompetitive communications among competitors.

Authored by Katie Hellings, Dan Shulak, Melissa Levitt, and Jill Ottenberg.