DOJ’s acting head of criminal antitrust enforcement recently warned that companies and individuals who reach collusive agreements using algorithmic pricing tools can be subject to criminal antitrust investigation and prosecution. In remarks at the Antitrust West Coast Conference, Acting Deputy Assistant Attorney General, Daniel Glad, highlighted that “algorithmic conduct” is not “beyond the reach of criminal antitrust enforcement.” These remarks follow years of civil actions targeting algorithmic pricing tools, particularly those that rely on non-public data from competitors to generate pricing recommendations.
Background – DOJ’s Stance Against Algorithmic Pricing in Civil Cases
In both the Biden and second Trump administrations, the Division has argued that the joint use of a shared pricing algorithm can subject the users to per se antitrust scrutiny, alleviating the need to show harm or effects beyond an anticompetitive agreement. In November 2023, the Division filed a Statement of Interest in In re Realpage, a large civil class action case alleging algorithmic collusion among some of the country’s largest landlords. That Statement argued that per se price fixing occurs when “competitors knowingly combine their sensitive, nonpublic pricing and supply information in an algorithm that they rely upon in making pricing decisions, with the knowledge and expectation that other competitors will do the same.” Per se treatment, the Division opined, is even appropriate if the algorithm “recommends, rather than mandates, certain prices.” This focus on per se has remained constant through the Trump administration. In March 2025, the Division issued another Statement of Interest in In re Multiplan Health Insurance Provider Litigation, reiterating that the per se rule can apply to “competitors’ joint use of a common pricing algorithm” even if “the competitors do not always use the algorithm in the same way.” Then, in November 2025 the Division entered a consent judgment with RealPage requiring certain restrictions on its algorithmic pricing tool. Under this judgment, RealPage cannot use real-time, granular pricing data.
New Horizons – Criminal Enforcement
Although the Department has thus far used civil tools to address algorithmic collusion, DAAG Glad’s recent remarks make clear the Antitrust Division is focused on criminal enforcement where they see collusion by code. “[T]he door to the per se rule, and therefore to criminal enforcement, is open” when competitors have used the same pricing tool and “have understood that their sensitive non-public data will be used to set prices for competitors and have participated on that understanding.” Glad pointed to the Division’s Procurement Collusion Strike Force, Leniency Program, and its new Whistleblower Rewards Program as investigative avenues for enforcement. At bottom, Glad made clear that the DOJ may criminally investigate any situation “[w]here competitors have agreed — through architecture, through information sharing, or through follow-the-algorithm understandings — to eliminate competition among themselves.”
Compliance Takeaways
- Businesses that use pricing algorithms must know what data goes into the algorithm and what output the algorithm generates that the business’s competitors can use.
- Criminal antitrust risk arises from inputting sensitive, non-public data into a pricing tool or algorithm that other competitors know about and eventually benefit from.
- Inputting aged, aggregated pricing data into a pricing algorithm carries less risk.
- Consult antitrust counsel if considering using a pricing tool where you supply sensitive non-public data.
- Autonomous AI pricing agents do not shield companies or individuals from criminal liability.