AI has continued to come under the scrutiny of government enforcers and private litigants. In July 2025, the White House released America’s AI Action Plan. As we noted in our January 5 Trends to Watch: 2025 Antitrust & Competition Law, on the competition side, plaintiffs have alleged that AI may be used in ways that harm competition, including as part of a conspiracy to use AI-supported algorithms related to pricing or other competitive datapoints. Additionally, control of the data on which AI, at least generative AI is built, is another area that may spur antitrust issues.
This GT Advisory explores the evolving antitrust landscape for AI in 2025, including federal policy developments, algorithm-related litigation, and regulatory scrutiny businesses should be aware of.
America’s AI Action Plan: Regulatory Relief and Innovation Priorities
AI and algorithms continue to be a topic of interest for U.S. and international antitrust enforcers. Antitrust enforcers in Trump’s second administration continue to be interested in “Big Tech,” though with a goal of also promoting certainty and clarity for businesses.
To that end, in July 2025, the White House released America’s AI Action Plan. America’s AI Action Plan outlines President Donald Trump’s perspective on AI and identifies specific steps to ensure the United States leads the race to achieve global dominance in AI. The plan includes three pillars of action—(1) accelerate AI innovation, (2) build American AI infrastructure, and (3) lead in international AI diplomacy and security—and contains 90 specific policy recommendations aimed at removing regulatory barriers to AI infrastructure development.
It also includes a recommendation to review all Federal Trade Commission (FTC) investigations, final orders, consent decrees, and injunctions “commenced under the previous administration to ensure that they do not advance theories of liability that unduly burden AI innovation … and, where appropriate, seek to modify or set-aside any that unduly burden AI innovation.” Additionally, the plan encourages open-source and open-weight AI (i.e., developers make it freely available for anyone to download and modify), which is relevant to antitrust considerations.
AI Algorithm Pricing Litigation: Class Action and Antitrust Risks
AI-powered algorithms are being watched for their potential to raise antitrust concerns. Using AI-powered algorithms in pricing decisions may create efficiencies, but may also raise concerns about potentially higher prices, including through an alleged conspiracy among competitors.
Several class action lawsuits have been filed across the country in a variety of industries—including hotels, multifamily residential rental units, student housing, mobile homes, and health care services—alleging that defendants have used some type of pricing algorithm to purportedly fix, stabilize, or raise prices for their respective products. These cases have had mixed success, with some being dismissed at the outset but others surviving dismissal and subjecting the defendants to extensive and expensive discovery on the merits, as well as the risk of class certification.
In addition to private litigation, the U.S. antitrust agencies are also active in enforcement against algorithmic coordination. For example, the DOJ Antitrust Division has amended its Guidance on the Evaluation of Corporate Compliance Programs to consider a company’s risk assessment related to AI, including such concerns as:
How does the company’s risk assessment address its use of technology, particularly new technologies such as artificial intelligence (AI) and algorithmic revenue management software, that are used to conduct company business? As new technology tools are deployed by the company, does the company assess the antitrust risk the tools pose? What steps is the company taking to mitigate risk associated with its use of technology? Are compliance personnel involved in the deployment of AI and other technologies to assess the risks they may pose? Does the compliance organization have an understanding of the AI and other technology tools used by the company? How quickly can the company detect and correct decisions made by AI or other new technologies that are not consistent with the company’s values?[1]
Most recently, in August 2025, DOJ Assistant Attorney General Gail Slater stated on social media that she anticipates the DOJ’s algorithmic pricing probes to increase as their use grows. Slater warned that “[f]irms should perform their own due diligence on shared algorithms inputs and functionality to prevent collusion that can harm consumers.”
AI algorithm pricing has been and likely will continue to be a hot area of antitrust litigation and enforcement, especially as the use of AI continues to spread across industries. Reliance on non-public information, especially if it was obtained from a competitor, may increase the risk of litigation and potential liability.
Open-Source AI Antitrust Concerns: Market Control and Regulatory Responses
Some have touted open-source models as key to democratizing access and promoting competition, but some regulators are skeptical that the models provide the hoped-for solution to antitrust concerns. Tech firms retain control over critical infrastructure like hardware, cloud platforms, and proprietary data, potentially limiting the competitive impact of open-source models.
For example, the strategic use of open-source AI might be used to gain market share, followed by a shift to closed models that regulatory agencies could argue restrict access and effectively bar entry of new competitors. Similarly, the lack of interoperability between open and proprietary AI systems may lock in customers, undermining competition. Additionally, as discussed above, companies might face allegations that shared AI tools unintentionally facilitate collusion among competitors even without express agreements to do so.
Regulators are considering different approaches to responding to these concerns. The European Union, for example, is considering expanding the Digital Markets Act to classify AI businesses as “gatekeepers,” and potentially mandating interoperability between systems. The FTC and DOJ, in contrast, are more focused on AI partnerships and acquisitions, emphasizing the use of existing antitrust laws to address access and control antitrust concerns.
AI Antitrust Compliance: Considerations for Businesses
The shifting legal landscape around AI and antitrust creates a complex environment for businesses. In this dynamic, changing area, companies should consider several approaches. Monitoring updates from antitrust authorities, including the FTC, DOJ, and international regulators can help companies adapt their practices to comply with the latest requirements and guidance. Companies using pricing algorithms may benefit from ensuring human oversight in ultimate pricing decisions and regularly reviewing algorithms to confirm that they are not using non-public data, particularly data obtained from competitors. Businesses may also consider keeping records showing that pricing and strategic decisions were made independently, even when using AI tools. Finally, in the mergers and acquisitions context, companies should consider evaluating antitrust implications of any AI-related partnership or acquisition and seeking antitrust legal guidance early in the process.
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[1] See U.S. DOJ Antitrust Division, Evaluation of Corporate Compliance Programs in Criminal Antitrust Investigations (November 2024) at 9 (DOJ Compliance Guidance). The Antitrust Division’s Guidance is aimed to the criminal context, however the Division notes that these same guidelines “should also minimize risk of civil antitrust violations.” DOJ Compliance Guidance at 2.