Emerging Antitrust Implications Of AI For The Hospitality Industry

As artificial intelligence continues to disrupt technologies in virtually every business and industry, regulators around the world are taking steps to police the anticompetitive use of AI in ways that harm consumers. One target of these efforts is dynamic pricing tools, commonly referred to as 'algorithmic pricing,' which utilize revenue, consumer demand, market conditions and past pricing data to make pricing recommendations for a product or service.

Government agencies, lawmakers and, more recently, class action plaintiffs' attorneys are scrutinizing whether these pricing tools, which increasingly use artificial intelligence to aggregate large volumes of data among competing firms to provide market intelligence, violate antitrust laws by enabling de facto collusion among competitors.

In one of the latest focal points of efforts to regulate the impact of AI on consumers, the hospitality industry is facing class action lawsuits in federal courts across the country alleging that hotels are using algorithmic pricing tools to unlawfully fix prices. These lawsuits seek to cast new suspicion on performance benchmarking and comparative analytics tools—such as those developed by industry leaders STR and Demand360—which have existed for years and on which nearly every owner, manager and brand relies.

In this article, we survey this emerging regulatory and legal landscape and consider steps the hospitality industry stakeholders can take to safeguard against potential exposure as they consider adopting AI tools to drive improved performance.

Increased Regulatory Focus on Algorithmic Pricing

Concerns over the anti-competitive effects of algorithmic pricing are growing worldwide and across industries. Since 2019, intergovernmental trade and governmental organizations, including the European Union, G7, and Organization for Economic Cooperation and Development (OECD), have been researching the impact of algorithmic pricing on competition. In October 2025, algorithmic pricing was the official topic of the G7's Competition Policy Makers and Enforcers Summit.

Several government regulators are now signaling a shift from research to enforcement, with the Competition Bureau of Canada recently opening an investigation into the use of algorithms to set rent in real estate markets. The Bundeskartellamt (Germany's Federal Cartel Office) also recently sent official notice to Amazon.com, Inc. that its algorithmic pricing models likely violate German and EU competition laws.

In the United States, in August 2025 the Department of Justice announced a settlement resolving price-fixing claims arising out of Greystar Management Services' algorithmic pricing software, which aggregates confidential competitor data to produce pricing recommendations, allegedly allowing landlords to align prices and raise rents.

The terms of the settlement included requirements that Greystar's software not use nonpublic competitively sensitive information or provide incentives for users to accept the software's pricing recommendations. In addition, last month California passed new laws strengthening its antitrust protections, including barring the use of algorithmic pricing in connection with a conspiracy in restraint of trade and increasing the criminal and civil penalties for violating the Cartwright Act (California's state-level version of the Sherman Act).

The Emergence of Class Actions Related to Algorithmic Pricing In the Hotel Industry

Private lawsuits targeting algorithmic pricing are also on the rise, including involving some of the hospitality industry's largest names and markets. Since 2023, five federal class action lawsuits have been filed alleging violations of the Sherman Act by the hotel industry related to the alleged use of algorithmic pricing software to fix pricing. See Richard Gibson, et al. v. Cendyn Group, LLC, et al., No. 2:23-CV-00140-MMD-DJA (D. Nev. May 8, 2024), aff'd, 148 F.4th 1069 (9th Cir. 2025) (asserting claims related to Cendyn's revenue management software products GuestRev, GroupRev, and RevCaster); Cornish-Adebiyi et al v. Caesars Entertainment, Inc. et al, No. 1:2023cv02536 (D.N.J. 2024) (same); Dai v. SAS Institute Inc., No. 4:24-cv-02537 (N.D. Cal. 2024) (asserting claims related to IDeaS' revenue management software); Portillo et al v. CoStar Group Inc et al, No. 2:2024cv00229 (W.D. Wash. 2025) (asserting claims related to CoStar's STR benchmarking and analytics tool); Segal v. Amadeus IT Group, S.A. et al, No. 1:2024cv01783 (N.D. Ill. 2025) (asserting claims related to Demand360, a comparable analytics tool).

Each of these cases, which named as defendants the third-party software company and various other industry participants, including brands and specific hotels, alleged violations of Section 1 of the Sherman Act, which prohibits contracts, combinations, and conspiracies in restraint of trade. Specifically, the plaintiffs alleged that by sharing non-public data with software companies using algorithmic pricing, and obtaining market pricing information in return, hotels collude on pricing and charge increased rates regardless of consumer demand. In a landmark 1969 case, United States v. Container Corp., 393 U.S. 333 (1969), the Supreme Court established that such an agreement among competitors to gather and exchange price information, with the effect of stabilizing prices across an industry, can violate the Sherman Act.

However, to successfully assert a claim for violation of the Sherman Act based on information sharing, a plaintiff must allege facts that make the existence of an actual agreement plausible. See "Conscious Parallelism and Price Fixing: Defining the Boundary," Michael K. Vaska, The University of Chicago Law Review (1985). "Parallel conduct," in which competitors independently reach the same business decisions in the face of certain market conditions, is not, without more, illegal.

To date, this is where each of the cases has fallen short: in the context of algorithmic pricing tools, federal courts have held that the mere sharing of competitive information with an algorithmic pricing software is not sufficient grounds for an antitrust claim. In dismissing each complaint, the courts have identified three main deficiencies. First, plaintiffs failed to allege that hotels entered into agreements with other hotels – rather than only with the software services – to make use of algorithmic pricing services. Second, plaintiffs could not show that the information the platforms relied on to produce recommendations was confidential. Last, plaintiffs failed to allege that the hotel industry is using algorithmic pricing software with the illegal purpose and effect of fixing hotel room rates.

For example, in Gibson v. Cendyn, the Ninth Circuit held that while "an agreement among Hotel Defendants to follow Cendyn's pricing recommendations would harm competition," Plaintiffs had failed to allege facts from which such an agreement could be inferred. Richard Gibson, et al. v. Cendyn Group, LLC, et al., No. 24-3576 (9th Cir. 2025). The Court reasoned that multiple hotels in the same market could have independently made the same decision to use the software, and more specific allegations (often referred to as "plus factors") are needed to adequately allege an agreement.

Courts have further held that not just any plus factors will suffice. In DAI v. SAS, No. 24-CV-02537-JSW, 2025 WL 2078835 (N.D. Cal. July 18, 2025), the Court rejected plus factors such as the unique impact of COVID-19 on competitive incentives, the alleged particular elasticity of the hotel market, and hotel industry stakeholders' participation in trade organization meetings held by pricing software companies.

It is well established that mere participation in trade organization meetings and other industry events, for example, is not suggestive of illegal activity, because it is an important and routine part of doing business. See In re: Musical Instruments and Equipment Antitrust Litigation, No. 12-56674, 2015 U.S. App. Lexis 14960, slip op. at 13-14 (9th Cir. Aug. 25, 2015).

Courts have further suggested that to prevail on price-fixing allegations under an information-sharing theory, the information exchanged must be directly related to the price of renting a room. In Segal v. Amadeus IT Group, S.A. et al, No. 1:2024cv01783 at 11 (N.D. Ill. 2025), the Court dismissed the plaintiff's second amended complaint in part because Segal based his allegations on the fact that the hotels shared occupancy data with Demand360. The Court held that this was too attenuated because a claim for conspiracy based on information sharing turns on the nature of the information exchanged—a claim that hotels are fixing prices must be based on the actual sharing of prices.

The Takeaway

While the class action lawsuits filed so far have been rebuffed by the courts, this is likely not the end of the story. It is anticipated that the putative class action plaintiffs will file amended complaints seeking to address the pleading deficiencies identified by the courts, and some antitrust scholars argue that, given the difficulty in applying century-old antitrust principles to the rapidly unfolding landscape of AI-assisted algorithmic pricing, a different paradigm for evaluating such cases needs to be considered.

For example, in Cornish-Adebiyi v. Caesars Entertainment, Inc., the American Antitrust Institute filed an Amicus Brief in the Third Circuit arguing that "because AI enables firms to effectively coordinate on price and output in ways that humans cannot, algorithmic collusion may not be accompanied by the same plus-factor evidence as human collusion." Brief for the American Antitrust Institute as Amicus Curiae in Support of Plaintiffs-Appellants and Reversal, Cornish-Adebiyi v. Caesars Entertainment, Inc., No. 1:23-CV-02536 (filed January 28, 2025).

Algorithmic pricing is just one of many privacy, property and competition questions raised by the burgeoning AI era. As hotel industry stakeholders take advantage of the incredible benefits of these new technologies, they must remain vigilant to avoid attendant legal and regulatory risk.

The analysis by courts to date, along with enforcement and policy efforts worldwide, has provided key insights into what activities may be viewed as anticompetitive. To best protect against exposure to regulatory and litigation risk, hotel industry participants should take care when entering into any agreement involving the exchange of proprietary revenue, occupancy, and room rate information.

In particular, they should avoid agreements which require users to provide the exact rate charged for any given room, lack privacy protections such as anonymization protocols that prevent users from identifying other users' proprietary data, or require or provide incentives for implementing the software's price recommendations.

While no plaintiff has successfully alleged that the hotel industry's use of algorithmic pricing violates antitrust law, efforts will surely continue.

Originally published in the November 4, 2025, edition of the New York Law Journal

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