Court signals AI hotel price-fixing claims have teeth

The Third Circuit's recent decision to revive antitrust lawsuits against prominent hotel operators such as Caesars and MGM marks a significant legal development in the realm of algorithmic pricing. These lawsuits allege that the companies engaged in price fixing by leveraging AI-driven pricing software that utilized shared, non-public data, effectively coordinating rates across competing hotels. This revival underscores the judiciary's growing recognition of complex, technology-facilitated collusion tactics, particularly the use of third-party algorithms to orchestrate pricing strategies in traditionally competitive markets.

The Third Circuit's decision marks a pivotal legal recognition of hub-and-spoke conspiracy claims grounded in the coordinated use of third-party pricing algorithms, specifically spotlighting Cendyn Group's Rainmaker software as the central hub facilitating this coordination. By pooling non-public, real-time pricing and occupancy data from competing Atlantic City and Las Vegas hotel-casinos, Rainmaker enabled the exchange of proprietary commercial information that generated AI-driven, synchronized price recommendations, thereby supporting an inference of price-fixing under Section 1 of the Sherman Act.

Crucially, the Third Circuit distinguished its ruling from the Ninth Circuit’s dismissal in Gibson v. Cendyn Group by emphasizing the presence of pooled non-public competitor data as a critical factor that elevates the plausibility of a hub-and-spoke conspiracy. This legal nuance underscores that mere parallel pricing is insufficient; instead, the sharing and integration of confidential commercial data through a common algorithmic platform substantively advances the claim of coordinated anticompetitive conduct.

The court further clarified that plaintiffs are not required to delve into the intricate technical workings of the pricing software or precisely how non-public data informs its recommendations at the pleading stage, noting that 'such level of detail into the software’s operations is neither required nor appropriate.' This pragmatic approach lowers the evidentiary threshold early in litigation, allowing antitrust claims to proceed based on the existence of coordinated data sharing and algorithmic pricing patterns alone.

Moreover, the Third Circuit rejected arguments that staggered adoption of the Rainmaker software over a fourteen-year span or the fact that hotel-casinos occasionally overrode price recommendations (approximately 10% of the time) undermined the inference of conspiracy. Instead, the court found that a high compliance rate—around 90%—combined with parallel conduct during the alleged conspiracy period, sufficiently supports the existence of a horizontal agreement among competitors facilitated by the shared algorithm.

The Third Circuit's recent rulings underscore a pivotal shift in antitrust jurisprudence by recognizing that the pooling of non-public, competitively sensitive data through third-party pricing algorithms can substantiate claims of collusion. Unlike earlier cases such as Gibson v. Cendyn Group, where courts dismissed claims absent allegations of shared confidential information, the Cornish-Adebiyi decision emphasized that plaintiffs need only allege that competitors contributed proprietary data to a common software platform, which then used this pooled information to generate pricing recommendations. This approach reflects a growing judicial consensus that the mere use of identical software is insufficient to allege conspiracy unless it involves the exchange of competitively sensitive data, thereby elevating the antitrust risk posed by such algorithmic tools.

The RealPage litigation serves as a compelling exemplar where the court found the submission of proprietary commercial data by multiple apartment owners to a single pricing software provider as persuasive evidence of a horizontal agreement. Similarly, in the Atlantic City hotel-casino cases, the Third Circuit highlighted that Cendyn Group’s Rainmaker software not only received non-public commercial information from each participant but also shared competitors’ confidential data back to users to formulate price recommendations. This reciprocal exchange of sensitive information via a centralized algorithmic platform significantly heightens the risk of coordinated pricing, distinguishing these cases from scenarios where pricing tools rely solely on public data or independent decision-making.

Importantly, the Third Circuit has lowered the pleading burden for plaintiffs by ruling that they are not required to detail the inner workings of the pricing algorithms or exactly how non-public data is pooled at the initial litigation stage. This pragmatic stance acknowledges the complexity and proprietary nature of such software, allowing claims to proceed based on the fact of data sharing alone without demanding granular technical disclosures before discovery. Moreover, the court found that staggered adoption of the software over 14 years and a high compliance rate—approximately 90%—with its pricing recommendations still sufficed to infer a conspiracy, reinforcing that algorithmic collusion risks persist even with partial or asynchronous use.