Third circuit revives hotel AI price-fixing claims

In a landmark move, the Third Circuit revived multiple lawsuits challenging algorithmic price fixing in the hospitality sector, notably in Cornish-Adebiyi v. Caesars Entertainment. The court underscored that pooling non-public, competitively sensitive data through shared pricing software like Cendyn’s Rainmaker—which aggregated real-time pricing inputs from Atlantic City hotel-casinos and generated price recommendations followed approximately 90% of the time—could plausibly constitute a hub-and-spoke conspiracy under the Sherman Act. This revival signals a judicial willingness to scrutinize dynamic pricing models more rigorously, moving beyond prior dismissals and emphasizing the antitrust implications of algorithm-driven coordination.

Central to the Third Circuit’s reasoning is the critical distinction between mere parallel conduct and the exchange of confidential, non-public information among competitors via pricing algorithms. Unlike the Ninth Circuit’s dismissal in Gibson v. Cendyn Group, where no data pooling was alleged, Cornish-Adebiyi’s detailed allegations of shared proprietary data among competing casino-hotels elevated the claims beyond speculation. The court highlighted that such data sharing creates a plausible horizontal agreement, reinforcing that companies must carefully assess the risks of using third-party dynamic pricing tools that pool sensitive competitor data, especially given the opaque terms of service governing these platforms.

The Third Circuit adopted a notably plaintiff-friendly stance, lowering the pleading bar by ruling that plaintiffs need not reveal the inner workings of the pricing algorithms or precisely how non-public data is utilized at the early litigation stage. This approach reflects an unusual judicial skepticism toward pricing algorithms generally, allowing circumstantial evidence—such as staggered adoption of the software over 14 years and high compliance rates with algorithmic recommendations—to support an inference of conspiracy. Consequently, this decision paves the way for more robust challenges to algorithmic collusion without requiring exhaustive technical disclosures before discovery.

Cendyn's Rainmaker software allegedly facilitates algorithmic collusion by pooling non-public, real-time room pricing and occupancy data from competing casino-hotels, which then generates coordinated price recommendations that hotels follow about 90% of the time. This pooling of competitively sensitive data is central to the plaintiffs' claims, distinguishing cases like Cornish-Adebiyi—where such data sharing was alleged—from others like Gibson, where no such pooling was pleaded, leading courts to view the presence of shared proprietary data as critical evidence of a horizontal agreement under antitrust laws.

Courts increasingly interpret the use of shared AI-driven pricing software as a potential hub-and-spoke conspiracy under Section 1 of the Sherman Act, with the software acting as the hub coordinating the spokes—hotel competitors. The Third Circuit in Cornish-Adebiyi notably lowered the pleading bar by accepting that alleging the software company pooled competitively sensitive information and used it in pricing recommendations suffices to plausibly infer an agreement among users, signaling a judicial recognition that AI systems functioning as de facto shared pricing agents do not eliminate antitrust risk.

While hotels retain nominal final pricing authority, courts have found this defense weak when evidence shows a high compliance rate—around 90%—with AI-generated recommendations, especially when overrides require special permissions. Additionally, staggered adoption of the same adaptive AI pricing system over many years does not negate the inference of collusion, as simultaneous use creates ongoing opportunities for coordinated pricing, reinforcing the courts’ view that parallel conduct combined with shared data exchange supports antitrust claims.

Beyond antitrust implications, the flow of competitively sensitive data through shared AI vendors like Cendyn raises broader concerns about data governance and information barriers. The reliance on a common vendor to pool and redistribute confidential competitor information spotlights potential contractual and regulatory gaps in how such data is managed, underscoring the need for clearer safeguards to prevent misuse and unintended facilitation of collusion.

The revival of antitrust lawsuits against major hospitality players like Caesars Entertainment and MGM Resorts by the Third Circuit in mid-2026 marks a pivotal moment of intensified judicial scrutiny over AI-driven dynamic pricing strategies. These rulings underscore that hotels leveraging sophisticated algorithms to set room rates are not insulated from antitrust liability, especially when such AI systems pool confidential competitor data to generate coordinated pricing recommendations, effectively facilitating collusion without explicit human communication.

This evolving legal landscape signals a broader regulatory recalibration where traditional antitrust frameworks are being rigorously applied to AI-enabled pricing tools, which can coordinate market behavior subtly through shared vendor platforms. The Third Circuit’s explicit warning that using AI or third-party software "does not eliminate antitrust risk" highlights the critical importance for hospitality companies—and other industries employing dynamic pricing—to implement robust compliance measures, including auditing data flows and ensuring genuine independent pricing judgments to avoid liability.

Importantly, the courts have clarified that ultimate liability for algorithmic price fixing rests with human decision-makers, not the machines themselves. As the Third Circuit emphasized, companies must meticulously document and exercise independent judgment even when relying heavily on AI-generated pricing recommendations, countering any assumption that algorithms provide a legal shield. This human accountability requirement elevates the stakes for corporate governance and internal controls in the hospitality sector’s adoption of AI pricing technologies.