Third Circuit Revives Atlantic City Casino AI Price-Fixing Lawsuit, Setting Landmark Antitrust Precedent

Third Circuit Revives Atlantic City Casino AI Price-Fixing Lawsuit, Setting Landmark Antitrust Precedent

On July 29, 2026, the U.S. Court of Appeals for the Third Circuit issued a landmark ruling in Cornish-Adebiyi v. Caesars Entertainment, Inc. (No. 24-3006), reversing the district court's dismissal of a putative antitrust class action. The lawsuit accuses major Atlantic City casino-hotels of conspiring to fix hotel room rates through their shared use of Cendyn Group's "Rainmaker" software, an AI-powered dynamic pricing program. This decision represents the first time an federal appellate court has sided with class action plaintiffs pursuing horizontal price-fixing claims centered on the shared use of a pricing algorithm.

The Alleged Scheme

The plaintiffs—hotel guests who rented rooms during the class period—alleged that the casino-hotel defendants inputted their current, non-public room-pricing and occupancy data into Rainmaker and received recommended room rates in return. According to the complaint, the defendants accepted Rainmaker's recommendations approximately 90% of the time, effectively delegating their pricing decisions to a common algorithmic agent. Cendyn allegedly urged "uniform adoption" of the algorithm and told the casino-hotels that Rainmaker "would generate significantly higher prices for each [casino-hotel] than if each one did so independently without use of that platform."

The Third Circuit's Landmark Ruling

The Third Circuit held that the plaintiffs had plausibly alleged a hub-and-spoke price-fixing conspiracy under Section 1 of the Sherman Act, with Cendyn acting as the "hub" and the casino-hotels as the "spokes." Crucially, the court ruled that the "rim" of the conspiracy was established by allegations that the competitors knowingly provided confidential, real-time data to a shared pricing agent¹, received recommendations informed by competitors' data, and overwhelmingly adhered to those recommendations.

Importantly, the court rejected the district court's requirement that the plaintiffs plead with specificity how the algorithm functions to facilitate information exchange. The panel reasoned that plaintiffs should not be expected to plead the precise technical operations of a complex software program before discovery.

Key Legal Takeaways

1. Delegation as Parallel Conduct: The relevant conduct is not the staggered adoption of the software over a 14-year period, but the continuous, synchronized deployment of the pricing tool and delegation of pricing decisions during the class period.
2. Pricing Control is No Defense: The court emphasized that retaining "final pricing authority" does not shield defendants from antitrust liability if they overwhelmingly follow the algorithm's recommendations. As the court noted, "[p]rices are fixed when they are agreed upon," regardless of whether conspirators always adhere to them.
3. Information Exchange via Algorithm: The decision establishes that using a common algorithmic vendor to pool and process non-public data can constitute circumstantial evidence of an agreement, signaling that courts will look past the technical "black box" of AI systems to examine the economic reality of coordination.