Another Signal on the Antitrust Risks of Pricing Algorithms

Alerts

August 3, 2026

On July 29, 2026, the U.S. Court of Appeals for the Third Circuit allowed a class action alleging antitrust violations against companies using a common pricing algorithm to proceed.¹ The Third Circuit held that when an “algorithm is in effect collecting non-public commercial information from [competitors] and utilizing the collective pot of data to ‘suggest’ prices to each [competitor],” that “surely raise[s] a plausible inference of collusion under Section 1 of the Sherman Act.”²

While several antitrust actions involving pricing algorithms have been brought in recent years, courts have sent mixed signals on what facts support an inference of plausible collusion under Section 1 might push using a common algorithm over the line to finding the plausibility of collusion. Importantly, the Third Circuit’s recent holding is further instructive on the risk factors to consider when deciding whether to develop or subscribe to such an algorithm.

The Third Circuit’s Decision: Cornish-Adebiyi v. Caesars Ent. Inc.

The Cornish-Adebiyi action was brought on behalf of a class of hotel guests against casino-hotels and a pricing algorithm supplier, Cendyn Group, LLC. The hotel guests alleged that the casino-hotels agreed to use Cendyn’s pricing algorithm software, “Rainmaker,” which allowed them to compare non-public pricing and occupancy rates of the hotels.³ The hotel guests further alleged that the “Rainmaker” algorithm used this non-public data to recommend pricing to the hotels, which the hotels followed 90 percent of the time.⁴ The hotel guests claim that this resulted in “anticompetitive prices across participating casino-hotels.”⁵

The question in front of the Third Circuit was whether the hotel guests plausibly alleged that the hotels agreed (colluded) to fix prices in violation of Section 1 of the Sherman Act. Before answering that question, the Third Circuit cited various literature that cautions how pricing algorithms could lead to anti-competitive conduct, observing that “AI software can . . . enabl[e] competitors to coordinate prices and share information without ever communicating with each other.”⁶ The Third Circuit found that its “review of Plaintiffs’ Section 1 claims [had to] proceed against this background.”⁷

The Third Circuit ultimately concluded that the hotel guests sufficiently alleged improper collusion. The Third Circuit keyed in on the following allegations to come to this conclusion:

“Since the software is allegedly integrated directly into a casino-hotel’s property management system, the pricing recommendations generated by the algorithm are directly and automatically uploaded into the casino-hotel’s system.”⁸
The software “allegedly conveyed to the casino-hotel Defendants that uniform adoption would enable their access, under the auspices of a single shared algorithm, to one another’s real-time, non-public pricing and occupancy data, and that the Rainmaker platform would generate significantly higher prices for each casino-hotel Defendant than if each one did so independently without use of that platform.”⁹
The casino-hotels used the software “contemporaneous[ly],” and there was “synchronous price and output movement” during the relevant time period.¹⁰
“Cendyn’s dynamic pricing algorithm . . . functioned as a shared pricing agent for defendants and generated recommended room rates for each of them using non-public pricing and occupancy data shared by each casino-hotel Defendant with the platform in real-time.”¹¹
“[A]ll of the casino-hotel Defendants were delegating their pricing decisions to Cendyn by accepting Rainmaker’s pricing recommendations 90 [percent] of the time.”¹²
As further support that the hotels reached an “agreement”—a core requirement of Section 1 of the Sherman Act—the Third Circuit credited: “(1) that Defendants had motive to conspire because of an extended period of financial hardship in the years preceding the class period; (2) that the casino-hotel Defendants’ adoption of Cendyn’s price recommendations when they could have competed more aggressively on price was against their economic interests; and (3) non-economic evidence showing exchange of non-public commercial information, opportunities to collude, and sudden changes in longstanding business practices.”¹³

Altogether, the Third Circuit “conclude[d] that, taking Plaintiffs’ allegations as true, the software is, in effect, facilitating collusive conduct by receiving from each client non-public commercial information, and in return, giving each client the benefit of their competitors’ non-public data in formulating a price recommendation which Defendants purportedly agreed to comply with.”¹⁴ The Third Circuit finally emphasized that the algorithm “is alleged to operate as a single decision-maker or hub, coordinating pricing for a majority of the market.”¹⁵

Instructively, the Third Circuit took issue with how the U.S. District Court for the District of New Jersey came to the opposite conclusion. The Third Circuit specifically disagreed with the District Court that “Plaintiffs’ allegations of collusion [were] premised merely on the casino-hotel Defendants’ ‘knowing’ and ‘purposeful’ use of the Rainmaker products” and that “the parallel conduct pled was not parallel because the casino-hotel Defendants signed up to use Rainmaker over a fourteen-year period.”¹⁶ The Third Circuit also disagreed that “Plaintiffs did not adequately show how Cendyn used the data it received from the casino-hotels” because the complaint “did not allege that the information was ‘pooled or otherwise commingled’ or somehow improperly exchanged,” and that “the casino-hotel Defendants continued to retain and exercise pricing authority.”¹⁷

The Third Circuit countered by highlighting allegations that “the casino-hotels overwhelmingly adhered to Cendyn’s price recommendation” and that “[e]ven more significantly, the [complaint] alleges that Cendyn’s former Vice President of Data Science and Analytics (who also served as Rainmaker’s Vice President of Revenue Analytics) encouraged casino-hotels to ‘avoid the infamous “race to the bottom” when competition inevitably becomes fierce within a market.’”¹⁸ It was also important to the Third Circuit that Cendyn allegedly “led discussions involving industry executives and managers, including personnel from [c]asino-[h]otel Defendants, on the best practices for maximizing room revenue and profitability while avoiding price wars, including through use of the Rainmaker platform.”¹⁹

Key Takeaways

The Third Circuit has now given a “win” to private plaintiffs and enforcers by crediting the allegations as supporting a horizontal agreement to fix prices at the pleading stage.²⁰ More importantly, the Third Circuit’s decision is instructive on the potential risks when developing or subscribing to a common pricing algorithm.²¹ The Third Circuit and other courts instruct that developing or using a common pricing algorithm can carry significant antitrust risk when:

the algorithm uses or discloses confidential, non-public pricing information;
the algorithm uses or discloses current (“real-time”) or future pricing data;
the algorithm provides a “default” or “recommended” price (even if only a “starting” price);
the algorithm encourages or enforces adoption of the recommended or default price; and
there is asymmetry in access to the algorithm, e.g., only sellers have access.

This list is not exhaustive, and no single factor establishes antitrust liability. This list is provided only as a guide when considering whether to offer or subscribe to a common pricing algorithm. It is important to seek legal counsel to assess the antitrust risk in detail before doing so. Please contact any member of Wilson Sonsini’s Antitrust and Competition team if you have questions relating to the antitrust risks of pricing algorithms.

[1] Cornish-Adebiyi v. Caesars Ent., Inc., 2026 WL 2182291, at *1 (3d Cir. July 29, 2026).

[2] Id. at *12.

[3] Id. at *2.

[4] Id.

[5] Id.

[6] Id. at *4.

[7] Id.

[8] Id. at *6 (quotation marks omitted).

[9] Id. (cleaned up).

[10] Id. at *7.

[11] Id. (cleaned up).

[12] Id.