Antitrust scrutiny of information exchanges and algorithmic pricing tools has intensified recently—from public enforcers and private plaintiffs alike—as companies across industries increasingly use algorithmic and AI-enabled systems to gather market data and generate pricing recommendations. These developments have generated significant uncertainty for businesses that rely on pricing and benchmarking tools, particularly given the still-unsettled state of the law.
For example, last year in the Gibson litigation (D. Nev.), the Ninth Circuit affirmed the district court’s dismissal, holding that plaintiffs failed to plausibly allege a horizontal agreement in an alleged algorithmic-pricing conspiracy case in the hotel industry.1 By contrast, in RealPage (M.D. Tenn.), the court allowed a similar algorithmic-pricing claim to proceed in part—emphasizing that, unlike Gibson, the RealPage complaint “unequivocally” alleged a “melting pot” of confidential competitor data being used to generate