In today’s digital economy, the prices consumers pay for housing, travel, and healthcare are increasingly determined by algorithms. Known as algorithmic pricing, this practice has existed for decades in industries such as airlines and retail. However, recent advances in artificial intelligence have expanded the reach and influence of these pricing practices, drawing scrutiny from regulators on both sides of the Atlantic.
Algorithmic pricing is the use of data-driven systems, often powered by machine learning, to recommend or set prices.Some systems monitor competitor prices, while others dynamically adjust prices in real time based on demand, inventory, or consumer behavior. Advocates argue that such tools allow firms to respond more efficiently to market conditions, reduce uncertainty, and optimize supply. Critics, however, contend that algorithmic pricing can obscure anticompetitive conduct, weaken meaningful competition, and enable coordinated outcomes that would be unlawful if achieved through agreement.
“Section 1 of the Sherman Act prohibits ‘[e]very contract, combination in the form of trust or otherwise, or conspiracy’ that unreasonably restrains trade. 15 U.S.C. § 1; NCAA v. Bd. of Regents of Univ. of Okla., 468 U.S. 85, 98 (1984). Under the statute, it is per se unlawful for competitors to join together their independent decision-making power to raise, depress, fix, peg, or stabilize prices. United States v. Socony-Vacuum Oil Co., 310 U.S. 150, 223–24 & n.59 (1940). And the Supreme Court has made clear that ‘the machinery employed by a combination for price-fixing is immaterial.’ Id. at 223.” Statement of Interest of the United States, In re RealPage, Inc. Rental Software Antitrust Litigation (No. II), No. 3:23-md-03071, at 1 (M.D. Tenn. Nov. 15, 2023), ECF No. 627.
At dispute is the growing concern over “algorithmic collusion.” Unlike traditional price-fixing schemes, which rely on explicit communication among competitors, algorithmic collusion can arise when firms independently adopt the same third-party pricing software. In this “hub-and-spoke” arrangement, the software provider acts as the hub, synchronizing pricing behavior across an entire market without direct coordination between competitors. This dynamic challenges conventional assumptions about how collusion forms and complicates the application of antitrust law in automated environments.
These principles are now being tested in litigation, most visibly in the U.S. housing market. In recent years, rental pricing software has become a focal point of antitrust enforcement, particularly tools developed by RealPage. Multiple lawsuits allege that RealPage’s revenue management software facilitated coordinated rent increases among competing landlords by aggregating and distributing nonpublic, competitively sensitive data. This conduct, if proven, may constitute the type of concerted price-setting activity long treated as per se unlawful under Section 1.
Federal enforcers have taken an aggressive stance in these cases, signaling a broader shift in how algorithmic conduct is evaluated under antitrust law. The DOJ and the Federal Trade Commission have filed Statements of Interest urging courts to treat the alleged conduct as per se unlawful, rather than applying a rule-of-reason framework. According to the agencies, when competitors rely on the same pricing algorithm, that system functions as a common pricing agent, effectively coordinating prices across the market. In the DOJ’s view, algorithmic price-fixing is traditional collusion carried out through code, and existing antitrust doctrines apply with equal force.
This enforcement approach has already produced results. In August 2025, Greystar, the largest apartment management company in the United States, reached a proposed settlement with the DOJ concerning its use of RealPage’s software. See https://www.justice.gov/opa/pr/justice-department-reaches-proposed-settlement-greystar-largest-us-landlord-end-its. If approved, the settlement would prohibit Greystar from using pricing programs that rely on nonpublic competitor data or incorporate specific anticompetitive features.
Scrutiny extends beyond housing to other sectors. The hospitality industry has also become a testing ground, with antitrust suits filed against MGM Resorts International and Caesars Entertainment over their use of algorithms to set hotel room rates. Although these claims were initially dismissed for failure to state a claim, they are currently under appeal, highlighting the unsettled nature of the law in this area. Healthcare has similarly emerged as a battleground, as ongoing litigation involving Multiplan raises concerns that algorithmic reimbursement recommendations may inflate medical costs to the detriment of patients.
In response, lawmakers are moving beyond case-by-case enforcement toward direct legislative intervention. At the federal level, Senator Amy Klobuchar and others have introduced, and reintroduced in 2025, the Preventing Algorithmic Collusion Act, which would prohibit firms from using algorithms to coordinate pricing or set higher prices. State and local governments have also acted. San Francisco and Philadelphia, for example, passed ordinances in 2024 banning certain forms of rental revenue management software that rely on nonpublic information. California has taken one of the most ambitious approaches with proposed Assembly Bill 325. The bill would impose sweeping restrictions, and potentially outright prohibitions, on the use and distribution of “common pricing algorithms.” It targets tools distributed to multiple competitors with the intent that they be used to set or recommend prices, particularly when those tools rely on nonpublic competitor data or when users know, or should know, that they are participating in a price-fixing scheme.
Internationally, the European Union (“EU”) has moved even more aggressively. Through the Digital Markets Act, Regulation (EU) 2022/1925 (restricting gatekeepers’ use of nonpublic business data), the Digital Services Act, Regulation (EU) 2022/2065 (imposing systemic risk and transparency obligations on online platforms), and the Artificial Intelligence Act, Regulation (EU) 2024/1689 (establishing a risk-based framework governing AI systems), the EU seeks to prevent dominant firms from exploiting nonpublic data and to limit automated practices that may result in discriminatory or unfair pricing. Together, these initiatives reflect a shared regulatory philosophy: algorithmic efficiency cannot come at the expense of competition, transparency, or consumer protection.
Algorithmic pricing stands at a pivotal moment. While these tools offer undeniable efficiencies, regulators increasingly agree that they cannot serve as a concealed mechanism for coordinated pricing, obscured behind proprietary code. As antitrust litigation advances, businesses should exercise care when adopting algorithmic pricing tools, particularly where those tools are shared across competitors or rely on pooled, competitively sensitive data. Algorithmic systems may still guide firms through complex markets, but their use carries meaningful antitrust risk where they have the effect of aligning pricing behavior across market participants.