A class action lawsuit filed in California on June 22, 2026 accuses BP, Marathon Petroleum, 7-Eleven, Walmart, Speedway, and Albertsons of using artificial intelligence to coordinate gasoline and diesel prices in violation of federal and state antitrust law, according to Reuters.

The complaint alleges that the AI-facilitated scheme inflated gasoline prices by up to 22 cents per gallon and diesel by up to 33 cents per gallon, per Insurance Journal. The plaintiffs — California drivers — are seeking damages for the antitrust violations, though the total damages figure has not been publicly specified in available reporting.

What Is Algorithmic Collusion?

At the center of this case is a legal concept called algorithmic collusion: the idea that competing firms can use pricing algorithms — whether shared or independently developed but functionally equivalent — to achieve prices higher than genuine competition would allow, without needing to explicitly agree to do so. Courts and regulators have grappled with this theory for years, and this California filing represents one of the more prominent applications of it to retail fuel in the United States.

The defendant list spans an unusual range: vertically integrated oil companies (BP, Marathon Petroleum), convenience-store and fuel-retail chains (7-Eleven, Speedway), and general retailers with fuel operations (Walmart, Albertsons). This diversity suggests plaintiffs are not alleging use of a single shared software platform, but rather that all defendants independently adopted AI-driven dynamic pricing tools that, taken together, resulted in price alignment across competing outlets.

The Technical Shift

Automated price-adjustment systems have existed in fuel retail for over a decade. What has changed is the power and speed of the underlying tools. Modern machine-learning models can now optimize profit margins across thousands of variables at once and adjust prices in response to competitor moves within seconds. The legal question—and the unsettled one—is whether that sophistication and responsiveness, even without an explicit handshake agreement, amounts to an illegal arrangement under the Sherman Act or California's Cartwright Act, which are the federal and state laws that prohibit price-fixing.

The Department of Justice and FTC have named algorithmic collusion as an enforcement priority. Academic experts, notably Ariel Ezrachi and Maurice Stucke, have long argued that existing antitrust law may be ill-equipped to address tacit coordination between machines. Yet no U.S. court has yet ruled definitively on whether independent deployment of functionally similar AI pricing systems constitutes an unlawful agreement. If this case survives motions to dismiss and reaches trial, it could become a watershed moment for how courts interpret competition law in the age of machine learning.

Why This Case Could Be Significant

Antitrust law requires plaintiffs to prove concerted action—not merely parallel price moves. The defendants will likely argue that price similarity in a transparent, commodity market simply reflects independent, lawful competition. The legal battleground will be discovery: if plaintiffs can demonstrate that defendants shared a common platform or data infrastructure, proving an "agreement" becomes much easier.

California is also a consequential venue. The state's gasoline prices rank among the highest in the nation, magnifying per-gallon damages. California's consumer protection statutes, including the Cartwright Act, offer tools that federal law alone does not, and state courts have historically read the Cartwright Act broadly when it comes to price-fixing claims.

Implications Beyond Fuel

AI-driven dynamic pricing is now standard across grocery, airline, and hospitality industries. How courts treat this case will send a signal about legal risk to every compliance team managing algorithmic revenue optimization. A decisive ruling or high-profile settlement could reshape pricing practices well beyond the gas pump.

None of the named defendants had publicly responded to the complaint as of the reporting reviewed for this piece.