A consumer recently filed a putative nationwide class action lawsuit in federal court in Illinois, Thomas v. McDonald’s USA, LLC, alleging that McDonald’s and its franchisees fixed menu prices through an AI-driven “pricing engine” built on pooled, nonpublic franchisee data. According to the lawsuit, it was an $18 Big Mac meal in Connecticut that first brought the alleged price-fixing to light. Reuters reports that this appears to be the first case to allege that franchises operating under the same brand are misusing shared pricing technology to overcharge customers by finding the highest price each customer will pay.

We have closely monitored the active litigation arising from use of AI in pricing decisions and written about risks of AI pricing lawsuits alleging antitrust claims (“What Does AI Have to Do With Your Pricing? Cautionary Antitrust Considerations,” The Franchise Lawyer, American Bar Association (Fall 2025)). The claims in the McDonald’s lawsuit put a different spin on the risks: they cast the franchisor as the hub of a price-fixing conspiracy among its own franchisees and company-owned locations. McDonald’s has denied the allegations, stating that its technology does not automate, coordinate, or fix pricing in any way, and that the claims are unproven. Even so, the complaint gives a roadmap of potential claims that could be asserted against franchise systems based on the use of AI-enabled pricing tools.

The Antitrust Framework

The Sherman Act prohibits any “contract, combination . . . or conspiracy” that unreasonably restrains trade. Competitors may not replace independent pricing decisions with coordinated pricing. Courts have long held that the mechanism or machinery used to carry out price-fixing is immaterial, and that principle applies to algorithms. Algorithmic pricing cases involving rental housing, hotels, and health insurance have produced mixed results at the lawsuit pleading stage. Recent federal appellate decisions include one court reinstating claims and another court affirming dismissal of claims. Across the recent AI pricing lawsuits, key questions have focused on whether the algorithms incorporate nonpublic competitor data and whether users retain and exercise independent pricing discretion.

The McDonald’s Allegations

The complaint against McDonald’s focuses on similar antitrust allegations:

Franchisees as horizontal competitors. Plaintiffs rely on McDonald’s own disclosures that franchisees have no exclusive territories, compete with one another and with company-owned restaurants, and set their own menu prices. The argument is that, absent the alleged agreement, each restaurant would be an independent price-setter.

Pooled nonpublic data. The complaint alleges that the Franchise Disclosure Document requires franchisees to use McDonald’s point-of-sale system that sends transaction-level sales data to McDonald’s, with no contractual limits on McDonald’s access. Franchisees allegedly may not share that data with anyone else, so McDonald’s is the “only channel through which one franchisee’s data can inform a rival’s price.”

Rules that track price moves. The complaint alleges that the pricing tool applies a “30% rule”: it recommends price increases on items that have already been raised in at least 30% of its stores. In other words, a franchisee may be told to raise a price based on what its competitors have done.

Pressure to comply. McDonald’s allegedly tracks deviations from the pricing tool’s recommendations and contacts franchisees who depart from them. Further, franchise standards require franchisees to engage with McDonald’s approved Pricing Consultant and Tools.  Allegedly, pricing non-compliance is considered as part of renewal and expansion decisions.

Antitrust disclaimers used as evidence. The pricing portal’s terms warn that users may be competitors of each other and must comply with antitrust law. Plaintiffs present this disclaimer as an acknowledgment of the risk, not as a defense.

Reference to DOJ’s Position. The complaint also cited the Department of Justice’s statement in a separate lawsuit that “any formula used to fix benchmark, component, recommended, or ‘starting point’ prices—even if end prices ultimately vary” can violate the Sherman Act.

The complaint asserts four types of claims: price-fixing under Section 1 of the Sherman Act (pleaded as per se unlawful or, alternatively, under a “quick look” analysis), a per se unlawful exchange of competitively sensitive information under Section 1, a claim under the Illinois Antitrust Act, and a claim under the Illinois Consumer Fraud Act based on McDonald’s public statements that franchisees set their own prices.

Practical Steps for Franchisors

Many franchise systems are exploring uses of AI, including in the pricing context. This case highlights some of the risks associated with embracing the new technology. The McDonald’s complaint applies horizontal-competitor framing to pricing in connection with franchisees. As a result, building on the best practices outlined in our earlier article, franchisors should consider the following:

1. Map the data flows. Determine whether any confidential data is used, directly or through model training, to generate recommendations for other franchisees or company-owned units. Consider firewalls or unit-specific models that rely on the unit’s own data and public market information. California’s recent amendment to its Cartwright Act, AB 325, also presents additional considerations as it expressly addresses pricing algorithms that use competitor data, which can include publicly available competitor data. Evaluate options to segregate franchisee-specific data and reduce any perception of collusion or data sharing.
2. Keep pricing tools truly optional. Avoid any mandatory adoption of pricing recommendations. Consider elevated risks if you track compliance with recommended prices or make renewals or other business decisions contingent on pricing behavior. Ensure that franchisees (and the company) have true pricing independence. High rates of adherence to algorithmic recommendations have been cited by plaintiffs in other litigation (where users are following the algorithm’s recommendation 80-90% of the time) as circumstantial support for alleged coordination. Accordingly, franchisors should consider not only whether recommendations are formally optional, but also whether their practices preserve franchisees’ actual ability to exercise independent pricing judgment in practice. Encourage franchisees, and company-owned units, to exercise and document independent judgment, including overriding the tool’s recommendations.
3. Audit the algorithm’s rules. Identify and remove any logic that conditions one unit’s price recommendation on price changes at other system units. The alleged 30% rule used by the pricing tool is the type of feature the McDonald’s complaint highlights. Consider the impact on consumer pricing as well. Importantly, the perception that the pricing tool used by McDonald’s materially increased consumer pricing at some locations could create reputational risk for the brand.
4. Review vendor contracts. Limit vendors’ use of system data beyond each user’s own environment, prohibit pooling nonpublic data across competing users, and address liability for violations. Carefully review legal compliance requirements as well as indemnity provisions.

Regulatory and litigation interest in algorithmic pricing continues, including reintroduced federal legislation and state-level activity, although the state-law landscape remains unsettled. We expect cases challenging algorithmic pricing to increase. Franchisors that use or are considering AI-enabled pricing, revenue management, or analytics tools should review their programs now.

UB Greensfelder continues to monitor these legal developments. Please contact the Franchising & Distribution Industry team with any questions.

The information provided in this alert reflects the information and guidance we have available as of the date of publication and is subject to change. This legal update was prepared by UB Greensfelder LLP and is not intended as a substitute for professional legal advice, nor does it create an attorney-client relationship.