“Same Big Mac, Different Prices”: AI Refines Third-Degree Price Discrimination as McDonald’s Faces Antitrust Litigation
Matthew Reuter is a senior economic correspondent at The Economy, where he covers global financial markets, emerging technologies, and cross-border trade dynamics. With over a decade of experience reporting from major financial hubs—including London, New York, and Hong Kong—Matthew has developed a reputation for breaking complex economic stories into sharp, accessible narratives. Before joining The Economy, he worked at a leading European financial daily, where his investigative reporting on post-crisis banking reforms earned him recognition from the European Press Association. A graduate of the London School of Economics, Matthew holds dual degrees in economics and international relations. He is particularly interested in how data science and AI are reshaping market analysis and policymaking, often blending quantitative insights into his articles. Outside journalism, Matthew frequently moderates panels at global finance summits and guest lectures on financial journalism at top universities.
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McDonald’s uses AI to differentiate menu prices across local markets Consumers file proposed class action over sharing of confidential transaction data Legal dispute centers on corporate pricing recommendations and franchisee independence
McDonald’s is facing a lawsuit alleging that it colluded with franchisees to fix prices through its use of artificial intelligence (AI) to recommend menu prices for individual restaurants. As the company deploys AI to charge different prices for the same products according to customers’ willingness to pay across local markets, the dispute turns on whether sharing confidential transaction data restricted competition among franchisees. McDonald’s has denied the collusion allegations, maintaining that restaurant owners independently determine final prices. With U.S. authorities tightening oversight of discriminatory pricing and the use of consumer data, the operation of the pricing recommendation system and franchisees’ authority to set prices are expected to be central to the case.
Sued Following Reports of AI-Based Pricing Practices
An antitrust lawsuit challenging McDonald’s AI pricing recommendation system has been filed in federal court in Chicago, Reuters and the Associated Press reported on October 6, local time. The plaintiff, Michael Thomas, a consumer residing in DeKalb, Illinois, filed the complaint on October 2. Thomas is seeking class certification to pursue the case on behalf of millions of McDonald’s customers in the United States. The complaint seeks damages for consumers and an injunction preventing the enforcement of agreements that restrain competition.
The lawsuit followed reports that McDonald’s uses machine learning (ML) to analyze millions of daily transactions across approximately 14,000 U.S. restaurants and recommend menu prices for individual locations. The system generates recommended prices for each menu item based on restaurant sales performance, local demand and nearby competition, among other factors. Its analysis covers a wide range of products, from Big Macs to discounted coffee for senior citizens. The same menu item can therefore receive different recommended prices depending on each restaurant’s circumstances. Reuters’ review of the McDonald’s app found that Big Macs at two company-operated restaurants approximately 3.2 kilometers apart in Fresno, California, cost $5.69 and $6.89, respectively—a difference of about 21%.
“Corporate Price Coordination” vs. “Franchisees Decide Whether to Accept Recommendations”
The plaintiff alleges that McDonald’s coordinated menu prices across franchised and company-operated restaurants using an algorithm trained on confidential transaction data from individual locations. The complaint specifically challenges the sharing, through the company’s pricing recommendation system, of sales information that competing franchisees in the same local market would not ordinarily disclose to one another. The argument is that such information sharing restricted competition among restaurants whose operators should have set prices independently. The plaintiff characterizes the coordination between McDonald’s and its franchisees as collusion prohibited under U.S. antitrust law, alleging that it forced consumers to pay higher prices. Counsel for the plaintiff also accused McDonald’s of using its extensive data and franchise network to extract more money from customers.
McDonald’s rejected the allegations in a statement, saying they rested on speculation and misunderstanding. “AI does not directly set the price of a Big Mac or any other menu item,” the company said, emphasizing that franchisees independently determine their selling prices and decide whether to accept recommendations. The information supplied by the tool is intended to support owners’ judgment, it explained, and they are under no obligation to follow its recommendations. Addressing price differences between restaurants, McDonald’s said that even nearby locations can face different operating costs, competitive conditions and other local circumstances. It added that it does not price products according to individual customers’ willingness to pay or change prices in real time or by time of day. The company also defended the purpose of its tool, noting that businesses across many industries have analyzed local costs and demand to inform pricing decisions for decades.
Table 1. Principles and Operating Conditions of Third-Degree Price Discrimination
| Category | Key Details |
|---|---|
| Pricing Method | Segment consumers by geography, age or other characteristics and charge different prices according to willingness to pay |
| Principal Example | Different prices for the same menu item across restaurants |
| Core Decision Criterion | Consider price elasticity of demand, which measures how the quantity purchased responds to price changes |
| Application Across Local Markets | Charge higher prices where increases cause less customer attrition and lower prices where more customers are price-sensitive |
| Analysis of Customer Characteristics | Assess purchasing capacity alongside the availability of substitutes, ease of price comparison and likelihood of switching to competing products |
| Expected Benefits | Generate new demand through lower prices and capture additional profit from groups that continue purchasing at higher prices |
| Persistence of Price Differences | The time and transportation costs involved in traveling between restaurants help sustain price differences across local markets |
Third-Degree Price Discrimination Targets Customers Who Keep Buying Despite Higher Prices
Charging different prices for the same menu item according to local purchasing power is a pricing strategy that applies the economic concept of third-degree price discrimination. Consumers are divided into groups by geography, age or other characteristics, with prices tailored to each group’s willingness to pay. Familiar practices such as student rates and senior discounts also fall into this category. Restaurant-level pricing can similarly increase revenue by charging more in markets where price increases cause less customer attrition and less where customers are more responsive to discounts. Even when consumers identify a cheaper restaurant, the time and transportation costs of reaching it may lead them to use a nearby location, allowing price differences across local markets to persist.
The central criterion for differentiating prices is price elasticity of demand, which measures how much the quantity purchased changes in response to a change in price. Even affluent customers may respond strongly to price increases if substitutes are plentiful and prices are easy to compare. Businesses must therefore assess both each group’s purchasing capacity and its propensity to switch to competing products. Lower prices can attract customers who would have declined to purchase under uniform pricing, increasing sales volumes, while customers who continue buying at higher prices can generate additional profit.
Labor Costs, Rent and Purchasing Power Embedded in Big Mac Prices
Geographic price differences also intersect with the Big Mac Index, which compares Big Mac prices across countries. Introduced in 1986 by British weekly The Economist, the index converts national Big Mac prices into a common currency to gauge currency purchasing power and exchange-rate levels. The product serves as a benchmark because it is sold in countries around the world using similar ingredients and preparation methods. National selling prices reflect local costs such as wages, rent and taxes, as well as income levels and demand conditions. The logic of geographic price discrimination is that the same principle of adjusting prices to consumers’ willingness to pay can also be applied to individual local markets within a country.
The Big Mac Index rests on purchasing power parity (PPP), the theory that exchange rates adjust over the long term to bring the prices of identical goods into alignment. For example, if a Big Mac costs the equivalent of $4.29 in South Korea at the hypothetical market exchange rate and $6 in the United States, the exchange rate implied by their prices would value each unit of South Korea’s domestic currency at $0.001. If the actual exchange rate instead values each domestic currency unit at approximately $0.000714, the Korean Big Mac converts to $4.29, implying that the domestic currency is undervalued by 28.6% against the dollar. However, Big Mac prices also incorporate restaurant rents and local labor costs that are difficult to trade across borders, making it inappropriate to treat this measure as a definitive estimate of a currency’s fair value. The Economist also publishes an adjusted index incorporating each country’s gross domestic product (GDP) per capita to account for pricing conditions that vary with income levels.
U.S. Oversight Expands from Preferential Pricing for Large Chains to the Use of Personal Data
The issue is that corporate price discrimination strategies can incur legal penalties when they restrict competition or improperly exploit consumer information. U.S. regulators have pursued a succession of rules and enforcement actions addressing fairness in pricing. On October 2, the Federal Trade Commission (FTC) announced a proposed settlement in its lawsuit alleging that major alcohol distributor Southern Glazer’s impaired the competitiveness of small retailers by providing discounts and rebates exclusively to large chains. The action was brought under the Robinson-Patman Act, enacted in 1936, and centered on preferential pricing that could not readily be justified by differences in distribution costs. The proposed settlement provides compensation for small retailers harmed by discriminatory transactions meeting specified criteria and requires an independent monitor to oversee compliance for six years.
Data used in pricing is also coming under regulatory scrutiny. Effective October 1, Maryland prohibited grocery retailers above a specified size and third-party delivery services from using personal information to impose higher grocery prices on particular consumers, a practice known as surveillance pricing. The measure restricts price increases based on estimates of individual customers’ ability to pay derived from information such as purchase histories and location data. Exceptions apply to voluntarily enrolled membership benefits and promotional discounts.
As scrutiny intensifies over the transaction terms and data practices underlying price discrimination, the McDonald’s case has brought the independence of pricing decisions between the company and its franchisees into focus. Whereas the preceding regulatory actions address preferential pricing among business customers and price increases based on personal information, this lawsuit asks whether an algorithm sharing confidential transaction data served as a mechanism for coordinating prices among independent businesses. McDonald’s emphasis on franchisees’ final authority over pricing appears intended to rebut the allegation that its corporate pricing recommendations constitute collusion.
Matthew Reuter is a senior economic correspondent at The Economy, where he covers global financial markets, emerging technologies, and cross-border trade dynamics. With over a decade of experience reporting from major financial hubs—including London, New York, and Hong Kong—Matthew has developed a reputation for breaking complex economic stories into sharp, accessible narratives. Before joining The Economy, he worked at a leading European financial daily, where his investigative reporting on post-crisis banking reforms earned him recognition from the European Press Association. A graduate of the London School of Economics, Matthew holds dual degrees in economics and international relations. He is particularly interested in how data science and AI are reshaping market analysis and policymaking, often blending quantitative insights into his articles. Outside journalism, Matthew frequently moderates panels at global finance summits and guest lectures on financial journalism at top universities.
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