Federal lawsuit alleges McDonald's AI pricing engine overcharges customers

A federal antitrust lawsuit filed against McDonald's alleges the fast-food giant uses an AI-powered "pricing engine" to set menu prices across U.S. locations, resulting in overcharges for items like Big Macs and fries. McDonald's has denied using AI to determine individual customers' willingness to pay, stating instead that it provides franchisees with "tools, resources, research and recommendations to help them make informed decisions."

The legal action arrives as food businesses globally expand their use of artificial intelligence in operations. Kroger, for example, announced earlier this year that it is using an AI platform called FlashFood to mark down perishables nearing the end of their shelf life and market them to shoppers via an app. The company also utilizes electronic shelf labels (ESLs), digital screens that display item prices, a technology increasingly adopted by supermarkets including Amazon Fresh, Walmart, and Whole Foods. In the U.K., Tesco, Morrisons, and Asda are also deploying ESLs, while financial platform Revolut recently trialed facial recognition checkout in select coffee shops.

Experts warn that the normalization of these tools could lead to more widespread dynamic pricing, defined as frequent, real-time price changes in response to market conditions such as demand, timing, or competitor prices. Miroslava Marinova, a senior lecturer of commercial law at the University of East London, noted that while dynamic pricing is not new, having been used by airlines and ride-hailing services for years, AI is changing the volume of consumer data available to retailers. This data includes transaction histories, browsing behavior, location, and purchasing patterns.

Clare Lombardelli and Rupal Patel, economists at the Bank of England, stated in April that sophisticated technology is leading to prices changing more frequently and becoming more individualized. They described this trend as moving toward "perfect price discrimination," where firms charge as close to the maximum price a consumer is willing to pay as possible. The economists argued that such frequent and individualized shifts undermine the reliability of standard inflation metrics, since the consumer price index depends on a representative sample of prices that loses its validity when rates vary continuously across different shoppers.

Marinova explained that the traditional distinction between dynamic pricing and personalized pricing is blurring. While dynamic pricing responds to market conditions, personalized pricing uses consumer information to estimate willingness to pay. As retailers combine market-level information with detailed consumer data, the boundary between the two becomes thinner. Although Walmart and Kroger have publicly insisted they do not use dynamic or surge pricing for individualized customer prices, citing operational streamlining instead, the rise of AI tools raises questions about how customer information influences visible prices.

Several U.S. states are moving to regulate these practices. New York requires businesses using personal data to set prices to disclose this clearly. Maryland has restricted food retailers and delivery services from using personalized, data-driven pricing to charge higher prices for certain foods, while New Jersey and Connecticut have enacted measures targeting "surveillance pricing." Marinova noted that while personalized pricing can make some products more accessible through discounts for certain consumers, it risks weakening the disciplining effect of consumer choice if shoppers cannot understand why they received a particular price or compare it with prices offered to others.