U.S. consumers are expressing growing concern and confusion over the use of artificial intelligence and algorithmic tools in pricing by major retailers and restaurant chains. The adoption of technologies such as digital price tags and AI-driven pricing systems has raised questions about transparency and fairness, particularly around dynamic and personalized pricing methods.
Dynamic pricing, which involves adjusting prices based on factors like supply and demand, is commonly seen in industries such as airlines and rideshare services, where prices fluctuate according to market conditions. However, this practice is becoming more widespread across retail sectors, with frequent price changes observed not only in food items but also electronics, clothing, and home supplies. Data from a web analytics firm tracking 2025 prices noted that about half of the price changes were reductions, highlighting that dynamic pricing does not only lead to increases.
Digital price labels, allowing stores to update prices electronically without paper tags, have been rapidly introduced in U.S. retailers including Walmart and Whole Foods. While companies emphasize that these labels help keep prices current and ease labor, consumer advocates and labor unions have voiced concerns that the technology could enable surge or personalized pricing in the future.
Personalized pricing—sometimes described as “surveillance pricing”—involves offering different prices or discounts to customers based on individual or group data such as geographic location, income level, and online browsing behavior. Although the full extent of personalized pricing use is unclear, regulatory and legislative bodies in states such as Maryland, New Jersey, and Connecticut have passed measures limiting the practice, especially in grocery retail. A notable legal case involved California prosecutors suing Target in 2022 over an app feature that allegedly showed lower prices before shoppers entered stores and raised them once inside. The settlement included a $5 million payment and a ban on such geofencing-based pricing tactics, with Target denying it personalized prices based on customer behavior.
In some cases, differing prices for identical products do not stem from the use of personal data. For instance, Instacart ended experiments that randomly assigned shoppers to various pricing groups without personalizing prices.
Algorithmic pricing, where software—including AI—helps set or recommend prices based on multiple factors such as competitor pricing and sales data, has been increasingly adopted across industries. A 2025 survey of over 2,200 business leaders worldwide found that more than 40% used AI to gather market intelligence, and nearly 30% utilized it for real-time price optimization. Industry representatives argue that algorithmic pricing continues traditional retail practices of adjusting prices according to market dynamics rather than representing a fundamental shift.
Nonetheless, concerns about price coordination through algorithms have led to legal challenges. The Federal Trade Commission’s 2023 antitrust lawsuit against Amazon targets a pricing tool called Project Nessie, alleging the company used it to raise prices in ways that generated over $1 billion in excess profits. Amazon denies wrongdoing, stating the tool was intended to prevent unsustainable price wars, was tested only briefly, and was discontinued prior to the lawsuit. Similarly, a consumer lawsuit against McDonald’s claims that its pricing system uses nonpublic data from competing locations to coordinate prices. McDonald’s has contested these claims, emphasizing that the tool is provided to franchisees to assist with setting independent restaurant prices.
As these technologies continue to evolve, lawmakers and regulators are increasing efforts to ensure transparency and protect consumers from unfair pricing practices. At least 30 states have considered bills addressing algorithmic and personalized pricing, and the Federal Trade Commission has proposed enforcement policies warning businesses to disclose their use of personal data in pricing decisions to avoid violating consumer protection laws.