The rapid integration of artificial intelligence into the consumer marketplace is being marketed as a revolutionary way for shoppers to save money. Tech companies are increasingly promoting AI agents—autonomous software capable of navigating the web on a user’s behalf—as the ultimate tool for finding discounts and optimizing household budgets. However, a growing body of research suggests that the reality for the average consumer’s wallet may be far less beneficial than the promotional materials claim.
At the center of this shift are tools like Meta’s Muse, part of a new wave of consumer-facing AI agents designed to handle complex tasks, including shopping. The promise from Silicon Valley is simple: these agents will scour the digital landscape to locate the lowest possible price for any given good or service. Yet, economic experts and researchers are raising alarms that these same technologies are fundamentally altering the concept of a “fair price” by enabling a sophisticated form of surveillance-based pricing.
Lindsay Owens, the executive director of the think tank Groundwork Collaborative, argues that the history of corporate pricing strategies does not support the idea that new technology will naturally lead to lower costs for the public. In her research and her new book, Gouged: The End of a Fair Price — and What That Means for Your Wallet, Owens details a shift away from uniform pricing toward a model where the cost of a good is determined by what a specific individual is willing and able to pay.
Research conducted by Groundwork Collaborative has found that some consumer-facing AI agents, rather than finding the cheapest option, may eventually begin serving customers more expensive items. This occurs as the algorithms learn more about a user’s specific financial situation through data collection and constant digital surveillance. When an AI agent understands a consumer’s income level, spending habits, and urgency of need, the “deal” it presents may be optimized for the seller’s profit margin rather than the buyer’s savings.
This evolution represents a departure from traditional retail models where a price tag was visible and identical for every person walking into a store. In the digital age, companies have moved toward dynamic pricing, often using the cover of inflation, supply chain disruptions, or global conflicts to pad profit margins. Owens suggests that while external factors like wars and inflation are real contributors to rising costs, many companies are leveraging technological surveillance to see exactly how much higher they can push prices before a consumer retreats.
Data collection is the engine behind this new pricing landscape. By tracking consumer behavior across various platforms, companies can create detailed profiles that predict price sensitivity. AI agents, while appearing to act as a personal assistant for the shopper, can also serve as a two-way mirror, providing corporations with the very data they need to justify higher costs. This dynamic raises significant questions about whether the technology is working for the consumer or effectively acting as a high-tech tool for price gouging.
As these AI agents become more prevalent, the definition of a “fair price” is being eroded. The transition from a single, transparent price for all to a hidden, personalized price for each individual could leave consumers vulnerable to extraction. While the marketing for tools like Muse emphasizes efficiency and savings, the underlying architecture of data surveillance suggests that the primary beneficiary of the AI revolution in retail may ultimately be the corporate entities looking to maximize their bottom line.
