Consumer Watchdog Raises Concerns Over Google’s AI Shopping Protocol; Google Refutes Claims
In January 2026, Google introduced the Universal Commerce Protocol (UCP), an open standard designed to enhance AI-driven shopping experiences. This protocol aims to streamline various stages of the consumer purchasing process, from product discovery to post-purchase support, by enabling AI agents to interact seamlessly across different platforms. Collaborations with major retailers like Shopify, Etsy, Wayfair, Target, and Walmart have been integral to the development of UCP, ensuring its applicability across a broad spectrum of e-commerce environments.
Shortly after the announcement, Lindsay Owens, Executive Director of the consumer economics think tank Groundwork Collaborative, voiced significant concerns regarding the protocol’s implications for consumer pricing. In a widely circulated post on X (formerly Twitter), Owens highlighted features within UCP that support personalized upselling. She interpreted this as a mechanism where AI agents could analyze user data to promote higher-priced items, potentially leading to consumers being overcharged based on their personal information.
Owens’ apprehensions were rooted in Google’s detailed specification documents, which outline functionalities that could allow merchants to adjust pricing strategies, including offering new-member discounts or loyalty-based pricing. These features, as described by Google CEO Sundar Pichai during the National Retail Federation conference, are intended to provide tailored pricing options to consumers.
In response to these allegations, Google issued a public statement on X, categorically denying any practices that would result in consumers being overcharged. The company emphasized that merchants are strictly prohibited from displaying prices on Google that exceed those listed on their own websites. Google clarified that upselling refers to the standard retail practice of presenting premium product options that might interest consumers, with the ultimate purchasing decision remaining entirely with the user. Additionally, Google highlighted the Direct Offers pilot program, which enables merchants to provide lower-priced deals or additional services like free shipping, explicitly stating that this feature cannot be used to increase prices.
Further addressing the concerns, a Google spokesperson informed TechCrunch that the company’s Business Agent lacks the capability to alter retailer pricing based on individual user data. This assertion aims to reassure consumers that their personal information is not being exploited to manipulate pricing strategies.
Despite Google’s reassurances, Owens pointed to technical documents indicating that the complexity of user consent should be minimized on the consent screen presented to users. She interpreted this as a potential attempt to obscure the extent of data usage and consent required from consumers. Google countered this interpretation by explaining that the intention is to consolidate user actions—such as getting, creating, updating, deleting, canceling, and completing tasks—into a streamlined consent process, rather than requiring separate agreements for each action.
This debate underscores broader concerns about the potential for AI-driven shopping agents developed by major tech companies to enable merchants to customize pricing based on individual consumer data. Owens refers to this practice as surveillance pricing, where merchants could analyze AI interactions and shopping patterns to determine the maximum price a consumer might be willing to pay, rather than offering uniform pricing to all customers.
While Google maintains that its current AI agents do not possess such capabilities, the company’s core business model, which heavily relies on advertising and data collection, raises questions about potential future developments. Notably, in the previous year, a federal court mandated that Google modify certain search business practices after determining that the company engaged in anticompetitive behavior.
The advent of AI agents capable of handling tasks like rescheduling appointments or researching products offers significant convenience. However, the potential for misuse, particularly in the realm of personalized pricing, remains a valid concern. The dual role of major tech companies as both developers of these technologies and beneficiaries of consumer data creates complex incentives that warrant careful scrutiny.
This situation presents an opportunity for independent startups to develop AI-powered shopping tools that prioritize consumer interests without the conflicting incentives faced by larger tech conglomerates. Emerging companies like Dupe, which utilizes natural language queries to assist users in finding affordable furniture, and Beni, which leverages images and text for thrifting fashion, exemplify early entrants in this space.
As the landscape of AI-driven commerce continues to evolve, consumers are advised to remain vigilant and informed about how their data is used and how it may influence their shopping experiences.
