Imagine waking up to find that, while you slept, an artificial intelligence (AI) shopping agent switched your auto insurer, rebooked your vacation at a lower price, and ordered running shoes matched to your stride and preferred terrain. That prospect no longer belongs to science fiction. AI companies, payment networks, and major retailers—including Walmart, Amazon, and Target—already offer shopping research tools and AI agent modes, and are racing to build tools that go beyond product recommendations toward automated transactions.
AI agents hold real promise for consumers. These agents can scan billions of product listings, track price histories, parse impenetrable terms of service, and synthesize thousands of reviews—all while avoiding the cognitive limitations and behavioral biases that often lead human shoppers astray. In a world of fake reviews, dark patterns, and choice overload, a competent digital agent could genuinely help consumers make better choices.
But regulators should not confuse convenience and efficiency with harmlessness.
Our entire regulatory apparatus for consumer protection assumes that a human is doing the shopping. False advertising statutes protect against deceptive claims. Disclosure mandates, such as truth-in-lending and nutritional labeling, put information in front of human eyes. Trademark law makes it easier for consumers to identify the source of a product. These frameworks share a common premise: Somewhere in the transaction, a person is deliberating.
When an AI agent shops on our behalf, that premise may collapse. As we argue in a forthcoming article, shopping agents will not simply help consumers navigate markets; they will increasingly govern how markets work and what shoppers choose. Once an AI tool decides what products to show, which sellers to prioritize, and when to execute a purchase, it becomes a form of market infrastructure.
If leading AI agents are controlled by dominant retail platforms, the risk is that these platforms will self-preference, creating a new form of corporate power. A biased algorithm, masquerading as neutral optimization, may be much harder to detect and contest than a biased salesperson.
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