MIT Researchers Say AI Shopping Agents Can Be Persuaded by Merchants: Reporter Tests Meta Muse, Pushed to Buy Target Membership Seven Times in Three Days
Scientific American reporter Mary Randolph connected Meta's Muse and OpenAI's Dots to her real accounts for a week-long test. After browsing the Target website, Muse pushed its membership program and discounted lamps seven times over three days. Manuel Cherep, a doctoral student at MIT Media Lab, points out that because there are limited mainstream model providers and their training methods are similar, persuading one agent could likely persuade many, potentially giving rise to a 'parallel economy' dependent on AI agents for purchasing decisions.
In October 2026, reporter Mary Randolph connected Meta's Muse and OpenAI's Dots to her real accounts during a move, naming them Mark and Sam, and conducted a week-long test of daily tasks. She asked researchers to help design testing methods, one of which was to see if the agents would proactively recommend products unrelated to user preferences.
The results were surprising. She asked Mark and Sam to use all their known personal information to suggest how to spend $50. Sam recommended a used book she had previously asked it to find—reasonable but mundane. Mark offered two suggestions: first, based on her Spotify collection, it recommended tickets to a January concert by Jess Williamson; second—and this happened seven times over three days—it pushed her to register for a Target membership and buy a lamp on sale. She did need a lamp, but she judged that Mark's recommendation stemmed from promotional content it read while browsing the Target website, rather than her personal needs.
Manuel Cherep, a doctoral student at MIT Media Lab who studies AI behavior, believes this discovery reveals a key issue: if merchants can convince agents that a product is worth buying, it could spawn a parallel economy driven by purchases made via AI assistants. His structural argument is that there are a limited number of mainstream model providers, and their training methods are highly similar; therefore, persuading one model is likely to persuade others simultaneously, thereby influencing many users who delegate decision-making to such models.