But Salakhutdinov says that having a wealth of information about how users go about common and important tasks like shopping might be a crucial ingredient for getting them to stay on track. “Data is going to be very important,” he says.

Ship It

Amazon’s agents are, of course, likely to be more focused on helping customers find and buy whatever they need or want. A Rufus agent might notice when the next book in a series someone is reading becomes available and then automatically recommend it, add it to your cart, or even buy it for you, says Rajiv Mehta, a vice president at Amazon who works on conversational AI shopping. “It could say, ‘We have one bought for you. We can ship it today, and it will arrive tomorrow morning at your door. Would you like that?’” Mehta says. He adds that Amazon is thinking about how advertising can be incorporated into its model’s recommendation.

Chilimbi and Mehta say that eventually, an agent might go on a shopping spree when a customer says, “I’m going on a camping trip, buy me everything I need.” An extreme, though not impossible, scenario would involve agents that decide for themselves when a customer needs something, and then buy and ship it to their door. “You could maybe give it a budget,” Chilimbi says with a grin.

Amazon’s new AI-generated shopping guides, announced at its Reinvent conference in Nashville today and initially available on the company’s US mobile website and app, are a small step toward the ultimate vision of a superintelligent shopping assistant. The Rufus LLM is used to autogenerate the sort of information and insights that could take someone hours of online research to gather. “If you ever try to shop in a category you’re not familiar with, it can be pretty time-consuming to understand the lay of the land, the different features available, and the different selections,” says Brett Canfield, a senior product manager on the personalization team at Amazon.

Canfield showed WIRED shopping guides for televisions and earbuds that noted important technical features, explanations of key terminology, and, of course, recommendations on which products to buy. The underlying LLM has access to the vast corpus of product information, customer questions, reviews, and feedback, and users’ buying habits. “This is really only possible with generative AI,” Canfield says.

The new shopping guides highlight generative AI’s potential in ecommerce, creating guides for product categories too niche to normally get the treatment. “The definitive hedge trimmers,” for instance.

Guide Supplies

The guides also, however, show how generative AI threatens to upend the economics of search and shopping while borrowing liberally from conventional publishers.

AI-generated search results often now provide product comparisons and opinions. This diverts traffic from outlets, like WIRED, that make money by producing shopping guides, reviews, and other articles, even though the AI results are produced using data scraped from such websites in the first place.

Canfield declines to say what additional training data was used to build the new AI shopping guide feature. (WIRED’s parent company, Condé Nast, entered into a partnership with OpenAI, the company behind ChatGPT, in August of this year.)