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.)
Concerns of this nature are unlikely to slow interest in AI at Amazon or any other ecommerce outlet. Machine learning is already widely used in ecommerce for analytics, search, and product recommendation. With LLMs opening up new use cases, one analyst report suggests that the market for AI in ecommerce will grow from $6.6 billion in value in 2023 to $22.6 billion by 2032.
“LLM agents are a customer service game changer,” says Mark Chrystal, CEO of Profitmind, a company that uses AI to provide retailers with analytics.
Chrystal says that big players like Amazon might benefit most from the rise of generative AI because they have so much data to feed to their models. This should “lead to increasingly capable AI systems that not only improve customer service but also lead to product and delivery innovations,” he says, although he notes that “in essence, the data-rich will continue to get richer and the data-poor will get poorer.”
Amazon says its Rufus LLM already demonstrates some unique abilities that are especially useful for ecommerce. Chilimbi recounts an incident involving an executive at Amazon who asked the LLM to recommend the best Batman graphic novels, and was surprised when it came back with a list that included the non-Batman dystopian classic Watchmen. When asked why it chose the book, the Rufus model stated that the themes and characters in Frank Miller’s popular 1980s Batman series The Dark Knight Returns carry a similar resonance to those in Alan Moore’s Watchmen. “Occasionally you say, ‘Oh wow, how does it do this?’” Chilimbi says.
Amazon’s Rufus LLM isn’t only fed a different diet from most LLMs; it also gets a different kind of fine-tuning. The additional training that normally helps chatbots engage in coherent conversation and avoid saying inappropriate things is used by Amazon to train its model to be a better “shopping concierge.” “There are multiple signals” fed to the model as fine-tuning, Chilimbi says, including whether someone clicks on a recommendation, adds it to their cart, and eventually buys it.
Chilimbi adds that Amazon has developed its own shopping benchmark for testing Rufus and helping it get smarter. But while a conventional LLM might be tested on its ability to answer general knowledge questions or solve math or science problems, Amazon’s benchmark tests the model’s ability to help a customer find what they are looking for more easily.
Amazon hopes that raising the shopping IQ of its AI might eventually enable its independent, shopping-centric AI agents.
“We aren’t quite there,” says Salakhutdinov of CMU, who notes that he wouldn’t be comfortable giving an AI agent his credit card just yet. “There are some actions you can’t really reverse from,” he says. “You know, like you already bought it.”
