GenAI Cracks the Code on Visual Search and Shopping at Pinterest

The promise of generative artificial intelligence is beginning to crack visual search, one of eCommerce’s most stubborn puzzles.

More specifically, generative AI is helping visual search understand, classify and recommend the billions of images consumers browse every day, even when they can’t describe what they’re looking for in words.

That challenge matters most to platforms built on pictures, and few know that better than Pinterest. The company’s 570 million monthly active user base arrives first to browse and later to buy.

Last month, Pinterest rolled out global “AI-modified” labels on image Pins, tapping its own classifiers to identify synthetic images and flag them for users in real time. It’s part of a broader push to keep inspiration trustworthy as the volume of AI-generated content accelerates.

The moves, as well as and the broader future of visual search, were the focus of a conversation between Pinterest Vice President of Design Dana Cho and PYMNTS CEO Karen Webster. Their discussion offered a window into how a design-driven social network is rewriting its search grammar for the generative AI age.

“GenAI makes everything interesting — and easier, in some ways,” Cho told Webster. “But the ‘why’ behind what grabs you in an image is still hard to define.”

Unlike keyword search engines that match words to indexed pages, Pinterest’s new visual search relies on a multimodal foundation model that digests pixels, metadata and user behavior in tandem. Users can long-press any Pin, zoom into a detail, whether it’s a hemline, a brass sconce or the gauzy vibe of a French-girl patio, and watch the platform present similar or adjacent options. Attribute chips such as “earth tones” or “athleisure” appear automatically, giving shoppers language they may not have at hand.

For Cho, the advance is less about precision than about what Webster called “guided serendipity.”

“There are image search engines that give you a near-duplicate,” Cho said. “We want to show you it, like it, and slightly adjacent to it — the rabbit-hole experience users expect from Pinterest.”

Those rabbit holes have commercial stakes. The same model that spots a boat shoe in a summer shot or the cut of a linen blazer also weighs first-party signals, such as the boards a user builds and the Pins they save, to decide which products, merchants or creators appear next. Cho said the goal is to avoid a one-size-fits-all “popular feed” and instead surface items that feel “delightfully personal,” a shift that could nudge Pinterest’s advertising and affiliate businesses deeper into the purchase funnel.

That funnel already looks different from a year ago. Pinterest has layered merchant catalogs onto millions of Pins and, under CEO Bill Ready, pushed retailers to upload structured product data so generative AI systems can tell whether the skirt a user loves is actually in stock and in her size. Labeling AI-generated images protects that integrity by warning shoppers when a cottage-core bedroom or avant-garde sneaker may not exist in the physical world, Pinterest executives have said.

Cho said she is cautious about declaring a winner in the emerging contest between text-heavy large language models and image-first platforms.

“The future of search is unwritten,” she told Webster, adding that agentic AI could redraw user expectations overnight. Visual search is “especially unwritten” because most rivals still lean on words, not pixels, to parse intent.

The subtext: Pinterest is fighting on two fronts. The first is against general-purpose search giants racing to add imagery to their models, and the second is against niche generative AI startups that generate pictures from text instead of finding them. Pinterest’s years of curating user-saved images give it an edge because the data is both visual and taste-signaling, Cho said.

“A Pin is a declaration of intent,” she said. “That makes the training corpus unusually rich.”

Where Pinterest once stopped at inspiration, Cho’s team now measures success by whether a user can move from “I like that aesthetic” to “I bought it” without leaving the app. Generative AI’s ability to label, localize and group look-alike products is central to that ambition.

“People slow down and take their time,” she said, especially younger shoppers for whom consideration is part of the fun.

The new flow lets them refine a Pin down to a colorway or price point, then click straight to check out on a merchant site or, increasingly, within Pinterest itself.

Webster pressed Cho on how far Pinterest intends to travel down the commerce stack. Will it become a merchant of record or stick to discovery and referral fees? Cho demurred on specifics but said the company’s mandate is to “collapse the distance between ‘love it’ and ‘own it.’”

Fashion was the obvious test bed. Apparel and accessories already sit atop Pinterest’s most-searched categories, giving engineers an ocean of labeled data and users a low-stakes place to experiment. The company’s new visual language model, which was released first for women’s fashion in the United States, Canada and the United Kingdom, generates descriptive terms on the fly, helping users articulate the cut of a jacket or the feel of “coastal granddaughter” even if they never type the phrase, she said.

“Pinterest is where people come to develop their own style sensibility,” Cho said. Moments of life change — a first job, a wedding, a move — drive surges in search volume, and visual search “lets them find what they’re looking for even when they don’t have the words.”

Asked by Webster for a take on the future of Pinterest and generative AI, Cho pointed to the same dual mandate: keep discovery joyful and make shopping frictionless.

“Technology has nailed the buying part,” she said. “Now, we’re bringing the joy back to shopping.”

That could mean deeper integrations between generative AI labeling, personalized recommendations and in-app checkout — advances designed to ensure that the platform where trends begin is also where transactions end.

For now, the roadmap remains fluid. Pinterest will expand its visual language model to home décor later this year and then to food and travel, categories where images often spark aspiration before words do. If Cho’s vision holds, tomorrow’s consumer may simply point a phone at an outfit, whisper, “Show me something like that,” and let a generative AI-powered Pinterest do the rest.

It’s proof, perhaps, that in visual search, a picture is worth 1,000 keywords.

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