OpenAI and Perplexity are launching AI-powered shopping assistants, but rival startups aren’t holding back

With holiday shopping just around the corner, OpenAI and Perplexity both announced AI-powered shopping features this week, which integrate with existing chatbots to help users research potential purchases.

The tools are remarkably similar to each other. OpenAI suggests that users can ask ChatGPT for help finding “a new gaming laptop under $1,000 with a screen larger than 15 inches,” or they can share photos of high-end clothing and request something similar for less.

Meanwhile, Confusion highlights how a chatbot’s memory can augment its users’ shopping-related searches, suggesting that someone could ask for recommendations tailored to what a chatbot already knows about them, like where they live or what they do at work.

Adobe has predicted that AI-assisted online shopping will grow by 520% ​​this holiday season, which could be a boon for AI-based shopping startups like Phia, Cherry, or Deft — but with OpenAI and Perplexity moving toward more AI shopping experiences, are these startups in danger?

Zach Hudson, CEO of interior design shopping tool Onton, believes AI shopping startups with a niche will still provide a better experience for users than general-purpose tools like ChatGPT and Perplexity.

“Any model or knowledge graph is only as good as its data sources,” Hudson told TechCrunch. “Currently, ChatGPT and LLM-based tools, like Perplexity, rely on existing search indexes like Bing or Google. This makes them only as good as the first few results that come back from those indexes.”

Julie Bornstein, Daydream CEO and longtime e-commerce executive, agrees — she noted to TechCrunch over the summer that she always views search as the fashion industry’s “forgotten child,” because it’s never done well.

“fashion […] “Finding a dress you love is not like finding a TV, which is uniquely nuanced and emotional,” Bornstein told TechCrunch on Tuesday. “This level of understanding of fashion shopping comes from domain-specific data and marketing logic that understands silhouettes, fabrics, occasions, and how people construct clothes over time.”

AI shopping startups are developing their own datasets so that their tools are trained on high-quality data — something that’s easier to achieve when you’re trying to index fashion or furniture, rather than the sum of human knowledge.

In Hudson’s case, Onton developed a data pipeline to index hundreds of thousands of interior design products in a cleaner way, helping to train its in-house models with better data. But if AI shopping startups don’t pursue this level of specialization, Hudson believes they’re bound to be overlooked.

“If you’re just using off-the-shelf MBAs and a conversational interface, it’s very hard to see how a startup can compete with larger companies,” Hudson said.

However, the advantage of OpenAI and Perplexity is that their customers are already using their tools – plus their large presence allows them to close deals with major retailers from the start. While Daydream and Phia redirect customers to retailers’ sites to complete their purchases — sometimes generating affiliate revenue — OpenAI and Perplexity have partnerships with Shopify and PayPal, respectively, allowing users to check out a conversational interface.

These companies, which rely on vast amounts of expensive computing power to operate, are still trying to figure out a path to profitability. If they are inspired by Google and Amazon, it makes sense for them to look to e-commerce as an option – retailers can pay them to advertise their products within search results.

But ultimately, this may exacerbate existing problems customers face in search.

“Vertical models — whether in fashion, travel or home goods — will outperform because they adapt to real consumer decision-making,” Bornstein said.

Additional reporting by Evan Mehta.