Here’s What You Should Know About Launching an AI Startup

Julie Bornstein thought it would be a cinch to implement her idea for an AI startup. Her résumé in digital commerce is impeccable: VP of ecommerce at Nordstrom, COO of the startup Stitch Fix, and founder of a personalized shopping platform acquired by Pinterest. Fashion has been her obsession since she was a Syracuse high schooler inhaling spreads in Seventeen and hanging out in local malls. So she felt well-positioned to create a company for customers to discover the perfect garments using AI.

The reality was much harder than she expected. I had breakfast recently with Bornstein and her CTO, Maria Belousova, to learn about her startup, Daydream, funded with $50 million from VCs like Google Ventures. The conversation took an unexpected turn as the women schooled me on the surprising difficulty of translating the magic of AI systems into something people actually find useful.T

Her story helps explain something. My first newsletter of 2025 announced that it would be The Year of the AI App. Though there are indeed many such apps, they haven’t transformed the world as I anticipated. Ever since ChatGPT launched in late 2022, people have been blown away by the tricks performed by AI, but study after study has shown that the technology has not yet delivered a significant boost in productivity. (One exception: coding.) A study published in August found that 19 out of 20 AI enterprise pilot projects delivered no measurable value. I do think that productivity boost is on the horizon, but it’s taking longer than people expected. Listening to the stories of startups like Daydream that are pushing to break through gives some hope that persistence and patience might indeed make those breakthroughs happen.

Fashionista Fail

Bornstein’s original pitch to VCs seemed obvious: Use AI to solve tricky fashion problems by matching customers with the perfect garments, which they’d be delighted to pay for. (Daydream would take a cut.) You’d think the setup would be simple—just connect to an API for a model like ChatGPT and you’re good to go, right? Um, no. Signing up over 265 partners, with access to more than 2 million products from boutique shops to retail giants, was the easy part. It turns out that fulfilling even a simple request like “I need a dress for a wedding in Paris” is incredibly complex. Are you the bride, the mother-in-law, or a guest? What season is it? How formal a wedding? What statement do you want to make? Even when those questions are resolved, different AI models have different views on such things. “What we found was, because of the lack of consistency and reliability of the model—and the hallucinations—sometimes the model would drop one or two elements of the queries,” says Bornstein. A user in Daydream’s long-extended beta test would say something like, “I’m a rectangle, but I need a dress to make me look like an hourglass.” The model would respond by showing dresses with geometric patterns.

Ultimately, Bornstein understood that she had to do two things: postpone the app’s planned fall 2024 launch (though it’s now available, Daydream is still technically in beta until sometime in 2026) and upgrade her technical team. In December 2024 she hired Belousova, the former CTO of Grubhub, who in turn brought in a team of top engineers. Daydream’s secret weapon in the fierce talent war is the chance to work on a fascinating problem. “Fashion is such a juicy space because it has taste and personalization and visual data,” says Belousova. “It’s an interesting problem that hasn’t been solved.”

What’s more, Daydream has to solve this problem twice—first by interpreting what the customer says and then by matching their sometimes quirky criteria with the wares on the catalog side. With inputs like I need a revenge dress for a bat mitzvah where my ex is attending with his new wife, that understanding is critical. “We have this notion at Daydream of shopper vocabulary and a merchant vocabulary, right?” says Bornstein. “Merchants speak in categories and attributes, and shoppers say things like, ‘I’m going to this event, it’s going to be on the rooftop, and I’m going to be with my boyfriend.’ How do you actually merge these two vocabularies into something at run time? And sometimes it takes several iterations in a conversation.” Daydream learned that language isn’t enough. “We’re using visual models, so we actually understand the products in a much more nuanced way,” she says. A customer might share a specific color or show a necklace that they’ll be wearing.

Bornstein says Daydream’s subsequent rehaul has produced better results. (Though when I tried it out, a request for black tuxedo pants showed me beige athletic-fit trousers in addition to what I asked for. Hey, it’s a beta.) “We ended up deciding to move from a single call to an ensemble of many models,” says Bornstein. “Each one makes a specialized call. We have one for color, one for fabric, one for season, one for location.” For instance, Daydream has found that for its purposes, OpenAI models are really good at understanding the world from the clothing point of view. Google’s Gemini is less so, but it is fast and precise.