Why is Julie Bornstein's AI fashion startup Daydream struggling?

Daydream is struggling because translating AI's complex capabilities into practical, user-friendly fashion recommendations is proving unexpectedly difficult due to model inconsistencies and 'hallucinations.'

What are the main challenges for AI in fashion retail?

AI in fashion faces challenges in understanding nuanced customer requests and matching them with product attributes, often requiring sophisticated interpretation of both shopper and merchant language.

When will AI deliver significant productivity boosts according to experts?

While AI has shown impressive tricks, significant productivity boosts have been slower than anticipated, with experts suggesting they are on the horizon but taking longer than expected to materialize.

Home / Technology / AI Fashion Fails: Startup Learns AI Isn't So Simple

AI Fashion Fails: Startup Learns AI Isn't So Simple

5 Dec, 2025

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Summary

AI fashion startup Daydream faced unexpected difficulties implementing AI.
Translating AI capabilities into user-friendly fashion applications is complex.
Productivity gains from AI have been slower than anticipated.

AI Fashion Fails: Startup Learns AI Isn't So Simple

Julie Bornstein, with a strong background in digital commerce, aimed to launch an AI startup, Daydream, to help customers find ideal clothing. However, the process proved more difficult than expected. Bornstein and her CTO, Maria Belousova, explained the complexities of translating AI's potential into tangible user benefits, a challenge faced by many AI applications.

Despite initial funding and partnerships, fulfilling simple fashion requests like 'a dress for a wedding' revealed significant AI model inconsistencies and 'hallucinations.' This led to a postponed launch and a strengthened technical team, emphasizing the nuanced understanding required for both customer and merchant vocabularies.

Daydream is now employing an ensemble of specialized AI models for color, fabric, season, and location to improve results. This strategic shift, alongside hiring top engineering talent, underscores the ongoing effort to bridge the gap between AI's impressive capabilities and its practical application in complex fields like fashion.