This article is part of the Future of AI, a collection of articles that investigates how artificial intelligence will impact the fashion and beauty industries in the years to come.
In many ways, sustainable fashion is an oxymoron — a utopian ideal at odds with the current system and the values powering it. It requires the kind of wholesale systems change that can take decades or longer to achieve.
But advocates of AI are quickly building infrastructure that they say could supercharge this process. At the very least, it could streamline the complex process of gathering and verifying data that has everyone from brands to suppliers sinking in the quicksand of compliance.
Vogue Business invited AI innovators to share their future visions for sustainable fashion (with the major caveat that poorly managed AI infrastructure can have disastrous consequences for the environment, and AI companies need much more robust ways of measuring and mitigating this to claim a net-positive impact). So, what are the seeds being sown today that could shape sustainability tomorrow?
Data-driven design
Among AI’s promises to fashion: its algorithms can aid in the design process, making it more efficient and reducing waste upfront.
Curbon was founded in 2025 by three Princeton University students to take the guesswork out of eco-design. “The majority of a product’s environmental impact is determined at the design stage through material selection, supplier geography, supply chain, and logistics, among other factors,” says co-founder Joe Wahba. “But it’s at this precise point that visibility into environmental data is at its lowest. The other issue is that sustainability teams often have competing targets for a given product compared to design, procurement, finance, and compliance teams. For sustainability to be incorporated, environmental modeling can’t just sit in spreadsheets or carbon accounting platforms; it needs to be integrated into product and supply chain decisions, and aligned with those competing targets.”
Curbon uses AI to aggregate supply chain and lifecycle assessment data, making recommendations to reduce the environmental footprint of a given product in the design stage.
Photo: Curbon
Like Curbon and Swedish competitor Material Exchange, UK-based Circkit uses AI to aggregate huge swathes of supply chain and lifecycle assessment (LCA) data, making recommendations to reduce the environmental footprint of a given product in the design stage. “Everything brands would normally only know post-production, we take it and stick it at the front, so they can almost reverse engineer their outputs,” says Circkit founder Joe Darwen.
Right now, these AI-powered systems are only as good as the limited data available to feed into them, but each startup has its own approach to filling these gaps in the near future. Circkit is working with traceability platforms, leveraging the data brands are already collecting to comply with digital product passport (DPP) and supply chain due diligence regulations, and plugging the gaps with in-house assessments of garment weight by category. Material Exchange is working with Worldly, the company behind the Higg Index, again utilizing data brands are already reporting elsewhere, but facing the same limitation, that most data today is aggregated rather than brand or factory-specific, meaning individual investments in decarbonization or energy efficiency are not factored in, giving inaccurate or incomplete LCAs.
