AI Turned Thrift Into a Profitable Fashion Machine

Secondhand is no longer a backup plan. Shoppers are going there first, and the platforms making that possible built their advantage in artificial intelligence.

The global secondhand market grew 12% this year to $289 billion and is projected to reach $393 billion within five years, according to ThredUp’s annual resale report. U.S. resale grew nearly four times faster than the broader retail clothing market in 2025. The acceleration isn’t consumer sentiment alone. Resale platforms were sitting on billions of dollars of inventory they couldn’t reliably price, tag or surface. AI changed that.

When Every Item Is a One-Off

Resale has a structural problem that standard retail doesn’t. Every item is unique. There are no shared SKUs, no brand-supplied product data, no taxonomy linking a garment’s condition and provenance to buyer intent. Discovery is expensive. Pricing is a lot of guesswork.

ThredUp trained a generative AI model on fashion data that let shoppers search using visual style language rather than text, PYMNTS reported. A query for “ugly Christmas sweater” returned precise results even when neither word appeared in the product database.

Beni, AI-powered resale search engine launched Beni Lens, a visual identification tool that let users photograph a garment and receive a curated list of comparable items across multiple resale marketplaces filtered by size, price, and preferred brands. The tool consolidated what had been a fragmented, platform-by-platform search.

Thirty-five percent of consumers across the Americas said they were looking for used items more than ever before, EMARKETER reported. Gen Z was the most likely generation to shift spending to secondhand if tariffs drove up new-goods prices, the outlet noted.

The Margin Argument

Discovery drives volume. Automation drives margin.

ThredUp posted 79.5% gross margins in Q2 2025, the result of more than $400 million invested in supply chain automation covering item identification, measurement capture, and product photography. Traditional apparel retailers rarely exceed 30%. The difference is what automated tagging, computer vision, and dynamic pricing produce at scale.

ThredUp managed more than 4 million listings, with tens of thousands added daily, using a reinforcement learning model that automatically lowered prices on slow-moving inventory by tracking real-time demand and revenue potential, PYMNTS reported.

The Opportunity

Forty-eight percent of shoppers said they used AI tools during their secondhand shopping journey and 63% said they were comfortable with agentic buying, according to ThredUp’s Resale Report. AI agents that browse, compare and complete purchases on behalf of users will push resale volume through automated transaction flows at a pace manual shopping can’t match.

ThredUp CEO James Reinhart said the next phase of the market would be defined by platforms that could best unlock supply and use AI to connect that inventory with the next generation of shoppers. Gen Z and millennials are expected to drive 71% of all market growth through 2030, with new shoppers making up the vast majority of that spending, according to the report.

Retailers standing up certified preowned programs face the same discovery and pricing problems ThredUp spent years and hundreds of millions solving. For payments operators, higher transaction frequency, faster inventory turnover, and rising agentic commerce mean more payment events per shopper. The global secondhand market grew 13% last year while new apparel sales were nearly flat, WWD reported.

That gap will only widen as AI lowers the cost of bringing inventory to market. Platforms that can tag, price and surface a one-of-a-kind item as efficiently as a mass retailer moves a SKU are the ones capturing the next wave of transaction volume.