Stripe took an unusual approach to generative AI: they told 100 employees to stop their regular work and just experiment with GPT-4. The result was 50 potential use cases, 15 strong prototypes, and one genuinely surprising finding — the model beat human reviewers at figuring out what a business actually does.
Product lead Eugene Mann described the moment his team realized the humans had it wrong. “When we started hand-checking the results, we realized, ‘Wait a minute, the humans were wrong and the model was right.’” The task involved scanning sparse, mysterious websites — nightclubs were a prime example — and returning accurate summaries. GPT-4 didn’t just match human performance. It exceeded it.
The payment giant had already been using GPT-3 for routing support tickets and summarizing user questions, but Mann says GPT-4 “was a game changer” that opened up entirely new areas. Engineers from support, onboarding, risk, and docs teams all contributed ideas. The 15 prototypes that survived vetting focused on three main areas: understanding user businesses, answering technical documentation questions, and spotting fraud in community forums like Discord.
On the documentation front, GPT-4 acts as a near-instant virtual assistant. It digests Stripe’s extensive technical docs, identifies the relevant section, and summarizes solutions without requiring fine-tuning. “GPT just works out of the box,” Mann said. “That’s not how you’d expect software for large language models to work.” For fraud detection, the model analyzes post syntax on community platforms to flag potential bad actors coordinating activity or trying to regain credibility after being removed. The team is now exploring GPT-4 as a business coach that could advise on revenue models and strategy — a canvas Mann says changes daily.
