Stripe's Payments Foundation Model: How Data & Infra Create Compounding Advantage, w/ Emily Sands

**Emily Sands** (0:00)
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**Nathan Labenz** (0:03)
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Hello, and welcome back to The Cognitive Revolution. Today, my guest is Emily Sands, head of Data and AI at Stripe, the programmable financial infrastructure company that in 2024 processed $1.4 trillion in payments, or roughly 1.3% of global GDP, for everyone from solo entrepreneurs to the Fortune 100, and which continues to grow at a blistering pace.
We begin by discussing the many fascinating details of Stripe's new foundation model for payments, and how Stripe is using this model to deliver improved performance across their broad suite of products. While it might seem unassuming at first glance, I would argue that the Payments Foundation Model has several important lessons to teach us. First, while payments are represented in text, the Payments Foundation Model is not a language model in the familiar sense. On the contrary, payments are treated as a distinct modality, and importantly, no payment is an island. To properly understand a single payment, requires Stripe to assemble extensive context, including recent activity associated with multiple entities, the buyer, the card, the device used to make the purchase, and the merchant. So much context quickly becomes overwhelming to humans, but this is exactly where neural networks can shine. And indeed, when Stripe first deployed this model to detect card testing, which is a process that fraudsters used to determine which stolen cards actually work, they saw a jump in their detection rate from 59% to 97%. Obviously, a massive win not just for Stripe, but for the entire e-commerce ecosystem that collectively bears the cost of fraud. Now, if you've listened to this show for a while, you know that one of my pet theories is that the surest path to super intelligence is to integrate today's reasoning models with models that are trained on other modalities that humans aren't well adapted to understand. I'd say it's safe to say that the payments foundation model is superhuman when it comes to understanding payments. And this conversation left me wondering how many other businesses are training foundation models on their own modalities, as well as how many other interesting modalities might still currently be hiding in plain text. I can imagine that this proprietary modality strategy might work on any number of domains, including health, cyber security, logistics, energy, and insurance. But to be honest, I haven't found too many other examples of this strategy being used today. So if you happen to know of any other foundation models being trained on any interesting proprietary modalities, please do ping me and let me know, as I would love to do more episodes exploring this theme. The next lesson, perhaps as important to Stripe Success as the model itself, is the way they are using it. Rather than trying to design the foundation model to support all use cases directly, they are exposing payment foundation model representations, and thus allowing engineers to use them as additional inputs to the many classification and other ML systems that they've already developed. The richness of the foundation model signal makes everything else work better, but doesn't require a major rethinking of existing systems. Again, outside of social network companies, who I do believe make their user and content representations available in this way, I've not heard of other companies taking this approach, and it seems to me now that more of them should consider it. Finally, the most important lesson from a societal standpoint might be that AI strongly favors the incumbent platforms that have the data necessary to train such differentiated models. The flywheel that Stripe has created here, which translates their incredible scale to commercial advantage, is allowing them to reduce the cost of fraud for their customers even as fraud is rising across the broader ecosystem. This makes Stripe the obvious choice going forward, which in turn further strengthens their data advantage and product lead. It is genuinely hard for me to imagine how anyone, aside from a few of the world's largest tech companies, could ever compete with Stripe. Meaning that even as history begins to unfold at a dizzying pace in many respects, competition in many key markets may effectively come to an end. This isn't necessarily a problem. I've never supported punishing companies for their excellence, and I've never been convinced that we should break up American tech companies. But it does seem like something that policy makers will need to think long and hard about as they envision the AI future and hopefully begin to imagine a new social contract. There's a lot more in this episode besides these key strategic insights, including how Stripe is designing processes to iterate quickly enough to stay ahead of fraudsters, including by using LLM as judge to fill in missing data. How they ensure reliability in their LLM-powered Talk to Your Data product experiences. How developers can accelerate product development by treating Stripe as their payments database of record. What Emily and team are seeing in agentic commerce today, and how they think about scoping their AI ambitions and investments. All in all, as you might expect from Stripe, it's a high alpha episode, with practical lessons for rank-and-file AI engineers and big picture implications for executive level AI strategists. Without further ado, I hope you enjoy this deep dive into how smart use of AI is transforming one of the world's most critical financial infrastructure companies. With Emily Sands, Head of Data and AI at Stripe. Emily Sands, Head of Data and AI at Stripe. Welcome to The Cognitive Revolution.