Stripe's Payments Foundation Model: How Data & Infra Create Compounding Advantage, w/ Emily Sands
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What is Stripe’s scale and why does it matter for AI?
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 twenty twenty four processed one point four trillion dollars in payments, or roughly one point three percent of global GDP for everyone from solo entrepreneurs to the Fortune one hundred, 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, the
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 superintelligence 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, cybersecurity, 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.
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Chapters
8 chapters
1
What is Stripe’s scale and why does it matter for AI?
0:01–12:43
2
How does the Payments Foundation Model turn transactions into dense embeddings?
12:43–22:51
3
Why did the foundation model boost card‑testing detection from 59% to 97%?
22:51–32:38
4
How does Stripe expose the model’s embeddings for reuse across other ML systems?
32:38–41:09
5
What are dynamic risk thresholds and how do they adapt during an attack?
41:09–50:30
6
How does Stripe use an LLM as a judge to validate noisy fraud labels?
50:30–1:00:09
7
What is agentic commerce and how might “business‑in‑a‑box” evolve?
1:00:09–1:08:28
8
What practical advice does Stripe give to startups building on its APIs?
1:08:28–1:17:52
Speakers
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