The Exchanges

Q the scale changed with AI? So before, you know, I used to run some software website and we would have the same issue. People buy the software and then gets charged back, but it's like 20 bucks. Like today, you could, like, you know, use the credit card and sign up for the OpenAI API and spend 10,000 dollars, 15,000 dollars, like much what's the shape of the fraud today?

A So friendly fraud is like not stolen card credentials, but something like non-payment abuse, free trial abuse, refund abuse. So it's me, they're my credentials, but I'm not actually creating a creative revenue for the business. This has happened for a while, and actually, if you, if you ask business leaders, like, I think something like 47%, payments leaders, like 47% of them will say that their biggest fraud challenge is friendly fraud. I would say this was, like, just much less of an issue for SaaS for two reasons. One, like, what were you stealing? You weren't stealing computer inference or whatever. And two, more importantly, even if you were stealing some service, like, the marginal cost of providing that good or service for Salesforce or whomever was like near zero, and so it didn't totally crush your unit economics. Now we're in the world where GPUs are expensive, inference costs are high, and free trial abuse or refund abuse or general non-payment abuse, right, you, you rack up these charges and you never pay, is like existentially threatening for AI businesses. I was talking to a small AI founder the other day because we're, we're building sort of a suite of Radar extensions that are explicitly targeted at this type of fraud. And everyone tells me it's a huge issue, and so with every company I talk to, I try to dig in on, for you, what exactly is the issue? And there's…

AI assessment note: “Now we're in the world where GPUs are expensive, inference costs are high”

Q So this is like from the outside how people perceive AI as Stripe. What about Insight? So you mentioned 3.5 was kind of like the moment you took it seriously. What were the first internal use cases and then how do you use AI as Stripe today?

A Yeah. First internal use cases were, you know, bottoms up experimentation, right? So we created, we call it go LLM, but it's like just a chat GPT like interface where you can engage with a bunch of different models. Um, it was the very, very first version actually wasn't like an LLM proxy where you could build production grade systems. It was literally just like chat GPT like stuff. And then we had this like preset feature, which was like prompt, Saving and sharing. And so you could share your temp. Oh, you know, this is how I figure out what customers to reach out to and generate reach outs or, um, you know, rewrite my marketing content in striped tone or whatever. And you had sort of hundreds of presets that came on like overnight because, uh, everyone was into it. And then we generated, so then LLM proxy was like, okay, now production grade access for engineers to these LLMs. And a lot of the early use cases there were actually around merchant understanding. So I mentioned a little bit ago, but we have like thousands of merchants that come under Stripe every day and we have to understand who are they? What are they selling? Is it supportable through the card networks? Like, are they credit worthy? Are they fraudulent? Um, and there's a lot that LLMs can do there. So those were some of our earlier, um, earlier use cases. Fast forward to today. I mean, you know, I, I actually …

AI assessment note: “First internal use cases were, you know, bottoms up experimentation... Fast forward to today”

Q transition and that, that, that, uh, is the perfect intersection of financial infrastructure and AI. So maybe, uh, could you tell this, the story of ACP, right? Like, I, I think, uh, this is one of the, the, the biggest launches of, I guess, like in the, in, in, in the second half of the year. And, and like, I guess a really important strategic move between OpenAI and Stripe.

A Yeah. So, you know, we talked a bunch about AI companies in general. One important slice of AI companies is AI commerce, agentic commerce. And, you know, I think just zooming back, like we're all spending more and more time in some combination of broad consumer-based tools like ChatGPT and AI dev tools like Replit or Vercel or whatever. And We want those agents, those tools to increasingly take action on our behalf. And I think, you know, we saw an early version of this in chat GPT with operator, but an important area we want them to take action is buying on our behalf. You know, sometimes it's recommending products, but often it's like literally getting it all the way over the wall. So, um, a couple of weeks ago, we announced our agentic commerce protocol, which is joint with OpenAI. And it's basically just a shared standard for how businesses can talk to agents. So if you think about it, like, it used to be that a human was buying from a business. Now there's an agent that's sitting in the middle. And that fundamentally needs to change how the financial infrastructure works. Like, checkout needs to look different. Fraud checks need to look different. Payment flows need to look different. But also, merchants are trying to figure out How they can efficiently expose their product catalog, their inventory, their brand, their pricing through a range of agents to have access to tha…

AI assessment note: “a couple of weeks ago, we announced our agentic commerce protocol, which is joint with OpenAI”

Q lot of internal tooling and that's great, but also there's a lot of great tooling out there. How do you navigate this? Obviously you have a lot of unique internal context, but you also have You work with external vendors. Like people, I guess, want to know how to work with you, but also people in your shoes at peer companies also want to know how you do this decision.

A Yeah. I think for us, it's not an either or. It's very much an and when it comes to build versus buy. And some of that and is sequential, right? So You, you and I were talking earlier about, like, when, when GPT, 3.5, I think, like, first hit the scene where, like, oh, everybody at Stripe needs to have access to LLMs, but, like, we don't quite know how to do that in a way that's, like, enterprise grade, safe, and we feel good about. Like, we don't see a provider there right now, and so we built it. But now we use, like, open source, LibraChat, you know, so I think there's, like, there's, like, an evolution over time. And one of the things that I think can be really hard, especially for the team who has tunnel vision for the products they own, You know, you love your product. You want to make it better over time is you can get stuck in a lot of hill climbing. Like we could have taken GoLM and been like, oh, we should figure out a way to like give it access to tool shed. Oh, we should figure out a way to make it do like deep research or, you know, like we, we, we could have done that. Um, and sometimes you just need like sort of more of an outside in perspective of, hey, if I ignore the sunk cost fallacy, ignore my emotional connection to the thing that I spent nights and weekends building, First principles, like if I were to do this today, what would I do? And some of that is al…

AI assessment note: “it's not an either or. It's very much an and when it comes to build versus buy.”

Q Have the foundation model enabled more data to be put in the embedding? I think like, you know, things like, you know, number patterns and like zip code versus like location, I think those you could do before. What are, are there any new data points that you get?

A Yeah. So, so I think there's, I think there's two big things. Like one, you know, when you're, when you're building a small model, you're usually like, oh, looking at the data that like has reasonable labels. It's like recent history. You probably have some like hand engineered features in many cases. If you're actually like building an FM and you're imagining there's many downstream use cases, you're putting, you know, tens of billions of transactions in it. Um, you're putting Like the entirety, like every detail of the payment in it, and, and letting the FM reason about what are the components that matter versus not. So it is literally all the things. But I think what's even more interesting is like the last K, like, like what matters is the sequence. What matters, like you can think of a payment sort of like a word, and so you can think of payments data kind of like language data. And what matters isn't the word, right? It is the word in relation to the words around it. But what's tricky about Payments is you don't see, like, you know, Emily on a podcast saying 20 words, and no, those are the words. Like, the sequence that could matter could be, you know, this particular retailer charges from this IP. It could be anyone on a Friday night with this credit card. And so you kind of have to choose a broad swath of relevant sequences to capture kind of the last, the last K that m…

AI assessment note: “you're putting Like the entirety, like every detail of the payment in it”

Q Does Stripe get involved there? Because that's not really within your normal wheelhouse.

A We do. So our, we have, so we have payments, and then we have a billing suite, and almost all the AI companies use our billing suite. Billing includes things like fixed fee subscriptions, but it also supports usage-based billing, so like metered billing, and you can define usage in All sorts of different units. And we have a number of customers, including Intercom, who actually define it in terms of the outcome. So in this case, it's like support cases resolved. Resolutions. Exactly. And, you know, I, I think it's really interesting. I'm an economist by training. I told you earlier in the elevator that I think physicists make the best, uh, scientists or MLEs, but I didn't know that at the time, so I'm an economist. Um, and I think a lot about, okay, like what makes the market Efficient or inefficient. Um, and one of the things that I worry about in AI is it's incredibly hard to take a product to market when someone has to pay for it before they see the value. Um, and that's especially true with AI because a lot of the buyers, especially enterprise buyers, don't understand how to evaluate the underlying technology. And so if you can get your foot in the door by saying not just, oh, you'll only pay for what you use. I mean, pay for what you use is kind of helpful because they're not committing up front to some huge contract, But they can come in with a fear like, well, what if my…

AI assessment note: “We do. So our, we have, so we have payments, and then we have a billing suite”

Q Exactly. It's like, well, but now if anybody can just have an agent that just goes on the website to buy them, now it's like, how do you do the queue? Because everybody gets there instantly, right?

A I won't say the company, but I had dinner the other night with the guy who's the CEO of, you can basically think of it as like StubHub for Country X. I won't say what country X is, and he was selling, I think it was like, 3000 Bad Bunny tickets, and he had 400,000 people come to buy the Bad Bunny tickets, except almost all of those people were actually bots. And this is, this is a great example of what we were talking about earlier, around like, it's not just about the, the fraudulent dispute. So the conversation I was having, he was like, you know, we have scalpers Scalpers who have bots, they, they end up scalping the tickets later. They come and they buy. It doesn't result in a chargeback. Like, they pay, but they're not the people that we want paying. And so, to our conversation earlier on suspicious transactions, like, there are lots of different types of fraud, and thinking of fraud as just things that result in a fraudulent dispute is actually overly narrow. And he wants to block, you know, all the scalpers, everyone who's, like, you know, enumerating through email addresses in their signup, and, and, and. Um, the other thing he said to me, which was interesting, is, He, for various reasons, and some of this is actually like the, the, the nuances of how his system is built. Um, but I think some of it generalizes. He wants to have those fraud signals before they even get …

AI assessment note: “He wants to have those fraud signals before they even get to the checkout page.”

Q Yeah. Um, well, so like, When you encounter those, like the StubHub for X, don't you feel the temptation to recommend an auction? Like, and there's so many auction mechanisms that can clear the market. This is clearly a market clearing problem. What's the market solution here?

A I don't know that I am usually tempted to recommend an auction. I am usually tempted to ask a lot of very probing questions about why they've designed the system the way they've designed it and then try to brainstorm. Yeah. Whether there's a, whether there's a more efficient path, but yeah, I feel that way about Um, most, most pricing matching discovery recommendations, like most markets are just inefficient. And so I think there's a lot of opportunity to make it better. Um, one of the reasons I joined Stripe and one of the things I have loved about being at Stripe for the last four years is when we see those opportunities to make the market more efficient, um, we can actually invest in doing them without optimizing them You know, without monetizing them directly. So you can think of it like incentives are very aligned. Anything we do to help the businesses on Stripe grow helps us grow because you know, they, they run their payments through us. Um, and so we do this all the time. Like, here's how to, you know, improve your checkout. And we just like update the checkout for them or like optimizing their payments acceptance or automating their retries. Um, So anyway, I, I think it's just, it's, it's very nice to be at a company where you don't have to worry about the go to market for something that helps the businesses that run on you. You just have to help the businesses that ru…

AI assessment note: “one of the reasons I joined Stripe and one of the things I have loved”