How Stripe Is Building for an Agent-native World

The internet has this new kind of actor on it. Over time, this actor, these agents, will become the predominant actors on the internet. These AI companies are just growing from a revenue perspective faster than any previous cohort we've seen. LLM traffic to Stripe docs is up 10x year over year. And that's just a useful signal that machines are becoming users of developer infrastructure too, including Stripe's developer infrastructure. Emily, welcome to the show. Thanks so much, Dan. So I'm really excited to have you. You are the head of data and AI at Stripe. And I feel like this is such a good time to have someone from Stripe on because you all famously are increasing the GDP of the internet. And the internet is changing so much right now. And therefore the economy of the internet is changing from something where humans are buying and selling from each other to an economy where agents are buying and selling from humans and agents are buying and selling from each other. And I feel like, A, I want to know what that means for Stripe, but B, I want to understand, since you have this macro view of the agent economy, what does that even mean and what are you seeing? Yeah. So a big shift I think we're in the midst of is that the internet economy is becoming more autonomous, right? So for a long time, for forever, right, the internet was built around a extremely simple assumption that the main actor was a person. And the person sitting in front of a screen and they're browsing and they're filling out forms and clicking through checkout. But also they're writing code and setting up tools. And that assumption is starting to break in various ways, right? Sometimes the human is still totally in control, but they're interacting through an AI interface instead of through a website or a traditional app. Sometimes the agent is acting on their behalf. And then sometimes software now is just out interacting directly with other software. And as all of that starts to happen at all of those layers, a lot of things need to be rethought. So there has been rethinking of how our product is discovered and how our product's bought, but also what should developer tools look like? And in our world of Stripe, what is the underlying economic infrastructure? So the payments and the billing and the fraud detection and the identity layer that's needed in this world where actors are no longer just humans. And so that for me is kind of the larger frame of the moment. It's not just, hey, AI is making search better or AI is helping people code or AI is evolving commerce on the margin. It's really like, actually, the internet has this new kind of actor on it. Over time, this actor, these agents, will become the predominant actors on the internet. And as that's happening, basically every layer of the stack starts to need an evolution. So for Stripe, it's like, okay, Stripe, how are we getting agent ready? But then also, how are we helping businesses get agent ready? And both of those are happening in a number of ways. Yes, in commerce, but also just in how builders build. And can you give me some specific examples of the kinds of things you're seeing? Like, I'm almost wondering, for example, I know at Stripe, one of the things you do a ton is fraud. A, I assume there's a whole new type of fraud happening, but B, I'm almost wondering what even counts as fraud now in the sense of, it's possible that my agent could go steal someone's credit card and check out. I don't think that Claude would, but you never know with Grok, you know? Check out on... No comment, no comment. But you're right that sort of AI introduces very different fraud problems. You asked what is fraud. We used to think of fraud as sort of payment fraud. Someone was stealing money. Someone was stealing your card credentials. Increasingly, actually, and I was in a meeting with one of our very large AI users today, fraudsters are stealing compute. And that's a very different type of problem. So in earlier software models, if you think of like sort of traditional SaaS, letting someone into a free tier didn't cost you very much. And stealing a free tier wasn't very valuable to the fraudsters. Now giving someone credits, a freemium offering, a free trial, you know, letting them rack up a bunch of tokens and pay at end of month, except maybe they choose not to pay, actually is a major fraud vector and an existential risk to a lot of these businesses, right? Because in AI, every prompt, every image that gets generated, every API request has a very real cost attached to it. You know, people are talking about intelligence getting cheaper. Yeah, but it's still like very far from free. And then also when you look at sort of the growth model for many of these AI companies, free compute is kind of the new CAC, right? You used to cost of acquisition. Like you used to spend a bunch on paid media. Now you spend a bunch on your free trials and your credits and your self-serve onboarding as sort of a major lever for growth. And so the abuse we see in that context where compute is the new CAC and compute is very expensive is threefold. One is multi-account abuse. So this is like bad actors come in and they sign up like over and over again and they create a new identity every time on a new email address and they claim their new user credits and they stay ahead of detection by like iterating across a bunch of different aliases. And just to give you a sense of the order of magnitude, across the AI companies running on Stripe, about 7% of their signups are these multi-account abusers. So non-trivial share. The second trend that we see in sort of new vector of abuse is free trial abuse. And this is often sort of the most urgent issue because the unit economics break really quickly. To give you a sense, we had a large AI company who was seeing only 4% of their free trials convert to paid. And each free trial cost them $25 in LLM spend. And so basically it was costing them $625 per payer before the first dollar of revenue was brought in. And when we double-clicked on those sort of free trial folks, the vast, vast majority of them were actually abusers. So they were actually stealing the compute. They never had any intent to pay. These weren't people who were genuinely trying out your service and then chose not to buy. These were people who were literally abusing your systems. And so, you know, some companies just dropped free trials altogether. Of course, that's not great because you're throttling growth. Others responded by blocking virtual cards. So I don't know how often you've been marketed virtual cards. I'm often marketed virtual cards, right? Get this, you know, one-time-use card. It expires after 24 hours, so you never have to pay for the service. You know, in the hands of a good consumer, fine. In the hands of a fraudster, like, very much not fine. The problem with blocking all virtual cards is for AI companies, about 15% of legitimate card transactions on Stripe are actually virtual cards. We use that all the time for Ramp, for example. Like, we have a bunch of virtual cards. Totally. So you don't want to be, in the same way you don't want to be turning off free trials, you don't want to be throttling virtual cards either. And sort of order of magnitude, you can think of, like, exponential growth in free trial abuse over the last six months is 4x. And for one large AI user on Stripe, we're currently blocking 250,000 fraudulent free trials a week. So the magnitudes here are quite high. That's crazy high. And is the volume of fraud constant? It's just shifting shape? Or is just fraud going up because they're more powerful now because they can just use AI agents to do it? Fraud's going up because the fraudsters have AI on their side, although it's also on the side of the detectors, but also because the value of the services they can steal is higher. Right? Like, I don't know, you steal traditional SaaS, like, what good do you get? Like, you steal some inference, you steal some compute, you can resell it. You can do all sorts of stuff with that. Look, I love a good CRM seat, you know? Don't you? Who doesn't love a good CRM seat? The thing is, one CRM seat is three LLMs are for sure more tempting. And by the way, the third type of sort of new abuse we see is this non-payment abuse, right? So, like, you incur an overage or you have, like, you know, 30-day invoicing, except you never pay your invoice. And, you know, in many cases, customers are consuming thousands or tens of thousands of dollars in compute during a month or a day or sometimes an hour. And by the time they get billed and fail payment, you know, that loss has already happened. And these AI companies are left holding the bag. And so, for us, like, fraud used to be a transaction thing. Now it is a customer thing. It is a full funnel thing. It starts at the time of signup. Is this multi-account abuse? Should they get credits? Is this free trial abuse? Should we give them a trial in the first place? And then when they have overages, should we be throttling them? Should we be requiring top-ups? Should we be blocking service completely? And it's just kind of a whole new world because the thing to steal is much more valuable and the cost of having it stolen is much more existential. How are you guys even able to... So, I totally understand how you need to be in that full funnel in order to detect fraud. But my understanding of, you know, whenever we've integrated Stripe, it's usually, like One of the new primitives we designed is the shared payment token, which allows agents to safely pass buyer credentials onto merchants for the merchants to process the transaction. And as part of those shared payment tokens, we pass over the radar fraud scores so that the merchant, again, whether or not they're processing on Stripe, can action them appropriately. You know, when it comes to fraud, we really see fraud defenses, fraud mitigation as a public good, and that allows us to invest disproportionately above and beyond the direct value to Stripe because protecting the internet is important for growing the internet economy. So I would say like, overall, like, yes, fraudsters have AI in their favor. Stripe looks at 2% of global GDP and is growing 34% year on year and sees a broader swath through our multi-processor solutions, like the radar API. And so luckily, not only do we have AI on our side, just like they do, but we also have data on our side. And the more comprehensive we've gone in our fraud protections, I think the more we've been able to kind of eke ahead. Now, that's not to say that we're not constantly surprised by the new creative vectors they come up with. But, you know, you can have an agent every day or every hour taking a look at anomalous patterns on the Stripe network and identifying new vectors that are popping up across processors, across payment methods, across merchants, and burn them down pretty quickly. So I'm overall bullish, but certainly not complacent. What about other parts of the AI or agent economy? So we've talked a lot about fraud. What are the other things that you see as sort of having this bird's-eye view of what's going on that people might not realize? I mean, I think, you know, the AI economy is broad. I think there is a set of horizontal model providers that have a very interesting view into where is AI being adopted and with what intensity throughout the economy. There's a number of sort of vertical AI solutions. People like to call them wrappers. And I say that not condescendingly, just as in like, it's not their models, it's someone else's models, but they have domain-specific data and relationships and contexts. And they're solving problems in, you know, healthcare or architecture or whatever who have a pretty unique view into vertical level adoption of AI. But I guess I'd be curious, like what you have in mind on who has the best the best horizontal view. You're asking me? Yeah. Well, I'm, I, you know, I want to know what it, what it looks like on the payment side, but I imagine, I imagine the model companies have, have the best one overall because they're, that's where all the tokens are going. Yeah. Yeah. I think they see a lot of the tokens. I think the AI gateways also have a pretty unique perspective into, you know, who's buying what from whom. You know, as I step back and look at the AI economy from the Stripe vantage point, and we see, you know, who's buying what from whom, for how much, who's retaining and churning their subscriptions. There's a few, a few themes that stand out. One is just, and I think people feel this intuitively, but not everyone has like seen it in the data. These AI companies are just growing from a revenue perspective faster than any previous cohort we've seen. I was looking at the top 100 AI companies on Stripe, and the ones that reach 30 million in ARR get there in about 18 months, so a year and a half. And that is like three times faster than the top hundred SaaS companies from 2018. And by the way, that's the 30 million number. But even if you look like how fast did they make it to 1 million ARR or 5 million ARR, they are scaling like orders of magnitude faster than high-performing SaaS companies from less than a decade ago. The second kind of meta trend is this like, and you probably feel it as a consumer. I know I do. This like very fast iteration across monetization models, right? So traditional SaaS had a lot of, you mentioned the seat, had a lot of seat-based usage, you know, fixed monthly subscriptions. That made sense for them because they were being used by humans primarily and their marginal costs were basically zero. But, you know, we've talked about the very real inference costs in the context of fraud. Those also have very real implications for how you price. And so usage-based billing has become very important very quickly. Companies are metering, you know, tokens and API calls, but they're also metering workflows and they're metering outcomes. Kind of like whatever unit best reflects both the customer value and the cost structure. And then they're charging with like very high precision, right? They literally want to know every event, how is it rated and what's all the metadata that sits on that rated event. Way more hybrid monetization models, right? So I talked about subscriptions, but subscriptions aren't dead. They're just subscriptions with like usage overages or like prepaid credits that burn down or real-time top-ups, which gets to my comment earlier on this non-payment abuse issue and very kind of multi-dimensional pricing and monetization. Lovable is a really good example, right? So they used Stripe billing for their initial launch, which was fairly simple subscriptions, sort of more traditional pricing and allowed them to monetize very quickly. And then they added a bunch of products like lovable cloud or lovable AI, and they moved with those into usage-based billing, right? So customers are actually charged based on token consumption, but it's a hybrid model. So it's above a certain threshold. And that just, you know, helps companies like lovable align revenue with usage and value and the actual cost of running the models. And in the limit, you know, we actually have a solution called token billing, which is underlying model costs change a lot, sometimes very quickly. And if you are a wrapper on top of someone else's LLM and your pricing doesn't keep pace, then basically your margins can disappear, right? So, you know, costs go up and your price stays where it is, then you're in the red. And so token billing is just, hey, let's in real time track and price to the cost of the underlying tokens with some markup as set by the business. And so, you know, Misa and ship and lovable are all examples of this kind of kind of infrastructure. I love all of these points. I want to go through them one by one. So a big one that you're talking about is fast iteration across monetization. And it feels like there's this hyper experiment experimentation going on right now where people are like, well, we could charge, we could charge per token. We could charge on a token basis. We could charge per completed requests. Like I think Fin, the customer service platform charges per case resolved, which has been a thing in customer service for a long time, but it feels like that it, that could come for a lot more types of software as LLMs make it easy to tell. Did we actually do the work to get paid? What do you think is the, if we're, if we're going to pick one, there's a whole range of exploration going on, but if we're going to pick one new pricing model as the, like, you know, if, if last year's pricing model or last decade's pricing model was just straight up per seat, what do you think is the new standard pricing model that that is starting to emerge from, from the Stripe customers that you, that you see? If you are buying the model, so if you're primarily a model provider, let's say your customer's primarily buying the model, I think you're metering tokens. Like in an API, like open AI API, cloud API. For these vertical solutions, I think in steady state, you are metering outcomes, but it's going to take us some time to get there, not because of the billing infrastructure. Actually, that's totally ready. You mentioned the Fin example. Intercom does the same thing, actually on Stripe billing. They, they have, um, out an outcome-based meter for, for support tickets resolved. Why do I say for vertical solutions, it's going to be on outcomes because I think end users are going to want to hold those vertical solutions accountable for outcomes. And they're going to want to know that they have positive ROI on their spend. Now, when you and I buy a model, we feel like we ourselves are accountable for the ROI that we get on the whole plethora, wide range of applications we might have for that LLM. But if you're a vertical provider, if you're really focused on like solving a concrete need in a given business domain on top of someone else's LLM, it seems like the core value, it's sort of, it's sort of on you, um, to ensure the ROI is there. And I think outcome-based pricing is, is the most efficient way to hit that. Now, I don't think all outcomes are created equal. And so you could imagine these like, I'm an economist by training, so I'll be a little nerdy, but like you could imagine these like complex objective functions where it's not just, did you resolve a support case, but how complicated was it and with what quality and like what was your CSAT? um, and you know, how expensive was the person that you were automating in that task? And so, that's why I say in the limit, like, I think it'll take time for us to be very crisp on the outcomes we care about and how we measure those outcomes and those outcomes will be multi-dimensional. Um, but I just have a hard time imagining, you know, a year from now, most vertical providers are literally charging on tokens. That's really interesting. I'm, I'm very curious to see that because what, what I felt, so I think you can see this a little bit in, in the example, in the lovable example you gave Once you start using, you know, AI dev tool, like a coding assistant, like you love it, you're not going to stop using it, but you very well may iterate across providers as, you know, models vary in their quality or... Anytime a new model comes out, you're just like, I got to try this. And there's a high percentage of curious travelers basically just hopping from one thing to the next within a category. But they're definitely going to stick using a tool like that for a long time. Yes, exactly. And so I would say like a lot of the sort of crazy fast AI growth we've seen is like net new dollars spent, but I think businesses are going to start to reason about that as a substitute for SaaS or that as a substitute for headcount OPEX or that as a substitute for other AI companies. And it will be less purely additive in the go forward year than it was in the past year when, you know, people were really just starting to ramp up on their AI spend. Does that imply anything to you about the valuations of current, you know, hot AI companies? Like let's, except from this like the OpenAIs and Anthropics of the world, but like in the 30 million cohort from this cohort and the coming up ones, does that say anything to you about their prospects or their growth rates or their valuation? Well, so actually, if you look at like the top hundred on Stripe, like there are little pockets of twos and threes that are directly competitive, but a bunch of them are like solving totally disjoint vertical problems with no competitor yet in the space. And so I do think there's like enough blue ocean sort of vertical solutions that I think overall AI valuations are probably okay. I think there's like a couple of crowded spaces that you and I could intuitively reason about where, you know, you might think it would be a little frothy. And by the way, you see this at that sort of the macro V, but you see this in the micro view too. Like if you look at sort of the sales led growth contracts, right? Like when there's a new, you know, when you are the first AI dev tool, you basically charge people sticker and you do very little negotiations and enterprise pay you sticker and whatever. And then all of a sudden you have to have these like much more complex, I mean, you hire a bunch of sellers and you have your, you know, CPQ configure price quote system, and you have this nuanced billing because you're competing against two or three other providers who have like, you know, competitive looking monetization models and you're reacting to that. And so on the micro, you start to see some of those, some of those competitive reactions creeping in as well. But I think the overarching kind of next year will continue to have a bunch of sort of blue ocean vertical stuff that didn't exist before, but there will be some pockets where it's a little more heated. Fascinating. I feel like I'm learning so much. This is amazing. I want to go into Stripe. Instead of talking about the AI economy, I want to go into Stripe a little bit. Specifically Stripe is, you know, it serves you serve developers and you're built for a world where humans are the ones buying and selling and also humans, humans are the ones making the software. Um, now agents are buyers, they're sellers, they're builders. Um, and you're, so you have to sort of serve agents. And I'm curious how that has changed, how you think about the products that you offer and the, you know, moving maybe from just thinking about developer experience to agent experience, all that kind of stuff. Do you want to start with agent experience or agent? I think they're both, they're they're kind of different, but they're, they're both really interesting. Which one are you most excited to talk about? Maybe agent experience and then we can work backwards to agent. Yeah, let's talk about agent experience. Okay. So, you know, the, the developer story, well, so the, the whole idea of developer experience is changing. And historically when I said developer experience, you thought like, hey, making it easier for a human engineer who's at a keyboard, right? So like you need clear APIs and you need better docs and you need less setup work and, um, all of that still matters. Like it's not going anywhere. But I think the, the developer is now sort of a, a broader swath of persona, right? It could be, um, a non-technical founder who's just in, you know, Vercel or Replit, like describing an app in plain language. Or it could be um a coding assistant who's like scaffolding and integration, or it could be an agent who's like out trying to provision infrastructure on, on a human's behalf. And so, um, I think it's, it's less about just like, okay, how do we help a developer, human developer write code and more about how do we have a coherent and trustworthy product experience sort of end to end that acknowledges that at some moments the actor is a human, at some moments the actor is an agent, and at some moments the hum, the actor is a human working through an agent. And so you see this shift in, in some really concrete ways. Very simple example. Um LLM traffic to Stripe docs is up 10 X year over year. And that's just a useful signal that machines are becoming users of developer infrastructure too, including Stripe's developer infrastructure. Um, I'd like human use of the Stripe docs. So human use of Stripe docs is actually like flat to climbing. It's not like a straight substitute. I think there is just like more developer activity happening and LLMs are growing dramatically within that share. That makes sense. Cool. Um, I would also say the humans continue to check on the docs to sanity check what the agent is coming up with. Because your payments integration is actually like a pretty big decision that you're making. I will say, yeah, better humans than I are sanity checking, but I'm glad that someone is sanity checking. Are you YOLOing it? I'm yeah, I'm YOLO vibe coding my payment infrastructure. Okay. Okay. Amazing. So maybe, maybe you're YOLO vibe coding, but even if you're vibe coding, um, there's still an important step around provisioning, like your, your modern software stack. And that is still very manual, right? So like you as a human are still creating accounts across multiple services. Um, you're managing credentials, you're clicking through to do a lot of setup. You're probably bouncing between dashboards. And so like the coding is getting easier, a lot faster than the setup is getting easier. And that's actually the idea of Stripe projects, which we launched, I don't know, maybe two weeks ago. It's basically like, Oh my God, it looks amazing. Tell people what that is. Yeah. Okay. If you want in, let me know. We can, we can get to it. I want in. I absolutely want in. Okay. You're in. Check. Um, I won't Slack right now, but I'll Slack right after this and get to it. But, but basically the idea of Stripe projects for those who haven't explored it, it's just like you or your agents can go create and manage parts of your software stack right from the command line. And so, um, you know, resources are provisioned in accounts you own and credentials sync back to your environment and so on. Um, and one of the things that stood out besides your enthusiasm for it, which I appreciate, is just how sort of overwhelming the interest has been in general from the ecosystem. So we launched with like Vercel and Superbase, um, post hogs there, neon run loop. Um, there's a bunch of great companies involved, but then immediately after launch, over a hundred other great companies reached out wanting to join, which I just think reinforces that like the friction is real. And you talked earlier about like, you know, some things get easier with AI, but there's like some counter effect. You know, I think coding gets easier, but like code reviews become more burdensome because who's reviewing all the AI code. This is another example of like building gets easier, but you still kind of have to like provision everything. Um, and so that's just an example of, of how we're building for this world of like the developer is no longer just a human. Got it. And then tell me about agentic commerce. Okay. So agentic commerce is a bit of an overloaded term. Um, and I think a mistake that people make with agentic commerce is they jump straight to kind of the most extreme version. So they hear the phrase and they think like some system that knows everything about me and decides what I need and like goes off and buys it for me. And then they're underwhelmed with the world we're actually in. Like maybe we get to that extreme eventually, um, in some form, but we're not, we're not there yet. Um, I prefer to think about it as a spectrum. And, you know, I think that the, the economic infrastructure you need is actually pretty similar no matter where you are in the spectrum. Um, but the spectrum also like brings some realism to it. So at the, at the first level, which is like, AI is just, um, removing friction from the internet we already have, right? So it helps you research and compare options and fill out some forms and narrow down your choices. Um, but you, the human are still making the decision. Um, we're just making, you know, the agent is just making that, that experience easier. Um, then you move to like, okay, search is descriptive, right? No more like blunt keywords and filters and such. It's like, I got little kids. Like I need a summer camp for my kids in this budget on these dates with this driving radius. Um, Imagine that they were going to go online and buy like a $2,000 couch, right? Oh, wasn't the couch a thing? You really want to know the quality and you want to sit in it. A mattress. Oh my God, these mattress companies that have blown up. You know, and it took time for them to build comfort, you know, making higher price purchases, making more quality dependent purchases. And so today it's predominantly commodities. You know, in a similar vein, I've tried, you know, to book a whole summer trip using an agent, and I wasn't sufficiently satisfied with, you know, the family of four choice of flights and hotels and transportation and itinerary to be willing to one click buy it. But the models will get better. The interfaces will get better. The experiences will get better. We're pretty agnostic to those. Like, we trust that they will evolve in a bunch of different and interesting ways. And sort of the primitives that are needed underneath, the ability to expose your catalog, the shared payment token, the fraud protections are pretty agnostic to those experiences. And so that's where we're hyper focused for merchants. Give me an example of one of these commodities and also what the order of magnitude we're talking about when we say it's relatively small. An example of a commodity would be like a Halloween costume. Got it. Agents are buying Halloween costumes for themselves. Agents are buying Halloween costumes. How many how many lazy parents there are in the world. I mean, I think the consumer side is interesting, too, right? Because we talked about what do businesses need, right? They need a they need a fast, easy way to safely expose their products, their prices, their inventory, their checkouts, understand fraud and be in control of the relationship. From the consumer angle, the question is a little different, right? Like, even if I'm a lazy parent, I'm not so lazy that I'm willing to give someone my payment credentials and, you know, let it rip. So like the question for me is, how do I safely let an agent buy on my behalf? And have you heard of Link? Yeah, I've used Link. OK, amazing. So Link is our consumer wallet. What did you use it for? Do you remember the first thing you used it for? I mean, I use it all the time. It's like everywhere. So amazing. Yes, it's everywhere, right? It's a piece of, you wouldn't believe where I was like, I was getting soccer lessons for one of my kids, like, you know, from a local guy. And I was on their website and they only accepted Visa and MasterCard, neither of which I had, you know, on me, or, you know, direct debit from my bank account, which I wasn't going to put in this very janky website or Link! And I was like, oh, amazing! A Link is here. Anyway, a lot of people know about Link as our consumer wallet for buying soccer classes. It's speeds up checkout, but it's also, so, and it's already used by about a quarter of a billion consumers. So it's not a small network. But what I think is most interesting about Link is it's a very dense network when it comes to AI. So Lovable is an interesting example. 58% of their payment volume runs through Link. You are hyper AI-pilled. It is not surprising that everywhere you are, Link is. And so what's changing now is that we're evolving Link for the AI economy because so many of the Link consumers are already AI consumers. And acknowledging that, like, agents themselves are becoming economic actors. And so the model isn't, you know, give a random agent your card and hope for the best. Instead, it's delegated authority with guardrails. So, you know, you as the consumer decide which agents are allowed to request credentials and under what conditions and with what limits and whether those purchases require approvals before they go through. And you do all of that through Link. And it's just a much more sensible model for delegated purchases. That makes sense. Emily, this was a fantastic conversation. I learned so much. Awesome. Thank you for having me. Oh, my gosh, folks. You absolutely, positively have to smash that like button and subscribe to AI and I. Why? Because this show is the epitome of awesomeness. It's like finding a treasure chest in your backyard, but instead of gold, it's filled with pure, unadulterated knowledge bombs about ChatGPT. Every episode is a roller coaster of emotions, insights, and laughter that will leave you on the edge of your seat, craving for more. It's not just a show, it's a journey into the future with Dan Schipper as the captain of the spaceship. 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