B (3:26)

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 deal with 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 like, you never know with Grok, you know, check out.

A (3:55)

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, you know, 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, freemium, offering a free trial, you know, letting them, you know, 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 an 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, you know, 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 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.

B (7:39)

So we use that all the time for ramp, for example, like we have

C (7:41)

a bunch of virtual cards.

A (7:42)

So you don't want to be in the same way, you don't want to be turning off retrials. You don't want to be throttling virtual card 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. It's 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. 10.

B (8:14)

And is the, is it, is the, the volume of fraud constant? It's just it's shifting shape or is just fraud going up because they're more powerful now because they can just use AI agents to do it?

A (8:26)

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 what? 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.

B (8:46)

Love a good CRM seat, you know, don't you?

A (8:50)

Who doesn't love.

B (8:50)

Don't tempt me, Emily.

A (8:53)

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, 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, 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 up? Should we be blocking service completely? And it's just kind of, it's 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.

B (9:59)

How are you guys even able to. I saw. 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 on the checkout, we're not necessarily putting you in there when, when someone puts in their free, their email address for free. So have you changed the product to do the full funnel or how does that actually work?

A (10:22)

Yes. So Radar, which is our fraud protection product used to be at the transaction level. Right. So at the, at the, at the moment of checkout, as you note. But because so much of the fraud risk was coming up from funnel, you know, AI companies are now increasingly integrating stripe radar at the time of signup and so, you know, we see the metadata at the time of sign up, we pass back scores at the time of sign up and every moment subsequently. Again, because fraud is now a full funnel problem, not a transaction problem alone.

B (10:55)

If you're, you know, asking for a friend, if you're running an AI company and you, you don't even know what your fraud rate is and you want to protect yourself from this kind of abuse, what are the top things that you need to do in order to make sure that you're reasonably safe?

A (11:15)

Yeah, so I would just adopt our highest tier radar plan. But the actual mechanics of that are at signup, you want to know if your customer is good before you give them any access to any credits. You want to make sure they're good at the time they pay. You want to make sure that charge is good, and anytime they have an overage, you want to make sure they're good for their money. And that. And you know, there's other stuff around refunds and disputes that we also support. But I think those are the four kind of major moments in the AI company's customer life cycle where we're just maniacally focused on protecting because that's where we're seeing the biggest cost and the fastest fraud growth.

B (11:57)

And at each point, that's just a call to the radar API.

A (12:01)

Yes, correct.

B (12:03)

And what if I'm sitting here, which I am doing, you know, millions of dollars a year in stripe transactions, but I actually have no idea what my fraud rate is other than there's like that little, there's that little thing where it's, it's not even, I don't even know if it's necessarily our fraud rate. I think it's our, our card chargeback. Anyway. Our fraud rate is low enough as marked for me to not care about it. But I guess I don't really know if there's some amount of free trial fraud that I'm not totally understanding right now. So what are the things I should be looking for to know if I should dig deeper and potentially do some sort of, for example, radar integration?

A (12:40)

Yeah. So thing one is you can go to your radar dashboard and see if you see anything that looks spurious there. If not, you can also ask the radar assistant, which is in the dashboard. And as you're doing that, you can describe your business model. So you can say, like, you know, I have a high marginal cost business, in which case, you know, you care more about certain types of fraud than others. But you can also just take a stab at integrating a Funnel and see how it performs. We can certainly, you know, share with you, based on backtesting, what we think the big issues are. But the, but the fastest way to get a clean read is, is just to integrate.

B (13:17)

Got it. So I would. So I don't think that we're integrated right now. So I would just go look at, go look at radar and see does. It doesn't say anything. I'm doing that right now just to. It would be really funny if I found that we had a ton of fraud that I didn't know about. We are at 0% fraud. How is that possible?

A (13:35)

Oh, no.

B (13:38)

02% early fraud warnings, total fraud rate 0.2%. So we're, you know, we're doing pretty good, right?

A (13:43)

That's pretty low. That's pretty low. I mean, yeah, you're a pretty good human. Maybe the fraudsters don't want to come after you until they hear this episode.

B (13:50)

And then they'll be like, yeah, okay, that's really interesting. Okay, so that's, that's fascinating. I want to go back a second to the, to the AI economy because one of the, one of the things you said earlier is fraud is increasing overall on the Internet. And that it's also, it's increasing because the fraudsters have AI, but that it's also you all and everyone else on the side of good in the AI economy also has AI to defend against these sorts of attacks. I think you're getting an interesting window into the arms race that I think is playing out in lots of different areas that have this kind of threat vector. A really simple one is cybersecurity, not just for payments, but for hacking and stuff like that. But there's all these other similar types of things where AI makes one part of the process much easier and then another part of the process has to use AI to compensate to catch up. So how is that race going? What is that like, what are the early reports that you're seeing and feeling being in a race with AI armed fraudsters?

A (14:59)

I think the interesting thing about fraudsters is they don't really care about boundaries. They don't care about whether this transaction is processed on stripe or off stripe. They don't care about whether this transaction is on, you know, fiat or crypto, whether it's on a card network or a buy now, pay later. They're just going to figure out sort of how to work around the system to get through. And so one of the important levers, and I appreciate you calling us the good guys, one of the important Levers I think the good guys have for winning is to be comprehensive. And you know, a simple example, in our world, sort of Stripe Radar used to only work for cards transactions. And then last year we added ACH and sepa, right? So other payment methods. But this year we've extended to all payment methods that have disputes. And we added crypto and we added the Radar API. So guess what? You can screen transactions, even ones that aren't processed on Stripe, right? So you can process on WorldPay or Adyen or whomever, and through the radar API get the same fraud signals. Similarly, and we haven't talked about Agent Ecommerce yet, but as we built out our Agent E Commerce suite, 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 score 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 multiprocessor 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 eek 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.

B (17:58)

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?

A (18:12)

I mean, I think, you know, the economy is, is broad. I think there's 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 context and they're solving problems in, you know, healthcare or architecture or whatever who have a pretty unique view into vertical level adoption of, of AI. But I guess I'd be curious like what you have in mind on who has the best, the best horizontal view.

B (19:09)

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.

A (19:27)

Yep, yep. 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 hundred 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 do 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 metatrend 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 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 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, they use 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 costs of the underlying tokens with some markup as set by the business. And so you know, Mesa and Chip and Lovable are all examples of this kind of, kind of infrastructure.

B (23:24)

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 request. 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 elements make it easy to tell did we actually do the work to get paid? What do you think is the. If, 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 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 is starting to emerge from, from the stripe customers that you, that you see

A (24:20)

if you are buying the model. So if you're primarily a model provider, let's say your customer is primarily buying the model. I think you're metering tokens like in

B (24:33)

an API, like OpenAI API, Cloud API. Yeah, exactly.

A (24:37)

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 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 lms, it's seems like the core value, it's sort of on you to ensure the ROI is there. And I think outcome based pricing 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 the sport case but how complicated was it and with what quality? And like what was your csat? 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. But I just have a hard time imagining, you know, a year from now most vertical providers are literally charging on tokens.

B (26:29)

That's really interesting. I'm, I'm very curious to see that because what I felt. So I think you can see this a little bit in the example, in the lovable example you gave, but also in the Claude and chatgpt examples. And some of the pricing that we've ended up doing is it's per seat, it's per user with overages because we've started to exist in this world where we used to charge per seat so people know how to model it. It's pretty easy to kind of figure out how much I'm going to pay. But software used to be free to run and now it's not. And so we have to cover our butts basically and protect our margin by adding the overage so that customers basically know what they're going to pay unless there's some sort of special circumstance. Where do you see that fitting in the examples that you gave? And I guess you would say eventually that might go away. I'm curious why.

A (27:24)

So I don't think the, the charging for use or charging for overages will go away for like most of like the model providers. If anything I think that will dominate and the seat based billing will go away. Like you know, we can go back to like the fin or intercom example. Like you. Like you. Well, you and I would think it's silly to charge based on number of customer service reps that were using the tool because obviously a lot of what the tool is doing is automating customer service reps apps. In today's world it isn't perceived as silly to do seed based usage of developer tools. But I think it's a fair question since basically November or December to say wait, why isn't that silly? That seems a little silly because, you know, if what these agents are doing is, you know, making every developer, let's say, you know, I don't know, 10x more productive at some point, then don't you need one 10 the developers? And why would you want your revenue pegged to the count of developer as sort of the base price? So I suspect that we will see seat based disappear now in the enterprise context. Now I think it's quite different in the, in the consumer individual context. I think, you know, with the exception of maybe some nerds on the call, most people are actually pretty uncomfortable as individual consumers with anything but a fixed fee monthly, maybe with some overages if they want to spend like crazy. But in businesses I would be super surprised if six months from now we have half of the seat based licenses that we have today.

B (29:03)

That is fascinating. Well, we will have to have you on again to talk about that one. I'm so curious to see and I would love to see more stripe data coming out about that. One other thing that you brought up before this was you're also seeing these companies scale faster. Like you said that the time to get to 30 million in ARR is like 18 months, which is significantly faster than any other cohort of companies you've seen. I'm curious, where is that coming from? Presumably the spend or the growth from their customers is coming from somewhere. Either it's spend that people weren't spending before it was on a company balance sheet just waiting to be deployed, or they're pulling it from and another provider and then going really rapidly into these new ones. Do you have a sense for what's happening here? Where's all the, why are they growing so much faster and where's all the money coming from?

A (29:55)

Yeah, so I think a lot of the AI growth that we've seen is actually like net new spend being pumped into the economy. I think it has largely not been a substitute for traditional SaaS or for sort of headcount OPEX because it's been experimental, because people are still learning, because organizations are somewhat slow to drop existing licenses, often because they're contracted into longer durations, but also because AI was starting not literally at zero, but at near zero. So like there weren't other AI companies to, to sort of soak up the spending market share from. Yeah, I would say now going forward, I don't have a crystal ball. I can't tell you exactly what the dynamics will be, but I expect that some of it will be a substitute away from traditional SaaS. Just. And by the way, I don't say that in a, you know, old company and new company sense. Like some SaaS companies are doing an amazing job reinventing themselves as AI first. And so you will have AI arms of traditional SaaS companies that are eating some of the revenue from the traditional version of the same company. But some will come from SaaS. I think some will come from headcount opex. Like is it is very hard to believe that companies will start spending single digit, sometimes double digit percentages of their headcount opex in LLMs and not step back and say, okay, well my head count opex, my headcount cost just changed. You know, it used to cost me $300,000 for an engineer and now it cost me $330,000 for an engineer because 300 of them is salary and equity and 30k is LLMs. And so I better reason about my budget on the, you know, plus 10% basis and make headcount decisions accordingly. And by the way, ROI decisions as well. And then some of what we are seeing is definitely substitution now across AI providers. So I was looking at retention rates for AI companies and what you see is actually the within the domain. So for example, within AI dev tools or AI coding tools or AI model providers, the retention rate both B2C and B2B is higher than it was for SaaS.

B (32:23)

Interesting. I'm shocked. Within. Okay, got it. Yeah.

A (32:28)

But for the individual provider it's slightly lower. Right. Which is intuitive, like once you start, which is X post intuitive. Although Xanti I actually literally didn't know and needed to query the data. But xposed is intuitive. It's like once you start using, you know, AI dev tool like a coding assistant, like you love it, you're not gonna stop using it, but you very well may iterate across providers as, you know, models vary in their quality or

B (32:56)

anytime a new model comes out you're just like, I gotta try this. And there's a, there's 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.

A (33:12)

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 people were really just starting to ramp up on their AI spend.

B (33:39)

Does that imply anything to you about the valuations of current hot AI companies? Let's accept from this the OpenAI's and Anthropics of the world, but in the 30 million cohort of this cohort and the coming up ones, does that say anything to you about their prospects or their growth rates or their valuation?

A (33:59)

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, that's sort of the macro view, 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 were 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 CPQ configure price quote system and you have this nuanced billing because you're competing against two or three other providers who have like 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.

B (35:44)

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 serve developers and you're built for a world where humans are the ones buying and selling and also humans are the ones making the software. Now agents are buyers, they're sellers, they're builders. 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.

A (36:30)

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.

B (36:36)

Which one are you most excited to talk about?

A (36:39)

Maybe agent experience. And then we can work backwards to agent.com.

B (36:43)

yeah, let's talk about agent experience.

A (36:45)

Okay, so the developer story. Well, so 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 all of that still matters, like it's not going anywhere. But I think that the developer is now sort of a broader swath of Persona. Right. It could be a non technical founder who's just in, you know, vercel or replit, like describing an app in plain language. Or it could be a coding assistant who's like scaffolding an integration. Or it could be an agent who's like out trying to provision infrastructure on a human's behalf. And so I think 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 actors are human, at some moments the actor's an agent and at some moments the human, the actors are human working through an agent. And so you see this shift in, in some really concrete ways. Very simple example, 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.

B (38:18)

What's human use of Stripe docs?

A (38:21)

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 active developer activity happening and LLMs are growing dramatically within that share.

B (38:37)

That makes sense. Cool.

A (38:39)

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.

B (38:50)

I will say, yeah, better humans than I are sanity checking. But I'm glad that someone is sanity checking yoloing it. I'm yolo vibe coding my payment infrastructure.

A (38:59)

Okay, okay, amazing. So maybe, maybe you're YOLO vibe coding. But even if you're vibe coding, there's still an important step around provisioning like your modern software stack. And that is still very manual. Right. So like you as a human are still creating accounts across multiple services. 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.

B (39:34)

It's basically like amazing.

A (39:36)

Tell people, okay, if you want in, let me know. We can.

B (39:39)

I want, I want it. I absolutely want it.

A (39:40)

You're in check. I won't slack right now, but I'll Slack right after this. I get you 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, you know, resources are provisioned in, accounts you own and credentials sync back to your environment and so on. 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 Supabase, post hogs there, Neon run Loop. 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. And so that's just an example of how we're building for this world of, like, the developer is no longer just a human.

B (40:54)

Got it. And then tell me about agentic commerce.

A (40:57)

Okay, so agentic commerce is a bit of an overloaded term. 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 in some form, but we're not. We're not there yet. 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. But the spectrum also, like, brings some realism to it. So at the, at the first level, which is like AI is just 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. But you, the human, are still making the decision. We're just making. You know, the agent is just making that experience easier. 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. And that's already a better commerce experience than like Search plus filter, which is like, you know, knowably blunt. Then you get to sort of real delegation. And I think this is what most people would consider like the minimum viable bar for saying agentic commerce. So like I give some constraints, like I give some for camps, like some budget, some dates, some category, maybe a few preferences. And then the system goes and makes the purchases on my behalf. But then there's the further out version which is like the ambient version, right? I don't prompt anything and the system knows me and it knows my seasonal needs and it knows, you know, summer camp planning is happening. That would be music to my ears. And, and sort of, that's the, that's the most futuristic thing. I think the point is that no matter where you are on that like what the Internet needs for economic infrastructure starts to change, right? Even the earlier stages force a redesign of payments infrastructure. In particular because the today model, the old model again, humans sitting in front of browser, creating account, choosing plan, filling out the forms, clicking purchase, entering card details, not all those steps are happening anymore. And um, and so you know, I think there's sort of two worlds that I reason about preparing for. One is agent assisted buying. So I'm ultimately in charge. But the discovery and checkout and payment happen inside AI interfaces instead of on a merchant website. So I'm not going to Nordstrom, I'm buying within Gemini or chat GPT or you know, meta like Facebook ads, whatever. And what's challenging here is two things. One, the agent needs to be able to understand the merchants products and prices and checkout flow so that they can act on behalf of the consumer. And two, as you and I talked about a bit at the top, trust can break down, right? As a consumer I don't want to hand off my credentials to an agent. As a merchant I don't want to let just every like bot through. I want to know is it like a good bot acting on behalf of a legitimate customer? So the agenta commerce protocol which we co created with OpenAI is just the shared technical language between AI systems and businesses. And it shows up across a lot of surfaces. Again we built it with OpenAI but Microsoft Copilot uses it. Meta's in ad shopping experience uses it and how it works is, is basically the merchant only has to integrate once with stripe for their product catalogs, their prices, their checkout flows and then they can literally from the dashboard turn themselves on through a whole host of agents. And be exposed through those, through those shopping experiences. Importantly, the merchant remains the merchant of record and that part really matters. Like businesses want access to these new storefronts, these new channels, but they don't want to give up the customer relationship. They don't want to give up control over trust or fraud. So kind of category one is the human is still sort of leading the buying, but the agent is like facilitating the transaction. You could call it agent of commerce, you could call it facilitated commerce.

B (45:30)

And how does that actually work? So is the experience something like I'm in charge of and it says hey, here's like a thing you might want to buy and I can Click checkout from OpenAI and that's using that protocol to then go send my information to the merchant and then send me back, hey, like here, here's your thing's on the way. That's kind of, that's what you're talking about.

A (45:51)

Exactly. So it's like one click checkout. Yeah, same thing like you're in Facebook, you get an ad in meta, let's say you do like a one click checkout. And you know, one of the primitives that we built for this is the shared payment token or spt and it's basically just, it just lets your payment credentials be passed securely from the AI agent to the merchant. So the merchant can process the transact and the merchant processing the transaction is important because that allows the merchant to remain the merchant of record. But you know, you don't want your credentials viewed by the agent, which is, you know why? It's why it's a token and not your actual payment credentials. And the merchant needs to know that you and the agent are good. Which is why as part of the shared payment token we pass over a whole host of, of fraud scores.

B (46:42)

And can I integrate this? Like so we have a cli, we have a bunch of software. Can I offer agentic checkout easily or does it have to go through the open eyes and the Facebooks of the world?

A (46:56)

So yes you can. And I think one of the, one of the premises here is you know, just like to date we haven't seen one model provider to rule them all or one model to rule them all. We don't think there's going to be one agentic shopping experience to rule them all. And merchants will literally break if they have to integrate with every single potential new storefront. Right. When they integrated with the Internet they built their own storefront and yeah, they iterated on it, but basically they built it once. But if you Tell them, hey, you need to build your storefront for, you know, agent startup, shopping startup x and FIA and OpenAI and Meta. Like their eyes are going to get, you know, bigger than their heads and they're not going to be able to handle it. And so we really want to abstract away that complexity for businesses. Like we spent the last decade plus helping businesses sell wherever their customers are. And first that was like on their websites and then it was in apps and then it was through platforms and marketplaces and actually some in person too with our terminal product. But now like, you know, where are the consumers? Where are they wanting to buy? Increasingly through sort of AI tools and agentic flows. And so similarly, we just want to make it really easy for merchants to agnostically participate in those, in those different storefronts. And again, where they want to sell, they can turn it on, little toggle in the dashboard. But it's not a different integration which is, which is the whole idea of the protocol.

B (48:21)

And then how often is this happening? Like what, what's the volume of agent. Com. Agent. Agent commerce.

A (48:28)

Right now the volume of consumer commerce is still relatively small, relatively small, relatively small as a percentage of all of the commerce we see. But it is growing quickly, particularly for, you know, what I would think of as commodities, right? So what is the first thing that people are comfortable buying through agents? It's like things that are reasonably known, reasonably observable, not super high priced. You know, I think when, when people started buying online, like you didn't imagine that they were going to go online and buy like a $2,000 couch, right? Oh, wasn't a 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, it's, it's predominantly commodities, you know, in a similar vein, I've tried, you know, to book a whole summer trip using, 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, 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, we're hyper focused for merchants.

B (50:12)

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.

A (50:19)

An example of a commodity would be like a Halloween costume.

B (50:23)

Got it. Agents are buying Halloween costumes for themselves.

A (50:27)

Agents are buying Halloween costumes. That's 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's a little different, right? Like, even if I'm a lazy parent, I'm not so lazy that 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?

B (51:05)

Yeah, I've used Link.

A (51:06)

Okay, amazing. So Link is our consumer wallet. What do you use it for? Do you remember the first thing you use?

B (51:11)

I mean, I use it all the time. It's like everywhere. So amazing.

A (51:14)

Yeah, it's everywhere, right? It's, 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, 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. Link is here. Anyway, a lot of people know about Lingq as our consumer wallet for buying soccer classes. It 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. 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. For delegated. For delegated purchases.

B (52:56)

That makes sense. Emily, this was a fantastic conversation. I learned so much.

A (53:01)

Awesome. Thank you for having me.

C (53:10)

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