The Agents Economy Backbone - with Emily Glassberg Sands, Head of Data & AI at Stripe

25:56I've tried, it just recommends things, but I always want to take over the last mile of checking it out myself, you know? Yeah, yeah, yeah, yeah. It's hard for me to like hand over control. Okay, yeah, and I think they're also still like iterating on their recommendations to some extent. Last night, my daughter was at a class and so I took my son out to dinner and he told me he has a school play and he is supposed to dress as a Spanish shopkeeper. And so I tried to search for like kids' Spanish shopkeeper outfit and they recommended me a$1 ,300, like$1 ,300 bolero off Etsy. The play is not that important.

26:31I love the child, but the play is not that important. But there is a lot of great stuff you can buy. And so in the initial Instant Checkout Lodge with ChatGPT, you could buy from US-based Etsy sellers. There's over 1 million Shopify merchants coming soon, including some really big ones like Glossier and Viore. This week, Salesforce announced that they're also in. And then my favorite is that a week ago, two weeks ago, you could have asked, hey, are the largest retailers going to get on board with this or not? In the last couple of days, Walmart and Sam's Club have just signed up to also make their inventory purchasable through ChatGPT and the Agentic Commerce Protocol, which I don't think that there is a bigger signal on a big retailer being up for it than Walmart.

27:27The world's biggest one. Yeah, yeah, yeah. So that's pretty exciting. And then one of the things that is important to us and to the broader ecosystem about the Agentic Commerce Protocol is it's not about Stripe. So that shared payment token I talked about or the Agentec Commerce Protocol, that works no matter who your payments provider is. We can pass the shared payments token over to any other PSP. You don't have to process on Stripe. It's also not just about OpenAI. And so in the same way that you and I are seeing new models come online all the time, we want to be able to move across models flexibly, there's going to be sort of new Agentec buying experiences coming online all the time.

28:08And we want to make it easy for merchants in kind of like one shot to integrate with all of the agents as they come online. And that's what the ACP really provides because it's a standard protocol versus needing to do custom integrations per agent. Did you guys see Karpathy's tweet about the, he basically like recreated. NanoChat? Okay. Yeah. So like if he can do that, 8 ,000 lines of code, less than$100, like you might think that a lot of companies are going to roll their own really soon. Using NanoChat? That would be interesting. I haven't made that connection yet. Let's see. Let's see. But I think this basic premise that it may be a winner-take-all, but it's not yet clear who the winner is.

28:48And by the way, I hope for the efficiency of commerce that it isn't a winner-take-all. Therefore, many merchants need to actually be having their products sold through many agents is kind of the premise of ACP. And we're just delighted to see the early traction and, you know, have just been flooded, honestly, with both merchants and AI platforms wanting to join. So I think we're on the right path. Yeah. And this is the Europe protocols kind of. Yeah. We did an episode with Crunch AI, which does web rewriting for agents, and they have him as a customer, they have skims. And I think every brand is like, I mean, for brands it's very easy.

29:29It's like, hey, I don't really care where it comes from. If you buy my thing, we're friends, you know? And I think like you take away the part which is like the most annoying to think about, you know, which is like the fraud. I do have a question on like the good bots, bad bots when it comes to like scarce releases, like tickets for events and things like that. I think that's gonna be interesting. Oh my God. 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.

29:59I 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 3 ,000 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 a great example of what we were talking about earlier. It's not just about the fraudulent dispute. So the conversation I was having, he was like, we have scalpers, scalpers who have bots. They end up scalping the tickets later.

30:37They come and they buy. It doesn't result in a chargeback. They pay, but they're not the people that we want paying. And so to our conversation earlier on suspicious transactions, 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. The other thing he said to me, which was interesting, is he, for various reasons, and some of this is actually like the nuances of how his system is built, but I think some of it generalizes, he wants to have those fraud signals before they even get to the checkout page.

31:17And so how can we understand the customer independent of them entering their payments credentials? And there are a bunch of ways we can, and we can get better there. His particular reason why is he's got, this is a little mundane, but once the ticket goes into the cart, it can't be touched for like 10 or 15 minutes. And so even though it's successfully blocked at the actual charge time, it's been held from those other good customers. So anyway, I think there's a lot of exciting work to be done that's actually increasingly possible, not just because of the scale of the Stripe Network, but also because of AI around understanding an expansive set of fraud vectors.

31:59And, you know, if you think about traditional deterministic systems, you know, you'd write rules like block, don't block. Now you can think about, okay, actually, foundation model, text alignment, like human readable description of like why we're worried about this charge. And then today, a human, tomorrow, an agent sitting on top of that and decisioning, like reasoning over the model outputs. I think that's the world we'll be in in the next three to six months. Yeah, I think we need to, we have to be careful about rolling those kinds of things out because people get very upset and justifiably so when they're denied something that they should have by a bot that won't explain itself.

32:41Yes. Right. There needs to be like an appeals process or like something like tier two human. For sure. Can I speak to the manager, please? For sure. And, yes, for sure. And, well, humans make bad calls too. Sometimes they make bad calls at higher rates than LLMs because they can't reason over as much information. But I agree, definitely appeals process. But then also, like, when we actually look at bad actors, it's like a tiny, tiny share of bad actors accounts for a huge volume of the bad outcomes. And so what ends up happening is actually good actors have worse experiences, which could mean they don't have access to free trials or they're gated and how many credits they can use prepayments or they're just charged a higher price because they're covering for the cost of the bad guys.

33:32and I actually think there's an opportunity to like, you know, I'm from Montana so sometimes I talk about sheep and goats, although I don't actually know which is good and which is bad. Separate the sheep from the goats in a way that's like really good for the good actors. Why are sheep and goats bad? No, only one's bad and one's good, but I don't know which is which. Why can't they both be good? That was like an idiomatic expression everyone used growing up to separate the sheep from the goats and I'm like, I don't know which one's the sheep from the goats. I think sheep are supposed to be cute, but I've always been impressed.

33:57Have you guys been to Yellowstone? Yeah. You go into Yellowstone National Park at like the mammoth entrance and there's this like sheer giant you know wall of rock and there's these mountain goats like exactly they're like scaling it at 90 degrees yeah like mountains i get but like dams i don't know why they love dams so much it's like it looks so dangerous and like there's babies just walking on there so dangerous yeah it almost looks like ai generated the image it's real it's real uh and okay i'm gonna do like economist corner just because like you know you You clearly still identify as an economist.

34:29I do identify as an economist. It's very strange. Yeah. Yeah. Well, so like when you encounter those like the StubHub for X, don't you feel the temptation to recommend an auction? 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? 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 whether there's a more efficient path. But yeah, I feel that way about most pricing, matching, discovery, recommendations.

35:12Like most markets are just inefficient. And so I think there's a lot of opportunity to make it better. 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, 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 run their payments through us. Um, and so we do this all the time.

35:49Like, 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 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 run on you do better. Um, and that in and of itself is, is a good outcome for your company and justifies the investment. Yeah, they're very incentivized. It's like top line and bottom line sometimes. Yeah.

36:18And by the way, like this isn't all causal, but last year, the companies on Stripe grew seven times faster than the S &P 500. And, you know, again, like not all causal and there's selection effects, but definitely some of the... It's just for sizing effects. Some good, some good tailwinds. Yeah. Yeah, so I mean, yeah, and I think the economist term I really like is deadweight lost. And like basically eliminating friction, it means improving surplus for both producer and consumer and Stripe benefits as a result. It's just like, it's nice. Everyone wins. It's a good fuzzy feeling. Coming back to the protocol, I think it's an interesting decision to actually release it as a protocol.

37:00Like you said, it's going to be many to many and like sometimes Stripe is not involved. You also mentioned like Stripe Link and Stripe Checkout and those are Stripe products, right? Those are not protocols. So I think it's a very interesting and pivotal decision to choose to release it as a protocol. As opposed to not, I was wondering if there's any internal debate or is there any internal color about the decision behind choosing a protocol? We didn't debate it. Stripe has moved fast for the entire four years that I have been there. I think it is accelerating. And I think it is accelerating because customers, users, changes in what the market needs, both what businesses need and what buyers need in the world of AI is accelerating.

37:40And so new products, new solutions like ACP, like token billing are literally being pulled out of us. And what we saw was a hole in the market. Like consumers, we can talk about developers too, but like consumers want to be buying through agents. Agents are ready to buy for them. merchants are ready to let agents buy on behalf of consumers. And yet, the market can't figure out how to make it work. And that's not about Stripe. That's about growing the GDP of the internet. That's about making sure commerce can flow. And, you know, so there was no debate. It's like, the ecosystem needs this. Like, we will pair with OpenAI to get it over the wall.

38:21And by the way, like, as I said, we're early days. You know, ACP is seeing a ton of traction. But if it wasn't, or if something different comes out six months from now, all we want is a shared standard. We don't need to have our name on the shared standard. I think it's absolutely the right thing to be doing. I have had folks ask me, is agentic commerce just a straight substitute for the commerce that's already happening on the internet? Yeah, it's just like fancy APIs. Exactly. And I think the answer is, it's not a straight substitute. I think it is actually like expanding the aperture of what commerce will get done.

38:57And the first person to put this bug in my head was Dwarkesh. And when he said it, it actually like took me a second. Like I didn't believe it. But I think it's right, which is if you look at the share of income that is spent on consumption as a function of how much income you have, you see that high-income people spend a much lower share of their income, low-income people spend a much higher share of their income. There are many reasons for that. But one reason is the biggest cost to very high-income people consuming is not the dollar cost, it's the time cost of consumption. And so I'm very interested in how agentic commerce can open the aperture for spending by high-income people because it's removing the most costly or binding constraint, which was their time.

39:47And we're actually then truly pumping incremental, not substituted, but additive dollars into the economy. And obviously, like, you know, first, second, third order effects of that. Yeah, well, it results in sometimes buying$1 ,300 costumes. I didn't buy that$1 ,500 costume. Trust me. I would rather spend an hour than buy a$1 ,300 costume. Well, you know, just work a few more years this trip, you'll get it. But so I think one, like there's some, I think, interesting, I love protocols, you know, as a developer tools person, I've been involved in designing a few of them, debating a few of them. What are the forks in the road that, you know, like someone else was like discussing something really strongly and we decided against it or maybe it's still an open question.

40:31I'll give you one and then maybe you can volunteer another. So I mentioned that both Solana and Circle have sponsored my conferences before, and they're also trying to build a protocol for agents. And both of them actually give agents a wallet, right, as opposed to a payment token. And I think having an agent with their own bank account effectively is an interesting choice. You didn't go for that. So is that a decision factor or is there a different one that you want to focus on? Let me parse two things. When I think of the commerce protocol, that's primarily around what's the standardized way that businesses expose their products and their inventory and their prices and their brands and make those available to agents to expose to the consumers and or to buy on behalf of the consumers.

41:21That really comes down to like, how should a product catalog be expressed? How should prices be expressed? I think the current version is like the bare bones version and it will continue to evolve. For example, like you could imagine like from a market clearing perspective that the merchant should also be as part of that articulating the cut that they're willing to give to the agent. Right. Like like a little bit. Yeah. In the classic, like if the human agent, I give them a budget, like what their negotiation target is and what their max spend could be. Yeah, exactly. And so in this case, I'd be like, okay, well, whatever, the Bolero that I didn't buy is a terrible recommendation.

42:03But they have many good recommendations, but that was a terrible one. This costs$1 ,300 on Etsy, but I'm willing to give X to any agent who facilitates the transaction, either on top of or underneath the$1 ,300. So anyway, I think there's going to be an evolution of the various parameters that should be included. But the basic set was, what do you have to be able to deterministically expose to an agent so that they understand what's available? and what representation of the product and brand and, you know, sometimes its size and number and whatever, like, has to be made available to the agent and or the human that's initiating in the first place.

42:52The shared payment token is a little bit different, which is like, okay, how do you actually get the transaction done? Like, how does the money flow? And even at Stripe, Like, that has been evolving. So shared payment token is what we built and launched and have in the background of the instant checkout implementation with ChatGPT. But a year ago, Perplexity launched a travel search and booking agent. Did you guys see this? That is also powered by Stripe. And there, the payment flows are a little bit different. So we have an issuing product, which allows you to issue virtual cards. And what happens in that sort of flow is the agent gets issued a one-time use virtual card to spend on your behalf.

43:40And, you know, people get very jumpy about that, but I like to remind them that like when I order from DoorDash my Phil's coffee, right, DoorDash is issuing a one-time use virtual card to the driver for$6, believe it or not, to spend on my behalf there. And so now you're just inserting, you know, AI agent instead of human agent. And in the same way that my DoorDash driver never saw my card credentials and couldn't spend, you know, more than$6 and had to spend it in a constrained time window near my house, same thing for the AI agent in the perplexity travel search and booking agent. And we still have agents doing commerce through Stripe using the virtual card implementation and their pros and cons.

44:26So I don't think that it's going to be all virtual cards, it's going to be all shared payment tokens, it's going to be all agent wallets. I think stable coins will be an interesting direction. I think wallets in general will be an interesting direction. I think stored balances will be interesting, especially as we're talking about kind of microtransactions, right? So we're talking a lot about buying goods. Buying goods are usually priced high enough that it's worth sort of like a card transaction type approach. But if you're talking about buying AI or buying some inference or buying content, you want to be able to make 5, 10, 25, 50 cent transactions.

45:03And those are hyper inefficient in the card world. So I think agent to agent payments are going to push us a little bit to the next frontier here. But ACP, again, is distinct from how the shared payment token works or how the money flows. And I think that will, A, continue to evolve just because the market needs are evolving and the technology possibilities are evolving. But B, also doesn't need to be standardized in the same way. What about the receive side? I guess, can my agent make money for me? Can your agent make money for you? Oh, I was thinking the opposite, which is like I'd be happy to give an agent, you know,$3 to go out and do deep research for me.

45:41And so we're trying to figure out how to enable that. Oh, no, that's research. No, I was like, you know, like spending, I think like we have a good mental model of how to spend, especially because we have human agents as well. But you guys can spend making money is obviously the original draw of a strive for any founder. Yeah. Yeah. I'm just kind of curious what you're thinking there. Well, so we make it easy to monetize your MCP server. So that's like one thing. We also are seeing an increasing number of new businesses get going in AI dev tools like Replit and Vercel. And so we want to make it really easy to spin up monetization also within those tools and within that flow, not be taken out of flow and go and create a Stripe account, authenticate, whatever.

46:23A couple of weeks ago, we released claimable sandboxes. Have you guys seen it? We've been in Vercel and seen it or anything? I know that you have a sandbox product, but I don't know about the same. So we have a sandbox product, which you've seen from like being within the developer experience on Stripe. Now that sandbox product can be like invoked, used, manipulated. You can create your products and your pricing and run test charges and generate customers. And in that sandbox before you even have a Stripe account, or maybe you have a Stripe account from your last business, but before you've linked it to this business.

46:56Right. And we call them claimable sandboxes. And Vercel and Replit were, I actually remember talking to Replit at Stripe Sessions in May about our sandbox product. And they were explaining to me how, you know, people are trying to build businesses end to end. And like one of the wonky parts of the flow is setting up the payments integration and get going. And so we started talking about like, okay, could you actually have like the sandbox environment there? And it was whatever, four months later and they had it. They launched it. We launched it with them and also with Vercel. Actually, Guillermo had a cool post a couple days ago that our Stripe integration is now one of their top.

47:40I think it's like the third highest integration that they're seeing in V0. And we're only two weeks into the launch. But it's basically like all of these people aren't going to create VZero just for fun, to create something, just a website or whatever, just for fun. They're going to create a business. And so making it really easy for them to do the business payments back end part of that. And how it actually works is literally like you're right there in VZero and you, you know, you have your plant shop and you create your products and you set your prices and you run your test charges and you click a button at the end.

48:15When you like what you see, you can go back and iterate later. But you click a button, and your tab opens in Stripe, and you can either sign into the account you have or create a new account, and you claim that sandbox. And that sandbox, you can take it live, and it becomes your business. And lots of people are taking it live every single day, and we're seeing new businesses get created that were never before. And one of the things that's really fun to see, I was going through the list of businesses. I probably can't name them live, but a chunk of them are AI companies. A chunk of the startups being created today are AI companies.

48:44But a chunk of them are like non-technical founders who may have actually struggled to like get going on Stripe had they not had it kind of within that V0 experience. Yeah, low-code is a huge enabler. Low-code is a huge enabler. And, you know, we've done some good work in our own onboarding experience to make it low-code and we have, you know, low-code subscription and whatever. We have our new onboarding experience. Internally, we call it future onboarding experience. And it kind of walks you through what's the business model you're trying to create and then sort of stands up the sandboxes for you.

49:16But what's cool is now you do a bunch of that in, say, V0, if you're in V0, and then you come over and your future onboarding experience has already learned your intent and your preferences and all of that from Vercel. And so you're dropped in X percent of the way through with the sandbox already spun up. 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 Strap today? Yeah. First internal use cases were, you know, bottoms up experimentation, right?

49:51So we created, we call it GoLLM, but it's like just a chat GPT like interface where you can engage with a bunch of different models. 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 template. Oh, you know, this is how I figure out what customers to reach out to and generate reach outs or, you know, rewrite my marketing content in Stripe Tone or whatever. And you had sort of hundreds of presets that came on like overnight because everyone was into it.

50:28And 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 on to 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? And there's a lot that LLMs can do there. So those were some of our earlier use cases. Fast forward to today. I mean, you know, I actually was looking at the dashboard earlier because I was planning for 2026 and some of our LLM costs, 8 ,500 stripes a day use our LLM-based tools.

51:10Okay, there's like only 10 ,000 stripes. Like not everyone is in every day. It's basically everyone. And I think people are getting pretty creative in the applications. I was talking last week to the LPM team, so local payment methods. You and I think a lot about cards or whatever, But local payment methods matter because the businesses on Stripe are almost always selling internationally. And when you're in other countries, having local payment methods, you know, JiroPay if you're in Germany. Yeah, I'm from Southeast Asia. Yeah, it's all over. Wait, what's your favorite? Well, I know. I mean, there's like GrabPay, I guess.

51:48I don't know. Yeah, exactly. And like if you don't see a local payment method, like if you only see a card, you might not have cards. Or if you only see cards, you might not feel like it's like localized to you or meant for you. Whereas if you see like in-market regional payment methods, you feel much more connected. You're much more likely to convert statistically. And oftentimes the fees also make more sense for the merchant. So we've invested a bunch in integrating local payment methods. Most businesses on Stripe use our Optimize Checkout Suite. Our Optimize Checkout Suite comes with over 100 payment methods out of the box.

52:18But one of the most requested features we get is payment method X, payment method Y, payment method Z because I also want to be in country HK. And so, you know, what is the challenge? The challenge is integrating with any new payment method. And it takes like two months for a couple engineers. It's not the end of the world, but Stripe's a pretty lean company and we got a lot of stuff to do. And so, you know, for the marginal payment method, is it worth it? Yes or no? Well, when you step back, what are you doing? You're really like looking at Stripe's code base. You're looking at like how the LPM works and like the integration guide for the LPM.

52:52And you're kind of like hooking the two up. And so the LPM team, it took them two weeks for the first one, but they just launched a new pan-European payment method in two weeks using an LLM to like build that integration. And I think they'll probably, you know, have it down to a day or two within a month. And so that's just a good example of like, it's kind of should be just like a machine talking to a machine and there's pretty good documentation on both sides of the house. And so the LLM can make it pretty far. We also use a lot of AI coding assistants. And I would say like 65, 70 % of engineers use them on the day-to-day.

53:32I have a really hard time understanding impact. I don't quite know what statistic to look at. I don't actually... It's not lines of code. It's number of PRs. I don't think it's lines of code because I have in the last week been sent three different documents that I know were like at least partially written by an LLM. Documents, not code. And in all cases, I went back to the individual and I said like, I actually just want to see the bullets that you put in ChatGPT or whatever, not the eight-page document because I have a really hard time reasoning about the eight-page document. And it sounds good, but I'm not quite sure it's like connected to reality.

54:11And I feel the same way about lines of code. Like I don't really want more lines of code, just like I don't want more pages of docs. So we're watching that. And then also the cost of a lot of these coding models is actually like pretty non-trivial. And so as we're planning forward to next year, we're reasoning a bunch about like, where can we get somewhat more efficiency there given like, obviously it's valuable and we want people to be using AI coding tools for sure. But and we want to make sure that we're getting the right returns for the business. And some of that is managing costs and some of that is getting a clearer read on impact and value.

54:46How do you feel the social contract is changing? Like you mentioned, just send me the bullet points, right? It's like, I could have sent you the bullet points before LLMs, but you were making me write this memo, right? I feel like in a lot of organizations, there's like a performative. And part of it is like, you know, wearing a suit to an important meeting. It's like, in a way, it's like, hey, I'm doing it. I don't wear suits. I hear some people do. You know, I'm doing it to show you respect. And in the same way, I could have just sent you this bullet point. So we could have had this meeting in shorts.

55:12Do you feel like with AI now, it's like, okay, if you're going to do it with AI, I just sent me the bullet points and we're kind of like breaking through in a way. Yeah. So that's really interesting. Okay. So I hadn't thought about this before, but here's my working hypothesis. Tell me if it checks. What I care about is that the expert in the area, like they're an expert in the area, otherwise they wouldn't be sending me a doc, right? The expert in the area has thought deeply. And what is writing like actual writing, typing, whatever, it doesn't matter, but like writing, not with an LLM forced you to do, It forces you to think deeply.

55:44It forces you to structure your reasoning. I don't know about you guys, but when I read a doc, when I write a doc, I've read the doc like 50 times and thought about, does this logic track? Are there gotchas I'm not considering? Is that the right train of thought? How might somebody else look at this? And it's not like that I wrote the doc to be performative and the bullets would have been better. It's that the careful, thoughtful, arduous, time-consuming construction of the doc forced me to appropriately reason from first principles. And I think LLMs do the opposite. Like, oh, you just throw in the bullets.

56:22You don't have to reason from first principles. And it sounds good. And people like, but I think that's extremely dangerous. I think that is true. But I feel like you still are not generating documents with AI. So because you are the type of person that uses the writing as the thinking, you still go through the process versus the people that use the AI still wouldn't have put that much thought into writing the long document anyway. I think to me, that's really the thing. Same way we were talking about this for code yesterday in another interview, which is like the slot machine effect of like cursor and like these tools.

56:53But at the end of the day, you got to merge the PR. So you got to come up with something that makes sense for the business. Like with these documents, it's kind of the same, right? It doesn't like you just need to come up with the right ACP design. I don't care if it's 10 bullet points or like 10 pages. Yes. To me, I think like things are changing now because also people read more summaries. So it's like, well, if you summarize my thing, then why should I write a long thing? I should just write a short one. So, I mean, I don't have an answer. Just like interesting to see how, you know, you're like, just send me the bullet points.

57:22Yeah. The primary thing that I, well, there's many things I care about, but like a very concrete non-negotiable is if an LLM was used in the generation of this content, please cite the LLM. Because my least favorite thing is to be like two pages into what I think is a thoughtful doc and find the annoying space, double dash, space and then... As a double dash guy, I feel like I was writing it before LLMs ruined it. No, that's not what tips me out. That's the only thing that tips me out. But you know what I mean? I do think we all need to be careful about LLM slop. And then I think there's like a societal behavioral thing here too, which is like, we can't turn our brains off.

58:05I mean, I actually think that like, what do LLMs make all the more important in the world? The ability to think and reason deeply. question to like tell the machine what to do, more so than like to do and execute the task. And so if you're looking at LLMs and you're like, oh, that means I don't have to think deeply because they're just going to do it for me, which is very natural because they do produce enticing output. I just think it's like, I think it's like risky for society. I mean, I think we've seen how like people who grew up on social media have like a lot of issues with attention. I think people who, I don't mean attention trying to get attention.

58:45I mean attention like staying focused on a task. I think in the same way, people who overgrow up in their work life on LLMs risk underinvesting in depth. And I think that's particularly dangerous in a world where with LLMs, actually, it may not appear this way in the moment, but like depth is the most important thing. I would push back a little bit in terms of, I think I'm maybe a bit more slot friendly than you guys. Are you? Just because like slot comes from humans and slot comes from AI. What just matters is when you sign off, when I send you the document or when I send the PR, I am signing off on the whole thing.

59:21I can't abdicate responsibility to the LLM. Maybe LLM had good output. Maybe not. But I'm the final judge. I'm the editor. So I actually think we're on the same page there. So I am all for using it in the generative process. Actually, it was really cool. It's a tool for thought. It's a tool for thought. And it's a tool for rapid experimentation and rapid iteration. And I love to look at like a demo or a prototype that like, I don't want to see a doc on it. I want to see like the quick thing that you spun in whatever tool you use. Actually, when we were working on claimable sandboxes, I'll never forget, Vercel sent us basically like the V0 of like how they thought it should be implemented.

1:00:03Just like the UI, like this is what we think the experience should look like. And it was extremely clarifying, like much more clarifying than hours of meetings and pages of design docs. And so all day long on the generative, but like you need to like deeply put your stamp of approval before you push the PR before you publish. I did want to double click a little bit on both, you know, like two primary use cases on RAG and writing code. Just on, just I guess on like internal information, there is obviously Glean, which we talked about yesterday and just all the other internal code search tools. You guys use Notion as well.

1:00:37Is RAG still relevant? Is that something that's inactive development? Or what's beyond that? What's the frontier? So we've actually been leaning in really hard on, we call it tool shed, but it's like an internal MCP server that basically has access to all the Stripe tools. And what I like about that is, and it's managed centrally. It's managed centrally and it plugs into, we've since killed that GoLLM thing I talked about. and we did a new implementation, like open source LibraChat situation, which is great. But it hooks up to all those same tool shed, like MCP servers. And it's got, I mean, everything you would think, like Slack and Google Drive and Git, but also access to Hubble so it can see our data catalog and all the data and it can query the data and, and, and.

1:01:26And I think that's been really powerful. I don't think RAG is dead. I do think there's an important name of the game around, it's not just like the information that's available in all those tools. It's also being able to interact with all those tools, right? It's like the tool calling. And so I think they coexist and I think they coexist together. Also, while Toolshed is owned centrally, anybody can, you know, the Salesforce team can add Salesforce and, and, and, because we don't want to be blocked on some central team in order to have those tools exposed to the LLMs and to the agents for Stripes.

1:02:04Yeah, you want to decentralize a bit. And then code-wise, you know, coming on the code side, obviously closer to home for me, I think it's also like an economist problem of measuring productivity. It is, okay. Well, now you're just making me feel guilty for not having cracked it. No, no, I mean, it's unsolved globally. I'm kidding, I'm kidding. Yeah, it's hard, it's hard. Yeah, and that's the thing. Like, you're looking at the cost and you're like, oh, it's pretty high. I don't know. Maybe we'd like move to open source models or something. But like you don't know the productivity gains you're getting in interim and you have pretty expensive engineers.

1:02:35Like it's hard to tell. Engineers are expensive. Engineers also, like all of us, right, are hyper motivated to do the best work of their lives. And so there's an important component of like if the people want it, like there's inherent value in providing it, right? Like when people have the tools that they want or learning the things that they want, like they work harder, they're more creative, they produce better output. So anyway, there's all this like sort of soft, fringy stuff that I think is added value above and beyond the actual, you know, production output. And then there's also like the learning curve, right?

1:03:09So, I mean, we think about this a lot, like when you launch a new traditional ML model or now like AI solution, right? Like, if you over-focus on the results in the immediate short-term window, you really risk getting a false negative. Like, it's not good enough yet. It's not tuned yet. It doesn't have the feedback loop yet. You know, it hasn't had time to get the training data to get better. And I think that's, like, kind of particularly true in, you know, AI because when you're working with LLMs, it's like, well, with, like, GPT-4, like, it didn't work. and then you swap in GPT-5 and all of a sudden it does.

1:03:46Or we use like a GPT-4-0 and it was like kind of a little bit expensive to justify the humans that it was replacing for a particular risk-related task. But then next thing we know, like O3 Mini is out and it's like, you know, $3 million a year savings for the business, both because the model is less expensive, but also more importantly, because it replaces more of the humans. And so I do think that like, when I think about the optimal adoption of these AI tools, it seems wrong to focus on in-year ROI. And it seems right to focus on two-year, three-year ROI. Now, inherently hard to know what two or three years is going to look like.

1:04:30But if we look at the history, models getting much better, much more quickly, models getting quite a bit cheaper quite quickly, it overall makes me bullish that we shouldn't over obsess around around in-year returns yeah in-year returns that's that's a good term i never thought about especially when they're amortizing it like that yeah um what about data what about this i was gonna move to the data side yeah you know it's like oh are the engineers more productive or not like uh what about yeah like i mean text is equal right it's kind of like the first iteration of this like what's the productivity like on like, I mean, you can generate any chart now, right?

1:05:10But like, doesn't mean it's good. Yeah. Yeah. So we have this, he's not really a guy, he's an AI, but his name's Hubert, which sounds like a guy. So we have this guy called Hubert, which basically is like natural language to ask questions about the business. By the way, we have a Sigma assistant. So like our users can query stuff about their business on top of Stripe data. That's a much more constrained problem because your Stripe data is like your revenue data. It's like very well-structured. It's very well-documented. It's available in the dashboard and in Sigma and in Stripe data pipeline. You can hook it up to whatever.

1:05:42And so, you know, a text to SQL experience sitting on top of that, like, it's not going to be perfect, but like, it's pretty airtight. And by the way, like, if you use natural language to describe what you want, we write the query and then we also tell you in natural language what the query does. So even if you're non-technical, you can validate it. Okay. Now imagine there's a lot of tables at Stripe. There's a lot of nuance in Stripe data. There's a lot of nuance in Stripe's business model. Hubert is the guy that sits on top of this Hubble tool, which you use to find, explore, and query Stripe data, that does that internally.

1:06:15And it's early. I mean, we have like 900 people who use it a week. We have tried to focus the people who use it mostly on technical folks who know the domain for exactly the reason you were citing earlier, which is it could get the answer fundamentally wrong. And technical folks are going to be better positioned to validate and provide feedback. One of the most interesting things to me as I was going through the Hubert evals was the place where Hubert did the worst was around data discovery. That is to say, it had a hard time finding which table and which field was best to answer the question at hand.

1:06:52And, I mean, personally, as a user, when I know the table, I actually now just articulate the table in the garage chat interface. But more importantly, we're doing a big push right now to deprecate low-quality tables and have the owners document high-quality tables. I haven't yet figured out if I trust an LLM to do the documentation. So for, like, the canonical data sets, we're kind of brute forcing it with humans who know the domain. The other thing that we are exploring but we haven't landed yet is offline it looks like there's actually really – like, Hubert does much better if you tell it where in the organization I sit.

1:07:27because it knows, oh, I'm interested in LPM data, or I'm interested in link data, or I'm interested in OCS data. People who work on the optimized checkout suite tend to query these tables, look at these fields, ask these kinds of questions, look at these types of metrics. Now that's not in production, but I think there's this interesting question of like, we can have humans do some like prompt engineering or documentation or whatever, but we can also give the LLM more just historic context about what people like me liked to do, basically. So that's the next step there. I'm bullish on it. I think, I don't know if it's going to be two months or two quarters before everyone's on it.

1:08:06I think it might take some time to get high enough conviction that we're not going to have an important wrong answer. Text-to-SQL is really easy, though, with really well-structured, well-documented data. It's just most data is not well documented and well structured. Well, so immediately before this, I was actually in the data engineering industry. We talked about DBT 5Tran and you said you didn't have an opinion. But I always thought that data discovery is the important victory or the ultimate victory of data catalog people and semantic layer people. Do you agree with those movements in the data world?

1:08:39Do you have any tweaks on the modern data stack that you have? So we are increasingly moving to like semantic events infrastructure. I think the value of near real time, high quality, well documented data is about to skyrocket because I'm pretty sure that nine months from now, no one is going to want to go and like look at a even like static dashboard and click around. they're going to want to be fed insights or they're going to want their agents to be fed insights and they're going to want to be able to just like pull real-time high-quality data. For us, what that looks like, the two most important domains for our users in that regard are payments and this like usage-based billing, which needs to be very, very real-time for all sort of like the AI business model stuff we talked about.

1:09:34And so there, our path is like semantic events, canonical datasets, available in near real time in dashboard, yes, because some people will still use dashboard, in Sigma, so like queryable, but also in sort of a Stripe data pipeline type, right? Because very few people want to look at Stripe data in isolation. They want to look at Stripe data connected to the other stuff, right? So, I mean, you could just even imagine like pulling into BigQuery or whatever. They want to see it connected to other stuff. And historically, honestly, it wasn't all the same data feed for all of those products. And that also creates confusion.

1:10:14So we're doing a bit of a re-architecture for that flow starting in the next six months, which is billing and payments. And then I think we'll expand from there. There's always going to be a bunch of data that for whatever reason doesn't fit your, I like to call it a North Star architecture, but like your North Star data architecture, right? And I wish that someone could figure out how to make sense of the old bad data. So you don't have to like re-architect everything and throw away the old, right? You'll always have like, okay, there's like the Stripe.com website, which happens before you even create an account.

1:10:52It's a very different type of data, but like, you know, how should I reason about that? How should I manage that? And I think traditional enterprises deal with this a lot. Like converting website analytics to signed up users and all that? Yeah. Oh, I've thought about that so much. Oh, yeah? You just need like, it's kind of fingerprinting, which is like something that people are kind of uncomfortable with, but you can, you can do it. Yeah, yeah, yeah. We have like a couple of questions on like, I think there's a build versus buy question on you're building a lot of internal tooling and that's great, but also there's a lot of great tooling out there.

1:11:26How do you navigate this? Obviously you have a lot of unique internal context, but you also have, you work with external vendors that 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. Yeah. I think for us, it's not an either or, it's very much an and when it comes to build versus by. And some of that and is sequential, right? So you and I were talking earlier about when GPT 3.5, I think, first hit the scene where everybody at Stripe needs to have access to LLMs, but we don't quite know how to do that in a way that's enterprise-grade safe and we feel good about.

1:12:06We don't see a provider there right now, and so we built it. But now we use open source, LibreChat. So I think there's an evolution over time. And one of the things that I think 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 Toolshed. Oh, we should figure out a way to make it do like deep research or, you know, like we could have done that.

1:12:36And 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 also making sure people feel a lot of confidence and conviction in their own abilities and the fact that there's a ton that they can contribute to the company across domains to kind of liberate them from needing to own this product. Another thing that we've done, and this is especially true in the AI space just because there's like so much new stuff coming online.

1:13:14We call it the spotlight program. But basically, we put out RFPs for products that we want to buy. So one example that we did recently, I guess it was like a year ago now, was evals. So we were like, okay, here's our problem with evals. Who's going to solve it? And we put out this RFP in the spotlight program. We obviously see a lot of these companies directly because they run on Stripe and or their investors have some affiliation with Stripe, and so we know them. So we actually had like more than two dozen applicants for this evals RFP. There's no way you can evaluate all of them. Well, we actually, we did.

1:13:47So they wrote like nice one pagers. We read them all. We narrowed it down to two finalists. Brain Trust ended up winning. We did a POC with them. They rock. We stuck with them. We also love like weights and biases, flight, Kubernetes, like sort of like the basic stack you can think of. But there have also been cases where we have had to build. So one example is, if you think about traditional ML for a second, also relevant in the world of our foundation models and that embeddings basically can become features, our feature engineering platform. So we had a homegrown feature engineering platform.

1:14:22It was old. It was on its last legs. We had a team internally that really wanted to adopt Tekton. We evaluated Tekton. This was a couple years ago now. Now, at the time, we couldn't wrap our heads around using it on the charge path, just from like a latency and reliability perspective. Like we've got to be operating at six nines. We've got to be like decisioning in, you know, tens of milliseconds for some of these models. Like we just we can't reason about that on the charge path. We ended up pairing up with Airbnb and building, we call it Shepard internally, but it's open source under the name Kronon.

1:15:01You know, I think there are cases for both. But we always start with like, what could we buy? Sometimes those are obvious solutions. Then we say, oh, there's no obvious buy solution, but like maybe there's some new startup thing. Let's run the spotlight program. And if we really come up dry, then we will build. And as we build, we reason about, okay, six months from now, 12 months from now, should we still be building or should we actually swap in a buy solution because the market has evolved? The other thing that we feel pretty strongly about in the world of AI is there will be many model providers, there will be many models, and we do not want to hitch our wagons to just one horse.

1:15:42You know, there's lots of enterprise grade versions of choose your LLM provider, like that's of much less interest to us than solutions that sit on top of many different providers and many different models and allow us to swap in and out. Yeah. And this is the Stripe Experiments team. When you decide something, we need to go, So you have the same people always do this? Or do you rebuild this team based on if it's evals or if it's like... It's probably a bottoms up, like whoever... It's bottoms up. So experimentation happens in a lot of different places. So like for the evals, we just did it like we ran it within ML Infra with like the new rebuild of GoChat.

1:16:19We paired up someone from EP with the people who had been owning GoLLM. You know, one of the things that's interesting about experimental projects is the goal is to learn quickly whether there's product market fit, whether that's with your internal users or your external users. But the goal is to basically get to escape velocity, like have a product that launches and goes live, whether that's like GoChat GA-ing and replacing GoLM, or whether that's like token billing, serving our users, or agentic commerce now being a thing. And what we found, and you know, this is, the experimental projects team has been around about a year and a half.

1:16:55And so these are relatively small samples we're talking about. But what we have found in that sort of ANIC data is what we call embedded projects, projects where you take a couple people from a product or infrastructure team and a couple people from the experimental projects and group them together are more likely to reach escape velocity. Token billing is a good example, right? Like, we need the billing team to take it forward. And if the billing team was like, core to it from the start, it's much easier for them to take it forward. Same thing, you know, If the ML Infra team deeply understands the new build and feels ownership over it, it'll be successful in the long run.

1:17:35Some people think of these teams as labs teams off in the side, off in a corner, operating totally in isolation. We do some of those. We're doing that for agent-to-agent payments because that just needs a big rev on what's the product shape, what's the technology shape. But wherever possible, we actually do it as a joint project, very deeply embedded with design partners, like with customers that want to do it with us, but also with other teams of Stripe. My typical line on just closing the loop on the build versus buy thing is usually buy then build. If you think about the sequencing, I think yours is much more nuanced in terms of like how close.

1:18:13Buy then build if a buy exists. Correct. If a buy doesn't exist, you might want to build, but pick up your head every quarter to make sure you can't buy. Yeah. And also, I think mostly because I see the opposite. A lot of people try to do the opposite of build, then buy, to reason things from first principles. But actually, the sheer amount of experimentation in the wider world means that a lot of people are being specialists in your thing, like evals, where you can just benefit from their experience instead of reinventing the wheel. So before Stripe, I was at Coursera. Have you guys ever looked at the EdTech platform?

1:18:48Okay, I was there for eight years. And I joined when we were less than 40 people. And it was a lot of absolutely brilliant folks from Andrew and Daphne's lab at Stanford who had never had a job before. And by the way, I do not count myself in the absolutely brilliant folks from Andrew and Daphne's lab. I was on the East Coast and not absolutely brilliant, but I had also never had a job before. And a bunch of us never had a job before, but really hardworking, determined people built a bunch of stuff, homegrown, that we shouldn't have, right? We had our own experimentation platform. We had our own analytics platform.

1:19:20There's learning value. We had our own machine learning platform. We learned a lot. We learned a lot. But like, what is Coursera's core competitive advantage? It's not their experimentation platform. And, you know, that was 2014. So actually a bunch of that stuff didn't exist, but it was painful in 2018, 2019, 2020 to rip and replace. And rip and replace was definitely the right thing to do. The other sort of thing I'll note on that is if you look at how Stripe is the skeletal system for all the AI companies, it very quickly becomes clear that when it comes to payments, billing, tax, revenue recognition, reporting, fraud protection, consumer checkout experience, pricing and monetization frameworks, they are completely buy.

1:20:07Like they're completely buying Stripe. That's all they're buying. And I think there's an interesting thing there where I was talking to an AI company the other day who uses another provider to block bots at the time of signup. And they said, it's actually really annoying to have multiple third parties doing my fraud protection. Like one doing it up funnel and the other doing it down funnel. Well, they asked to switch to Stripe. We don't yet have that particular functionality, but we could build it. But I think it's also important to reason about what is the third party that you can be, not for everything, but more all-in on so that it plays nice internally, so that you have fewer relationships, so that you have preferential pricing, et cetera, et cetera.

1:20:48And when you think about cloud providers, that often happens a lot as well. Vercel is definitely doing that. Totally. Trying to bundle. Yeah. So this is the economy section. We were saying you're more interested in sort of the AI economy takes. The obvious big one is, are we in a bubble? Are we in a bubble? Okay. So it depends what you mean by a bubble. But I think, you know, one question... Classic economist answer is like, it depends. It depends. I know, I know. There's always, there's like two armed economists on the one hand, on the other hand. Okay. The question I got a lot a couple quarters ago, especially because all of these AI companies are private, is are they creating real value?

1:21:26Is there real revenue coming in? Right? Because it's pretty clear to see that there are real costs. There's very big fundraisers. There's a lot of capital that's flowing out. Is there dollars flowing in? And so that actually forced us to step back. You know, one of the fun things at Stripe is just like, you just see it going through, right? You see each successive wave of startups. You see, you know, people retaining and churning their subscriptions. You see who's buying what for how much from whom. And when we stepped back and we said, okay, like, let's just look at this AI cohort. And there's lots of different ways to define it.

1:22:00But for simplicity, one cohort that we looked at was the 100 highest grossing AI companies on Stripe. And you kind of need a reference point. And so we were like, let's compare them to the 100 highest grossing SaaS companies from five years prior. And we looked at things like how quickly do they get to a million or 10 million or 30 million in ARR? And the answer is two to three times as fast as a SaaS cohort. We looked at questions like how diversified, global is their customer base? And the answer is at the end of the first year, at the end of the second year, basically whenever you look, they are twice as global.

1:22:42Like they're selling into twice as many countries. They have majority of their revenue coming from outside their home market, even if their home market is the US. And in some cases, you know, there's a startup in France. who's in that list, who's like 95 % of their revenue is outside of France, right? They're very global. And then you start to look at things like retention, which also comes up a bunch, right? Is this truly ARR or is this like revenue popping and then falls off? Time's 12. Exactly. And this one was a little more nuanced. So if you squint at the data, you can actually see that these AI companies on a per company basis have slightly lower retention than the SaaS companies.

1:23:24Not like dramatically lower, but slightly lower. And that's also consistent with being like relatively early in the adoption curve, but even correcting for that slightly lower. But then if you bundle that, if you look at, okay, like these SaaS companies are doing this wave of things, these AI companies are doing this wave of things. What's interesting about SaaS is the churn is churn from the entire vertical. In AI, they're just churning from that company and flipping to another company. And then if you keep watching them a few months later, they flip back to the first company. So that tells me it's actually like a very competitive market.

1:23:54People like the product, they want to use the product, but there's a bunch of good products and the best product is changing over time. And so people are happily flipping across. Oh, well, so in SaaS, you had this cool trend of you started horizontal, you started with Salesforce, and then you went vertical, like you have the vertical SaaS, the toasts and the whatever else. We talked a bit about wrappers earlier. AI has done the same thing. You start horizontal, right? You're like the infrastructure, you're the model providers, you're the purely horizontal. And then all of a sudden it's like all of these verticals.

1:24:23It's like, okay, we're in healthcare and there's Nabla and there's a scribe, or we're in architecture and there's studio, or we're in law and there's Harvey, just like all of these verticals popping up and popping up much faster than in SaaS. And I think there's two things happening there. One is you can get to those verticals very efficiently because you're sitting on top of someone else's LLM. So you don't actually have to do the research and it's like a quite lightweight build. But the other thing is, because these AI solutions are so borderless, niche markets, vertical markets at a global scale are actually quite large markets.

1:24:59And so there are incentives to specialization in a way that maybe there weren't five years ago. So anyway, is it a bubble? I don't know. It depends how you to find a bubble. I'm a two-armed economist. But what I will tell you is these are companies that are growing very quickly, faster than anything we've ever seen, very diversified in their customer base, which makes me feel better about them. Very sticky in their customer base, not always on a per company level, but on like a problem to be solved level, which tells me that the customers are getting recurring value from the product and are willing to pay for it.

1:25:34Yeah. What I'm hearing is like, there is some real, like better quality businesses being built at the same time that has no, the expectations can raise ahead of those. Yeah. And that's not within the Stripe observable universe. Yeah. The part we didn't, I mean, the part we didn't talk about was the cost profiles. And, you know. Oh, the margins. When I reason about cost profiles, there's really like two, there's like the fixed costs and the marginal costs, or there's like the people costs and like the, I mean, in the case of AI, like the inference costs. Right. And so the people costs for these AI companies are quite small, right?

1:26:08You look at a lovable, you're talking crazy revenue milestones with 10, 20, 30, 40 people in the early days. And even today, right, when you look at most of the top 100 AI companies on Stripe, their revenue per employee is unlike any other business, including public companies who are known for being incredibly efficient companies. That, of course, people cost ignores the inference costs. And so I think we absolutely need to model assumptions around the efficiency and where the inference costs is going in order to be able to reason. But in the same way we were talking about how do we think about the ROI on these coding agents, I think we would be unwise to measure the value of these companies under the assumption of today's costs.

1:26:56And we need to model out, based on reasonable things that we've seen and reasonable expectations we have about the world, like those costs going down quite a bit, at which point, you know, in traditional senses, like very interesting businesses. Yeah. I would say like, you know, there's the benign element and then there's the less benign in terms of the cost profile, which is, yes, as AI is increasingly doing more and more labor that you would otherwise have hired for, then it should rise as a part of your spend. And then there's the less benign one, which is like people are selling dollars for 50 cents.

1:27:27And that's why you're seeing such revenue attraction because obviously you're kind of giving money away. For sure. And, you know, like I was a grad student in the early days of Uber and DoorDash or, you know, I was year one, two of Coursera, which was like, that's still a.org at that time, right? Like basically a nonprofit. And I remember my lifestyle was subsidized by the VCs who were paying for part of my Uber and paying for part of my DoorDash. So, you know, we've lived that pain. Those two worked out. Some of those prices going up. But increasingly what we're seeing from the AI companies on Stripe is they do want to have healthy unit economics.

1:28:01I mean, let's not talk about like the big labs that are pouring crazy money into research. But if you're talking about like the vertical kind of wrappers, which are themselves also doing very well as businesses, they are building quite healthy unit economics. And the demand we've seen for token billing, I think, is actually in part a testament to the fact that they really do want to have unit economics. So not their overall book, but literally like the marginal person I serve, the revenue I get from them versus the cost I incur for them, they want those to already be in the green. So I think there are some very good businesses that are being built.

1:28:37We've kept you for a long time. You've indulged us in so many different topics. Do you have like any other like hot takes on just like AI in the economy that you want to indulge in? Like my classic hot take is how come AI doesn't show up in the GDP per capita numbers, right? Which is part of the whole productivity discussion, but it's really, really driving home. Like we have to see this show up somehow, right? And that's part of the bubble discussion. But I think to me, like any story where technology, you know, as a factor in the macro economy equation is supposed to be a big driver, you should see it in GDP at some point.

1:29:13The GDP doesn't measure everything, but like at some point. We should see it in GDP. There's a lot of noise in GDP because there's a lot of other drivers of GDP. How quickly we see it is, I think, an open question. It should be fast. It should be fast-ish. And the funny hot take is the only way we're seeing GDP is the data center build-outs. Oh, interesting. Oh, yeah. I'm less close to that. It could be. I mean, hot takes. I think AI should make markets more efficient. I think agents should make commerce more efficient, which should genuinely expand the aperture of what people buy. I think agents are already making business creation more efficient, which should accelerate new startup growth, which we are also seeing.

1:30:03And if you just think about the tens of thousands, hundreds of thousands of businesses that are getting started in these AI dev tools that would not have been getting started before, I think that's incredibly promising. I think we have a real cost question on our hands, but I think it will be solved for many domains. I think there will be cheap enough models to do the job that create meaningful value. I think the AI companies are being quite savvy. We talked a bit about the unit economics, but also like what are they pricing to, right? So SaaS was mostly seat-based. And you could imagine sort of a death spiral where like AI is seat-based, but AI is replacing the seats.

1:30:49And so you need fewer seats and you monetize less. Like you don't want to peg your revenue to the thing you're trying to replace, right? And I think outcome-based or usage-based will be much more powerful. I'm seeing more adoption of AI outside of the U.S. in a broad range of markets than I expected. Brazil is huge. Brazil is huge. It's a little bit hard to parse what is like, well, Stripe is opening up more markets and having more payment methods and giving them more exposure versus like literally there is an expansion in adoption. But I think it will be promising for the world if there is more.

1:31:30it's not really equality of access because like on paper anyone has access, but like equality of adoption so that we don't lead to sort of, we don't end up with very uneven economic growth as a result of AI. So we'll have to watch. I don't think it's going to show up next year. I don't think most businesses are targeting employee efficiencies next year, but I think Every business is targeting employee efficiencies for 27 and for 28, which is suggesting more efficiency. And if you can couple more efficient production with more efficient consumption, which frankly is what agents do, then one would expect, yes, GDP to rise meaningfully.

1:32:17And I think you could debate, is it one percentage point more growth per year? Is it three percentage points more growth per year? Like, I don't know. I don't think it's 10. I don't, I mean, I'd love it, but I don't think so. I think we'll have to see. Because that thing compounds, right? That thing compounds. GDP is a big number. But yeah, the term I've had for the movement of employee efficiency is tiny teams. Like, you know, teams with more millions in revenue than employees, which like completely changes the startup structure because you are probably profitable from like maybe your first round of funding.

1:32:51I have one more hot take, which is it's easy to think in a world of like really exciting, powerful tech that somehow brand doesn't matter. It's all about the technology. But actually, if you look over the last year, so much of the value created in AI companies has actually come from, and Lovable was brilliant in branding themselves, Lovable, right? So many of these rappers are actually winning on a really differentiated user experience and a really compelling brand and a really compelling community. And so, I don't know, I just, you know, sometimes, you know, investors, friends, whatever, will be like, oh, like, what do you think of this?

1:33:32What do you think of that? And it's like, you know, great founders who are highly technical are amazing, but you also need them to be hyper-focused on like the user and the product experience and really creating something like beautiful and crafty. And I think some people are like, oh, like AI should replace that. And like all that matters is the tech. And there's not going to be a human internet. It's going to be agents talking to agents. And it's like, I don't know, maybe, but like so far what I'm seeing is brand matters more than ever. Yeah. The Silicon Valley phrase is you need Riz and Tiz.

1:34:00And if you don't have Riz, then it's all for it. I think something in Stripe is always embodied. Very good technologies with also industry-leading design, which I think is very important. I'm a Katie Dill fangirl. Katie Dill's our head of design. Actually, I was co-hosting Friday Fireside, which is our weekly company thing today. And I cited something that Katie's team did and made it clear that I was a fangirl. And Tanya, who's our head of PMM, said she was going to have to fight me for like Katie's biggest. Anyway, we decided that the three of us would just go to a spa and take mimosas instead of fighting.

1:34:36But for a hot second in the company's Slack chat, there was like maybe a fight between TK and I. Okay. I mean, while you're on this topic, what's something that you learned from her that like has really driven design and stripe? Yeah. Katie does not give an inch on quality. And it doesn't matter if it's like one banner that 2 ,000 users see that has some font that's slightly bigger than it should be. Like, that's a bug. That isn't SLA. That needs to be burned down. And actually, now every two weeks we have a run the business review where it's like 60 folks get together and talk about the whole business.

1:35:20And literally each of us has a slide that's like, did we meet our bug burndown SLA for these like largely, not exclusively quality, but often quality issues. And I think she builds like beautiful experience. She and her team design like beautiful experiences and sort of like macro are like extremely innovative. But there's something that I've learned around like the micro. Like you have to obsess over every detail. and one tiny thing that's not good enough is worth all of us sweating until it is good enough. It can be a little exhausting at the scale of Stripe, but it's also like very grounding to just know like there's a clear line and if it doesn't meet the quality bar, like you just gotta fix it.

1:36:08Wow. Well, thank you for spending some time with us and explaining how things work at Stripe. I mean, everyone's always curious and you feel very generous with your time. Oh, thanks for having me. and it's been really fun. Call to action. Hiring, I assume? Yes, we are hiring. We are hiring. I mean, we're hiring everybody. But we are particularly hiring machine learning engineers slash scientists, a lot of back-end folks. So if you're excited to build the infrastructure for building agents or build the infrastructure to do machine learning, a lot of those, we are recognizing that data is increasingly an asset that our users want real-time and high-quality and well-documented.

1:36:50So if you're big on data engineering or building data platforms, also hiring there. But just across the stack, it's a great team and fun project. So yeah, we're hiring. Thanks, Emily. Awesome. Thanks, folks.