The Rise of Agentic Commerce — Emily Glassberg Sands (Stripe)

30:58So chargebacks, let's start with the user pain, the business needs. So chargebacks are really painful. Merchants lose about$55 billion a year to charge backs. And fighting disputes is also really costly for the business. Fighting a single dispute can mean putting together a 12-page evidence packet, digging up receipts, looking at IP logs, tracking down delivery confirmations, pasting everything into this dozen-page PDF. Most businesses are only bothered by the biggest ticket items. And for lean teams, which includes like basically all of the startups out there, they rarely bother. Like it's just not worth their time.

31:39They don't have the expertise in house. I was talking to a friend of mine the other day who runs a like jobs marketplace. And she's one of the few marketplaces that monetizes off of the job seeker instead of monetizing off of the employer. And she's just getting crushed by disputes. And she told me like, Hey, Emily, it's crazy that these people are disputing because they're saying that they never used my service, but they've literally uploaded their resume. Nobody else has their resume. Nobody has benefits from uploading their resume. It's called friendly fraud, but that's kind of a misnomer because it's not friendly.

32:15But she literally doesn't fight them. She has all the evidence, but she doesn't fight them. And if you ask her, she's like, it's just not worth my time to put together these crazy packets. Okay. So a small improvement in dispute win rates would translate into hundreds of millions of across the Stripe network. So it sort of satisfies the first bill of there's a real user pain and there's real business opportunity here. Okay, then the second is kind of do we have the data? Well, we already see which disputes are being won and lost of those that are being fought. We already store most of the data an issuer would want to see when it decides a chargeback.

32:50So it's a great candidate, which is why we launched Smart Disputes. And it's basically just a classifier that grades every incoming chargeback as it comes through on its likelihood of success. And if the model thinks that the merchant can win, then we overlay this LLM-powered agent that goes out and gathers the right proof, right? Like, you know, IP address matches for the digital services and screenshots of like the usage and, you know, whatever the issuer historically prefers. And then it just bundles that evidence into the format that the bank expects and files the response without any human having to touch the case.

33:25And then, of course, it watches the ruling and then feeds the outcome back into training so it keeps getting smarter. And Vimeo and Squarespace were our two first adopters, but they're recovering 13 % more revenue on disputed charges from adopting it. And they're doing that with zero extra labor. You literally don't even have to click a button. You just toggle once to turn it on. And then, you know, the impact is even greater for these tiny merchants who never used to contest chargebacks at all. And they now have kind of this like AI paralegal that's working for them. And so you weren't asking about smart disputes.

33:59You were asking about the mental model. But it's basically like big user pain, abundant Stripe-only data, a clear kind of model-driven fix. And that's how we decide where's the next sort of place that Stripe AI should go. And a lot of what we talked about so far has had to do with stopping bad things from happening. So fraud, card testing, illegal chargebacks. Are there examples where you use AI to generate revenue? I guess the example that you just mentioned does generate increasing revenue. But whether that's, I don't know, a smarter route or faster checkout, any of those things? For sure. And by the way, fraud done well also generates revenue in the sense that the alternative is usually doing fraud poorly, which has a bunch of false positive, which means you're blocking some good users.

34:43But the way we think about it is like, we use AI across every stage of the payments lifecycle. So like from the second a customer lands on the checkout page, like all the way through, right? To handling those refunds and disputes. And if you think about that lifecycle, there's kind of five like meaty steps. There's checkout, there's authentication, there's fraud detection, which is where we've spent most of our time talking. There's authorization and then there's the downstream of events like the refunds and disputes. Checkout is sort of like the easiest for you or like me pre-stripe to reason about because we all experience it as consumers.

35:21And I think we could all agree that like checkout experiences feel pretty sort of stayed and inefficient. Like no matter who you are, no matter where you're shopping from, no matter how you like to pay, you usually get like ish the same old form. It doesn't adapt. It doesn't know you. So, and a lot of times that's kind of all it takes for a customer to drop off at the finish line. Some of that is little stuff, but some of that is big stuff. Like, you know, if I only have an Amex on me and Amex isn't shown, like I literally would have to text my husband to get a Visa card. And if I'm in another country and have no access to any of the payment methods that are listed, then you've basically shut off my market entirely.

36:01So, we've been working a lot on fixing that in checkout. AI is our magic wand here. We call it Stripe's optimized checkout suite. And it's just about making the checkout experience increasingly personalized for our users' customers, right? So dynamically tailoring that experience to each of the end users, again, our users' users, in real time. So like Turo, maybe you've used it there like the world's largest car sharing marketplace. they moved over to our checkout suite and saw a 5 % increase in recaptured revenue, which for them was, I think, like$100 and some million a year. Payment methods are a really interesting subcomponent of checkout.

36:42So there have been a proliferation of payment methods in the world, which from a market efficiency perspective is probably a great thing. Stripe now supports well over 100 payment methods. So like Apple Pay, Ideal, Buy Now, I'll pay later. And so what we do in the optimized checkout suite is like more payment methods is better for business because it comes kind of out of the box for businesses. They can reach more customers with what they need. But actually showing more payment methods to their customers is suboptimal because people get choice anxiety. If they don't see what they need in the first three, they give up.

37:18And so we provide all these payment methods, but then we automatically surface the most relevant payment methods based on who the customer is and what they're buying. And it works. like businesses that show at least one relevant payment method beyond just cards see like a 12 % increase in revenue and more than 7 % lifting conversion. So like conversion goes up and the size of the transaction goes up. And that's like a really big deal for something as small as kind of the order of buttons on a screen. So that's checkout. Can we nerd out on data infra for a few minutes? I'd love to talk about lessons learned operating data infrastructure specifically for data science, machine learning, and AI at this scale?

37:58What tools do you use? What worked? What didn't? Any lessons around scaling and operating at that level? We use ML infrastructure that we've developed over time at Stripe and that relies on open source where available and sort of third-party buy solutions where it's not differentiated for us and where there's a third party that meets our reliability and latency and cost consideration needs. So for example, the data scientists and MLEs and even some of the software engineers here use notebooks for experimentation. We use Databricks notebooks. We use Flight for orchestrating our training runs. We use NVIDIA GPUs and PyTorch for model training.

38:51feature computation, including those LLM embeddings and feature serving is done in Shepard, which again, we built in partnership with Airbnb and have since open sourced under the name of Cronon. You know, I think, so Shepard is new for us. Actually, we just completed the full migration to Shepard a month and a half ago. That migration took, you know, on the order of about six months. But one lesson learned is to really make sure that we're investing sufficiently in the horizontal infrastructure layer so that individual product teams snap to the same infrastructure versus allowing their sort of golden workflows to diverge and everyone to spin their own.

39:43um transparently the the original feature computation and feature serving system we built which was called semblance um had a number of limitations it was pretty hard to develop on and as a result one of our largest uh machine learning groups at stripe decided to fork um buy tekton you know a third-party solution um we couldn't adopt tekton across stripe because Tecton was only useful for batch solutions and didn't meet the latency and reliability requirements of the charge path. So you could use it, for example, to score merchant risk at onboarding because you have a couple minutes to make that decision, but you couldn't use it to score a charge because you have tens of milliseconds to make that decision.

40:28And we ended up in this fractured world, which led to all sorts of issues, including actually one of the most valuable signals for understanding whether a merchant is fraudulent is looking at the transactions that are happening on that merchant because there are certain patterns of transactions. Oh, many of your buyers are from the same IP or there's a big jump in prices. You used to be selling everything at$2 and suddenly you're selling everything at$2 ,000 that in and of itself indicates that the merchant is fraudulent. And those features actually couldn't be shared because we're bifurcated.

41:00Plus, just from an investment perspective, you basically have like like mini ML infra teams within the applied teams that are operating kind of inefficiently. And so we brought all that together under Shepard. It was a bit of a long journey, but it was definitely worth doing. And then we put enough work into it that we were like, we should just open source it and make sure other people can build on it as well. You have obviously a key real-time aspect to what you do. You need to detect fraud in real-time. Is there a specific way this translates into infrastructure tools that you use for that real-time component?

41:35I think it results in us. It's a combination of the latency requirement and the reliability requirement. We run on five, six, nines reliability. You can't have downtime. And that's not just downtime of the core payments APIs. Downtime of the radar API is super, super costly to the businesses that run on us. And so the SLA is needed for us to be able to buy are quite high. There's also pretty stringent security requirements. So there are often new startups, less so on the infrastructure side and more so on the applied side, who we would love to buy from, partner with, but they don't have the security protocols and controls in place for us to feel comfortable operating in their stacks.

42:23And so I do think that the nature of what we are doing. Yes, the timeliness requirements, but also the reliability requirements and the security requirements, you know, push us to, and I'm a big proponent of like only build where you have a core competitive advantage, but like on the margin for ML infra, do push us a little bit more towards build than we would have in other contexts. All right, let's switch to the rise of urgentic commerce. So obviously, urgentic is one of the big words of the last year or so. How do you all envision this? Do you view autonomous shopping agents as a part of that future?

43:12And where do you fit? Well, reasoning models are on the rise. And with that, AI is no longer just about getting answers to your questions, right? It's starting to do things for you. I think most individuals first felt that like our individual aha moment was maybe with the shift from chat GPT to operator, right? Answer questions to like go out and execute tasks in a browser. But that shift from knowing to doing is a big deal. And I think one of the earliest places we're seeing it's going to change things is commerce. We've all seen those cool demos of agents buying stuff for people. At Stripe, we started leaning into this about a year ago.

43:58And back in November, we launched a toolkit that makes it easy for agents to transact on someone's behalf. So I like coffee. I drink a lot of coffee. You might be able to tell by the pace of speaking, but there's this barista agent that is out there today. And you tell it what kind of coffee you like, and then it just scours the internet for the best beans, and then it buys them for you. But what's interesting about the barista agent is it's not a traditional coffee shop. It doesn't own any of the inventory. It is literally just doing the discovery matching, plus I'll talk a little bit about the payments flows.

44:40That is the entirety of the app. And I think that's just a glimpse of how kind of who is doing the buying is starting to shift. Like agents are buying on behalf of humans. And then there's another big shift that's kind of happening in parallel. And by the way, both of these are early, but I think just given the pace at which we're seeing things change, we'll probably move pretty quickly here. It's like where the buying happen. So like more people and more businesses are spending time inside AI tools. And with that, product discovery and browsing and now even buying are starting to happen in those tools.

45:22So like Perplexity, you may have seen that they recently launched hotel discovery and booking in the app and it's powered by Stripe. But, you know, unlike most hotel discovery and booking surfaces you might think of, you're not linked out to a merchant website. You aren't taken to separate checkouts. You stay within the perplexity app. And I think that kind of like in situ commerce is really interesting. We're also working with Hip Camp. It's like summer season, So maybe a good time to mention this. They just use agents to book campsites at state or national parks on the camper's behalf, even off platform.

46:05The agent goes and completes the booking. They do it really safely with these virtual cards in terms of the money flow. And it just gives campers access to sites that aren't normally all bookable in one place. So behind the scenes, how does that translate into requirements, whether that's, I don't know, speed data formats, authentication, the checkout experience that requires you guys or not to just change the way Stripe works? Early days, the biggest change is around the money flows. But I would caveat because, you know, people get really jumpy about like, oh, an agent buying for me, that sounds super scary.

46:48I'd argue that like in practice, agents have actually been buying for us for years. They were just human agents, right? Like when I order my salad from DoorDash, DoorDash charges my credit card and then it issues a single use virtual card to the driver, right? The driver is my human agent who goes and buys the salad on my behalf. And they can only buy at Sweet Greens and they can only buy for$25 and they can only buy in this two-hour window in my town. But it is very controlled. And that single-use virtual card in the DoorDash case happens to be powered by Stripe. And so what we're doing here in the first most simple iteration in your mental model should be swap out the human agent for an AI agent.

47:35And that's how Bursta agent works, right? It's just using a single-use card from Stripe issuing to make the purchase just like the DoorDash driver does. So the transaction's controlled and your data stays safe. Now, I don't think that that will be the only mechanism for agentic commerce or the limit to what gets done, but it is sort of the first instantiation that we're seeing is this just difference in money movement or replicating kind of human agent money movement with machine agents. The other thing that's kind of interesting, you know, those were all B2C, like consumer examples. But just like you and I are spending a bunch more time in chat GPT or perplexity or whatever we like to use, developers are spending a lot more time in Cursor and various AI dev tools to code faster.

48:32And so another example of agentic commerce, which maybe isn't the first thing that comes to mind for people, is like, you're in Cursor, you're building your product, you want to set up some like front end thing, bot protection, whatever, something like Vercel, normally what do you do? You like stop coding, open a new tab, go to Vercel, sign up, get your API keys, bring them back to cursor, like total context switch. But now you can just buy Vercel from inside cursor, like right there in the code editor. So you don't break your flow. It saves time for the user. It also creates sort of a whole new channel for Vercel and cursor to sell software directly right where the work is happening.

49:11So I mentioned like in-situ commerce for the consumer, but this is like in-situ commerce for the developer or B2B. And Stripe enables those transactions too. So I don't know. I think there's a brave new world of agentic commerce and who's doing the buying is different and where they're doing the buying is different. But there's a bunch of other stuff that's going to need to evolve too. And as you push the reasoning further and think of like multiple agents that need to coordinate and everything happens through code. Do you then get into a different world where Stripe needs to sort of behave differently?

49:50I don't know if you know Daphne Collar, but she hired me at Coursera. She co-founded Coursera back in the day. And I remember talking to her in the parking lot one night. She was notorious for staying very late. So it was always dark when we talked to the parking lot and also driving very quickly. So you really had to get out of the parking lot before she got in her car. But she was saying to me in the parking lot late one night, we were so, everyone was so young and like half those people ended up married to each other. We were like, they're all hours. Anyway, the first movie, the way she described it to me was like, because we were talking about, you know, where were we going to, this was literally 2014, like, where do we need to evolve the learning platform and teaching platform to be?

50:30And I think her words were something like, you know, the first movie was just filming a play on stage. And then you think about whatever, today's latest Hollywood release, and it's like this whole set of experiences that are only possible because it's on film. And sort of the analogy she was drawing is like the very first MOOC, like literally what we had in 2014, massive open online courseware was like recording Andrew Ng up in the front of his Stanford classroom, right? But today, companies like Coursera and Khan and whatever, like we've actually built learning experiences that are only possible because of the data, because of the technology because of what you can do sort of through this new medium.

51:08And just bear with me, like, I kind of think it's going to be the same for commerce, right? So the earliest versions of agentic commerce have looked a lot like flipping an AI agent for a human agent, right? Like, instead of the DoorDash driver doing it, like the barista agent is doing it. And actually, you know, we didn't talk about order intense, but one of the things that we're also enabling is right down to the agent navigating a web browser and filling out the human optimized checkout form. And that feels like a very reasonable place to start. But that's not what agentic commerce will be, right?

51:45Like imagine now you're like no longer selling to a person who's scrolling through your site. You're selling to a piece of software that has already read all the reviews and has price compared the market and is now in a hurry to kind of tick payments off its list. That's like how the AI agent is going to feel. And it's going to buy very differently from you and me. And so, you know, we're still working through a ton of this. A ton of this is yet to be built. But you asked, like, what's it going to demand of Stripe? And I think sort of some high level design principles, like the first is just that intent is the interface, right?

52:18Like humans click around, agents just declare what they want. So in perplexity, a traveler will type, you know, find me a flight to New York under$300. But perplexity is going to turn that sentence into like a single JSON blob. It's like origin, destination, budget cap. It's going to like fire that at the seller. And so every merchant API is probably going to need one canonical kind of intent endpoint that accepts those structured desires instead of sort of this UI click world that we live in today. Second, I think it's pretty clear that product data is going to have to be machine readable, right?

52:53Like, I don't know if you've ever played around with like United's FAIR database. It is not perfect for humans. Sometimes it's like intentionally opaque for humans, but it is definitely useless for code. And so I think early adopters who want to sell through regentic channels are going to need to expose kind of an open product schema, like the SKU and the inventory and the price and the constraints and, you know, maybe even the wedge that you're willing to give to the facilitator agent who's facilitating the commerce. Like that's not CSS, like that's not JavaScript. And then the agent's going to be able to run kind of a SKU level search and know with like cryptographic certainty, right?

53:32Like flight UA 263 for$250 is still available, right? So I think that'll change. I think latency budgets are going to shrink to machine time. We talked about latency about this in the context of the charge path, but like, you know, people will wait three seconds for a spinner. I think an agent's just going to retry somewhere else after a couple hundred milliseconds. And so it's all going to have to be like pretty fast. And then we touched on this briefly, but just like a ton is going to have to evolve in the risk space. You know, today, I think human buyers think of themselves as owning their credentials.

54:07I own my card numbers. credentials are going to have to move from like being possessed, being owned to being permissioned. Right. Someone gets like a one time scope limited token to spend that$250 on United Airlines before midnight. And that token, you know, can't be used, replayed at another provider and it evaporates after use and it has all sorts of limits, whatever. Trust is going to have to be super programmable. Like some developer IDE is going to be buying GPUs on behalf of 50 different startups and will want it to attach sort of a verifiable business profile, including a risk score so the downstream sellers can accept or refuse the purchase.

54:53We're going to need a lot more observability. We want to kind of, if you take the hip camp example, right? Like it's camping bot should be able to book federal park campsites. But it also needs to be able to expose these real-time logs so that hosts can reverse anything that looks odd. And then it's probably obvious, but good bots need to be very distinguishable from bad bots. And a lot of the classic fraud tools might mistake a good bot as a bad bot or just consider a bot to be bad. Like speed, data format, auth are all going to change when the buyer's a bot. And I think it's just going to require designing for intent and publishing those structured catalogs and signing and scoping every credential, instrumenting everything.

55:38And then just like we're all going to have to teach the risk stack to tell the good from the bad. Fascinating. Where does MCP fit in that picture? So MCP being the emerging agent to agent protocol and Stripe was early in setting up your own MCP server. where does that fit and any lesson learned with your experimentation with MCP so far? I think there's two bits. One is like our own MCP server and the other is how we enable MCP payments. And they're different, but I think they're both kind of interesting in their own right. On the former, we talked a bunch about commerce-related examples, but there are AI agents out there now, probably a greater number actually than commerce agents, that are helping you run your business.

56:25So not the transaction commerce part, but the running your business, like doing the boring admin stuff you hate. So generating the invoices off of messy spreadsheets and updating cards on file and changing billing plans and analyzing business metrics and doing support stuff. And they're doing that without needing a human. And MCP model context protocol is a critical enabler here, right? So yes, it can be agent to agent. MCP can also be a translator between LLMs and, you know, SAS APIs, like more deterministic SAS APIs. And so you can think of it as like the simplest version, just the LLM reads like a menu of tools.

57:02So for example, a menu of Stripe tools. And then when you ask a question, the model picks the right tool and fills in the JSON. And then the MCP server sort of fires, in this case, the actual Stripe call. And it's sort of the same principle as a browser hitting a REST endpoint, but the client is just a bot instead of a person. And so what does this actually let you do today? Stripe's MCP server lets you do all the most common kind of low risk tasks. that you can do on Stripe or through our API, right? So list customers or create customers or find your product prices or spin a payments link or issue a refund or pull up your balance.

57:44Like all of the kind of boring but essential stuff you do a hundred times, right? While you're wiring up your Stripe integration or trying to serve your customers. One of the most interesting use cases I saw recently was actually Decagon. Are you familiar with? Yep. Yeah, so... The customer AI company, right? Customer AI. And in less than one week, one engineer at Decagon built an integration with Stripe through our MCP server that just lets Decagon's customer support agents securely access all of the info for their users, right? So their users' invoicing info and subscription cancellations and whatever.

58:27So now Decagon's customer support agent can, on behalf of the business, find the business's customers' invoicing info or cancel their subscription or deliver their refund directly from their customers' Stripe accounts. And the first Decagon customer that they released this to reported a 65 % drop in support costs. It's kind of striking how much of support is cancel my subscription, give me a refund, explain my invoice, right? Stuff that actually can be done in a fully automated way if you have clean access to your Stripe systems. So where do we think this is going to go? I mean, it's pretty clear that MCP is becoming the default way that any single service, Stripe or GitHub or Notion, talks to an LLM.

59:17And so, you know, naturally, I think MCP also needs to support monetization, which is why we've enabled MCP payments. So you can seamlessly monetize your MCP server using Stripe as well. We previewed talking about the new AI economy earlier in the conversation, Stripe, for the reasons that you describe as a very unique vantage point into what companies do and their growth and all the things. And you release from time to time really interesting stats. And perhaps we'll put some of those as a link in the show notes. To start at a high level, what do you see that's different in this generation of AI companies from your vendetta point?

1:00:06One of the things, you know, from my economist hat that I love about working here is just kind of this front row seat to, hey, what's the growth trajectory of each successive wave of startups in particular? And, you know, the current wave, of course, is AI. We work with AI companies across the stack. So when I talk about the AI companies on Stripe, you should think of this as everything from infrastructure and modeling to full-blown applications, OpenAI, Anthropic, Suno, Perplexity, Cognition, Eleven Labs, Decagon, Sierra, and a long tail of others. We recently looked at the Forbes AI50, and 78 % of them are Stripe users.

1:00:45That 78 % reflects 100 % of the Forbes AI50 that accept online payments. And, you know, I think there's a lot of hype around AI tech and I think fair questions around the monetization. And so we took a look at, hey, with this current wave of AI startups, what do we see in their monetization trends and in their growth trajectories? The long and short of it is like they are monetizing super fast. They are monetizing faster than any previous generation of startups that we've seen. we focused in just for concreteness on the top 100 highest grossing AI companies on Stripe. And we asked, okay, for the median in that cohort, how long did it take them to hit various revenue milestones?

1:01:29And what did their customer base look like? What did their monetization strategy look like? And those that already hit 30 million in annualized revenue got there in about a year and a half. For comparison, many of us were around five years ago, the fastest growing SaaS startups on Stripe took five and a half years to hit that same mark. So this kind of AI wave is scaling revenue. You can think of like 3x the speed of the SaaS boom. And it's not just the big players. If you look at the newest AI startups, the ones just getting going, they're ramping even faster. The ones that hit a million, the median gets there in five months.

1:02:10They're earning 4X more in their first year than peers who launched just a couple years earlier. I was at Stripe Tour Paris a couple weeks ago and was looking at some of the European breakouts. Lovable out of Stockholm hit 50 million ARR in six months and is now for sure the fastest growing startup in Europe. Cursor, which of course we mentioned earlier, helps developers code with AI. They only launched two years ago. They recently announced that they're over 300 million in ARR. So just like really astounding growth rates. And I think it doesn't mean it comes without cost, including inference costs.

1:02:51But like this is a real wave of businesses building real value in the market, else they wouldn't be able to monetize it. And they're doing that way faster than we've seen in any previous tech cycle. You mentioned Paris and Europe. do you find that those companies are global earlier in their life as well? For sure. These AI companies are going global way faster than their predecessors. If you look at the AI, that AI 100 group, and you ask the median, the median is in 55 countries in their first year and 80 countries by their second year. And that is twice the internationalization of equally promising earlier SaaS companies at the same stage of their evolution.

1:03:33And it's real money that they're getting cross-border. Like today, these companies generate the majority of their revenue. I think the median is 56 % of revenues from international customers. Back to France, like PhotoRoom is very cool. It's like, you know, one of the darlings of France, the AI photo editor. It helps you clean up images. They went from, I think, zero to 50 million ARR in three years. They already sell into 184 markets. Like, I mean, you don't have to go that deep into your geography background to know there aren't that many more markets. to sell into, right? And well, some of it is that they're selling, you know, infrastructure and models and digital art and music and stuff that just works across borders.

1:04:11Some of it is that LLMs are good at translation, but some of it, honestly, to our conversation earlier on Optimized Checkout Suite, is just that the bar to going global has gone down, right? So almost all of these guys adopt our Optimized Checkout Suite. It comes with over 100 payment methods out of the box. That gives you global reach and conversion. But also there's all the hassle of, global for managing tax and regulations. And a bunch of our solutions, like Stripe Tax, help these businesses scale up globally with very lean teams. Because that's another trend we didn't talk about yet, but these folks are building very real businesses with 10, 20, 30 people in a way that's quite striking and actually never been seen before.

1:04:56And look, 100%, I mean, not Not that you need praise from me, but you guys should absolutely take a victory lap for enabling a whole generation of startups around the world. A combination of AWS, Stripe, companies like Deal and others. You can just launch your business globally in a few days after you incorporate the company, which is insane. which is partly the reason why you see this generation of companies growing so fast. Yes, AI is hot, but the enabling layer now exists in a way. And I would add that on top of that, you've got the global communication layer where everybody, at least in tech, is on X.

1:05:44And this whole world of problems that were just abstracted away in a way that was just completely unimaginable 15 years ago. So I have a hypothesis about a second order effect from that, which I haven't robustly validated, but I'm going to say it anyway because I'd love for you to chew on it, which is an interesting corollary of being so global from day one is that today's vast internet markets enable and reward specialization. The markets today are so much bigger than they were a decade ago. And correspondingly, what people are building with AI is starting to look a lot like what we saw with SaaS, like first horizontal and now vertical, right?

1:06:28SaaS was like first Salesforce and then Toast. AI was like, you know, first broad tools like ChatTPT and then highly specialized industry specific applications in healthcare, in real estate, in architecture, in restaurants. But the switch from horizontal to vertical, which definitely happened in SaaS, happened so much faster with AI. And I think part of that is for sure that like the models enable these, we talked earlier about wrappers, enable these sort of specialized products to spin quickly and find product market fit without having to sort of invest a bunch in upfront research. But also, I think the fact that they are global provides additional tailwinds to that, which is when stuff is truly borderless, specialization is rewarded because the markets are bigger.

1:07:14And so even a very specialized niche is a very large business. Yeah, no vertical is narrow, is too narrow when you can do it globally. Really interesting. Yeah, I love that thought. Yes, I think part of it is also seeing the LLMs go up the stack and go from being foundation models to increasingly application companies and covering a lot of the broadly horizontal stuff, which pushes people to the vertical aspect of things. But I love that thought that the international, you know, the given vertical makes your vertical market very, very big. What are you seeing in terms of, you know, in your world that's different with AI companies in terms of like billing, pricing, business models?

1:07:58Okay, so selling software used to be you build it once, you incur a fixed cost of building it once. I'm slightly oversimplifying. Obviously, you continue to do R &D, but it is a high fixed cost of building. And then you sell it by seat over and over and over again at very high margins, because the marginal cost of providing the software is low. Okay. That is not true with AI. As products get more AI-centric, at least today, inference costs are more meaningful. and so companies are shifting from this sort of per seat billing. And by the way, if the AI does really well, there might also be fewer human users who need such seats, so it's not clear that per seat billing was going to get you the revenue, but companies are shifting to usage-based billing first to align pricing with costs.

1:08:56And then second, a trend, and this one's earlier, But I think it's where actually like the market equilibrium, like where clearing will actually happen, you know, two, three, five years from now is experimenting with new pricing models like outcome based pricing and actually increasingly using outcome based pricing, which, you know, provides flexibility and really only charges you for the stuff that works as a competitive, competitive differentiator. So it can be hard to evaluate whether AI is going to work or not. And if you can go in and say, look, we're only going to charge you for what works, like that is a much lower risk proposition for the business than saying we're going to charge you per seat or we're going to charge you for usage, right?

1:09:36It's like, well, what if I use it, but it doesn't work well enough. And so like I'm paying the inference cost, but it's not like moving the needle for my business. And so Intercom is an Irish-founded company, and they're also reinventing customer service. There's a lot of interesting stuff in the customer service space. But they're moving their support product from charging per seat, the olden days model, which is how most SaaS is built, to charging per resolved case, which is it aligns incentives with their customers. And it is actually an outcome-based version of pricing. And so I think stepping back, AI is changing everything.

1:10:13It's increasing productivity. We think you don't want a pricing model that is static. You don't want a pricing model that depends on your customers hiring ever more people. You also don't want a pricing model that is assuming near zero marginal costs given inference costs. And so we do see these businesses iterating very quickly to figure out kind of where to supply and demand intersect. And correspondingly, we're sort of arm and arm with them working on our billing solutions, including usage-based billing and outcome-based billing, and really partnering with this current wave of AI startups to make sure that their pricing and monetization approaches, A, work for the market, and then B, can be like very fast evolving and highly unconstrained.

1:10:56constrained. So maybe as a last theme to close the conversation, you know, a topic du jour is how companies use AI internally. And it's a little bit of, you know, AI coding, vibe coding on the one hand, and then on the other hand, you know, the Toby memo about AI literacy, and then you saw Aaron at Box do the same, and the CEO of Zapier, and so on and so forth. How do you all think about this in terms of building or governing, you know, AI literacy inside Stripe? For us, I think it really starts with a culture of experimentation. And I actually like to tell the story of how back, boy, like two years ago now, right?

1:11:42A couple of engineers hacked together a little internal beta for an LLM Explorer. And the basic idea was like, hey, let's get a chat GPT-like interface in the hands of thousands of talented Stripe employees and just have them figure out how to apply it to their work. And, you know, Stripe is coming into this from kind of a long-running culture of bottoms-up experimentation all the way up to kind of Patrick and John. Leaders here have very intentionally crafted that we think a lot about sustaining experimentation and innovation internally as we grow. And so in the case of LLMs, for us, this was like, hey, let's just quickly unlock internal experimentation.

1:12:22And obviously that needs to be done safely, right? People are going to experiment. The enthusiasm was palpable. They better not be in their personal chat GPT accounts, especially given the sensitivity of Stripe data. So we decided fairly early on to organize cross-functionally and just set up the tools and policies so that any Stripe could safely play with LLM capabilities. We also decided early on to decouple from any one model because we saw the models evolving quickly. So the first version of this LM Explorer had just checked GPT 3.5 and GPT 4. But today we serve dozens of models through the tool.

1:13:00We assumed collaboration. So people are very social. And the returns you get from building something are almost never worth it if that thing only works for you. And so we enabled these things called presets, which are basically shareable prompts. And basically overnight, the Stripe community developed hundreds of these reusable LLM interaction patterns. And I think from there, we were kind of off to the races. And we had a bunch more to do. Like, hey, let's make sure that once we built LLM proxy, any engineer should be able to hit a standard API to get access to LLMs and build their production grade applications.

1:13:38we actually only relatively recently GA'd like an agent builder internally that hooks up to what we call Toolshed so it has access to the MCP servers for Google Cloud and Jira and Slack and whatever else but it started with just a small number of engineers saying everyone at Stripesh have access to LLMs they should be able to share what they build with LLMs then they should be able to access those LLMs programmatically and then they should be able to build agents on top Zooming out, anything you can talk about in terms of like roadmap, what you're currently working on, what should we expect in the next 12 to 18 months?

1:14:13Anything you can share? I mean, it's a lot of going big on what we talked about today, like deploying our foundation model across applications, building really robust risk as a service, helping our users prepare for commerce in an AI era. I can't share any super specifics, but I think you kind of see where we're headed with foundation models, with MCP, with order intent, And with sort of the perplexity shopping example, and you can expect to see more of that from us in the coming months. Brave new world. All right. Thank you so much. This was fantastic. Love the conversation. Thank you so much for spending time with us.

1:14:51Super. Thanks for having me, Matt. Hi, it's Matt Turk again. Thanks for listening to this episode of the MAD podcast. If you enjoyed it, we'd be very grateful if you would consider subscribing if you haven't already, or leaving a positive review or comment on whichever platform you're watching this or listening to this episode from. This really helps us build a podcast and get great guests. Thanks and see you at the next episode.