How I AI - How Stripe built “minions”—AI coding agents that ship 1,300 PRs weekly from Slack reactions | Steve Kaliski (Stripe engineer)

Starting point is 00:00:00 At Stripe, we're landing about 1,300 PRs that have no human assistance besides review per week. A lot of where our work begins is it could be in a Google Doc as we're planning a new feature, or maybe a GR ticket comes in, or we're talking about something in Slack. I can click an emoji, and then the menu will sort of attempt to one-shot resolving that prompt, using all the tools that are available at Stripe. When you're in larger organizations, there's so much friction that can come between a good idea and getting it into the world. Not only can I have one of these, but I could have many, many of these running in parallel, in isolated environments, making isolated changes all at the same time.

Starting point is 00:00:33 How are you getting all this code review done? Whether the text has been written by Steve or the text has been written by Steve's robot, you still want that CI environment that's providing confidence that the code that's being changed is safe and that as it rolls out, we're having blue-green deployment so you can roll back to. All that is super critical, independent of the nature of the authoring of it. No matter how juiced these laptops are, you get three or four work trees in and like it starts to sound like an airplane taking off. It's no good. And so I do think on this multi-threading, agentic engineering work, cloud environments and

Starting point is 00:01:05 virtual environments are so important to unlock velocity. Welcome back to HowIAI. I'm ClaraVo, product leader and AI obsessive here on a mission to help you build better with these new tools. Today we have Steve Kaleski, a software engineer at Stripe, and he's going to show us how the Stripe team deploys a bunch of minions to do their engineering work. We'll also watch an agent spend a little bit over $5 to plan a birthday party all in ClaudeCode. Let's get to it. This episode is brought to you by Optimizely. Most marketing teams aren't short on ideas, but what they are short on is time. And that's exactly what Optimizely Opel gives you back, with AI agents that handle real marketing workflows. You know, like creating content and

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Starting point is 00:02:29 Find out more at optimizly.com slash how I.A.I. Attend live and you'll get a free pair of Rayban meta AI glasses. Steve, I'm so excited to have you on How IAI because I saw the Stripe minions on the timeline. And one, exceptional branding, don't sue us. And two, I just love the idea that you and your colleagues in the team at Stripe have created not just one agent, but minions all across the company that can help with development work. And I'm so excited for you to show us how that helps you in your day to day here. So welcome to how IAI. Thank you for having me. So tell me what has been the effect that minions

Starting point is 00:03:17 have had on you personally at Stripe and at the Stripe team as a whole. Sure. So for me personally, I think sort of anecdotally, I don't remember the last time I started work in the tech center. Right. So I do end up there often. But, you know, what I found is that, you know, a lot of where our work begins is, you know, it could be in a Google Doc as we're planning a new feature or maybe a GER ticket comes in or we're talking about something in Slack. And those are sort of like the more natural entry points to starting work, right? And then you end up in a tech center when it's time to, you know, actually do the work or make the final tweak. And it just felt very natural. And I think in particular the sort of like activation energy of starting work feels a lot lower, right? So if you know, you're in a Slack thread and maybe there's a piece of user feedback and it's something simple like, you know, we have to update the docs or maybe it's something more consequential and we just want to build a prototype. I can click an emoji and like the work begins. And often the work finishes too. You know, we at Strip, we're landing about 1,300 PRs that have no human assistance besides review per week. But at the minimum, the activation energy of like starting your write code, seeing test pass,

Starting point is 00:04:29 Maybe a test fails occurs without me even, you know, participating. And then I can jump in and I can tweak and I can kind of like have that momentum sort of, sort of like generative momentum, you know, that I can hop in halfway through. What I think is magical about this. And I won't call Stripe a big company, but you do have a decent amount of employees and very, very large business is I love that concept of activation energy going lower because when you're in larger organizations, there's so much friction. that can come between a good idea and getting it into the world.

Starting point is 00:05:03 And it's not malintent, right? It's nobody's like, oh, man, I really want to slow this process down. Yeah. It's either, you know, functional. I don't have access to a technical area of expertise to actually get from here to there. It's operational. I don't know how to organize people and communicate effectively to get the next step done. Or it's just kind of like people get siloed in their day to day and don't think of new ways to get work done.

Starting point is 00:05:28 And one of the things that has been so revelatory about AI for me personally is like all that just kind of goes to zero because coordination cost can go down, execution cost can go down, communication costs can go down. You just get closer to the work, which I think is the fun part we all really care about. So show me how you actually activate a minion. And, you know, we skip this a little bit, what a minion is. The quick spiel of a minion. When I, as an engineer, sort of at pre-AI time, you know, want to make a modification to Stripe. Well, Stripe is a huge code base with tons of services. It can't run on my computer alone.

Starting point is 00:06:07 So Stripe already has a long history of investing in great developer tooling, having hosted development environments that I can spin up, that, you know, have all the code already there and services running. And I can S.S.H.N. and make modifications. And we have a ton of great CI tooling around that. So that's the context. We have all that. The idea with the minion is that I can provision one of those environments seated with a prompt, and then the menu will sort of, you know, attempt to one-shot resolving that prompt

Starting point is 00:06:38 using all the tools that are available at strike. So all of our internal documentation, our internal CI, our test data, so on and so forth, and it will loop through that in an attempt to, you know, solve that prompt. So let's go ahead and jump in see what sort of a pro-typical experience might look like. So I'm in a Slack channel. It's called Steve Cliskey Robots dash Claire. I actually have a Steve Cliskey Robots channel that has 76 humans in it, but I do have every... It sort of is just me and my robots, and now there's some sort of, you know, like audience observing.

Starting point is 00:07:14 But let's imagine that, you know, maybe I'm thinking of a new feature idea or I want to improve documentation that we have. So we have a launch coming up soon. And I want to sort of embellish the documentation. So I'll say, I have this cool idea for docs at striped.com slash payment slash machine. This is our new machine-to-machine payment work, which we'll look at later in our call. And I want to make sure the landing page really sticks

Starting point is 00:07:44 and gives a good code example of how to get started quickly. Right. So maybe someone posted a message. like that or came in through a ticket or whatever the origin may be. All I have to do now is add a reaction, which is create Minion Pay Server. This is a particular repository within Stripe. We get the one sec cooking from the Dev box agent. And then we got a reply in here saying, your Minion for Pay Server, it's the repository, for

Starting point is 00:08:15 a new branch that's created, landing page code example has been created. And it's going to kick off our Doc service. so I can eventually preview it. Now, I'm going to click follow along. So right now, what it's doing is it's provisioning that development environment I was talking about earlier. Right. So this part isn't new. It is excellent, but it is not new.

Starting point is 00:08:33 And basically, it's going to spin up an instance in the cloud. It's going to apply all the configuration that's required for both me and the agent to do coding within Stripe. So this will just take a few seconds. It's going to check out that repository with a new branch, configure the, local database, apply my Git config. It's going to set up a Vs code server so I could connect to it just through the web or locally. So with some extensions. So what's really great about minions is, you know, obviously there's the agent loop that's, you know, making the code modifications.

Starting point is 00:09:12 But it's built on top of like a ton of incredible work that are developer productivity done around just making it easy to get like a perfectly operating Stripe development environment. for coding, which means that, you know, not only can I have one of these, but I could have, you know, many, many of these running in parallel, in isolated environments, making isolated changes all at the same time. So, you know, that little one-click emoji, I could have done that with a few messages at the same time, which is really great. So. Yeah, one thing I want to call it here is we had my friend Zach from Launch Darkly on. And one thing he said was, look, what's good for the developer is good for the agent. So there's this virtuous loop of, if you have or do invest in developer experience for your human engineers, your agents will benefit off of that. And in turn, if you invest in developer experience or agent experience for your agent engineers, your development team benefits from that. And so I always tell people, you know, engineering team, you've always asked, like, can we just give a little bit more time on the roadmap to DX? Like, pretty please, can we invest here? And I think if you attach it to an AI initiative, that's like the secret way to get some of that good stuff.

Starting point is 00:10:23 Totally. Yeah, I mean, imagine your, you know, some codebases are small, but, you know, Stripes is huge. You know, imagine you show up day one and there's no documentation and there's no tools and they say, good luck. Like anyone would have trouble. And even if you threw the agent at it, it's very likely that the context window would be blown by the whole code base, just scanning through it to understand all the intricacies would be like impossible or extremely expensive. So, you know, if there's a very blessed path for 90% of the common activities in being, you know, an engineer at Stripe, that makes it, that makes the propensity that the agent succeeds really high too, right? So, you know, imagine we wanted to make an API change, which we do, you know, hundreds or thousands of times a year. We have really good documentation on, you know, how to add a new field or a new method or a new resource that the minion would read and would execute against. And then, you know, the propensity of it would one shot is very high. So, Good docs for developers are equally important for the agent to your point. So we've now transitioned from, you know, booting up the development environment to now we're in the first agent run.

Starting point is 00:11:30 So we have that prompt that I posted in Slack here. And now what it's going to do is boot up an incident of Goose that's basically the harness that's, you know, going to run through all this. We did have an episode with the Block Team about Goose, this, this open source agent harness that got set up. And I want to call out one thing for folks that are not watching and are listening, which is I love your system prompt. So sophisticated. It says, implement this task completely, colon, and then just whatever you put in. No mistakes. No mistakes. You forgot no mistakes. But, you know, I think people really think they have to overarchitect their initial prompt. And I think if you have a great hardness, it can go a long way to extracting out a successful outcome from a pretty loose prompt.

Starting point is 00:12:16 Totally. And we, a lot of this is an experiment in some way, right? You know, as new models come out and, you know, we build new tools, like there's this sort of dynamic nature to it. And we've built a lot of interesting, you know, bots that help write the prompt, right? So, you know, maybe first it will do the task of searching through the code base or looking at all the poll requests or Google Docs or whatever may be. I think now it's straight. Most things that could have an MCP server have an MCP server. So we're able to interact with a lot of the internal data we have. And then it can, make a prompt that I could then paste in here or I get assigned to the agent. So that's sort of part of why I wanted that public channel we were looking at is like, you know, working on the sea of that we don't pair program anymore, but we, you know, pair prompt, right? And that activity could be with other engineers or other data sources or other agents too, right, to figure out if we can, you know, properly explain to the agent, you know, how to do it correctly. In any case, you know, what it's doing now is it's taking the link I gave it, which is to public documentation. It's going to search through the code base and use some of our, you know,

Starting point is 00:13:22 code searching tools to locate where that change in particular should go. It's going to execute a whole sequence of tools. And over time, as it figures out where in the code base it should work, what the modification should be, they'll ultimately commit those and make those available in a pull request that me and my fellow colleagues can review. Yeah, I have a couple questions on this, because we've seen a few examples of folks building their own cloud agents and kind of, and I'm curious, you know, why, why Goose, you know, versus doing something on your own or doing sort of a more commercial solution? I'm curious if there was an internal discussion or how this, or did this happen organically because it worked for one engineer. Curious how you kind of ceded the idea of minions on top of your development tools.

Starting point is 00:14:12 Yeah, sure. So we also make Claude Code and Purscher and tools like that widely available to engineers at Stripe. So I think our general sentiment is like we want to accelerate development so we can build new features for our users. And there are going to be new models coming out and new tools. And we want to be able proliferate those as much as we can. In the particular case of Minion, it's very, I don't want to say very specific, but it's very specific to like the Stripe developer experience in the Stripe developer experience in the Stripe Developer. environment. And we had been experimenting with Goose early on, and I think in this particular case, we'd forked it to make some modifications as well. And really what we were looking is

Starting point is 00:14:52 like sort of a base harness and loop to apply all of our own tools and software to. So we spent a lot time on like making good tools available and making sure that the sort of routes that the minions go through, you know, work closely with like the most common Stripe developer workflows. So it's sort of like commercial versus, you know, custom things. Like there are things that are very specific about Stripe's code base and being developed in the way we build things, that it was just sort of easier for us to build and deploy that. But the commercial solutions are great, and we use those extensively. Even later on this demo, I can sort of show like I can, you know, for example, I can pop into

Starting point is 00:15:33 BS code web where I could manually edit some of the code that's going on here as well. But I can also boot up Claude and I can have. sort of the typical clot experience with all the Stripe MCP tool, internal Stripe MCP tools available as well. So, you know, there's no singular tool to rule them all, but I think the like overall end-to-end development story at Stripe is built on minions. So you can see I'm in that dead box and clot now. Yeah.

Starting point is 00:16:00 Cool. I have one other question and then an observation. I want to make sure that the listeners don't miss. So my first question is, you know, Stripe is a very well-resourced, I would say, engineering organization. So I'm presuming you have a team dedicated to working on not just your dev tools, but as well as as minions and managing that as an internal product itself. Has that team been sort of built as a standalone team that's focused specifically on internal developer experience? Is that how it works?

Starting point is 00:16:29 Yeah, we've had a developer productivity team for as long as I can remember, I think, about six and a half years now. And, you know, that team's focused on all the tools that I engage with and making them more. useful, right? So that's all the way from, you know, how we interact with Git and version management to our tech centers and our configurations there, to our development environment and how that whole story pieces together. And, you know, we, you know, just as, you know, as a product engineer in Stripe, I care deeply about our external users and them being successful at Stripe. That team cares equivalently about engineers at Strike being successful and being able to build things quickly. And I think that's been even more accelerated by AI in the last couple of years. And then one of their observation I want to make, because I think you glossed over it a little bit at the beginning, but it's so important for folks that really want to go ham on coding with AI.

Starting point is 00:17:23 Sure. Which is, look, all of us engineers have a MacBook Pro that weighs 8 million pounds. I can do some damage. Mine for anybody who wants to know, its nickname is Big Boy. So whenever I need my kids to get my coding laptop, I say, can you bring me Big Boy? because I call it San Francisco rucking when I carry two of them in my backpack. Oh, my God. But, you know, no matter how juiced these laptops are, you get like three or four work trees in, all running.

Starting point is 00:17:52 And like it starts to sound like an airplane taking off. It's no good. And so I do think on this sort of like multi-threading, agentic engineering work, cloud environments and virtual environments are so important to unlock velocity. And that's one place where I haven't seen enough large engineering teams invest in those environments to really unleash the power of either AI-assisted coding for their software engineers or agents in general. So if there are any CTOs, BPs of Engineering listening, if you were to invest in something to really unlock growth in the next year, getting that situation locked up would be really good because, again, I hear so many people, like, oh, I can clod code everything. I can spend, you know, I can codex anything. I can spin up all these work trees. I'm fine. And I'm like, are you running all these local? Like, what are you doing? And so that's one thing I just want people to not miss is the limitations of your actual machine on how multi-threaded you could be, especially in a complex road base like stripes. Totally. And, you know, I have slack on my phone, right? So I can even kick off one of these minions on the way to work, right, as I'm sort of going through slack on the subway.

Starting point is 00:19:08 And then, you know, by the time I'm there, I can jump an halfway through. And I think like maybe like the hyperbolic thing here is like, imagine if all engineers at a company could only like work on, didn't have Git. We all had to like coordinate working on the one code base together. Like that would be crazy. And, you know, the equivalency here is like, imagine if I'm bounded by, you know, my agents are bounded by just what's available and can work on my computer. The 10x thing to do is be able to have 10 of them run in parallel, but also not be contingent on my like, it's like everyone's playing a Mac mini, right? So it doesn't fall asleep. Right.

Starting point is 00:19:47 It's like there's a whole business around just the computer not falling asleep. I legitimately, first of all, I have like four Mac minis upstairs. And one of them is just basically a laptop that doesn't close. Like I use it as a laptop that does not shut. And it's really unlocked my, my velocity. So, okay, we thank you for going on this side quest about virtual environments and local host and all those things. I'm a founder, so I know most people don't start companies because they love running payroll or managing compliance. But somewhere between hiring your first employee and raising your next round, you end up in the weeds with HR, IT,

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Starting point is 00:21:11 Focus on what you're building and leave the rest to Rippling. Okay, so you are now running this. You're going to, it's, it's, you said one shot at the beginning. Really, you're trying to take one, one prompt and not a single reply gets you. gets you what you want, but it goes into the harness, it goes through its own loop, hits the tools it needs, and ultimately you as the end user get one response back, which is here's the successful implementation. Exactly, right.

Starting point is 00:21:40 So we can already see that it's identifying the relevant files. It's keeping track if it's on to-dos. That's something that we've codified in it to focus on. It's making changes. It's, you know, preparing the commit and so on and so forth. And ultimately, you're sort of like taking out of the oven. We'll see a response at the end of just like, it finished. You know, you can go ahead and look at the pull request and the sort of normal human review part continues.

Starting point is 00:22:06 Let's talk about that really quickly. You said 1,300 code or agent initiated PRs per week, something like that. And then humans are involved in code review. How are you getting all this code review done? Well, you can make the argument that, you know, if I'm spending less time actively writing code, I can, you know, re-centre my time on reviewing the code that's being written or working with users and so on and so forth. So I think that's a big part of it. I think the other side of it, like it comes back to that CI environment, right?

Starting point is 00:22:36 So having really good test coverage, having synthetics that run to, you know, simulate end-to-end interactions with your product. Those all help inspire confidence in the code you're reviewing, right? So absent those, like, it would be really difficult to look at codes, especially in a huge code base and have high confidence that it works. So, again, whether the text has been written by Steve or the text has been written by Steve's robot, you still want that CI environment that's, you know, providing confidence that the code that's being changed is safe, and that as it rolls out, you know, you're having sort of blue-green deployment so you can roll back to, like, all that is super critical, independent of the nature of the authoring of it. I do believe, like, if coding becomes easier and coding historically has been the bottleneck in product development, it's just going to shift to other areas, right?

Starting point is 00:23:28 So if, like, coding in effect becomes free, the review is going to be really challenging, right? Or getting enough ideas in the first place could be a big problem or distributing them, right? So I think the attention is just going to move around it to other areas. Great. And then one other question before we go on to your next work. which I am so excited about, spoiler alert, is, are more than engineers using minions? Are you seeing product managers, designers come in? How is this going across the company and across functions?

Starting point is 00:24:00 Yeah, I think, you know, part of why I like the Slack example is the entire company is in Slack, right? And, you know, to a point of activation energy, you know, even if, like, you had the tech setter on your computer and I gave you the docs and whatever it may be, you know, to someone who's like, not an engineer, it could be really challenging or intimidating or whatever it may be. And, you know, for whether you just want like a proof of concept or you're going to make a docs change or whatever it may be, like, you can, you can probably write out in plain text the thing you want to occur, right? You might be writing the product brief or you might be giving design feedback. Like, you're, in effect, just writing a prompt at some point. So being able to just click an emoji or, you know, tag the robot to spend an opinion. We're trying to see.

Starting point is 00:24:48 more non-engineering usage there. Yeah. Amazing. Okay, so let's go to our next workflow, which I am. Yes. As somebody with a stack of Mac minis downstairs, I am excited about. So, you know, at Stripe, we're, you know, we're thinking about AI in a few ways, right? So the demo we just showed us how we're thinking about using AI internally to accelerate our product development and engineering.

Starting point is 00:25:14 The second way is, you know, thinking about how we're supporting all these businesses that are, leveraging AI in their own products and how we can support their business models. And that's with things like usage-based billing. And we just announced our beta of our LM token billing product. But there's a third side, which is like this sort of idea of agents as economic actors or agents that can spend money as part of their attempt to solve a prompt. And before we jump to down, like just the thing I'll illustrate is like, you know, often you give a prompt to Clod or some other agent, and it will use its own model to generate text and response, right?

Starting point is 00:25:55 Or maybe it will do a web search or call an MCP tool or whatever it may be to gather information or to affect change as part of that response. And, you know, of course, there's the shopping cases, but we imagine a future where, like, third-party services are going to want to sell into these kinds of experiences and that those interactions will cost money. So we have to equip our agents with the capacity to spend so that they can not only consume tokens, but so that they can also pay services as part of achieving the prompt. So I'm going to give an example. Jen, who's a proclamation I work with, is awesome. I think her birthday is coming up soon. If not, the demos, it's her birthday party.

Starting point is 00:26:38 And we're going to ask Claude to help plan it. And along the way, it's going to interact with a bunch of different real third-party services that are really going to accept money over a payment protocol. we're calling them machine payment protocol, which we've co-designed with tempo, and we'll see some real transactions along the way. So I have a sort of pre-baked prompt. We'll paste in just to skip that part, and I will go ahead and give it. So I told it to research Jen Lee, who's my product manager, figure out what would be a good idea for her birthday, find a place to have the birthday, send invites to the birthday, and then, And, you know, we've burned all these tokens along the way, so we should probably donate to Stripe Climate at the end to make up for all the energy consumption.

Starting point is 00:27:27 So right now, we're still getting the environment set up, just setting up our ability to pay tempo. The first thing we're going to do, we can see right here, is that we've actually paid browser base to create a new browser session. So I didn't sign it for browser base beforehand. I'm just paying for this one session. It's going to do that. I gave it her website somewhere up here. So it's going to go ahead and spin up that environment. You can see right now it's writing some playwright code locally,

Starting point is 00:27:56 which will connect to that browser-based session. It got to her website, right? So Jen likes, I think she bakes and she cooks. So it actually found out by running that browser session that she's a macha-obsessed baker working on a cookbook. We're going to go ahead and turn off that browser session. And we can see the net cost is just a fraction of a cent. And again, we really paid that business just now.

Starting point is 00:28:22 The next thing it's going to do is using its knowledge of Jen and her interest in matcha. It's going to search online using parallel AI to find relevant venues in New York that we could host this party, something that matches her matches her matcha interest. I'm going to just do, again, a side quest, a call back to our episode with Andrew. and Nabil who used AI to set up a tabletop gaming business they were building in the East Bay. And my friend texted me and she said, this is the most San Francisco thing I've ever seen, which is two dudes that need AI to all plan their game night. And I was looking up at your original prompt.

Starting point is 00:29:07 And I was like, this is such an engineer's prompt for how to plan a birthday party. It's like source end. and then insert Jen's name. You know you're doing something wrong if I have to load environmental variables to celebrate someone's birthday. Exactly. It's just like so funny. Yeah. So I found this macho cafe in New York on Bowery that's, it thinks it's a perfect fit for a matcha interest, which is great.

Starting point is 00:29:31 Now we should, you know, send an invite in the mail. You know, we're taking it offline. So now we're interacting with this service called Postal Form. Postal Form will take a PDF. and actually send it in the mail. So, again, right now what we're doing is we're, the LM is writing code locally to generate a PDF image of the invite. So there's this sort of interesting balance of like,

Starting point is 00:29:55 what can the LM do itself, right, with its own tools in my local machine, versus what it needs a third-party service for. Like, obviously the robot can't send mail, and I think if the robot could send mail, that would be kind of concerning. So, you know, that's trying to fix a couple things with the PDF. I'm sure the invite looks, it'll be very interesting.

Starting point is 00:30:16 It looks machine generated. It looks, yeah, it's just a bunch of binary. No one's going to come to the party. How do you? I mean, I know this is a little bit of a demo you're giving us here, but I think so many of these, even consumer, you know, facing products. Like, I've never heard of postal form. It sounds amazing. Where it solves like a very, you know, individual user problem of like, how do I get mail

Starting point is 00:30:42 out the door, so many of them are going to be interacting with agents and, like, the API as the interface. And you and I were talking about that a little bit before the show. And you were saying you were getting user feedback recently that sort of spoke to that. Yeah, we've been talking to, you know, I think maybe including Postal Form. We've been talking to a lot of users as we've been integrating this machine payment stuff. And, you know, it's very normal stripe to ask for feedback. And, you know, typically they go, I'll get back to you and write up some notes.

Starting point is 00:31:12 And I would get these, like, in 30 seconds, I'd get two pages back. And the engineer over there had used, you know, Clod or Codex to, you know, read the Stripe Docks and implement the feature. And then figured since, like, they hadn't really written it themselves, but they'd ask Clot or Codex to send feedback back to me. And, like, it happened once. I thought, okay, that's funny. And it happened, like, four or five times that week. And it was just extremely jarring. And it added the sort of physicality to who the new.

Starting point is 00:31:42 user is here, right? That like the, we'd have to hear from the agent directly. All right, we're just going to check in quickly. We sent it in the mail and then, you know, we burned, we burned some tokens along the way. So we actually made a $1.65 donation or a contribution to strike climate to erase 4.4 kilograms of carbon based off of our 70K token usage. And you can kind of see here an agent receipt of the services it interacted with and the cost. of each. So at some point, I'm going to get an invite to a party in the mail. I want to just recap those for folks that are not watching. So we started with a prompt and Claude code that said, plan my friend Jen, a birthday party. This is what we know about her.

Starting point is 00:32:27 It preceded. There was some like movie magic here where it preceded. Here are some tools I know can take agent payments that might be useful in the pursuit of this. And instead of a human having to go into those tools, log in, drop a credit card, buy a plan. There was a machine-to-machine transaction that happened that gave micro access to the tool for the capacity the agent needed to do the job at hand. And we see it used browser base and parallel in postal form. And it issued those payments programmatically, acts at just what it needed, did a little offset, Stripe Climate purchase and then got your party planned. And what I like about this is what's really interesting about this particular example is it makes it very clear the economics of doing something

Starting point is 00:33:22 agentically. I like this little, you know, we got a little Stripe Climate shout out here. But it also just calls out like this actually does cost you in tokens whether or not your agent is doing outside transaction. So we're already operating in an economic framework, right? Yeah. I think I'm on a strike plan here. But in general, like, you know, people have a subscription relationship to, you know, these providers. And that costs money. And we get a certain number of tokens. And any prompt I give, even though I'm not like seeing the penny count move by, has a ultimate dollar cost to it. Right. And, you know, maybe in the typical coding example and, you know, consuming tens of thousands, hundreds of millions of tokens, we've sort of justified the value

Starting point is 00:34:10 of that, right, because the code has business value and the size of monetary value. But the sort of like token and the currency that backs it, like, they feel closer than ever. And, you know, whether I'm spending a penny or a dollar on a third-party service or I'm spending, you know, tens or 100,000s of tokens with the LM. We're sort of doing a similar activity, right, which is that we need intelligence or we need data or we need operations or we need a service to execute on that prompt and, you know, achieve some outcome. And I think it's like it, even just this view feels very provocative and it feels early,

Starting point is 00:34:52 but I think it's going to feel very natural over time to see the token and the dollar side by side. And, you know, for me, it's like, you know, I planned to, birthday party for, I mean, it's probably, I don't know if it's any good, but I plan a birthday party for $5.47. That doesn't seem too bad. Again, we're doing this episode in the year of our Claude 2026. Like, we're going to show the terminal, the terminal example. And most people watching this and again, how AI is for everybody, super technical and not, they're going to look at this and we're like, okay. But yeah, like, I'm not going to plan my birthday party in the terminal. But let's just pull that thread six months in the future or 12 months in the future. There's going to be a bunch of of builders out there that are going to wrap this in a much more consumer-friendly user-user experience. And then you're going to be able to build such interesting products that can interact and transact in just a much more human way, which, again, can just solve problems in a different mindset. Yeah, I think it would be really interesting to build a business

Starting point is 00:35:52 where your primary consumer sort of wants an ephemeral interaction with you. And it doesn't necessarily require you having a dashboard or an admin panel or a landing page or, you know, all the other typical things that are really useful, you know, when a human or a business is interacting with you. And instead you could focus on like just a hyper useful single API and monetize that directly and make your, you know, audience primarily agents. I think a lot of just like really interesting businesses can emerge out of that opportunity. I completely agree. and then we're going to have agents identify what those businesses are, build them, transact with other agent customers, agents all the way down. Well, Steve, this is awesome.

Starting point is 00:36:39 Just to recap for folks, we saw minions and how to kick off development work from Slack and the benefits of investing in developer experience. Again, the piece of engineering, just like carve off a devax team and give it some love. And product managers get out of the way. You'll get more product at the end of the day if you just give some time. and effort towards developer experience. And then we got to see these machine-to-machine payments, which I think by the time the episode is live, we should be able to maybe talk about or see.

Starting point is 00:37:10 So fingers crossed. This will be live by the time our episode goes live. And we showed you how to plan a, I guess, got to zoom in, a matcha cheesecake birthday party in New York. Jen Lee's Macha Party, April 19th, apparently. All things much. I guess I didn't pick the date. So the robot has decided that will be a good birthday.

Starting point is 00:37:29 Saturday, April 19th, 3 to 6 p.m. Sounds perfect. We planned a birthday party for $6.00. Carbon neutral. Steve, this is awesome. Before I send you off, couple lightning round questions. Sure. You know, we showed kind of a contrived personal use case, but what are your personal workflows for AI? The thing I've been really interested in is the sort of like disposability of software. And I have a four-month-old now and a almost two-and-a-half-year-old now. And the two and a half year old keeps grabbing my phone to try to change music. So I've toyed around with like music apps that are extremely controlled to just six songs.

Starting point is 00:38:06 I have no idea how to build iOS apps, but the robot does. So I've been touring around like little, little engagements like that. And then I use, you know, all the AI apps sort of in the normal way, I guess, in addition. Yeah. Well, if folks want to create an app like that, we just did an episode with Jesse Jinnay, who built a like minimalist YouTube for kids where it can only, like her kids can only watch the videos that she pre-approves and you can only swipe and you can't do any, like no other buttons. It's very, very streamlined. So very similar to your music example. Okay. And then my last question, which got a sneak preview of a little

Starting point is 00:38:44 up on this quad example, but when AI's not listening, you know, when your minion does not one shot, what is your prompting strategy? And you're a parent. So like, do you gentle parent, your AI? Are you like, I know you can do it? Or do you, you know, do you bribe it? Do you offer it 15 cent? Carbon neutral? Like, what do you do? This sounds crazy. Like, I have made a concerted effort to always be polite. And and I don't, I mean, like, I like, I like, I like, I like, I like sci-fi. I like, I like, I like, there's this sort of like, who knows if that's going to happen or not. But, like, I definitely don't want to be caught being rude. Even though, like, I think I've read some stuff of, like, you know, being more intense or

Starting point is 00:39:32 being rude can result in better. It's like, I don't want to, like, I'd rather have to do a little bit extra work than have it on the record that I was mean, because you never know. You never know. But the more serious answer is, um, one, asking to explain or justify itself has helped quite a bit. And then I think in other cases I've, I've tried. Like, in other case, I know the right direction to go.

Starting point is 00:39:58 I will start going in the right direction and then I will ask it to look at sort of like the get status to look at the diff or like look at other sort of like breadcrumbs that I've left as like the directional thing to help guide it. And then of course, like if I'm doing a thing that's not recurring, but like that I'm going to do again, I try to keep that in some skill or prompt or otherwise that I can inject back in later. Got it. So you're doing like the dad teaching his kid to write. ride a bike move where like your hands on the back of it and then you let it let it like you're like it didn't really hit me until you said that there's something really it's really weird about raising kids at the exact same time that the the the robot emerges that hadn't really clicked with me yet so I don't know what's informing what but they are happening at the same time yeah I said something

Starting point is 00:40:48 like it's really interesting to be raising kids and literally writing like sold.md.files into my agents like I guess that's a virtuous cycle of skills. Well, Steve, this has been awesome. Where can we find you and how can we be helpful? We can learn more about the work we're doing at Stripe at stripe. At striped.dev, which is our blog. You can learn all about some interesting things we're building. The demo I just showed you, you can learn more about at docs.stripe.com slash payments slash machine. And I guess I'll plug my Twitter, which is just at Steve Kaliski. So those three. Yeah. Thanks for joining how I This was awesome.

Starting point is 00:41:24 Awesome. Thank you so much for having me. Thanks so much for watching. If you enjoyed this show, please like and subscribe here on YouTube or even better, leave us a comment with your thoughts. You can also find this podcast on Apple Podcasts, Spotify, or your favorite podcast app. Please consider leaving us a rating and review, which will help others find the show. You can see all our episodes and learn more about the show at how IAIIPod.com.

Starting point is 00:41:52 See you next time. Thank you.

How I AI - How Stripe built “minions”—AI coding agents that ship 1,300 PRs weekly from Slack reactions | Steve Kaliski (Stripe engineer)

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