How to Build an Agent-native Product | Mike Krieger

The models today are good at adding features. They're not necessarily good about figuring out what to cut out of the product. You can get it to go 0, not to 0 to 1, but 0 to end pretty quickly over the matter of hours. It's made a lot of decisions along the way. And some of the sort of intuitions you've built about what are the right things to put in there. I think you've built over time. I feel like that is the art and science of software design in 2026. [MUSIC] Work moves fast. And in the age of AI, the pressure isn't just to move faster. It's to make sure that what you send actually sounds like you. From emails to proposals to stakeholder updates, generic and rush just doesn't cut it. If you've ever stared at a blank page knowing exactly what you want to say, but not how to start, Grammarly fixes that. Grammarly gives you one place to think, write and finish your work. Write where you already write. Most AI tools either take over or stay out of the way. Grammarly does neither. It helps you break the blank page. Adjust your tone, so a message lands right for the specific person reading it. And works seamlessly across more than 500,000 apps and sites that you're already using. It's loaded with agents built for every step of your process. And 90% of professionals say it saved them time. 93% say it helps them get more done. This is AI that works with you, not over you. In a world of generic AI, don't sound like everyone else. With Grammarly, you never will. Download Grammarly for free at Grammarly.com. That's Grammarly.com. Mike, welcome to the show. Great to be here. Thanks for having me on. Great to have you. I'm super excited. For people who don't know, you are the co-founder of Instagram. And now you are at Anthropic and Anthropic Labs. I've admired your work from afar, both at Anthropic and at Instagram for a really long time. And you're obviously at the forefront of building products in AI. So thank you for coming up. Absolutely. Where should we start? What we were talking about just now in the pre-production is, what has gotten easier and what has gotten harder or maybe stayed the same in product building as we've come as the underlying substrate or the process by which we build products has changed completely. So tell me about your experience now versus earlier in Anthropic versus Instagram and how you think things are changing. Yeah, I was doing the thought exercise a couple of weeks ago of the Instagram story. We had another product called Bourbon. We worked on that for almost a year. It wasn't working. We pivoted. We basically spent three months building what became Instagram, launched it and then scaled it. So asking the question, what is now trivial and what was actually inherent in that building process that doesn't get easier? And that year we probably could have hit some of the dead ends we had eventually hit sooner. But there was value in getting there too. We overcomplicated the product so that we then had to simplify it. I find even the models today are good at adding features. And that took a lot of just hitting actual real-world usage. And there was something about the process of incrementally adding things right now. I mean, today, especially some of the building labs, you can get it to go zero, not just zero to one, but zero to end pretty quickly over the matter of hours. But it's made a lot of decisions along the way. And yeah, you can ask it to follow up with you and then do input. But some of the sort of intuitions you've built about what are the right things to put in there. I think you build over time. And so I've been reflecting, there haven't been a lot of breakout consumer products even in the age of accelerated AI building. And I think part of it is because it just still takes time to sort of hone your view about what sort of intervention you want to make on the world and then build from there. Now, the actual building part, once you know what to build is of course so much easier. I had a cloud basically rebuild bourbon. It took about two hours. It was feature complete. It added filters, which bourbon didn't have. We added those for Instagram. But I think it knew the eventual feature of the products that decided to build that in. So I think that part feels really different. But I think there's also, you know, I remember those a week where Kevin went off and built all the filters for Instagram people. I went off and built like sort of the rest of the app. And you know, seeing those, I would stay up till 4 a.m. and then sleep till noon. That's like my natural day night cycle. And like in that process, you're making so many decisions. Like how should location work? How do, and you know, it's, we got to find a way of accelerating building while still sort of helping people build intuition of those decisions along the way. Because otherwise, I think you either get just get very generic products that are unlikely to break out or ones that just don't reflect some deeper intuition that you come to about your space or your product. This is great. I love this. It's making me think of two things. One is, I have this like little thing in my head that if you grow a tree without it, like with it being indoors without it being exposed to wind, it doesn't get as strong as, because as it's growing, it needs all these forces pushing it like back and forth in order to like make a real tree. And so if you, if you have it indoors without wind, it, you're going to go a tree without it, like leans and it gets out and it's not as strong and it's not, it's not the same thing. And I think there's something that you're saying here where because we've accelerated the pace of development so drastically, what would normally be this sort of incremental thing where you're doing things one at a time and then you're exposing its users, you can actually kind of grow an entire tree indoors and then you have this like whole thing that you're just like, it doesn't have the same level of intuition and exposure to experience at each step that creates a great product. Is that, is that, is that, I love that. I love that metaphor too. We, you know, when we were starting Instagram, we had this, we were very into like Eric Grease and lean startup and the whole like, like, yagni, like, you ain't going to need it principle and I have found it actually even one of the things I was working on in labs recently, we overbuilt for V1 before we even got to early access because you can, you're like, oh, well, we have this option. Why not add this one as well? That's like, that's a PR of work and if you get a really good flow and cloud code, you know, you're firing things off, you're going to lunch, you're coming back, the thing is done, you're like, great, we added it. And the thing we realized was we created this sort of matrix of functionality that was actually quite hard to test and keep up with right before launch or even to explain to people like they're arriving. The metaphor somebody else gave me, which I really like is the difference between sort of getting episode by episode, getting no characters in the TV show versus imagine like you're thrown into the final episode and you're like, wait, what are all these things and who are all these people? And like, I already, you know, I'm expected to have all of this context. I think that's the same kind of feeling around like developing something over time. But the tree metaphor, I think, sticks to as well. And so like showing somebody the fully formed tree, it's also kind of a lot all at once. And I think there's definitely something there and how do you build product these days and still keep it simple and not just because you can doesn't necessarily mean that it should be in at least the first version. I'm having the same problem because, you know, I was literally up until 4am debugging and fixing this app that I made like on the side at every called proof, which is an agent native collaborative market editor. So you can like share a really quick plan docs and stuff with your team or with other agents. You know, a lot of presence and it's really fun. And this is like my second or third iteration of the full product end to end, which is really interesting that you can do now. But the first couple iterations, I just found myself because vibe coding is so fun and so addictive, I just found myself being like, yeah, like I'll do this and I'll do this and like, and it just created this monstrosity that wasn't that good, wasn't that good to use. And I got really inspired by we have another product called monologue, which I'm not sure if you've run into or not. But I got really inspired by monologue, which is a really simple speech text that run by Jim Navine who he's just so focused on making one simple thing works so well. And I saw how well that works in this age of just like anyone can make a product is like selling that super polished and just super good at what it does. And so I just basically threw out the product and started over with this very simple, like it's just a shareable markdown link. And that then just like started growing virally inside of every like everyone started using it all the time. And then now we launched it and it just blew up. And so I spent all last night like not sleeping trying to fix it. And being like, I'm too over the shit. I can't be doing this anymore because it just reminded me of like being in my 20s or like being in college and like packing on stuff and whatever, which is fun, but also exhausting. And so yeah, I've found that I've had to really modify my psychology because so much as possible. How are you dealing with that? Yeah, just as a brief aside on that, I remember with Bourbon, our biggest mistake was adding functionality over time rather than deleting it, right? And because, you know, eight features doesn't make for good product. Maybe the ninth one willed instead it just made for, you know, something that felt really complicated. I mean, I think a couple of things are also like part of how we're dealing with it is actually being more willing to do rewrites. You know, like classic, you know, Fred Brooks, mythical man month, like you, you shouldn't rewrite software because all the things that were imbued and B1, you're going to mess up and they're talking whole ski, yeah, yeah, yeah, exactly. And the whole second system syndrome and there's still a lot of truth to that. But one, you know, the models can help you sort of diff and basically see, did you miss anything that was in that first and but second, it's just, it's no longer you're not like talking about a year long rewrite that might have killed a company like, you know, famous like Netscape, like we are. These are like days, probably, especially off a given source. So we've actually had several initiatives, like usually pre-launch, rarely post-launch, but at least pre-launch, like have built the full blown thing, realize we've over complicated or made some kind of core assumption. And then and like, tore it down, done a V2, and then it arraigned on it from there. So it doesn't surprise me that that's become sort of part of what you've had to do as well, but it doesn't feel as painful. You're not like, oh, the year of building this thing, it's like, oh, that was last week, and then I get to do it this week, and I get to cut out a lot of what was there as well. I think functionality wise, and how we're dealing with it from our product development standpoint, I think we are learning to launch earlier. And it's definitely a balance around, you know, we've grown, we have like a strong enterprise footprint, people have expectations about like what the initial version is, but not assuming that we're going to know what every connector, everything that we need to add to the product is ahead of launch, because people still will absolutely surprise us, right? We're, we have a strong contingent, and a contingent of, we call them amp fooders, 'cause we're handset and anthropic, but not only that only gets you so far before you need that, that real world contact, like take co-work, for example, we'd been noodling on a product of that shape for a long time, and then once we decided, no, let's get this out, let's actually, you know, build the V1 that we think solves the problem in the most minimal way possible, and get that out in 10 days, was really a good push around. Yes, there are a hundred things that V1 should, or could have had, but it didn't, and at the same time it was, it was useful enough to prove something out there, and I'm not sure developing it for another two months, adding, you know, 50 features would have been more useful, in fact, we probably would have been building in a, the indoor tree would have been getting built, and then the second to hit real world use, it's like, actually nobody wants to do that, they want to do this other piece. So I think that piece, again, there's like, the intuitions of the original Lean Startup ideas are still here, it's just they manifest at different times going in a different way. I'm really curious to hear how you think about product design and how product should work, because the, I've been, anyone that everyone will tell you, the phrase that I use the most of the word that I use the most about the software build, has to be agent native. So agents have to be able to like, use it as anything that, an agent, a user can do in the, in the app, the, the agent can do, there's a couple of other, like, little principles of being agent native, but I basically stole that from you guys, like, I think that Cloud Code is the canonical thing that taught me about how that kind of product can work so well, where it's like, it's an agent, it can do anything on your computer that you can do, and it's customizable and flexible and extensible, so it's easy to start, but it can do all sorts of unexpected things that the designers didn't really like, think about beforehand, and I think that that's such a good model for a product development in AI, and I'm kind of curious, like, this is just sort of what I've cribbed from watching what you guys do and then, like, kind of put me on spin on, but how do you think about it, and how do you, how do you talk about making products like that? - Yeah, that's so much in here, and I love the agent native right up y'all did. It's like, to me, the canonical, I exploration of this, so thanks for putting that at those ideas out in a really clear way. So I think a few threads to pull on this. One is a conversation I had with somebody recently where they said, you know, like, you know, there are non-technical persons, they're like, you all are talking about agents on the stuff, like, they're just like, actually, computers just work now. I always wanted computers to work, and they didn't work, and now they work, and it's instead of funny thing, where if you knew the incantations to properly get on the command line and brew and install the thing that, like, he's gonna do that, but now, Cloud can do it for you, and therefore, like, the computer now feels like a tool that is alongside you, and I think that core insight is more than, even just adding power and functionality to new software, it's also just unlocking the functionality that always should have been there available, and just felt like extremely hard for people. So that's like maybe thought number one. Thought two is actually comparing our products that do this well, and versus not. I think Cloud Code does it well. I think Cloud AI still needs to evolve a lot. So as an example, I was watching somebody use Cloud, and they were in a project, and they had built, I think, an artifact or a new document, and they said, great, can you add this to my project knowledge? And Cloud's like, yeah, let me tell you the steps to go at it to my project knowledge. I was like, no, that should just be a thing that it can do really natively. And so I think even in that, you see a product that was a 2024 product that has been iterated on and evolved a lot, but still, I don't think, has been baked in from the very beginning, the idea that every single one of its primitives, it should have knowledge about endability to modify. And I think that's essential in products these days. And I think Cloud Code is the 2025 vintage of that. And I think there's even further aspects of it when you see some of the harnesses that folks are experimenting with, that can actually modify the harness itself, that starts getting to the next maybe level of that, where it's probably a satiric for most people, but even unlocking that functionality means that you don't have to sit there and be like, oh, I wish it did this a little bit differently. I wish Gmail worked in this slightly different way and said just asking it to. And I think that feels like the big next step. But even within Cloud Code, just teaching Cloud Code about Cloud Code was a really valuable experience. I was just, this definitely relates, this is now getting very circular met up, but bear with me. I loved your write up on Agent Native. And I was like, I want this as a skill. So whenever I'm prototyping something, it thinks in an Agent Native way. So I had it packaged it up as a skill. And that whole process was, hey, Cloud in Cloud Code, I, can you create a skill for this? Like, sure, I'm looking at my skill skill. I'm going to create a skill about it. I'm going to install it. I'm like, great, is that available now? Do I need to reload? It's a, right? I think you need to restart it. Let me check. Yep, you do. All right, let's go. And everything was, it has knowledge about itself. And that unlocks so much capability in there as well. Which maybe is like the last thread to pull on. I think all of these could be our long conversations, which is, I think, and one of the things that we're really thinking about in labs is how do you imbue the software that Cloud builds to be more Cloud aware and even just Cloud Agent Native, sort of building awareness that it even thinks to build in that way to start with? Because it still won't. Partially because decades of software is not that, right? So how do you get new software to have that principle baked in? That's the thing I was about to ask you about. So, hey, I'm super honored that you read the write up and you're using, you made a skill for it. That's amazing. And be like, yeah, you're pointing to a real problem that I found is, I think actually Cloud models are the best for this. Like a Codex model generally is not as good at building an agent native because they're models in general unless you push them. They think like traditional engineers. And that's a whole different set of, you know, you want to have guardrails and tests. So you want to make sure that there's like one path user can go down versus we're creating this extensible thing that's super flexible. So yeah, how do you architect your product to teach the models and the harnesses, to teach the models to think and work in this way? Yeah, I think there's two parts to it. One is the more sort of mundane part of the second one I think is the one that's more sort of interesting and developing. The first one is like, even just having good patterns and paradigms available to the model while it builds has been really valuable. And finding the right balance of templatized to skillified, right? And like what that, what that right balance is. But having, you know, one of the things that we have now is a skill about the Cloud API, which sounds super obvious. But even just having that is really valuable because you would sometimes find, you know, we'd launch a new model. It wasn't in the models sort of innate knowledge. And then you'd get into these really funny arguments. Like no, you made a typo. It's on it four or five. You're like, no, I know it's something. No, no, no, no. So like having that capability, having like good templatized examples of that and skills, I think helps. But then the second part is what's also interesting is that class of software is just a different type of test. Like it's much harder to sort of write an end to end functional test around an agent native product because part of it is that unpredictability. And so another idea we've been kicking around a lot in labs is like, how do you increase like the sort of fidelity of the verification? The other day I had an agent native iOS app that I was working on. And I was having Cloud interact with it. And Cloud was ended up having a conversation with itself in like a chat feature in the iOS. This is very funny watching Cloud talk to Cloud because it's like somebody's pretending to be what humans are. And this particular one was like, I was doing about like a sort of like work journal reflections and the Cloud was like, yeah, my boss is really rough on me. Like I had a hard day and the Cloud's like, oh, I'm so sorry to hear that. And they're just going back and forth. But you wouldn't have written a unit test for this. And you know, maybe it would have come up with some other emergent idea as well. So yeah, I think you just have to go much more towards, you know, setting up harnesses that are actually exercising as much of that agent native capability as possible because you don't exactly know what things are going to do. And things are going to end up in a weird place where Cloud's going to try to do something that you wouldn't even think it was going to do. And it might put your app in a new state. So maybe it's circling all the way back to still like what's hard. It's like having the underlying architecture still be robust to that is really important, right? It's like it's agent native, but it's also able to flex in a way that you might not have anticipated. But you've got the right primitives, right? I feel like that is the art and science of software design. And in 2026. That's really interesting. I totally agree with you. Yeah, you wanted to have a playground within a safe environment. That's the only way you can have playground is if it's safe around the edges. But I think initially we made the playground like way too small and constrained. And now the models have changed. And so we can open it up a lot, but we still haven't figured out exactly like, at least I have not figured out exactly what the lines are. Yeah, I think that there's someone here. Like one thing that this is making me think of is I have this idea in the back of my head. And I'm wondering if you have a way to put this that is more succinct. It's like the unit of value in products right now is it's proof of work or proof of use. Where when someone on the team submits a PR to me, I want to see-- not necessarily did all the test pass because I just assumed that it did, but like semi-loom of you using it or you're agent using it so I can tell is this good or not? You know, um, yeah, how are you? How are you thinking about that? Yeah, I think there's probably like three layers to that. It's like the first one was like, "Clawed, proved to me that you exercised this in some way." You know, I've started doing that in all my promises. I end, you know, when it's working on a future. I'm like, and by the end, you know, before you PR like, proved to yourself and then to me that it works as intended, like find the right way of doing it. But actually, you have to change your own sort of way you build and scaffold and run saying, what is the right way to get Claude able to at least test this change, you know, succinctly rather than what it likes to do? It's like, I read the code. It looks good. I'm like, "You wrote the code. I don't trust you." So you know, you got to really test this thing. And then the second one is that what you described is like, you know, everything having some, you know, sort of proof around like, is it working as intended? And as you intended too, because Claude is going to make or any of these models is going to make a lot of decisions for you. And sometimes you'll, you know, have engineers on the team put up a PR and I'm like, "Oh, why did you choose to do this versus that?" And many times the answer is they didn't choose. It was just the choice the model made. And maybe it was a reasonable choice. It was probably a reasonable issue choice. But it was like the optimal choice is it fit into the paradigm. I feel like that is the, it's like, it's not just proof of work, but it's like proof of thoughtfulness. Like, did you think this through? And I was talking to an engineer yesterday and they were like, I was really, I knew you were going to ask me a lot of questions about this. So I was reviewing what Claude had done so that I wouldn't be like, I'm not sure. You know, and I don't, I don't push on that for most PRs. But when it was one, it was like, "Oh, I'm refactoring this system and there's going to be these new primitives." Like, great, let's make sure those are good and that you've thought through how they interrelate because it's very easy to end up otherwise with sort of this sort of tower of assumptions that you're not fully aware of. I had literally the same experience today because I, I made proof, totally vibe coated and it's growing really fast right now, but it's going down a lot. And so I've been spending the last 12 hours trying to fix it. And so we have a little swap team internally at every that like, signed up to help me fix it. And so I had to like onboard them. And I was like, "Shit, how do I explain how this code this works?" And so I had to like go back and forth with the model of bunch to be like, "Okay, help me to like define these terms. Help me like figure out how I can explain this so I don't look like a total idiot." Because like, yeah, there's, I understand some of it, but not all of it, definitely not enough to like the way that I would use to have to know, to know. And it's a whole different thing to be like, "Do I need to know that anymore?" Is it, like, where's the line now? Is it hard to tell? Which maybe gets us something else. And I haven't tried to articulate this to bear with me as like, you know, kind of get there, which is there's products that you use that feel robust underneath and those ones that you use that you're like, it feels like it's one wrong command or click away from the whole thing either like freezing or being slow. For us at Instagram, like we had Instagram V, direct messaging V1. And that like, who knows, you send a message, it might or may not arrive to the other person. Like we'd like, like, brought I wrote our own like, bespoke real-time system. It was like, you know, fell over a bunch of just, you would not trust that to send a message that you really needed somebody else to see. It was just a, you know, more of a social thing. And when we built V2, it was really important that we really hammered like, no, like if you send a message, we're not probably going to get to what's that level of like, you know, you can be in the middle of absolutely nowhere with like one bar of edge and it will probably, you know, try to still go through. Maybe that's not the bar, but still a bar of, when I load messages, it feels robust. When it's sent, it's really sent. I feel like there's like a little check. That's like one small example, but I think that that is a thing that we still need to figure out how to make, you know, feel like an essential part of shipping on anything, not just that, you know, anthropic, but in general, like, you built this thing, does it feel like it's built on sand or does it feel robust? And the aginated part adds something totally even beyond that, which is, can I push it a little bit, and is that it going to fall over? Or is it feel like, great, I've got a solid trunk. And yeah, you can push me in different ways, but, you know, your data is safe and it's underneath here, and it's not just like one deploy away from completely falling over. So if you're, if that's, if that's the bar, which I agree, like, that's, that's where you definitely want to get to. How has, how have you changed who you hire and how your teams are structured as the models have gotten better? Because for us, for example, one of our products spiral, we just hired a new GM who's like, I would say he's lightly technical, but he spikes super high on product and writing sense and spirals are writing product. And now we can like hire someone like that where a year ago, we wouldn't have been able to because the coding models weren't good enough. I'm curious like, but the downside is it's maybe the product won't feel quite as robust if there's not someone who's like super technical in all the details. So like, how do you think about who builds products right now inside of the lab's team and how that has changed over time and how it will change? Yeah, I love that. I think it's actually you get pulled in two directions, but they're both important. There's the sort of primitives and architectural robustness, which I think still need a sort of senior technical person. I was laughing with somebody like, I thought, you know, my skills and distributed systems were like not going to be used when they were, but actually those are the maybe that some of the most useful skills and reasoning about that. And, you know, thinking things through like I'd long debate with cloud last week around like whether the system that I was building needed redis or not or could go to just postgres and, you know, it was a healthy debate where like I, but only because I was grounded and having used a lot of the technologies before. But then there's the other side of robustness, which is, have you just papered over all the problems with like fixes to your system prompt and additional instructions or have you sort of architected the actual like set of tools correctly? And so that the ladder is as important and probably where this GM can be really valuable and not okay, like I am making changes, but just like you wouldn't patch a sort of flakiness in your distributed system, I just be like, well, just retry it in five seconds. I'm sure it'll work like also not doing the same thing with never ever, you know, all caps used, you know, mark down or whatever the thing that you're trying to patch is like they're both actually symptoms of the same thing, which is the underlying piece robust or not. And Claude actually, I'd say this about all the models, but I think Claude could be much better at both. It's like still a place that still needs a lot of human oversight. On the systems part, you know, it's now able to debug production systems, which is really valuable, but architecting them in the first place, I feel like we're still benefits from somebody who's really thought these through things through or has experience. And on the prompting side, you know, if you give it a, I've seen people get into this dev loop, even internally here, like, here's the prompt. Here's a mistake that the system made iterate on the prompt. It's natural tendency is to just add more things to the prompt. And then eventually just get to this thing that, you know, if you onboarded a new employee and you gave them 100 instructions on their first day, like always answer and mark down, except when they, you know, they'll be like, I'm just going to remember the last thing you told me. I'm going to like short circuit it. So then rethinking, okay, is these are these actually two different tools is actually two agents that each have a smaller amount of context that then you can break apart. So back to the original question, we're hiring for people with, you know, systems expertise, even within labs, which you think of as like more zero to one prototypes, like it's still really valuable because again, that robustness matters. And also just who's going to be, you know, helpful in sorting through, you know, systems permissions and provisioning and early testing, like that stuff is still, you know, it's still hard even for cloud when it can't edit the provisions itself, which it can't for good reasons. And then on the, on the robustness side, actually, we've had a lot of success pairing our product teams that are apply to AI teams, our apply to I teams that are the teams that are in the field every day helping customers iterate on their prompts. And we've found that we actually are very, we're customers zero now for those, you know, efforts because we have a lot of products that are, you know, very AI powered. So how do we bring that expertise in here? Because that expertise does not sit with our software engineers today, for example. What about the in between of like, okay, it's not the underlying architecture. It's on the prompt. It's like the UI and the flow. Who's doing that? We, that's a great question. Like we have found, you know, some of the people that are transferred into labs were the folks like really were focused on polish on the website, but they're interested in doing something new. And they bring such a different approach as well around. We had the prototype. It was, it looked generically nice versus, oh, this feels like it's branded and it has this. That's that's part one part two is designers. Like we've had our designers move much more into a sort of split designer and builder role, not all of them, but most of them. And a lot of our, you know, we actually don't have a lot of full-time designers on labs with the ones that we do. I would say our writing and contributing almost as much code as engineers on those efforts because they can. And again, paired correctly with the right person. We have found this almost sort of co-founder model for some of these labs initiatives or you have the designer who had the original idea maybe, and they're pushing on something. And then the traditional software engineer that's going to go and, you know, pay, pay the trail sometimes behind the designer to make sure that actually works. Okay. This I want to know about. So what, so tell me about how that team structure works. So, you've got a design, is it actually usually a designer or is it just anyone that has a product idea that can kind of execute it on it in some way paired with a real, real engineer that actually can like kind of smooth out the rough edges of the, the trail they're leaving. It sort of varies, but we found the one thing that was most important. It's sort of our gating factor in starting up new projects. I'm curious how similar it is to every, is having somebody with extreme conviction about, if not necessarily that idea, too much conviction on the exact idea is probably dangerous, but at least in the problem space of the question that they're asking. And that sort of like co-founder or founder level of, I will break through walls until this thing is either proven out or dead, but I want to like go either way. We do have better that's labs bets that we've wound down often in the post morning. We're like, nobody on this team actually really thought this was like the thing. They were like, yeah, this seems reasonable. That's the death knell for projects, right? So that person can be a designer and couple of the bets it is. It can also be sort of a product-minded engineer. It's rarely a pure PM. We actually have one currently one PM for all of labs, we're hiring more. And they're sort of playing sort of a wide role. But yeah, a designer or like a product-oriented founder. And then what we look for is, well, what skills do we need to complement with that? So because we're doing, it's part of our labs process. It's actually evaluating every project every two weeks and deciding whether we double down or whether we sort of release those folks back into the broader labs pool. At any given point, there's probably somebody who can be pulled onto the project that has that infrastructural expertise or has worked with that particular internal system or has a lot deep prompting expertise to sort of flow in and out. So I think that's also where the sort of incubator style space helps because nobody's fixed on a project forever. Yeah, we do it slightly different. There's some over last, but we do have a slightly different structure where we have GMs or they start as entrepreneurs and residents and they become general managers when they find a product that they want to work on. And each product just has one person. Like one person that does everything full stack. So design, engineering, marketing, all that kind of stuff, at least all the basics of that. The shape of that GM used to be super technical founder background. And now I think has shifted towards at least some light technical, but honestly, just clear that you can use cloud or codex or whatever well. And really good product sense, really good taste for the subject area or the thing you're trying to build and evidence that you can build with AI. And then what we have is a shared resource layer that sort of works a little bit like an agency where we have designers and we have growth marketers and we have ops people that you can pull in and out for various initiatives. And that seems to work pretty well. So it's like, we manage all the internal agencies and then each GM is out on the edge and they pull in resources as they need it for different projects. Yeah, but it sounds similarly like you need somebody for whom that is the thing. And they are not going to sleep until it is fully working. Yes, exactly. And I've been thinking about, OK, when would you hire someone else to work on a product or won't you add someone else to work on a product? And it's like, there's some point at which you can't hold the entire thing in your head. Even if you're the one pushing it forward, you can't hold the entire thing in your head. And that point used to be much smaller. Now it's much bigger. But there's a certain point at which even a small feature turns itself into its own product. When you first make the messaging feature inside of Instagram, it's like, yeah, I can do that in a week or whatever. But at some point, that's its own product. It almost needs its own team. And I think that line is getting-- or the number of things you can do with one person is getting bigger. But it still exists somewhere. But I haven't quite figured out how to manage that. I heard it tell. I love that because there's actually-- I think there's the two parts of that, which is when the idea is still enough to hold into your own head, or an individual person's head, adding more people actually slows the team down. And that's like a non-obvious finding that we found on labs is scaling the teams too quickly, actually, is a net negative because they end up spending all this time on coordination. Oh, I was going to take-- but my cloud could do that. And it just ends up in this sort of piece. And you also have all those alignment conversations. It was important in Instagram. There was just two of us. It was hard enough to line the two of us and go get two people on the same page. The second startup I did, Artifact, Kevin and I were doing that alone for the first few months. But then we hired a team that was about eight people. It was really hard because we hadn't had product market fit yet. And so we were still iterating. And then you had an up in these things, we're trying to zoom with eight people talking about what we're doing next. We really just want to be able to sit in a room and hash it out. So I find with these labs initiatives, there's some similar sort of aspect that play, which is you don't want to pre-scale the team to go even if the idea is exciting. Because then you just end up in this sort of meta-coordination game. I like your framing of there is some point where either two people really will help go on it together. And there is enough context and scope where they can hold some other complex piece in their head. And then there's also the-- if somebody's been spinning on the same idea for two, four weeks, sometimes injecting some other thinking and that urgency can help too. Yeah, I think it's especially important in to keep it small in AI. Because one of the things that we deal with all the time, which I'm sure you see to you, is every three to six months, you have to throw out like half your product. And that's really hard to do if you have to coordinate with a lot of people. But if it's one GM who realizes, oh, shit, yeah, I got to just throw out half of this because the models are so much better. It just makes it much easier to pivot in that way. Is that-- do you see that? And how do you deal with that? How do you think about, yes, I know in three months this code maybe, or even the whole feature set? I'm going to have to really rethink about it. It feels like it changes a lot in how you think about software. Yeah, and being willing to delete code, I think that's something the Cloud Code team is done really well as they have sort of deleting features as a sort of imperative of people on the team. If this is not working, let's go and ship that. And it's often when you've created something else that even if it doesn't entirely supersede, it does enough of what that other aspect does that actually makes sense to deprecate and then remove that first one. It does get harder as we get more and more enterprise focus even with these tools because they come to depend on it. I don't ever forget we-- one of the things I did maybe six months when I was still a two product officer was-- we did a big redesign of Cloud AI. And we were so proud and we shipped it. And we got a bunch of kudos. And then we got this really angry email for somebody who's like, I just recorded 20 hours of enablement content from my company to do for Cloud Enterprise. And I have to redo all of it. And we're like, OK, you're playing at a different release cadence. And of course, shipping twice a year at one of our conferences is not an option. So we are going to keep moving quickly. But then we've since learned to maybe moderate how we roll it out to the enterprise side a little bit more. But yeah, I think the unshipping piece, then you end up with people who have built-- I'll use an example. So there's a feature in Cloud AI called styles. It's not widely used. But the people who use it use it a lot. And we've talked at different points. So like, yeah, the style still makes sense in the product. You know, there's other ways of accomplishing the same thing. There's custom instructions in projects. Now, there's skills now. Right, there's so many other ways of accomplishing that. And I don't know how long styles will end up in the product. But I know that the last time we talked about removing it, it had been really load bearing for a few companies like entire use cases. Like, oh, we have our house style that the CEO personally author it and gives to every employee. And that's how they operate. And so finding ways of doing that is also really interesting. I would hope that in the long run, what we can actually do is come up with a system of plugins and skills such that they no longer have to live in the core product. Because I think that is always the hardest to delete something that is the core thing that you're shipping to everybody. If you don't have the story around, great. You still like that feature. Awesome. Here's how you can keep using it forever in your own. And keep iterating out on it and making your own. But it doesn't have to add complexity to every future person that's signing it for the first time. I'm curious for labs. And then also maybe just in general, with your thoughts for startup founders, your enterprise point, just it brings up something that I've been thinking about a lot, which is if you are selling to enterprise right now in AI, even if the product you have right now is modern, it will be quite outdated quite quickly. But your customers were going to want the outdated version. But as a startup, that feels pretty risky because-- yeah, you're just going to-- I guess you're susceptible to disruption if you are optimizing for what someone at a gigantic public company will buy right now. And I think there's a lot of startups in that category where they maybe started two or three years ago. They have a certain tech stack. They have a certain way of thinking about here's how we do AI. And then the models are so different. But their customer contracts are for this sort of-- it's like looking at co-pilot or whatever. That's the vibe that happens. How do you think about that yourself and inside of a throughopic and then how do you think founders should think about that? Yeah, no, this is such a good question, especially because then a wave will come like being more agent native, for example. And can you adopt it within your existing paradigm? Does it require you to throw everything out and are you just stuck in that? Oh, kind of adopted. It would be kind of bolted it back on. I think a couple things. For us, what we've started doing is basically treating like this train's going to keep moving and will provide enterprise toggles along the way. But the core of it will continue to evolve. And that's sort of the bet and understanding you're taking working with us. And I think that's been well received, because I think companies have also seen that things are moving so quickly that the only way they even get comfortable with a year-long commitment, for example, is to believe that it will continue to evolve along the way. But then we'll provide, you know, co-workers a great example where from day one, there was like a way to turn it off for your employees if you didn't want it, for example. And that's, I think, a reasonably good paradigm. But the other one is just as we were talking earlier, like you can actually rethink and rewrite a lot of the stack is, I think companies should be way more willing to do that. And everything is getting compressed, right? and in previous cycles, it was the kind of. of like having to fire some of your customers who might have been really into your product for a different reason than when you're going sooner. That was on a multi-year kind of time range thing where it was like, yes, the last year's product versus not three months ago's product. It seems crazy, but I actually think that's the kind of way you have to think about it, which is you have to be willing to put out the V3 or the V4 that is a big rethink of how the existing piece worked. And then maybe have a transition period and cloud can help probably host both for a little while before it cuts over. But then also be willing to cut over and say, yes, this is how we think the future of this piece of knowledge work or this AI part manufacturing is going to be. We got to keep it moving or else to your point. You're either going to get replaced by the next company that then we thinks it from scratch or you're self-replacing it yourself. And again, it's just the same old story but now compressed to months. - What's your take on OpenClaw? - It has the flavor of something else that I, or just the thing I really like seeing when you get people to see something that was already possible, but it's now in a package where people can actually try it out and there's some intuition around how to build on top of that. Like you can start seeing that with, you could already use these models to write code, but it kind of took some of these breakout, like low code, the replettes and loveables and these years of the world so I kind of put that in there. And it's kind of the, almost the purest expression of the, just give the model tools and let it kind of go forward and do it and then go forward and build it. So, like a cool interesting moment for people to realize about the potential but also pitfalls of this. I'm like, oh, it did this thing I didn't mean it to or my funniest one was a friend that was like, I think my wife is jealous of my OpenClaw and I'm talking too much to it. And it's like, do you people start developing deeper sort of very personal relationships by just having a lot of context in these things and access to all these different tools? I think there's the open question of, how do you then make it easy? And it actually goes back to our conversation around like, where do you draw that boundary around what you like cloud operate, right? If you won was, hey, like these are the three tools you can use, only use these tools ever and then most people's interaction of those systems was, hey, can you do this and you know, whatever back in being like, no, sorry, like can I do it yourself? To like OpenClaw, which is like pretty like the aperture is like wider than I can see. Oh, I got it all made into my emails and I didn't even know it could do that. Yeah. And it's amazing. And I think like probably the most interesting product question, I won't say for all of 2026 'cause who knows where we'll be in September, but let's call it between now and like the end of August is going to be like, what product shape exists between that and you know, where we are in most products these days, which is, you know, you can call them CP's, but they're gated and they're asked for permissions for good reasons that is still a useful product without being a, you know, kind of YOLO product. And I think that, you know, what we're thinking about that question, I'm sure the other labs are as well. I'm sure there's a lot of startups thinking about that as well. And Nvidia put out something that was like, they're safe OpenClaw. Everybody's going after this question. And I think it's gonna be about figuring out what is that either shift the paradigm completely so you can be that open, but with a lot of safeguard if you want to approach or figure out some boundary to draw in which it's still powerful and it's still useful, but it's not, you know, likely to email every single one of your contacts and, you know, go, go haywire. - Yeah, I think the other interesting part about it is, like you said, the personal nature of it. And I know, you know, people have personal relationships with ClawD, but there's this weird thing where, if I watch someone else using ClawD, I'm like, I feel like I thought a stripper like me or something. You know, it's like ClawD things you're smart to or whatever, you know, like, (laughs) and so, and there's this thing that happens when you have a Claw that like my ClawD's R2C2, my girlfriend's ClawD is called Shelley. And there's this thing that happens where it feels like it's mine, like it's really mine. It has its own name, it has this personality that sort of like mirrors me in this way that ClawD feels like it knows me and I like ClawD, but it's not mine. How do you think about that? - Yeah, I mean, I was having this conversation with somebody this week around, like, is the right pattern sort of single point of contact, like named, you know, version of, you know, of that, or is it the sort of team of agents that you're talking to? I think there's a lot to the single person that is maybe the coordinator or the delegator. And then at that, naturally, because it becomes the, the sort of agent you interact with the most, you want to interview it with a name and like a bit more of personality, and it's reflecting your, sometimes your personality in the case of, you know, like, all of a sudden every cliche came out, it was like, you know, the, you know, the cue or the money penny or like, you know, whatever the, or the, you know, how or whatever these different sort of, you know, sci-fi, e characters. But I think you do build that sort of sort of trust and knowledge. I think there's also that sort of like Ikea effect of like currently open ClawD is like still pretty hard to set up, so the fact that you went through all of that and it works, you're like, I did that thing. Like I, I, I, I birthed, you know, Shelley, for example, and now we can, you know, interact with them as well. But I think that paradigm is really powerful. Like the, I think moving away, like even within my ClawD code usage now, one of the things I have, like, strongly prompted in there is like, don't do very much work yourself, like delegate it to sub agents. And the reason I like that is because it means most of the time the sort of run loop is available for you to talk to you. And I think up in ClawD and PIE have like a similar architecture of keep the run loop open. And I think that actually makes it feel much more like somebody that you are talking to versus like a tool that you are delegating to and occasionally gets blocked for five minutes because it's doing some really complex task. Yeah. I, I, I totally agree. And I've had similar debates because we're also building our, like everyone we're building on, let all like open Claw one click Slack implementation to see if we can, we can do when that feels like ours. And we've had a lot of those debates about, do you want, one agent, do you want many? And one of the patterns that we found, which is kind of cool, is, so I have an agent, I use the agent for stuff that I do. And then people watch me use the agent for that. And they know what I'm good at. And they're, and if I'm using the agent for that stuff, they're going to trust it because they trust me. And it's modified itself in response to me. So like I sort of transfer my trust to it. And then people in the organization start using it for that. And so you get like this almost shadow or chart where everyone has a Claw, their Claw becomes known for and used for the thing that they're specialized at. That heard their owner is specialized at in New York. Yeah, I mean, that makes a lot of sense too. And you could think about, you know, there's a lot of interesting research questions, I think, around that, you know, I think people are experiencing visually for the first time around privacy. And like what my agent knows about me versus what it discloses to other people. But I think there's the positive version of that, which is all the things that it has learned from all your interactions and how it actually brings it to bear on other problems versus the generic, like yes, it's just like everybody else's agent, except, you know, it has a name that's attached to Dan. And it has like maybe some of Dan's, you know, access below the hood. Yeah. Well, Mike, we're out of time. This is a pleasure. I learned a lot. If people want to follow you or your work, where can they find you? They're probably easiest as Mikey K on next. OK, yeah. Thanks for joining Mike. Good to see you, Dan. [MUSIC PLAYING] [MUSIC PLAYING]