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 zero, not just zero to one, but zero 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 build about what are the right things to put in there, I think you build over time. I feel like that is the art and science of software design in 2026. Book 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 rushed 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. 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Where should we start? Like what we were talking about just now in the, in the pre-production is the, what has gotten easier and what has gotten harder or stayed, maybe stayed the same in product building as we've come as, as the underlying substrate or the process by which we build products has changed completely. So like, tell me about your experience now versus, you know, earlier in the topic versus Instagram and how you think things are changing. Yeah. Yeah, I was doing the thought exercise a couple of weeks ago of, you know, we know 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. And I was asking the question, like, what is now trivial? And what was actually inherent in that building process that doesn't get easier, right? And that year, we probably could have hit some of the dead ends we had eventually hit sooner, right? But there was value in getting there too, right? Like we over complicated the product so that we then had to simplify it. I find even the models today are good at adding features. They're not necessarily good about figuring out what to cut out of the product. And that took a lot of just sort of, you know, 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 stuff we're building in labs, like 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 build about what are the right things to put in there, I think you build over time. And so I've been reflecting, like 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, you know it knew what the eventual future of the products have decided to build that in. So I think that, that part feels, feels really different, but I think there's also, you know I remember there was a week where Kevin went off and built all the filters for Instagram. I went off and build like sort of the rest of the app and, and, sitting there, I would stay up till 4am 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? 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. 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, cause 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 grow a tree, but it like leans and it gets all, 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 what would normally be this sort of incremental thing where you're doing things one at a time and then you're exposing it to 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, um, level of intuition and exposure to experience at each step that, that creates a great product. Is that, is that, is that, I love that. I, I, I, um, I love that metaphor too. We, you know, when we were starting Instagram, we had this, we were very into like Eric Reese and lean startup and the whole like yakni, like you ain't going to need it principle. And, um, yeah, I have found, and actually even one of the things I was working on in labs recently, we way overbuilt for V1 before we even got to early access because you can, and you're like, oh, well, we have this option. Why not add this one as well? That's a PR of work. And if you get a really good flow in cloud code, 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'd 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 to know 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 there's the same kind of feeling around like developing something over time, but the true metaphor. I think sticks too as well. And so like showing somebody the fully formed tree is also kind of a lot all at once. And I think there's definitely something there in how do you build product these days and still keep it simple. And 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... I was literally up until 4 a.m. debugging and fixing this app that I made on the side at Every called Proof, which is an agent-native, collaborative, marketing editor. So you can share really quick plan docs and stuff with your team or with other agents. And you have a little presence. And it's really fun. And this is 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 of 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 app run by GM Naveen. who he's just so focused on making one simple thing work so well. And I saw how well that works in this age of just like anyone can make a product. It's like something that's 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. Yeah. 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. I'm being like, I'm too old for this 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 hacking 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 is possible. How are you dealing with that? Yeah, and 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, oh, you know, eight features doesn't make for good product. Maybe the ninth one will, and 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 shouldn't rewrite software because all the things that were imbued in B1, you're going to mess up. And it's not going to be B2. Yeah. Yeah, yeah, exactly. And that whole second system syndrome. And there are 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 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 overcomplicated or made some kind of core assumption, and then like tore it down, done a V2 and then iterate 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, they... 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 a 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 or 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 contingent of we call them ant fooders because we're ants at Anthropic. But the only that only gets you so far before you need that, that real world contact, like take cowork, for example, we've 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 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 100 things that V1 should or could have had, but it didn't. And at the same time, it was useful enough to prove something out there. And I'm not sure developing it for another two months, adding 50 features would have been more useful. In fact, we probably would have been building The indoor tree would have been getting built. And then the second they hit real world use, it's like, actually, nobody wants to do that. They want to do this, this other piece. So I think that piece of that, again, there's like the intuitions of the original lean startup ideas are still here. It's just, they manifest at different timescale and in a different way. I'm really curious to hear how you think about product design and how products should work. Because anyone at every will tell you the phrase that I use the most or the word that I use the most about the software build is it has to be agent native. Um, 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 agent can do. There's a couple 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. Um, 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 product development and 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 my own spin on. But how do you think about it? And how do you talk about making products like that? Yeah, that's so much in here. And I love the agent native write up you all did. To me, the canonical exploration of this. So thanks for putting 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 all, they're a non-technical person. They're like, you are talking about like agents and all this 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 a sort of funny thing where if you knew the incantations to properly get on the command line and brew install the thing that like, nobody is going to 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's, that core insight is, it's more than even just adding power and functionality to new software. It's also just unlocking the, functionality that always should have been there or 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 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 add it to my project knowledge. It's 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 is a product. it should have knowledge about and the ability to modify. And I think that's essential in products these days. I think cloud code is the 2025 vintage of that. And I think there's even further aspects of it. When you see what some of the harnesses that folks are experimenting with, where that can actually sort of modify the harness itself, that starts getting to the next maybe level of that, where, you know, it's probably esoteric 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. You know, I wish Gmail worked in this slightly different way instead of just asking it to. And I think that feels like the big next step. But even within like Cloud Code, just teaching Cloud Code about Cloud Code was a really valuable experience. I was like, this definitely relates. This is now getting very circular meta, 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 up as a skill. And that whole process was, you know, hey, cloud and cloud code, can you create a skill for this? It's 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 or do I need to reload? It said, 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. a capability in there as well, which maybe is like the last thread to pull on. I think all of these could be hour 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 aware so 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 A, I'm super honored that you read the write-up and you made a skill for it. That's amazing. And B, yeah, you're pointing to a real problem that I found. I think actually cloud models are the best for this. A codex model generally is not as good. Yeah. 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. You want to make sure that there's like one path the user can go down versus for creating this extensible thing that's super flexible. So yeah, how are you, how do you, How do you architect your product to teach the models and the harness that you 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. 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. Finding the right balance of templatized to skillified, right? And like what that right balance is. But having, you know, one of the things that we like 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 model's 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 four or five. 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 a agent native ios app that i was working on and i was i was having claude interact with it and claude was ended up having a conversation with itself in like a chat feature in the i was very funny watching claude talk to claude because it's like somebody's pretending to like be what humans are in this particular one it was like a prototype 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. I know 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 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 Claude'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 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 a 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 so much here. One thing this is making me think of is that 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 like 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... send me a loom of you using it or your agent using it so I can tell is this good or not, you know? Yes. Yeah. How are you thinking about that? Yeah, I think there's probably like three layers to that. The first one was like, Claude, prove to me that you've exercised this in some way. I've started doing that in all my promises. And when it's working on a future, I'm like, and by the end, before UPR, prove to yourself and then to me that it works as intended. Find the right way of doing it. But actually, you have to change your own sort of way you build and scaffold around saying, what is the right way to get Claude able to at least test this change succinctly rather than what it likes to do is 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 to? Because Cloud is going to make, or any of these models is going to make a lot of decisions for you. And sometimes, you know, I'll 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-ish choice, but it wasn't like the optimal choices that 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 there was like, I was really I knew you were going to ask me a lot of questions about this. So I was reviewing what Cloud had done so that I wouldn't be like, I'm not sure. You know, and that's I don't I don't push on that for most PRs. But when there's one that's 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 tower of assumptions that you're not fully aware of. I had literally the same experience today because I made proof, totally vibe coded. 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 signed up to help me fix it. And so I had to onboard them. And I was like, shit, how do I explain how this code base works? And so I had to like go back and forth with the model a bunch to be like, okay, help me define these terms. Help me figure out how I can explain this so I don't look like a total idiot. Because like, yeah, I understand some of it, but not all of it. Definitely not enough to like the way that I would used to have 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? It's hard to tell. which maybe gets to something else and i haven't tried to articulate this so bear with me as i 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 are 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 um instagram view direct messaging v1 and that like who knows if you send a message it might or may not arrive to the other person like we'd like 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 WhatsApp 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 better. like an essential part of shipping on anything, not just at, you know, Anthropic, but in general, like you've built this thing, does it feel like it's built on sand or does it feel robust? And the agent native part adds something totally even beyond that, which is, can I push it a little bit? And is that going to fall over? Or does 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 that's the bar, which I agree, that's where you definitely want to get to. 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 Spiral is a 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 maybe the product won't feel quite as robust if there's not someone who's super technical in all the details. How do you think about who builds products right now inside of the labs 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 useful anymore. But actually, those are maybe some of the most useful skills and reasoning about that. And, you know, thinking things through, like I had a long debate with Claude last week around like whether the system that I was building needed Redis or not, or could go to just Postgres. And, you It was a healthy debate, but only because I was grounded and having used a lot of those technologies before. But then there's the other side of robustness, which is, have you just papered over all the problems with fixes to your system prompt and additional instructions, or have you architected the actual set of tools correctly? And so the latter is as important, and probably where this GM can be really valuable, and not, OK, I'm making changes. But just like you wouldn't patch sort of flakiness in your distributed system by just being 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 use you know marked out or whatever the the the thing that you're trying to patch is like they're both actually symptoms of the same thing which is the underlying piece um robust or not and um Claude actually, I'd say this about all the models, but I think Claude could be much better at both. It's still a place that still needs a lot of human oversight. On the systems part, 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 things through or has experience. And on the prompting side, if you give it a... I've seen people get into this dev loop, even internally here. Here's the prompt. Here's a mistake that the system made. Iterate on the prompt. Its 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'll be like, I'm just going to remember the last thing you told me. Or 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 your 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 helpful in sorting through systems permissions and provisioning and early testing. That stuff is still hard even for cloud when it can't edit the permissions itself, which it can't for good reasons. And then on the robustness side, actually, we've had a lot of success pairing our product teams with our applied AI teams. Our applied AI teams 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 customer zero now for those – efforts because we have a lot of products that are very AI powered. So how do we bring that expertise in there? 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 not the prompt. It's like the UI and the flow. Who's doing that? That's a great question. Like we have found, you know, some of the people that transferred into labs were the folks like really were focused on polish on the website, but they were interested in doing something new. And they bring such a different approach as well around... We had the prototype. It looked generically nice versus, oh, this feels like it's branded and it has this. That's part one. Part two is designers. 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 designers, We actually don't have a lot of full-time designers on labs, but the ones that we do, I would say, are writing and contributing almost as much code as the 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 where 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 make the... pave 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 yeah It sort of varies, but we found the one thing that was most important, sort of our gaining factor in starting up new projects. I'm curious how similar is this 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 or 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. Yeah. When we have labs bets that we've wound down, often in the post-mortem, we're like, nobody on this team actually really thought this was like the thing. They were like, yeah, this seems reasonable. Like that's the death knell for projects, right? So that person can be a designer and a couple of the bets it is. It can also be sort of a product-minded engineer. It's rarely a pure PM. We actually only have one, currently one PM for all of labs. We're hiring more. And they're sort of playing, you know, 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, you know, because we're doing, as 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 of 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 is fixed on a project forever. That's really interesting. Yeah, we do it slightly different. There's some overlaps, but we do have a slightly different structure where... Yeah, 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 like, I honestly just care that you can use Claude or Codex or whatever. Well, and really good product sense, really good taste for, for the, the, the subject area or the thing that 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, the internal agencies. And then each GM is out on their on, on, you know, 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 like the thing and they are not going to sleep until it is fully working. Yes, exactly. Like, and, and I've been, I've been thinking about, okay, when, when would you hire someone else to work on a product or when would 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 that, uh, I think that line is getting, uh, 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 like how to, how to manage that. I heard it tell. No, I love that because there's actually, I think there's the two parts to 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. Like, oh, you were, I was going to take, oh, but my cloud could do that differently. And it just ends up in this sort of piece. And you also have all those alignment conversations. Like it was important in Instagram that it was just two of us. Like it was hard enough to align the two of us like, and go like get two people on the same page. Right. Uh, the second startup I did artifact, you know, 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, you know, we hadn't had product market fit yet. And so we were still iterating and then you would end up in these things where you're we're in a zoom with eight people talking about what we're doing next. And you 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, there's some similar sort of aspect at play, which is you don't want to pre scale the team to it, even if the idea is exciting, because then you just end up in this sort of like meta coordination game. But I like your framing of there is some point where either, you know, two people really will help go on it together and there's enough sort of context and scope where they can hold some other complex piece in their head. And then there's also the, if somebody has been spinning out the same idea for two, four weeks, somebody is injecting some other thinking and that urgency can help too. Yeah. I think it's especially important to keep it small in AI because one of the things that we deal with all the time, which I'm sure you see too, is every three to six months, you have to throw out like half your product. Um, 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 like throw out half of this. Cause the models are so much better. It just makes it much easier to, to like pivot in that way. Is that, do you see that? And like, how do you deal with that? Like, how do you think about, yes, I know in three months, this, the code maybe, or even the whole feature set, I'm going to have to like 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 has done really well is they have sort of the leading features as a sort of imperative of people on the team. Like, if this is not working, let's go unship 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 it actually makes sense to deprecate and then remove that first one. It does get harder as we get more and more enterprise-focused, even with these tools, because they come to depend on it. I'll never forget, we... One of the things I did maybe six months into when I was still Chief Product Officer was we did a big sort of 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 that's like, I just recorded 20 hours of enablement content for my company to do for Cloud Enterprise and I have to like redo all of it. And we're like, okay, like you're playing at a different release cadence. And of course, like shipping twice a year at one of our conferences is not an option. So we are gonna keep moving quickly. But then we've since like, Learn 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 IAC called styles. It's not widely used, but the people who use it use it a lot. And we've talked at different points like, yeah, does styles still make sense in the product? You know, there's other ways of accomplishing the same thing. There's custom instructions and processes. projects now, um, there's skills now, right? There's so many other ways of accomplishing that. Um, 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 ended up being really load bearing for a few, a few companies, like entire use cases, like, Oh, we have our house style that the CEO personally authored and gives to every employee and that's how they operate. And so, uh, finding ways of doing that is also really interesting. I would hope that in the long run, what we can actually do is, is, um, 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. Like here's how you can keep using it forever in your own and keep iterating on it and make it your own, but it doesn't have to add complexity to every future person that's adding that's signing up for the first time. I'm curious for labs and then also maybe just in general, what are your thoughts for startup founders? Your enterprise point, 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 are going to want the outdated version. But as a startup, that's like a little bit, 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 like sort of out. It's like, you know, looking at looking at Copilot or whatever. It's that's the sort of vibe that happens. Like, how do you think how do you think about that yourself and inside of Anthropic? And then how do you think founders should think about that? Yeah, no, it's 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? Or are you just stuck in that like, oh, we kind of adopted it. We kind of bolted it back on. Yeah. I think a couple of things for us, what we've started doing is, um, basically treating like this train is going to keep moving and we'll provide enterprise toggles along the way, but the core of it will continue to evolve. And that's sort of the, 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, you know, 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, uh, Co-workers is a great example where, you know, 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 sort of. rewrite a lot of the stack is I think companies should be way more willing to do that. And everything is getting compressed, right? In previous cycles, it was the kind of idea of having to fire some of your customers who might've been really into your product for a different reason than where you're going sooner. But that was on a multi-year kind of time range thing, where it was like, yes, 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 know, 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 like, yes, this is how we think the future of this piece of knowledge work or this, you know, AI-powered manufacturing is going to be. we got to like keep it moving or else to your point, you're just going to, you're either going to get replaced by the next company that then rethinks it from scratch or yourself replacing it yourself. And again, it's just the same old story, but now compressed to months. What's your take on Open Claw? It has the flavor of something else that I, or just the thing I really liked 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, you know, around how to build on top of that. Like you sort of seen that with, you could already use these models to write code, but it kind of took some of these breakout, like low code, the replets and lovables and v0s of the world to kind of put that in there. And it's kind of 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... cool interesting moment for people to realize both the like potential but also pitfalls of this of like oh it did this thing i didn't mean it to or you know my funniest one was a friend was like i think my wife is jealous of my open claw and like i'm talking too much to it and it's like people start developing like deeper sort of like very personal relationships by just having a lot of context in these things and access to all these different tools um i think there's the open question of how do you then make it easy, and it actually goes back to our conversation around where do you draw that boundary around what you'd like cloud operate, right? If V1 was, hey, these are the three tools you can use, only use these tools ever, and then most people's interaction with those systems was, hey, can you do this and whatever, back in being like, no, sorry, like, you can do it yourself to like open claw, which is like pretty, like, the aperture is like wider than I can see. Oh, my God, it called me to keep my emails. And I didn't even know it could do that. Yeah, exactly. And it's emerging. And it's amazing. And I think, like, probably the most interesting product question, I won't say for all 2026, because 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 MCPs, but they're gated and they ask for permissions for good reasons. That is still a useful product without being a, you know, kind of YOLO product. And I 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. I think NVIDIA put out something that was like, they're safe, open, everybody's going after this question. And I think it's going to be about figuring out what is that, either shift the paradigm completely so you can be that open, but with a lot of safeguards, that would be one 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 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 Claude. But there's this weird thing where if I watch someone else using Claude, I'm like, I feel like I like thought a stripper liked me or something, you know, it's like, Claude thinks you're smart, too, or whatever, you know, like, And so, and there's this thing that happens when you have a claw that, like my claws are to see too. My girlfriend's claw is called Shelly. there's a thing that happens where it feels like it's mine. Like it's really mine. It has its own name. It has a personality that sort of like mirrors me in this way that Claude feels like it knows me. And I like Claude, 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, it naturally, because it becomes the sort of agent you interact with the most you want to imbue it with a name and like a bit more personality ends up reflecting your, sometimes your personality in the case of, you know, like... all of a sudden every cliche came out. It was like the Q or the money penny or whatever the, or the Hal or whatever these different sort of sci-fi-y characters. But I think you do build that sort of trust and knowledge. I think there's also that sort of like IKEA effect of currently open clause is 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. I birthed Shelly, for example, and now we can interact with them as well. I think that paradigm is really powerful. Like the, I think moving away, like even within my cloud 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. And I think open claw 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 tasks. Yeah, I totally agree. And I've had similar debates because we're also building on like everyone, we're building on little like open claw one click Slack implementation to see if 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 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. Yeah. And so you get like this almost shadow org chart where when everyone has a claw, their claw becomes known for and used for the thing that they're specialized at, that their owner is specialized at in the org. 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 down below the hood. Well, Mike, we're out of time. This was 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. Yeah. Thanks for joining, Mike. Great to see you, Dan. Thank you. Oh my gosh, folks, you absolutely positively have to smash that like button and subscribe to AI and I. Why? Because this show is the epitome of awesomeness. It's like finding a treasure chest in your backyard. But instead of gold, it's filled with pure unadulterated knowledge bombs about chat GPT. Every episode is a roller coaster of emotions, insights, and laughter that will leave you on the edge of your seat, craving for more. It's not just a show. It's a journey into the future with Dan Shipper as the captain of the spaceship. So do yourself a favor. Hit like, smash subscribe, and strap in for the ride of your life. And now, without any further ado, let me just say, Dan, I'm absolutely hopelessly in love with you.