How to Build an Agent-native Product

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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 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've built 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.

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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, Enanthropic Labs.

I've admired your work from afar, both at Enanthropic and Instagram for a really long time and you're obviously at the forefront of building products and AI.

So thank you for coming on.

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 the underlying substrate or the process by which we build products has changed completely.

So tell me about your experience now versus earlier in Enanthropic versus Instagram and how you think things are changing.

Yeah, I was doing the thought exercise a couple of weeks ago of...

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, 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 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 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 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 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, 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 Claude basically rebuild bourbon.

It took about two hours.

It was feature complete.

It added filters, which bourbon didn't help.

We added those for Instagram.

But I think it knew the eventual future of the products that decided to build that.

And so I think that part feels really different.

But I think there's also-- I remember there was a week where Kevin went off and built all the filters for Instagram P1.

I went off and built the rest of the app.

And sitting there, I would stay up till 4 AM and then sleep till noon.

That's like my natural day/night cycle.

And in that process, you're making so many decisions.

How should location work?

We've got to find a way of accelerating building while still helping people build the intuition of those decisions along the way, because otherwise, I think you either 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 little thing in my head that if you grow a tree without it-- with it being indoors, without it being exposed to wind, it doesn't get as strong.

Because as it's growing, it needs all these forces pushing it back and forth in order to make a real tree.

And so if you have it indoors without wind, you're going to grow a tree, but it leans and it's not as strong.

And 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 incremental thing where you're doing things one at a time and then you're exposing it to users, you can actually grow an entire tree indoors.

And then you have this 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-- I love that.

I love that metaphor too.

When we were starting Instagram, we had this-- we were very into Eric Ries and Lean Startup and the whole like, yag me, like, you ain't going to need it principle.

And 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.

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 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.

They're arriving.

The metaphor I have somebody else giving, which I really like, is the difference between getting episode by episode, getting no characters in the TV show versus imagine you're thrown into the final episode and you're like, wait, what are all these things?

And who are all these people?

And I already am expected to have all of this context.

I think there's the same kind of feeling around developing something over time.

But the true metaphor, I think, sticks too as well.

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 not because 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 4am debugging and fixing this app that I made on the side at every called Proof, which is a agent native collaborative marketing editor so you can share a really quick plan docs and stuff with your team or with other agents and you have a little presence.

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, I'll do this and I'll do this.

And it just created this monstrosity that 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 Jim 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 is 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.

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, 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 a good product, maybe the ninth one will.

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 and be one you're going to mess up and 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 the company, like 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 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, 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 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 a contingent of we call them ant fooders because we're ants at 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 build a V1 that we think solves the problem 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.

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 the indoor tree would have been getting built.

And then the second behavioral world use, it's like, actually, nobody wants to do that.

They want to do this, this other piece.

So I think that piece that again, there's like the intuitions of the original lean startup ideas are still here.

It's just they manifested different times going in a different way.

I'm really curious to hear how you think about product design and how products should work because the I've been anyone at every will tell you the the the phrase that I use the most of the word that I use the most about the software build is it 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 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.

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 my own 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 exploration of this.

So thanks for like putting that ideas out in a really 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 know, they're a non technical person.

They're like, you are talking about like agents on 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, he's gonna do that, but now Claude 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 that core insight.

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 and versus not I think Claude code does it well.

I think Claude AI still needs to evolve a lot.

So as an example, I was watching somebody's 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 Claude's like, yeah, let me tell you the steps to go add it to my project knowledge.

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 and the ability to modify.

And I think that's essential in products these days.

I think Claude 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 they 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 that feels like the big next step.

But even within like Claude code, just teaching Claude code about Claude code was a really valuable experience.

I was like, this definitely relates.

This is not getting very circular meta, but bear with me.

I loved your write up on agent native.

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, you know, hey, Claude and Claude code, I, you know, 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?

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 get 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 hour long conversations, which is I think and one of the things that we're really thinking about allows is how do you imbue the software that Claude builds to be more Claude aware and even just Claude 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.

Like, so I'm super I'm super honored that you're you 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 Claude 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, you want to make sure that there's like one path the user can go down versus we're 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 harnesses 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 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 models sort of innate knowledge and then you'd get into these really funny arguments like no, I know you made a typo it's it's on it for five like no, I know it's for something.

No, no, no, so like 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 eye.

It was very funny watching cloud talk to cloud because it's like somebody's pretending to like be what humans are.

And this particular one was like 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 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 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 didn'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 not a primitive 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 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 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.

The unit of value in products right now is it's 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.

Yeah, how are you thinking about that?

Yeah, I think there's probably like three layers that it was like the first one was like Claude proved to me that you exercise this in some way.

I started doing that in all my promises.

I end when it's working in the future, I'm like and by the end before you PR, like prove to yourself and then to me that it works as intended like find the right way of doing it but actually that ends up 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 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 got to really test this thing.

And then the second one is that what you described is like everything having some sort of proof around like is it working as intended and as you intended to 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 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 choice but it was like the optimal choice as it fit into the paradigm.

And that is the 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 that's I don't I don't push on that for most PRs but when I was 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 sort of 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 like 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 a 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 used 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 hard to tell which maybe gets us 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 it being slow for us at Instagram like we had Instagram view 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 right 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 is really important that we really hammered like no like if you send a message we're not probably gonna 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 entropic but in general like you 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 kind of push it a little bit and is that gonna 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 is like he 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 it's maybe the product won't feel quite as robust if there's not someone who's like super technical and all the details like 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 force I was laughing with somebody they're like I thought you know my skills and distributed systems were like not going to be useful anywhere 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 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 know it was a healthy debate where like I 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 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 latter is as important and probably where this GM can be really valuable and not okay like I'm 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 use you know marked out 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 three 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 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 a hundred instructions on their first day like always answer and mark down except when the you know there'll be like I'm just gonna remember the last thing you told me I'm gonna 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 gonna be you know helpful in sorting through you know systems permissions and provisioning and early testing like that stuff is it's still you know it's still hard even for Claude when it can't edit the permissions 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 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 found that we actually are very work customer zero now for those 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 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 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 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 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 gonna go and you know make 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 it sort of varies but we found the one thing that's most important sort of our gating 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 when we have bets labs bets that we've wound down often in the post morning like nobody on this team actually really thought this was like the thing they were like yeah this seems reasonable like that's the the death knell for projects right so that person can be a designer and couple the bets it is it can also be sort of you know product minded engineer it's really a pure PM we actually 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 a compliment with that so you know because we're doing it's part of our labs processes 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 pull at any given point there's probably somebody who can be pulled onto the project that has that infrastructure 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's 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 we yeah we have GM's or they started as entrepreneurs and residents and they become general managers when they find a product that they want to like work on and each product just has one person like one person that does everything full stack so you know design engineering marketing all that kind of stuff at least the all the basics of that the shape of that GM used to be like super technical founder background and now I think has shifted towards at least some light technical but like I honestly just said that you can use clod or codex or whatever well and really good product sense really good taste for 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 you know ops people that you can like 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 sound 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 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 like even a small feature turns itself into its own product you know when you when you first make the messaging feature inside of Instagram it's like yeah I can do that in like a week or whatever but at some point that's its own product it almost needs its own team and that I think that line is getting or the number of things you can do it with one person is getting bigger but it still exists somewhere but I haven't quite figured out like had it had it managed that I heard it tell no I love that because there's actually I think there's the two parts that which is when the idea is still enough to hold into her 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 gonna take oh but my cloud could do that then it just ends up in this sort of piece and you also have all those alignment conversations like it was important as to kind of there was just two of us like it was hard enough to line the two of us like and go like get two people on the same page right with 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 end up in these things were trying to 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 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 is 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's been spending on the same idea for two four weeks somebody's 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 too is every three to six months you're 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 like throw out half of this because 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 gonna have to like really rethink about it feels like it changes a lot and 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 as they have sort of deleting features as a sort of imperative of people on the team like if this is not working let's go and ship that you know 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 never forget we one of the things I did maybe six months into when I was still a 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'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 like okay like they're 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 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 act called styles is not widely used but the people who use it use it a lot and we've talked at different points 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 and 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 and I've been really load-bearing for 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 finding ways of doing that is also really interesting I would hope that in the longer and what we can actually do is 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 it's 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 your own but 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 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 and but your customers were 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 gonna 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 co-pilot 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 entropic 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 to throw everything on or are you just stuck in that like oh kind of adopted it we kind of bolted it back on I think a couple things for us what we've started doing is basically treating like this trains gonna 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 better 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 would 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 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 and and and sort of rewrite a lot of the 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 like having to fire some of your customers who might have been you know really into your product for a different reason than where you're going sooner 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 have to be willing to put out the the v3 or the v4 that is a 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 a I powered manufacturing is going to be we got to like keep it moving or else to your point you're just gonna 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 cloth 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 started seeing that with you could already use these models to write code but it kind of took like some of these break out like low code you know the replants and loveables and these years of the world so I kind of put that in there and it's kind of the like almost the purest expression of they just give the model tools and like let it kind of go forward and do it and then like go forward and build it so like it was a 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 that 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 the 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 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 what where do you draw that like boundary around the way you let cloud operate right if you one was hey like 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 you know whatever back and be like no sorry like you can I do it yourself to like open claw which is like pretty like the aperture is like wider than I can see and I got it all made it to 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'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 the 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 what is that either shift the paradigm completely so you can be that open but with a lot of safeguards of you want 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 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 and so and there's this I think that happens when you have a claw that like my claws are to see to my girlfriend's call is called Shelley and 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 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 you know the you know the queue 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 claws like still pretty hard to set up so the fact that you went through all of that and it works like I did that thing like 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 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 it's blocked for five minutes because it's doing some really complex task yeah I I totally agree and I've had similar debates because we're also building aren't like everyone we're building on little 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 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 there and if I'm using the agent for that stuff they're gonna 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 org chart where everyone has a claw their claw becomes known for and used for the thing that they're specialized that that per their owner is specialized that in the org yeah I mean that 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 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 is Mikey K on next yeah thanks for joining Mike good see you Dan 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