Building Is the Easy Part Now | Mike Krieger on What AI Changed

The models today are good at adding features. They're not necessarily good

features. They're not necessarily good about figuring out what to cut out of the product. You can get it to go zero,

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

way. And some of the sort of intuitions you build about what are the right things to to put in there. I think you build over time. I feel like that is the art and science of software design in in 2026.

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That's grammarly.com. Mike, welcome to the show.

>> Great to be here. Thanks for having me on.

>> Great to have you. Um, I I'm super excited. For people who don't know, you

excited. For people who don't know, you are the co-founder of Instagram and now you are at Anthropic Antropic Labs. Um,

I'm, you know, I've admired your work from afar both at Anthropic 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? Like what we were talking about just now in the in the pre-production is um what has gotten easier and what has gotten harder or stayed maybe stayed the

same in product building as we've com as as the underlying substrate or the process by which we build products has changed completely. Um so like tell me

changed completely. Um so like tell me about your experience now versus you know earlier in anthropic versus Instagram and how you think things are changing. Yeah, I was doing the thought

changing. Yeah, I was doing the thought exercise a couple weeks ago of, you know, we know the Instagram story. We

had another product called Bourbon. We

worked on that for almost a year. Uh, it

wasn't working. We pivoted. We basically

spent 3 months building what became Instagram, launched it, and then scaled it. And asking the question like what is

it. And asking the question like what is now trivial um 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, but there was value in getting there too, right? like we over complicated the

too, right? like we over complicated the product so that we then had to simplify it. I find even the models today are

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 of the product and that took a lot of just uh sort of you know hitting actual actual real world usage. Um and there was something about

usage. Um 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 and and do input, but some of the sort of intuitions you build about what are the right things to to put in there, I think

you build over time. And so I 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 of what sort of intervention you want to make on the world and then build from there. Now the actual building part

from there. Now the actual building part once you know what to build is of course so much easier. I I had uh Claude basically rebuild Bourbon. It took about two hours. It was feature complete. It

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 uh it knew the eventual future of the product so it decided to build that in. Um so I think that that part feels feels really different. But I

think there's also, you know, uh I remember there was a week where Kevin went off uh and built all the filters for Instagram v1. I went off and built like sort of the rest of the app. And

you know, sitting there was I would stay up till 4:00 a.m. and then sleep till noon. That's like my natural dayight

noon. That's like my natural dayight cycle. And like in that process, you're

cycle. And like in that process, you're making so many decisions like how should location work? how and you know it's we

location work? how and you know it's we got to find a way of accelerating building while still sort of helping people build intuition of those decisions along the way because otherwise I think you either get just get very generic products that are

unlikely to break out or ones that just don't reflect some deeper intuition that you come to about your space or your product.

>> This is great. I love this. Um it's

making me think of two things. One is um I have this like little thing in my head that if you grow a tree without it like with it [clears throat] being indoors,

without being exposed to wind, it doesn't get as strong as because as it's growing, it needs all these forces pushing it like back and forth in order to like make a 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

thing. And I think there's something that you're saying here where because we've accelerated the pace of development so drastically um what what would normally be this sort of

incremental thing where you're 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 a great product. Is that is that is that

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

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 Yagne like you ain't going to need it principle and um 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

can. You're like, oh well, we have this option. Why not add this one as well?

option. Why not add this one as well?

That's like that's a PR of work. And if

you get a really good flow in cloud code, you know, you're firing things off, you're going to lunch, you're coming back, the thing is done, you're like, great, we added it. And the thing we realized was we' created this sort of matrix of functionality that was

actually quite hard to test and 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 a TV show versus imagine like you're thrown into the final episode. You're like wait what are all these things and who are all

these people and like I already you know am expected to have all of this context.

I think there's the same uh kind of feeling around like developing something over time. But the tree metaphor I think

over time. But the tree metaphor I think sticks too as well. And so like showing somebody the fully formed tree is also kind of a lot um all at once. And I I think there's there there's definitely something there. And how do you build

something there. And how do you build product these days and still keep it simple and not be just because you can doesn't necessarily mean that it should be in at least the first version. I'm

having the same problem because, you know, I was I was literally up until 4 a.m. debugging and and fixing this app

a.m. debugging and and fixing this app that I made like on the side at every called Proof, which is a agent native collaborative marketing editor. So, you

can like share share really quick plan docs and stuff with your team or with other agents and you have little presence and it's really fun. And

this is like my second or third iteration of the full product end to end which is really interesting that you can do now. But the first couple iterations,

do now. But the first couple iterations, I just found myself because vibe coding is so fun and so addictive. I just found myself being like, "Yeah, like I'll do

this and I'll do this and like and it just created this monstrosity that wasn't that good wasn't that good to use." And I got really inspired by we

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 to text app run by um 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 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 sharable markdown uh link and that then just like started growing virally inside of every like everyone started using it all the time. And then

now I we launched it and it just blew up and so I spent all last night like not sleeping trying to fix it and being like I'm too old for this [ __ ] I can't I can't be doing this anymore. uh because

it just reminded me of like being in my 20s or like being in college and like hacking [clears throat] on stuff and whatever which is fun but also exhausting. Um and so yeah, I've I've

exhausting. Um and so yeah, I've 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

oh, you know, eight features doesn't make for good product. Maybe the ninth one will. Instead, it just made for, you

one will. Instead, it just made for, you know, something that felt uh really complicated. I mean, I think a couple of

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. Um, you know, like classic, you know, Fred Brooks mythical man month like you you you

shouldn't rewrite software because all the things that were imbued in B1, you're going to mess up and >> yeah, >> exactly. And the whole second system

>> exactly. And the whole second system syndrome and there's still a lot of truth to that, but one, you know, the models can help you sort of diff and basically see did you miss anything that was in that first one? 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 re these are like days probably especially off a given source. So we've actually had

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 fullblown thing realized we've over complicated or made some kind of core

assumption um and then like tore it down a V2 and then and then iterate on it from there. It doesn't surprise me that

from there. 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, like a 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. Um, I think functionality wise and

well. Um, I think functionality wise and how we're dealing with it from a product development standpoint. Um, I think we

development standpoint. Um, I think we are learning to launch earlier. Um, and

it's definitely a balance around, you know, we've grown, we have like a strong enterprise footprint. People have

enterprise footprint. People have expectations about like what the the initial version is. Um, 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 uh contingent of we call them ant foodters because we're ants at anthropic. Um, but 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. Um, and then uh once we decided

time. Um, and then uh 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 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. Um, but it didn't.

And at the same time, it was it was useful enough to prove something out there. And I'm not sure developing it

there. And I'm not sure developing it for another two months, adding, you know, 50 features would have been more useful. In fact, we probably would have

useful. In fact, we probably would have been building in a the indoor tree would have been getting built and then the second it 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 like again there's like the intuitions of the original lean startup ideas are still here. It's just

they manifest at different time scale and 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 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

agent native. Um, so agents have to be able to like use it as anything that an agent a user can do in in the app, the 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 claude code

you guys. Like I think that claude 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 um the designers didn't really like think about beforehand. And I think that that's such

beforehand. And I think that that's such a good model for AI product development and AI. And I'm kind of curious like h

and AI. And I'm kind of curious like h this is just sort of what I've cribed from watching what you guys do and then like kind of put my own spin on, but how how do you think about it and how do you how do you talk about making products

like that?

>> Yeah, there's so much in here. Um, and I love the agent native write up you all did. It's like to me the canonical like

did. It's like to me the canonical like exploration of this. So, thanks for like putting that those ideas out in a in a really in a really clear way. So, I

think a few threads to pull on this. Um,

one is uh a conversation I had with somebody recently where they said uh you know like you all they're a non-technical person. They're like you

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 And it's just such a 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 uh alongside you.

And I think that's that that core insight is it's more than even just adding power and functionality to new software. It's also just unlocking the

software. It's also just unlocking the functionality that always should have been there or available and just felt like extremely hard um for people. So

that's like maybe thought number one.

Thought too 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

um as an example, I was watching somebody use uh cloud and they were in a project. And they had built I think an

project. And they had built I think an artifact or 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 it should have knowledge about and the ability to modify and I think that's essential in products these days and I think cloud code is the 2025 vintage of that and I

think there's even further aspects of it when you see what some of the 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 or you know it's probably esoteric for

most people but even unlocking that functionality means that you don't have to um sit there and be like oh I I wish it did this a little bit differently you know I wish Gmail worked in this slightly different way and instead just

asking it to and and I think that that feels like the big next step but um even within like cloud code just teaching cloud code about cloud code was a really valuable experience I was you this

definitely relates this is now getting very circular meta but bear with me uh I I loved your uh write up on agent native and I was like I want this as a skill.

So whenever I'm prototyping something it thinks in an agent native way. So I had it packaged it up as a skill and that whole process was you know hey cloud in cloud code I you know can you create a skill for this? It's like sure I'm

looking up my skill skill. I'm going to create a skill about it. I'm going to install it. I'm like great is that

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,

it. Let me check. Yep you do. All right,

let's get and everything was it has knowledge about itself and that unlocks so much uh uh capability in there as well which maybe is like the last thread to pull on. I think all of these could be hourlong 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 claude builds to be more claent 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 uh decades of software is not that right. 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 a I'm super I I'm super

about. Like, so a I'm super I 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 that's amazing. And

B, like yeah, you're pointing to a real problem that I have found is um I think actually cloud models are the best for this. like a codeex model generally is

this. like a codeex model generally is not as good um uh at building an agent native because they're uh 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 guard rails 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. Um, so yeah, how how are

super flexible. Um, so yeah, how how are you h how do you how do you architect your product to teach the models and the harness to teach the models to think and

work in this way?

>> Yeah, I think there's two parts to it.

One is the more sort of mundane part and the second one I think is the one that's more sort of interesting in developing.

The first one is like even just having good patterns and paradigms available to the model while it builds has been really valuable. Like finding the right

really valuable. Like finding the right balance of templatized to skillified, right? and like what that what that

right? and like what that what that right balance is. Um, 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 the model's sort of innate knowledge, and then you'd get into these really funny arguments like, "No, no, you made a typo. It's it's set 45."

You're like, "No, I I know it's like, no, no, no." So um 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

test. Like it's much harder to sort of write an endto-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

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 Cloud was ended up having a conversation with itself in like a chat feature in the It was very funny watching Claude talk to Claude

because it's like somebody's pretending to like be what humans are. This

particular one 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." And then the

me. Like I had a hard day." And then the clad's like, "Oh, I'm so sorry to hear that." And you're just going back and

that." And you're just going back and forth. But you wouldn't have written a

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 um 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 and things are going to end up in a weird place where cloud's going to try to do something that you wouldn't even think it was going to um

do and it might put your app in a in a new state. So maybe circling all the way

new state. So maybe circling all the way back to still like what's hard. It's

like um 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

primitives, right? I feel like that is the art and science of software design in in 2026.

>> That's really interesting. I to I totally agree with you. Uh yeah, you wanted to have a playground within a safe 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 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.

Um yeah, I I think that there's there's there's so much here. Like one thing that this is making me think of is I have this idea in the back of my head and I'm wondering if you're if you if

you have a a way to put this that that is more succinct is like the unit of value in in products right now is um it's 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 tests 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 is good or not, you know. Um,

>> yeah. How are you how are you thinking about that?

>> Yeah, I think there's probably like three layers to that. Like the first one is like Claude, prove to me that you've exercised this in some way. You know,

I've started doing that in all my promises. I end you know when it's

promises. I end you know when it's working on future I'm like and by the end you know before you PR like prove to yourself and then to me that it works as intended like find the right way of doing it which actually ends up 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 you know succinctly rather than what it likes to do it's like I read the code it looks good I'm like you wrote the code I don't trust

you so you know you got to really test this thing um and then the second one is that what you described is like you know everything having some you know sort proof around like did is it is it

working as intended and as you intended too because cloud is going to make or any of these models is going to make a lot of decisions um for you and sometimes you'll you know I'll have engineers on the team uh 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 reasonableish choice but it was it

like the optimal choice does it fit into the paradigm I feel like that is the uh it's like it's not just proof of work But it's like proof of thoughtfulness, like did you think this through? Um, and

uh, I was talking to an engineer yesterday and they they they were like, "Oh, I was really I knew you were going to ask me a lot of questions about this." So, I was reviewing what Claude

this." So, I was reviewing what Claude had done so that I wouldn't be like, "Uh, 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

primitives." Like, great. Let's make

sure those are good and that you've thought through how they interrelate because uh, 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 um I 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 [ __ ] how do I explain how this codebase works and so I had to like go back and

forth with the model a bunch to be like, "Okay, help me like define these terms. Help me like figure out how how I can explain this so I don't look like a total idiot because like yeah, there's I understand like some of it, but not all

of it. Definitely not enough to like the

of it. Definitely not enough to like the way that I would used to have to to know to know. Um, and it's a whole different

to know. Um, and it's a whole different thing to be like, do I need to know that anymore? Is it like where's the line

anymore? Is it like where's the line now?" It's hard hard to tell. Which

now?" It's hard 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 yeah there's products that you use that feel robust underneath and there's ones that you use that you're like it feels like it's one wrong

command or click away from the whole thing either like freezing or being slow for us at Instagram like we had um uh Instagram v 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 like wrote I wrote our own like bespoke real-time system it was like uh you 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

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

sent. I feel like there's like a little check. Um that's like one small example

check. Um that's like one small example but I think that that is um a 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 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 is 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 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

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.

Spiral as 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

enough. I'm curious like but the downside is it's maybe the product won't feel quite as robust if there's not someone who's like super technical in all the details. So like h 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 in 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 uh 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 Reddus or not or could go away with just Postgress and you know it was a healthy debate where like I only because I was grounded in having used a lot of those technologies before

but then there's the other side of robustness which is um 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? Um, and so that the

tools correctly? Um, and so that the latter is as important and it's probably where this GM can be really valuable and that okay like I'm making changes but

just like you wouldn't patch a um sort of flakiness in your distributed system by just being like well just retry it in 5 seconds. I'm sure it'll work like also

5 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 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 like still a place that still

both. It's like still a place that still needs a lot of of human oversight on the systems part. You know, it's now able to

systems part. You know, it's now able to debug production systems, which is really valuable, but architecting them in the first place. I feel like we're still benefits from somebody who's really thought these through things through or has experience. And on the prompting side, you know, if you give it

a I've seen people get into this dev loop even internally here, like here's the prompt, here's a mistake that the system made, iterate on the prompt. Its

natural tendency is to just add more things to the prompt. Um, 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 in markdown except when

they, you know, they'll be like, I'm just going to remember the last thing you told me um 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

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 you know helpful in sorting through you know systems permissions and provisioning and early testing like that stuff is still you know it's still hard even for cloud when it can't edit the

permissions itself which it can't for good reasons. And then on the on the

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. So our applied 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 you know efforts because we have a lot of products that are you know very AI powered. So how do we bring that expertise in here because that expertise does not sit with our

software engineers today for example.

What about the in between of like okay it's not the underlying architecture it's not the prompt it's like the UI and the 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 who 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 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

builder role. Not all of them, but most of them. And a lot of our um you know,

of them. And a lot of our um you know, 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 um on those efforts because they can and again

paired correctly with the right person.

We have found this almost sort of co-founder model for some of these labs initiatives or you have the designer who had the original idea maybe and they're pushing on something and then the traditional software engineer that's

going to go and you know 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

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 a real real engineer that actually can like kind of smooth out the

rough edges of the the the trail they're leaving. It sort of varies, but we found

leaving. It sort of varies, but we found the one thing that's most important and sort of our gating factor in starting up new projects. I'm curious how similar is

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. Um, and that sort of like

asking. Um, 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. Um, when we'd have

bets uh, labs bets that we wound down often in the postmortem, 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 the the deathnail for projects, right? So that person can be a

projects, right? So that person can be a designer and couple of the bets it is.

It can also be um sort of a you know product-minded engineer. Um it's rarely

product-minded engineer. Um it's rarely a pure PM. Um we actually only have one currently one PM for all of labs. We

we're hiring more. Um and they're sort of playing you know sort of a wide role but yeah a designer or like aoriented founder. Um and then what we look for is

founder. Um and then what we look for is well what skills do we need to complement with that? So, you know, because we're doing it's part of our labs process is actually evaluating every project every two weeks and deciding whether we double down or

whether we sort of release those folks back into the 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's fixed on a project forever. That's really

project forever. That's really interesting. Yeah, we do it slightly

interesting. Yeah, we do it slightly different. There's some overlaps, but we

different. There's some overlaps, but we do have a slightly different structure where um we yeah, we have GMs or or they start as entrepreneurs and residents and they become general managers when they find a product that they want to like

work on. Um and each product just has

work on. Um 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. Um 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 that you can use claude or codeex or whatever well um and uh really good uh product

sense really good taste for for the the the subject area or the thing that you're trying to build um and evidence that you can build with AI. Um and then

what we have is a shared resource resource layer that sort of works a little bit like an agency where we have designers and we have growth marketers

and we have um 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

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 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

head. And that point used to be much smaller. Now it's much bigger. But

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 a week or

whatever. But at some point, that's its

whatever. But at some point, that's its own product. It almost needs its own

own product. It almost needs its own team. And that uh I think that line is

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 or how to tell.

No, I love that because there's actually I think there's the two parts to that which is um when the idea is still enough to hold into your own head or an individual person's head. uh 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 clude could do that and it just ends up in this sort of piece and you also have all those alignment conversations like it was important in Instagram there 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 end up in these things where 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 um uh sort of aspect at play which is you don't want to pre-scale the team too quickly even if the idea is

exciting because then you just end up in this sort of like meta coordination uh game. But I I like your framing of there

game. But I 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.

Um, and then there's also the if somebody's been spinning on the same idea for two, four weeks, sometimes injecting some other thinking and 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. 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 [ __ ] 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

with that? Like how do you think about Yes. I know in three months this the

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 like it feels like it changes a lot in in how you think about software.

>> Yeah. And being willing to delete code.

But I think that's something the cloud code team has done really well is 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 un 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 it actually makes sense to deprecate and then remove that first one. It does get harder um as we get

one. It does get harder um as we get more and more enterprise focused even with these tools because they come to depend on it. I I I'll never forget we um one of the things I did maybe six months into when I was still a chief

product officer was we did a big sort of redesign of 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 now I have to like redo all of it and we're like okay like there 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 going to 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

more. But yeah, I think the unshipping piece then you end up with people who have built um I'll use an example. So,

uh there's a feature in in Claire called styles. It's not widely used, but the

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, the styles still make sense in the product. You know, there's other ways of

product. You know, there's other ways of accomplishing the same thing. There's

custom instructions in 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 we talked about removing it ended up being really loadbearing for a pe 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

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 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 it your own but it doesn't have to

add complexity to every future person that's adding that's uh signing up for the first time. I'm curious for for labs um and then also maybe just in general

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 uh but your customers are going to want the outdated version

but as a startup that's like a little that that feels pretty risky because um yeah, you're just going to I guess you're you're you're susceptible to to

disruption if you are optimizing for what uh someone at a gigantic public company will buy right now. Um 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

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 and [snorts] 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 uh can you adopt it within your existing paradigm does it require to throw everything out and are you just stuck in that like oh we kind of adopted

it we kind of bolted it back on. Um I

think a couple 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 wellreceived 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 we'll continue to evolve along the way but then we'll provide you know uh 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 um and that that 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 the stack is I think companies should be way more willing to do that and it everything is getting compressed right in in previous cycles there 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 when we were going sooner.

But that was on a multi-year kind of time range thing where he was like, "Yes, last year's product versus not 3 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. Um, and then maybe have a

worked. Um, 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, uh, 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 you're selfrelacing it yourself. And again,

it's just the same old story but now compressed to months.

What's your take on Open Claw? It

>> it has the flavor of something else that I or just the the thing I really like seeing when you get people to see something that was already possible, but it's now in a package where people can actually try it out and there's some

intuition, you know, uh around how to how to build on top of that. Like you

sort of seeing that with you could already use these models to write code, but it kind of took um like some of these breakout like low code, you know, the replets and lovables and vzeros of the world to like kind of put that in

there. Um, and it's kind of the the like

there. Um, and it's kind of the the like almost the purest expression of the 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, um,

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 that my 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

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 like what where do you draw that like boundary around what

you let cloud operate right if v1 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 backend being like

no sorry like can I 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 emergent and it's

>> exactly. And it's emergent and it's amazing. And I think like probably the

amazing. And I think like probably the most interesting product question, I won't say for all of 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 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 think that you know 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 um

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 what is that either shift the paradigm completely so you can be that open but with a lot of safeguard that

would be one approach or figure out some boundary to draw in which um 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, um, like you said, the personal nature of it. Um, 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 [laughter] and and [clears throat] so and there's this thing that happens when you have a

claw that like my claw is R2C2.

My girlfriend's claw is called Shelly.

Um and there's this thing that happens where it feels like it's mine, like it's really mine. It has its own name. It has this

mine. It has its own name. It has this 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. H how do you think about that?

mine. H how do you think about that?

>> Yeah. I mean I was having this conversation with somebody this week around uh 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 the coordinator or the delegator and then at that it naturally because it

becomes the 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 Q or the money penny or like you know whatever the or the you know Hal or whatever these different sort of you know sci-fi 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 claw is 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 I

birthed uh you know Shelly for example and now we can you know interact with them as well. But I think that paradigm is really powerful like the um I think moving away like even within my cloud

code usage now uh one of the things I have like strongly prompted in there is like don't do very much work yourself like delegate to sub aents 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 openclaw and pi have like a similar architecture of keep the run loop open and I think that actually makes it feel much more like somebody that you are talking to versus like a tool that you

are delegating to and occasionally gets blocked for five minutes because it's doing some really complex task.

>> Yeah, I I I I totally agree and I've had the similar debates um because we're also building our like everyone we're building our own little like open claw oneclick Slack implementation to see if we can we can do one that feels like

ours and we've had a lot of those debates about do you want one agent, do you want many? And one of the patterns that we found, which is kind of cool, is

so I have an agent. I use the agent for stuff that I do and then people watch me use the agent for that and they know what I'm good at and they're and if I'm using the agent for that stuff, they're

going to trust it because they trust me and it's modified itself in response to me. So like I sort of transfer my trust

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 when everyone everyone has a claw, their claw becomes known for and used for the thing that they're specialized at that per their owner is

specialized at 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

other people. But I think there's the positive version of that which is all the things that it has learned from all your interactions and how it actually brings it to bear on other problems versus the generic like yes it's just like everybody else's agent except you

know it has a name that's attached to Dan and it has like maybe some of Dan's you know access below the hood.

>> Yeah. Well Mike we're out of time. This

was a pleasure. I learned a lot. Um if

people want to follow you or your work, where can they find you?

>> Uh they probably easiest Mikey Konx.

[ __ ] yeah. Thanks for joining, Mike.

Great to see you, Dan.

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