Building Anthropic with Mike Krieger: Product Playbooks In The Age Of AI, Why Memory Is Key, And Instagram Lessons

0:00When I think about your role on the topic, I do want to connect it to Instagram. out. Let's say you were posting a photo is very clear. Oh, there's a list of filters. Oh, I tapped one and my photo looks different. Or, oh, there's this thing called boomerang. Oh, I tried it once and I can see what it does. That sort of like building of mastery is I think more complicated in AI. But I think the thing I've most changed my mind on is when I got here, I was like, I can't believe we're still using chat boxes. Like that feels like we should be in a completely different thing. It's actually a very useful way of expressing like an open-ended problem.

0:31The thing that needs to change, though, is what happens after you hit enter.

0:39What does building Instagram, one of the most influential social networks in the world, with over 3 billion users, have to do with building one of the best AI assistants in the world? As it turns out, quite a lot. My guest today on Building One is Mike Krieger. He's the co-founder of Instagram, and he's the chief product officer at one of the most interesting AI companies in the world, Anthropic. And in this conversation, we cover a lot of ground. What made Instagram such a breakout success? Why Mike decided to shut down Artifact, his second startup after Instagram? And the key lesson he would share with founders about when to stop or where to persevere?

1:17The surprising similarities between building Instagram and building Anthropic, and why he's still joining Anthropic, he has completely changed his mind on how to build great products. And we actually spent quite a bit of time on this part. How Anthropic, an AI-identive company, is completely rewriting the playbook for how you build great products with AI. Here's my conversation with Mike Krieger.

1:44Mike, it's a pleasure to have you on the show. Thank you so much for joining me. It's great to be here. Thanks so much for having me. So, you know, we'll start right from the start. So you co-founded Instagram just two years after studying symbolic systems at Stanford. Now, for our listeners, they might have heard this program, but this is a special program that blends computer science, linguistics, philosophy, psychology. I'm curious just why you chose that program. I think you came from Brazil for that program. I grew up in Sao Paulo. And, you know, it's funny, you have to rewind your memory of social networks.

2:19So I was graduating high school and the biggest thing out there was Orkut in Brazil. This is like a network. I don't know if it started inside Google or whether Google, but it was that social network that was basically... It was Brazil and India. Orkut was massive. Exactly. And it was huge. And the summer before I went to Stanford, I was really curious to connect with other people. And this is what Facebook would barely emerge, right? So I think we maybe had our first Facebook accounts. But I went on Orkut and I searched for Stanford. I was like, is there anybody out there? and there was a very small Stanford page, but there was also a Stanford Symbolic Systems page and it had like six people in it.

2:53And I'd never heard of this program before and I clicked through and, you know, sort of you see your, you know, life reflected in front of you. You're like, this is all the things I'm interested in. I was already interested in programming. I liked thinking about like language. I was a big reader at the time. I was interested in psychology and linguistics. It was like, in some ways, the perfect degree if you want to do, you know, be an entrepreneur or be a builder or really combining how to build, but also who you're building for and how people think and how machines think. And it was even some early AI in that program too.

3:24So I feel very lucky that I found that program at the right part of my life. What part of that was kind of instrumental or important for founding Instagram literally two years after college? I think there were probably three pieces. One was the design thinking and human centered, you know, interaction piece. So we, Stanford D School, the design school was just starting to kind of emerged of my third year at Stanford. And they didn't take undergrads, but I basically kept showing up until they couldn't kick me out anymore in these classes because I really wanted to take them. I was so interested in kind of the IDEO sort of design thinking HCI field.

4:00And I got to take classes and really do the whole process of what's the problem you're solving? What's the user need? What are the range of ideas around how you can solve it? And then how do you go through that process of discovery. So that piece was really, really important. The second part was knowing enough to build. And the last piece was, there was a program called the Mayfield Fellows Program, where it's nine months, you do three months of case studies and learning about startups and how they succeed and failed. And then three months interning at a startup, and then three months of debriefing and writing your own case study on what you learned that summer, which is very accelerated.

4:34It's a very fast, you know, sort of timeline. But I loved it. And even and Kevin, who ended up being my Instagram co-founder, did the same program a couple of years prior. It ended up making us have this shared language around some really difficult moments. And we were able to sort of look back and say, hey, that reminds me of that case study. It's hard for me to think about going and learning something for three to four years and a curriculum based on something that somebody designed, you know, if you're lucky, six years ago. Right. And then waiting for the job market to wait for you on something that you took, you know, six plus years for the curriculum, four plus six to learn.

5:06and then expecting that your skill set will match what the market needs versus being able to learn something that is mostly, you know, based on market needs right now, going and actually getting your hands dirty, doing some work, coming back, potentially refining, but that model needs to evolve. So you mentioned Instagram. It's hard to talk to you and not to talk about Instagram. You know, back in the day, Instagram grew from zero to one million in users in two months. Today, even today, that's not easy. in today's craziness, but back then it was one of those like rarefied areas, simply incredible, kind of unbelievable moments.

5:44And today obviously needs an introduction, one of the most used, loved consumer apps in the world. I'm curious, when you reflect that on those early days, you mentioned the conversation you and Kevin, your co-founder had, what was one of your key learnings when you reflect back that actually had that incredible product market fit that got you to that incredible milestone? I think the first key is that it didn't get there sort of in a narrow or sort of one linear way. Like we were initially building, you know, you have to remind yourself to 2009, you know, we were initially building a check-in app for exploring cities and making, you know, plans with your friends.

6:27And I think what was interesting, and it's like the first lesson learned was it's very rare that a product succeeds because of the incremental N plus one feature if the core is not actually working. And so what we were finding is that some people really liked the product, but it wasn't growing enough and it wasn't really sort of lighting the world on fire. And we would do our planning every week. It was just Kevin and I. And we'd say, all right, what else could we add? And it's funny because I just mentioned the Mayfield Fellows Program. One of the things that we had sort of emerged from there was this idea of really simplifying and distilling down to like your core elements.

7:01I still carry that all the way through to Anthropic. When we have a product where we're figuring out like, all right, this initial part works in this way, but it's not growing as much as we'd like. It's always tempting to say, and then this, and then this without like sort of rethinking the core. And so that was one big lesson. And with Instagram, we kind of hit this kind of crisis moment where we'd been going at it for probably six months at the time with Bourbon, which was our check-in app. And we had an investor check-in. I remember walking up to, you know, afterwards and we had seen on the investor's face that they'd given up on us.

7:34Like it was like, okay, this is, I'm doing this because I have to, but it's not going to work. And so that was a good moment to say, right, if not even they believe in us, like what should we be doing differently? And that was a really good moment of then like pairing it all the way back. We had a really good conversation where there were two things that I think were kind of working with Bourbon. One was the photos part. The other one was making plans with your friends. And my pitch to Kevin was like, let's do, let's, let's break off and each of us prototype one of those and then come back in two weeks.

8:01And he said, well, it's better if we put our heads together and do one thing really well. And then if that doesn't work, we'll try the next. I think it was totally right. Actually, it was actually much better to have both of our attention on one problem rather than sort of doing this divide and conquer. And we chose photos, which within a week of having a better prototype of a sort of photo sharing app, it was clear that that was going to be more intuitive, exciting, you know, clearer for people than all of what we had built before with bourbon so you felt that the core was just strong with kind of the instagram and photos and filters and before it was the core felt meh and you were iterating trying to make the numbers look better like the same way as if you pick up a tool or you pick up a you know have a coffee mug in front of me you would know how to hold it and what to use it i think that what bourbon had become was like you could kind of intuit how you might use it but there were a lot of questions like, well, how does this fit in my life?

8:53Versus you open up to a feed of photos, which at the time was novel. There was not really any other product that was actually just doing that even. And you're like, oh, okay. It's photos from people that I know. And maybe some people that I don't know. And here's a big plus button. We spent a lot of time on that. The thing ended up getting like, it became a UI pattern for a couple of years where it was the bottom tab bar with the big, you know, sort of bump in the middle of around like, here's how you share. And, you know it was very optimized towards that you know when uh instagram joined facebook and even think about the core of the facebook product when zach did it there was that initial insane product market fit and then there was all the incrementality and iterations on top of that do you see this as almost like two different schools of art so it's kind of like three like three types of builders it's like the ones that can do the initial core the ones that can do the initial core and then like scale it up and actually like reiterate and continue to evolve it we could talk about that for a second because I think that's interesting too.

9:46And the third people that like don't ever ask the question of do we just need to simplify down to some other you know deeper core. I think you have an amazing case study with Artifact which was an AI news app that you started and you decided to shut it down and if I recall it was actually had a pretty strong core base when you started to shut it down and this is really hard for entrepreneurs. What advice would you give entrepreneurs on like when to persist and when to stop? So the leading indicator for me was, and I can't quantify this, but it's something around the energy in the system. With Artifact, especially by the end, it felt like we would put in 10 units of effort and get one to two units of output.

10:27What I mean by that is, we would completely revamp how the social features worked and a whole new commenting system that was actually reputation-based and had some novel ideas around how how to sort of track and rank reputation over time. And like some people used it, but it wasn't like even within the core of people we had, it didn't really change the dynamic or bring in new people or we would try a completely new way of ranking. And it was just sort of this feeling of like, there's a lot of things that go in and it doesn't feel like it's really like tilting. Whereas I've had the experience at Instagram and at times here at Anthropic as well, where you're kind of layering things on And you're almost in this almost jazz-like back and forth with your customer or user base.

11:11We're like, oh, that's cool. Oh, yeah. Oh, look what they built with that. That's really exciting. All this energy. It's a very different feeling. So that's number one, which is, are you feeling like, yes, startups are hard, but it still should feel. If things are working, you feel that kind of engagement back and forth. It doesn't have to be with a million people, but at least with some people that have that engagement. The second piece, which was how do you even go about doing that in a way that minimizes regret? We wrote down the list of things that we would feel dumb not having tried before shutting it down.

11:40Because there's some point where you realize, okay, is this working? And that was sort of mid-2023. Kind of gave ourselves to the end of 2023 to say, all right, what are all the things that we, you know, we won't, we will feel dumb having not done this at least. And we tried them all basically. And we kind of like shipped the last one in mid-December. and that kind of let us enter 2024 and say, all right, it wasn't that we didn't try the things that we were going to try. We've checked it off. Yes, we could generate another list, but it's also time to call it now. So when I think about your role in Entropic, and I do want to connect it to Instagram, Instagram launched very simple, right?

12:17It was like just photo sharing and filters. And simplicity was the key of the app. And you mentioned a few of those aspects. And in Entropic, you are building this AI that can do everything potentially. but the user interaction model, the UX, is also very simple, right? It's still that chat interface, at least on the experience side. Is this for you the same kind of principle of simplicity between the kind of non-AI feature-based model of Instagram versus this new way of working? Is this the same kind of, it's still the same chat, right? Or are you thinking of those very different? In similar ways, you know, we're talking about the sort of intuitive shape of something, like to see a text box, you kind of know how to ask it.

13:01But it is interesting how many people still will ask a almost sort of search query type thing into, you know, cloud.ai, because that's what you're used to when you see a box or you don't know the full potential of it. And so a lot of what we've tried and we're still iterating on is how do you keep the sort of basic interaction simple? And also for two reasons, one for that intuitive nature of it, but also because the more sort of extra UI you put on top, the less you're letting the model just do what it wants to do and kind of solve the problem in its own way. So you want to provide a fair amount of room to run.

13:38But at the same time, also express the full list of potential capabilities that exist there. And the ideas that we've tried that I don't think have worked very well are things like suggestion chips, like, did you know you can use this to answer a question about your calendar? And the problem is, if you're not hitting somebody at exactly the moment where they have that need, it's like, cool, but I came here to do this other thing and maybe I'll remember or forget. So I think a big piece of what we're trying to do now is can you get Claude itself to be better at knowing its own capabilities so that in the conversation, if it has a skill that it can pull off the shelf, it will do that.

14:13Or if it can suggest that, actually you might want to incorporate this additional piece, or I need this additional data, can you connect your Gmail so I can actually answer this question for you? So the product can still remain simple on the surface, but there's like this progressive disclosure. But I think sort of AI and more conversationalized really need to do that because there is that sort of progressive disclosure of capabilities as well. That sort of like building of mastery is I think more complicated in AI. But I think the thing I've most changed my mind on is when I got here, I was like, I can't believe we're still using chat boxes.

14:45Like that feels like we should be in a completely different thing. We should be, you know. And I think my, it's that like meme of like, you know, you have an opinion, you have like the other opinion, and then you come back to your digital. And I'm like, actually, it's actually a very useful way of expressing like an open-ended problem. The thing that needs to change though is what happens after you hit enter. You know, is it just the model answers your question or is it the model's guiding you, connecting the right sources, doing some work independently, showing its state, and it's much more of a collaboration than it is a sort of question-answer situation.

15:17I think when it comes to building AI products, when you think about this fundamentally different way of working, what other things come to mind for you? So when you think about Instagram, we were generally on like a two halves a year, high level plan, and then sort of quarterly kind of roadmaps. And sometimes those had blank space in them. We're not sure exactly how we're going to solve this question of how to get people to share more informally. But we know that is a problem we're going to solve. And there's a method towards it. And it was sort of within product development, that convergence, divergence, but still towards a fairly set set of goals.

15:52And it was rare that we didn't ship something. And if it was, it was because maybe in testing it didn't work well, but not because there was some underlying capability that we didn't know about. Whereas at Anthropic, what I learned is I actually started with that process and it kind of runs into this very natural sort of mismatch, which is the model not only is not deterministic, the model research process is not deterministic. And it involves a lot of flexibility. So I think it's a few different things. And by the way, this feels different in API and platform land where there is more. Sometimes they'll align some feature release with the model capabilities, but it's less like this API is going to look totally different depending on the model.

16:34But very much so on the first party app side. I think it's a few things. It's preserving space in the roadmaps for prototyping and experimentation. We used to do that by having sort of a dedicated prototyping team. And we still have some efforts that are explicitly carved out for that. But even from our sort of core roadmap, you know, there's more room now than definitely there was at Instagram around like experimentation and prototyping, like not just doing hackathons, but using them as inputs for what we did. We shipped CloudSkills a couple of weeks ago. That was seeded by first an internal prototype, then a hackathon, then the desire to actually build it.

17:08And then that became a roadmap item that we were able to pull together. When you go and you think about, sorry, for the lack of a better word, agent, you know, when I think about, maybe I'll call it assistant for a bit. When I think about what would it take to win in building the ultimate assistant, and maybe I am primarily thinking about consumer, it's hard for me to not think about, you have to bring the relationship. Like it has to be an intimate relationship with the user, but the user has to feel you're establishing something which is more than transactional. It's more than I ask for something and you gave me something.

17:41There is a sense of I'm going to invest in you and you invest in me back and we're going to build this amazing thing forever, which is hard in enterprise because the moment I'm out, I'm losing that relationship. But let's put it aside. That's just complexity. I'm just curious. Do you see that similarly? And then how do you build for that relationship? Because that is, whether it's enterprise or consumer, that is kind of the notion of me coming and investing. We've seen internally that like when somebody invests in the experience and products we build in the agentic sphere, they stick around. The result is like, we love the product.

18:17It's amazing. But when they don't, it's literally the opposite response. So ultimately, it's about that time spent, that investment, but investment in making it yours. I'm curious how you see it. they will learn a lot about you and there are these memories and i think that those are really important to developing that sort of trust and long horizon kind of um relationship with with even an agent but using that judiciously rather than yes like i know you know that about me and now it actually i feel creeped out that you know that about me rather than that it feels uh it feels empowering um overall the memory unlock do you see it as a tech problem right now or this is like a product thing you're thinking about how to orchestrate that, like what type of memories you look at, or is this almost like the original models?

19:05Let's just wait for that to be better, and then we can build something incredible on top of it. Is this a layer you are waiting for, or is this a layer you are shaping as well? I think of it as essential to everything we're doing. There's the in-context memory, there's the user-level memory of who you are, what you know, there's procedural memory, which we're solving with skills, is then the thing that we don't talk about as much is the organizational memory around, you know, who knows what, who does, you know, who should I talk to for this aspect? What have we learned about this? For previous agents, what kind of instructions have they've gotten?

19:34So that whole layer, I think is, I completely agree with you. It's a big unlock around really feeling like it's a kind of trusted collaborator over long time periods. It's hard to talk about product without talking about metrics. So I'm going to ask you one metric question. You know, I think of the true north as a relationship, but that's too amorphic or abstract. So then I kind of bring it back to like retention. You come back to the thing you're using it. But that's also, you know, more of a long-term way to do it. What is your proxy? How do you think, is it like the depth of the conversation?

20:09I've seen this with many labs. Is it the first conversation, the first five conversations? Like, it feels like, is it just that you're still like, hey, we're still iterating. We're going to figure this out as part of this. How do you think about the proxy for that true north? I think there's two things I look at a lot. One is, are the conditions met? And then are the sort of features you've built not being used? So are the conditions met? We look at a lot of, you know, have people connected their Google Drive if they want to use their MCP? Have they used memory? Have they enabled this? Like, do we even have enough context and state and connectivity with this person to be able to do useful things for them?

20:46There's an organization level here, which is, has your organization enabled these features too? Because now it's an enterprise product. So there's kind of two layers right there. And then there's the other side, which is now are people actually engaging with it? And so at Instagram, we looked a lot at participation rate. I look a lot at participation rate here as well. And I'm assuming the first informs the ladders or is a causal driver. Exactly. Or it's sort of like, yeah, it's the top of that funnel as well. Like if you don't know. And to our earlier conversation, engaging with a feature that then requires some connectivity might be a moment where you can upsell that so they can have that interplay as well.

21:19But we look a lot at, you know, if we're really pushing forward the ability of Cloud to actually generate professional-looking office documents, well, are people using it to generate professional-quality office documents? Do they then stick around and retain on that feature as well? That's probably the best proxy we have for now beyond evals, which are like the thing that I didn't have at Instagram, which is upstream of all of this. You know, how are we doing on the things that we do? And how much can we actually sort of tailor evals even more so to the problems that people are hitting in the real world, right?

21:50So if it's generating really professional-looking PowerPoints, how do we actually, you know, either bet that with real people or incorporate that judgment in the model so that we can actually do that better over time? Those are the best I found. And then we have our more classic growth team that, you know, if you squint your eyes, it looks like any, you know, general product with, you know, retention, activation, resurrection, all of those different pieces. But when I think about the more AI-appealed features, it's very much in the like, are people discovering it? Are they using it? Do they have the necessary conditions?

22:21And they kept coming back to it too. What is your favorite non-software, non-digital product? I don't know if the 8 Sleep counts as a non-software because there's definitely software in there as well you know what it is actually this is a very funny thing because I shared this with another co-founder or another founder that I've talked to a bunch it's like good self-massaging back things such a life changer because you're tense at the end of the day having a really, really good body back buddy like$15 on Amazon but high quality if you're going to get retargeted with an ad after this we are not sharing any information No PIIs are shared here out of this conversation.

22:59And then lastly, folks listening, saying, hey, I want to be Mike like Mike. I want to learn a lot. I want to build at the frontier of what does it mean to actually build amazing products right now. What would be your advice for them? I think just being constantly always experimenting with anything that you see out there. I'm notorious in our team because I will add myself to the internal feature flag for features that are like, like not even half baked, like the 10th baked. And the team's like, it's not ready. I'm like, I don't care. I just want to experience, you know, the early promise of it and see what it is.