Lenny's Podcast: Product | Career | Growth - Anthropic’s CPO on what comes next | Mike Krieger (co-founder of Instagram)

Starting point is 00:00:00 90% of your code roughly is written by AI now. The team that works in the most futuristic way is the Claude Code team. They're using Cloud Code to build Claude Code in a very self-improving kind of way. We really rapidly became bottlenecked on other things, like our merge queue. We had to completely re-architect it because so much more code was being written and so many more poll requests were being submitted that it just completely blew out the expectations of it. You guys are at the edge of where things are heading. I had the very bizarre experience of I had two tabs open.

Starting point is 00:00:27 It was AI 2027 and my product strategy. and it was this moment from like, wait, am I the character in the story? It feels like Chad GPT is just winning in consumer mindshare. How does that inform the way you think about product, strategy, and mission? I think there's room for several generationally important companies to be built in AI right now. How do we figure out what we want to be when we grow up versus like what we currently aren't or wish that we were or like see other players in the space being? What's something that you've changed your mind about what AI is capable of and where AI is heading? I had this notion coming in like, yes, these mobile.

Starting point is 00:00:59 are great, but are they able to have an independent opinion? And it's actually really flipped for me only in the last month. Today, my guest is Mike Krieger. Mike is Chief Product Officer at Anthropic, the company behind Claude. He's also the co-founder of Instagram. He's one of my most favorite product builders and thinkers. He's also now leading product at one of the most important companies in the world. And I'm so thrilled to have had a chance to chat with him on the podcast.

Starting point is 00:01:25 We chat about what he's changed his mind about most in terms of AI capabilities. abilities in the year since he joined Anthropic, how product development changes and where bottlenecks emerge when 90% of your code is written by AI, which is now true at Anthropic. Also, his thoughts on open AI versus Anthropic, the future of MCP, why he shut down artifact to his last startup and how he feels about it, also with skills he's encouraging his kids to develop with the rise of AI. And we close the podcast on a very heartwarming message that Claude wanted me to share it with Mike. A big thank you to my newsletter Slack community for suggesting topics for this conversation. If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube.

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Starting point is 00:04:03 Stripe has been leveraging AI for the last decade to make its product better at growing revenue for all businesses, from smarter checkouts to fraud prevention and beyond. Join the ranks of over half of the Fortune 100 companies that trust Stripe to drive change. Learn more at stripe.com. Mike, thank you so much for being here and welcome to the podcast. I'm really happy to be here. I've been looking forward to this for a while. Wow, I'd love to hear that.

Starting point is 00:04:32 I've also been looking forward to this for a while. I've so much to talk about. So first of all, you've been at Anthropic for just over a year at this point. Congrats, by the way, on hitting the cliff. Thank you. Not that we're tracking. That's right. So let me just ask you this.

Starting point is 00:04:47 So you've been an Anthropic for about a year. What's something that you've changed your mind about? from before you join Anthropic to today about what AI is capable of and where AI is heading. Two things. One is like a pace and timeline question. The other one is a capability question. So maybe I'll take the second one first. I had this notion coming in like, yes, these models are great. They're going to be able to produce code. They're going to be able to write, you know, hopefully in your voice eventually. But are they able to sort of have an independent opinion? And it's actually really flipped for me only in the last month and only with Opus 4, where my go-to product strategy

Starting point is 00:05:26 partner is Claude, and it has been basically for that full year. Well, I'll write an initial strategy. I'll share it with Claude, basically, and I'll have it, you know, look at it. And in the past, it's pretty anodyne kind of comments that it would leave like, oh, have you thought about this? And it's like, yeah, yeah, I thought about that. And Opus 4, I was working on some strategy for our second half of the year was the first one. I was like, Opus 4 combined with our advanced research, but it really went out for a while And it came back and I was like, damn, you really looked at it in a new way. And so that's like a thing that I've, maybe I didn't feel like it would never be able to do that, but I wasn't sure how soon it'd be able to like come up with something where I look

Starting point is 00:06:01 at it. I'm like, yep, that is a new angle that I hadn't been looking at before. And I'm going to incorporate that immediately into how I think about it. So that's probably the biggest shift that I've had is like, I don't know about independence is the right word, but like creativity and sort of novelty of thought relative to how I'm thinking about things. And the timeline one, it's like so interesting because, you know, I was sitting next to Dario yesterday. And he's like, I keep making these predictions and people keep laughing at me and then they come true.

Starting point is 00:06:27 And it's like, and it's funny to have this happen over and over again. And he's like, not all of them are going to be right, you know, but even I think as of last year, he was talking about, you know, we're at 50% on sui bench, which is like, you know, benchmark around how well the models are at coding. He's like, I think we'll be at 90% by the end of 2025 or something like that. And sure enough, we're at about 72 now with the new models. we're at 50% when he made that prediction and it's like continued to scale pretty much like as predicted. And so I've taken the timelines a lot more seriously now. And I don't know if you read AI 2027. I have.

Starting point is 00:07:01 It was made by heart race. Yeah. And I had the very bizarre experience of I had two tabs open. It was AI 2027 and my product strategy. And it was this like moment where I'm like, wait, am I the character in the story? Like, how much is this converging? But, you know, you read that and you're like, oh, 2027, that's like, that's years away.

Starting point is 00:07:20 You're like, no, mid-20205 and like things continue to, uh, to improve and the models continue to be able to do more and more and they're able to act genetically and they're able to have memory and they're able to act over time. So I think my like my confidence in the timelines and I don't know exactly how they manifest have definitely just solidified over the last year. Wow. I, I wasn't expecting to go down that because that, that paper was scary. And I'm curious just, I guess, I can't help but ask just thoughts. just how do we avoid the scary scenario that that paper paints of where AI getting really smart goes. Yeah.

Starting point is 00:07:55 I mean, this maybe ties into like, I've been here a year, like, why did I join Anthropic? I was watching the models get better. And even, you know, you could see it in 24 and like, you know, early 2024. And looking at my kids, I'm like, right, they're going to grow up in a world with AI. It's unavoidable. What is the thing that I can, like, where can I maximally apply my time to like nudge things towards going well. And I mean, that's a lot of what people think about across the industry, especially at Anthropic. And so I think, you know, coming to an agreement and a shared framework

Starting point is 00:08:27 and understanding of like, what does going well look like? What is the kind of human AI relationship that we want? How will we know along the way? What do we need to build and develop and research along the way? I think those are all the kind of key questions. And, you know, some of those are product questions. And some of those are research and interpretability questions. But for me, it was like the strongest reason to join was, okay, I think there's a, there's a lot of contribution that Anthropic can have around, like, nudging things to go better. And if I can have a part to play there, like, let's do it. I love that answer. Speaking of kids, so you've got two kids. I've got a young kid. He's just about to turn two. I'm curious just what skills you're

Starting point is 00:09:05 encouraging your kids to build as this, you know, AI becomes more and more of our future. And some jobs, you know, will be changed. And just what do you, what advice you have? We have this, you know, breakfast, feed breakfast of the kids every morning. And sometimes some question will come up, you know, like, you know, something about like physics and our oldest kid's almost six. But, you know, they ask like funny questions about like, you know, the solar system or physics or, you know, in a six-year-old way. And before we reach for Claude, because at first, you know, my instinct is like,

Starting point is 00:09:34 oh, I wonder how cloud will do this question? And like, we started changing, like, well, how would we find out, you know? And the answer can't just be the last Claude, you know? So, all right, like, well, we could do this experiment. We could have this thing. So I think nurturing curiosity and like still having a sense of, I don't know, the scientific process sounds grandiose to instill in like a six year old. But like that process of like discovery and asking questions and then, you know,

Starting point is 00:09:57 systematically working right through it, I think will still be important. And of course, AI will be an incredible tool for helping like resolve large parts of that. But that process of inquiry, I think is still really important and independent thought. My favorite moment with my kid, because she's very headstrong or six year old. She's, you know, I was like, she said something. And I was like, I wasn't sure if it was true. It was, um, oh, is that coral is a, is an animal or like coral is alive. I don't even remember the details of it.

Starting point is 00:10:21 And I was like, I don't know if that's true. And she's like, it's definitely true dad. I'm like, all right, like, let's ask Claude on this one. And she's like, you can ask Claude, but I know I'm right. And I'm like, I love that. Like I want that kind of level of, you know, not just sort of delegating all of your cognition to the, you know, to the eye because it won't always get it right. And also, uh, kind of like, you know, kind of short circuits, any kind of independent

Starting point is 00:10:43 thought. So the skill of asking questions, inquiry, uh, and independent thinking, I think those are all the pieces. What that looks like from a like job or occupation perspective, like, I'm just keeping an open mind. I'm sure that'll radically change between between now and then. It's interesting. Toby Luckie, a Shopify CEO on the podcast and he had the same answer for what he's encouraging his kids to, uh, to develop his curiosity. And, uh, and so it's interesting. That's a common threat. The, you know, K through eight score, a kid goes through head. and an AI sort of AI and education expert come in. And I had a very low bar or like a very low expectation of what this conversation was going

Starting point is 00:11:20 to be like. And actually, I think it went over most of the people in the heads, the audience's heads because he was like, all right, well, let me take it all the way back to Claude Shannon and an information theory. And I could see people's eyes grow like, what did I like sign up for? Why am I here in this like school auditorium hearing about, you know, information theory? But he did a really nice job. I think of also just imagining like, you know, there will be different jobs.

Starting point is 00:11:40 And we don't know what those jobs are going to be. And so, like, what are the skills and techniques and remain open-mindedness and around, like, what the exact way we recombine those things? And even those will probably change three times between now and when they're 18. I want to go back to, so we're talking about timelines and how things are changing. So I've seen these stats that you've shared other folks at Anthropic have shared about how much of your code is now written by AI. So people have shared stats from like 70% to like 90%. There was an engineer lead that shared like 90% of your code roughly is written by AI. now, which first of all is just insane.

Starting point is 00:12:15 It went from zero to 90%, I don't know, a few years, something like that. I don't think people are talking about this enough. That's just wild. You guys are basically at the bleeding edge. I've never heard a company that is this higher percentage of code being written by AI. So you guys are at the edge of where things are heading. I think most companies will get here. How has product development changed knowing so much of your code is not written by AI?

Starting point is 00:12:38 So usually it's like PM. It's like, here's what we're building, engineer builds it, ships it. Is it still kind of roughly that or is it now PMs are just going straight to claw, build this thing for me. Engineers are doing different things. Just what looks different in a world where 90% of your code is written by AI. Yeah, it's really interesting because I think the role of the role of engineering has changed a lot, but the kind of suite of people that come together to produce a product hasn't yet. And I think for the worst in a lot of ways, because I think we're still holding on some assumptions. So I think the roles are still fairly

Starting point is 00:13:11 similar, although we'll now get, in my favorite things that happen now, or sometimes PMs that have an idea that they want to express, or designers that have an idea they want to express, we'll use Claude and maybe even artifacts to, like, put together an actual, like, functional demo. And that has been very, very helpful. Like, no, this is what I mean. Like, that, that makes it tangible. That's probably the biggest, like, role shift is, like, prototyping happening earlier in the process via more of this kind of, you know, you know, code plus design piece. What I've learned, though, is, like, the process, of knowing what to ask the AI,

Starting point is 00:13:45 how to compose the question, how to even think about structuring a change between the back end and the front end. Those are still very difficult and specialized skills, and they still require the engineer to think about it. And we really rapidly became bottlenecked on other things, like our merge queue, which is the sort of get in line to get your change accepted by the system that then

Starting point is 00:14:06 deploys it to production. We had to completely re-architect it because so much more code was being written and so many more poll requests were being submitted that it just completely blew out the expectations of it. And so it's like, I don't know if you've ever read, is it the goal, the classic like process optimization book? And you realize there's like this like critical path theory. I've just found all these new bottlenecks in our system. You know, there's an upstream bottleneck, which is decision making and alignment. A lot of things that I'm thinking about right now is like,

Starting point is 00:14:32 how do I provide the like minimum viable strategy to let people feel empowered to go run and prototype and build and explore at the edge of model capabilities? I don't think I've gotten that right yet, but that's the thing I'm working on. And then once the building is happening, other bottlenecks emerge. Like, let's make sure we don't step on each other's toes. Let's think through all the edge cases here ahead of times that we're not blocked on the engineering side. And then when the work is complete and we're getting ready to ship it, what are all those bottlenecks as

Starting point is 00:14:57 well? Like, let's do the air traffic control of landing the change. Like, how do we figure out large strategy? So I think we're the, there hasn't been as much pressure on changing those until this year. But I would expect that like a year from now, the way that we are like conceiving of building and shipping software just changes a lot because it's going to be very painful to do it the current way. Wow, that is extremely interesting. So it used to be, here's an idea. Let's go design it, build it, ship it, merge it, and then ship it. And usually the bottleneck was engineering, taking

Starting point is 00:15:28 time to build the thing and then design. And now you're saying the two bottlenecks you're finding are, okay, deciding what to build and aligning everyone. And then it's actually like the cue to merge it into production and and I imagine review it too is probably a Reviewing has really changed too and in many ways our most perhaps unsurprisingly, the team that works in the most futuristic way is the Claude Code team because they're using Claude Code to build Claude code in a very self-improving kind of way. And you know, early on in that project they would do very line by line pull request reviews in the way that you would for any other project and they've just realized like

Starting point is 00:16:05 Claude is generally right, and it's producing, you know, pull requests. They're probably larger than most people are going to be able to review. So can you use a different cloud to review it? And then do the human almost like acceptance testing more than trying to like review line by line. There's definitely pros and cons. And like so far it's gone well, but I could also imagine it going off the rails and having a completely both unmaintainable or even understandable by cloud code base that hasn't

Starting point is 00:16:28 happened. But watching them like change their review processes definitely has, has been, has been interesting. And yeah, like the merge two is one. sense of the kind of bottom bottleneck that forms down there. But there's other ones, which is, how do we make sure that we're still, like, building something coherent and, like, packaging it up into, like, a moment that we can share with people. And whether that's around a launch moment, whether that's about, like, then enabling people to use this thing and, like, talking about it, like, the classic things of building something useful for people and then making it known that

Starting point is 00:16:56 you've built it and then learning from their feedback, like, still exists. We've just, like, made a portion of that whole process much more efficient. I heard you describe this as you guys are patient zero for this way of working. Yes. I love that. Do you have a sense of what percentage of Claude Code is written by Claude code? At this point, I would be shocked if it wasn't 95% plus. I'd have to ask Boris and the other tech leads on there. But what's been cool is so nitty-gritty stuff. Cloud code is written in TypeScript. It's actually our largest TypeScript project. Most of the rest of Anthropic is written in Python, some go, some rust now. But it's not, you know, we're not like a typescript shop. And so I saw a great comment yesterday in our

Starting point is 00:17:38 Slack where somebody had this thing that was driving them crazy about Claude code. And they're like, well, I don't know any type script. I'm just going to like talk to Clod about it and do it. And they went from that to pull request in an hour and solve their problem. And they like, you know, submitted a pull request. And that kind of breaking down the barriers. One, it changes your sort of barrier to entry for any kind of newcomer to the project. I think you can let you choose the right language for the right job, for example. I think that helps as well. But I think it like also just reinforces like Claude code being that patient alpha of that, you know, or like contributions from outside the team can be cloud coded as well. Wow. This is just, it's just going to continue

Starting point is 00:18:18 to blow my mind. Like all these things that you're sharing. 95% of cloud code is written by Claude That's my guess. Yeah. I'll come back with the real stuff. But it's good. I mean, if you ask the team, that's how that they're working and that's how they're getting contributions from across the company too. It's interesting going back to your point about strategy being assisted by Claude itself and your point about how a lot of the bottlenecks now are kind of the top of the funnel of coming up with ideas aligning everyone. It's interesting that Claude is already helping with that also of helping you decide what to build. So if those two bottlenecks are aligning, deciding what to build and then just like merging and getting everything, where do you see the most interesting stuff happening to help you

Starting point is 00:18:57 speed those things up? Yeah, I think that on that first row, like I started. I started. the year by writing a doc that was effectively like what how do we do product today and where is Claude not showing up yet that it should and I think that upstream part is the next one it goes interesting like at your conference I talked to somebody who was working on like a PRD GPT kind of like chat PRD I think was chat PRD yeah so you know can we push more on you know can Claude be a partner in figuring out what to build what the market size is if you want to approach it that way, what the user needs are if you look at a different way. Like, we think a lot about the virtual collaborator Anthropic and one of the ways in which I think

Starting point is 00:19:37 that can show up is, hey, I'm in the Discord, the cloud Anthropic Discord. I'm in the user fora. I'm on X and I'm reading things. And like, here's what's emergent. That's step one. Models can do that today. Step two, which the models probably can do today, which have to wire them up to do it is like, and not only are the problems, here's like how I think you might be able to solve them. And then taking that through to like, and I, put together a poll request to like solve this thing that I'm seeing like feels very achievable this year and stringing those things together and we're limited more this is why mcp is excited to me like we're limited more around like making sure the context flows through all of that so we have the

Starting point is 00:20:14 right access to those things more than the model's capability to to reason and propose now the model in mind i have like perfect ui taste yet so there's definitely room for design to intervene and be like oh that's not quite how i would solve the problem of this not showing up but i you know i would get very excited. I would give you a really small example, but we changed the on Cloud AI. You should be able to just copy Markdown from artifacts or code from artifacts. And we change it so you can actually download it and export it. We changed the button to export. We got a bunch of feedback like, how do I copy now? And the answer is like you drop it down and it's copy. It's just like, my, you know, one of those things where it's like made sense, but we probably got it like not

Starting point is 00:20:50 quite right. That feedback was in the RUX channel. Like I would have loved like an hour later for a plot to be like, hey, if we do want to change it back, here's the PR to do it. And by the way, eventually and then I'm going to spin up an AB test to see if this changes metrics and then we'll see how it looks in a week like this stuff feels if you told me that about a year and a half we're going to be like yeah maybe like 27 maybe like 26 but it's pretty much like it really feels you know just at the tip of capabilities right now wow okay so you mentioned the lending friend summit I wanted to talk about this a bit so you were on a panel with Kevin wheel the cpo of open AI I think it was the first time you guys did this maybe the last time for now we haven't done it's not for any reason I had a lot of fun. What a legendary panel we assembled there with SerraGuo moderating. And you made this comment actually ended up being the most rewatched part of the interview, which is that you've kind of, you were putting product people on the model team and working with researchers, making the model better. And you're putting some product people on the product experience, making the UX more intuitive, making all that better. And you found that almost all the leverage

Starting point is 00:21:55 came from the product team working with the researchers. Yes. And so you've been doing more of that. So first of all, does that continue to be true? And second of all, what are the implications of that for product teams? It's continued to be true. And in fact, I think that the, if the proportion was already like skewing towards having more of that embedding, I've just become more and more convinced. Like I have this, I didn't feel as strongly about it during your, you know, the summit. And now I feel really strongly about it. Just if any, for shipping things that could have been built by anybody just using our models off the shelf, there's great stuff to be built by using our models off the shelf, by the way, don't get me wrong.

Starting point is 00:22:30 But where we should play and what we can do uniquely should be stuff that's really at that, like, magic intersection between the two, right? Artifacts being a great example. And if you play with artifacts with Cloud 4, that's an actually really interesting example where we took somebody from our, we call it Claude Skills, which is a team that really is doing the post-training around teaching Claude, you know, some of these like really specific skills. And we paired it with some product people.

Starting point is 00:22:53 And then together we revamped how this looks in the product today and like what Cloud can do way better than just like, yeah, we just like use the model and we like prompted a little bit. Like that's just not enough. We need to be in that like fine tuning process. So so much of what, you know, if you look at what we're working in right now, but we've shipped recently between like research and all the other things like are things that we like the functional unit of work at Anthropic is no longer like take the model and then like go like work with design and product to go ship a product. It's more like we are at like we're in the post training conversations around how these things should work. And then we are in.

Starting point is 00:23:28 the building process and we're like feeding those things back and looping them back. Like I think it's exciting. It's also a new way of working that like not all PMs have, but the PMs that have the most sort of internal positive feedback from both research and engineering are the ones that get it. That like, I was in a product review yesterday. I was like, oh, you know, if we want to do this memory feature, like we should talk to the researchers because we just shipped a bunch of like memory capabilities in the platform.

Starting point is 00:23:52 They're like, yeah, yeah, we've been talking to them for weeks. Like this is how we're manifesting it. It's like, okay, feel good. I feel like we're doing the right things now. So let me pull on this thread more. There's something I've been thinking about all these lines. So essentially there's like a big part of Anthropic that's building this super intelligent gigabrain

Starting point is 00:24:08 that's going to do all these things for us over time. And then there's, as you said, there's the product team that's building the UX around the super intelligent gigabrain. And over time, this super intelligence is going to be able to build its own stuff. And so I guess just where do you think the most value will come from traditional product teams

Starting point is 00:24:27 over time. I know this is different because you guys are a foundational LM company. And most companies don't work this way, but just I don't know thoughts on just where most value will come from product teams over time working on AI. I think there's still value, a lot of value in two things. One is making this all comprehensible. I think we've done an okay job. I think we can do a much better job of making this comprehensive. Well, it's still like the difference between somebody who's really adept at using these tools and

Starting point is 00:24:53 their work and most people is huge. And I mean, maybe that's the most literal answer to your earlier question around like what skills to learn. That is a skill to learn and use it. In the same way that I remember, I read like computer a lot class when I was in like middle school. I remember being like really good at Google. And that was actually a skill back in the day, you know, like to think in terms of like this information is out there. How do I query for it? How do I do it? I think it actually was like an advantage of the time. Of course, now Google is pretty good at figuring out where you're trying to do it if you're like are only in the neighborhood. And like, there's less of that research kind of need.

Starting point is 00:25:24 But I still think that's a necessary part of like good product development, which is like the capabilities are there. And even if the like, even if cloud can create products from scratch, what are you building and how do you make it comprehensible? Like still hard. Because I think that like gets at like this much deeper empathy and like understanding of human needs and psychology.

Starting point is 00:25:43 Like I was a human community interaction major. I've still been talking to my book here. Like I still feel like that is a very, very, very, very necessary skill. So that's one. Two is, straight to call back to another one of your guests, like strategy, like how we win, where we'll play, like figuring out where exactly you're going to want to, like, of all the things that you could be spending your time or your tokens or your computation on, like, what,

Starting point is 00:26:09 what you want to actually go and do? You could be wider probably than you could before, but you can't do everything. And even like from an external perspective, if you're seen to be doing everything, like, it's way less clear around like how you're, how you're positioning yourself. Like strategy, I think is still that the second piece. And then the third one is opening people's eyes to what's possible,

Starting point is 00:26:27 which is a continuation of making it understandable, but we were in a demo with a financial services company recently. And we were like working on like, here's how you can use our analysis tool and MCP together. And like you could see their eyes light up. And you're like, okay, like there's still set.

Starting point is 00:26:42 We call it overhang, right? Like the delta between what the models and the products can do and how it's being used on a daily basis. huge overhang. So that's where it's still like a very, very strong, necessary role for product. Okay. That's an awesome answer. So essentially areas for product teams to lean into more is strategy, just getting better and better at strategy, figuring out what to build and how to win in the market, making it easier to help people understand how to leverage the power of these tools,

Starting point is 00:27:09 the comprehensibility. And kind of along those lines is opening people's eyes to the potential of these sorts of things. That's where product can still help. Exactly. Awesome. So kind of along those lines, actually, do you have any just like prompting tricks for people, things you've learned to get more out of clot when you chat with it? Sometimes, you know, it's funny because we, in some ways, we have like the ultimate prompting job, which is to write the system prompt for Claudia. And we publish all of these, which I think is like a, you know, another nice area of transparency. And we are always careful when giving prompting advice because, at least officially, but I'll give you the unofficial version because like you don't want things to become like,

Starting point is 00:27:46 like we think this works, but we're not sure why, you know, but I, I'll do small things. Like in Cloud Code, and we actually do react to this very literally, but I always ask a to like, if I wanted to use more reasoning, like, think hard and it'll like, you know, use a kind of a different flow. And I usually start with that, you know, um, nudging. There's a great essay around like, make the other mistake. Like if you tend to be too nice, can you focus on like, even if you're trying to be more critical or more blunt, you're probably not going to be the most critical blunt person

Starting point is 00:28:13 in the world. Um, and so with Claude sometimes I'm like, be brutal, Claude, like, roast me, like, tell me what's wrong with this strategy. I think, I know, we were talking earlier about the, you know, Cloud as thought partner around, like critiquing product strategy. I think I previously would say things like, you know, like, what could be better on this product strategy? I'm just like, you know, just roast this product strategy. And Cloud's like a pretty nice, you know, editing.

Starting point is 00:28:34 It's not going to be, it's hard to push it to be super brutal, but it forces it to be a little bit more critical as well. The last thing I'll say is, so we have a team called Applied AI that does a lot of like work with our customers around optimizing Cloud for their use case. And we basically took their insights and their way of working and we put it into a product itself. So if you go to our console or work. bench, we have this thing called the prompt improver, where you describe the problem and you give it examples, and cloud itself will agentically create and then iterate on a prompt for you, I find what comes out of that ends up being quite different than what my intuitions would have been for a good prompt. And so I encourage folks to also check that out, even for their own

Starting point is 00:29:13 use cases, because while that tool is meant for an API developer putting a prompt into their product, it's equally applicable for a person doing a prompt for themselves. It'll insert XML tags, which no human is going to think to do ahead of time. It actually is very helpful for Cloud to understand what it should be thinking versus what it should be saying, et cetera. So that's another one is like, watch our prompt improver and then note that like Cloud itself is a very good prompter of Cloud. Awesome.

Starting point is 00:29:38 Okay, so we're going to link to that, the prompt improver. The core piece of advice you shared early is just kind of do the opposite of what you would naturally do. So if you're like trying to be nice, just like be brutal, be like very honest and frank with you. Exactly. I find that worked quite well. Like what are the thought patterns that I've like fallen into that you want to break me out of? I saw you guys just today maybe launched a Rick Rubin collab or it's that vibe coding.

Starting point is 00:29:59 What's that all about? That was a, you know, what I've heard about that. And again, like this, a lot of coalesce this week between model launch, developer event and the way of code. We had our, one of our co-founders, Jack Clark is our, you know, head of policy. And he got connected to Rick Rubin because I think he's been thinking a lot about coding the future of coding and creativity. And they've stayed in touch. And, you know, Rick got excited about this idea of. Like he was creating art and visualizations with Claude.

Starting point is 00:30:27 And then he had these ideas around like the way of the vibe coder. And they put together this actually I love the, I mean, I love almost everything Rick Rubin. So like the aesthetic of I think is just like so on point too. But yeah, this sort of like meditation is probably the right word meditation on like creativity working alongside AI coupled with this like really rich interesting visualizations. But it's one of those things for like, you know, internally they're like, oh yeah, and we're doing this like recruiting collab, we're like, we're doing what?

Starting point is 00:30:56 Like, that is, that's amazing. I love the, I looked at it briefly and there's like that meme of him like just like thinking deeply sitting on a computer with the mouth. Yes. And like ask yard, I think. It's totally. It's like AskiR 5. I'm excited to have Andrew Lueh, joining us today. Andrew is CEO of One Schema, one of our longtime podcast sponsors.

Starting point is 00:31:16 Welcome, Andrew. Thanks for having me, Lenny. Great to be here. So what is new with One Schema? I know that you work with some of my favorite companies like, like Ramp and VANSA and Watershed. I heard you guys launched a new data intake product that automates the hours of manual work that teams spent importing and mapping and integrating CSV and Excel files.

Starting point is 00:31:34 Yes, so we just launched the 2.0 of one schema file feeds. We've rebuilt it from the ground up with AI. We saw so many customers coming to us with teams of data engineers that struggled with the manual work required to clean messy spreadsheets. File feeds 2.0 allows non-technical teams to automate the process of transforming CSV and Excel files with just a simple prompt. We support all the trickiest file integrations, SFTP, S3, and even email. I can tell you that if my team had to build integrations like this, how nice would it be to take this off our roadmap and instead use something like One Schema?

Starting point is 00:32:09 Absolutely, Lenny. We've heard so many horror stories of outages from even just a single bad record in transactions, employee files, purchase orders, you name it. Debugging these issues is often like finding a needle in a haystack. One schema stops any bad data from entering your system and automatically validates your files, generating error reports with the exact issues in all bad files. I know that importing incorrect data can cause all kinds of pain for your customers and quickly lose their trust. Andrew, thank you so much for joining me. If you want to learn more, head on over to one schema.com. That's one schema.c.c. Actually, going back to kind of the beginning of your journey at Anthropic,

Starting point is 00:32:47 what's the story of you getting recruited at Anthropic? Is there anything fun there? But it all started, and I actually sent my friend this text. So Joel Lewenstein, who I've known, he and I built our first iPhone apps together in 2007 when the app store was just out. And you could still make money by selling dollar apps on the app store back in the day. And we were with the Stanford together and we were friends. And we've stayed in touch over years. And we've never gotten to work together since then. We just remained close.

Starting point is 00:33:14 And I was coming out of the artifact experience. I was trying to figure out, do I start another company? I don't think so. I need a break from start. starting something from zero. Do I go work somewhere? I don't know, like what company want I want to go work at? And he reached out and he's like, look, I don't know if you'd at all consider joining

Starting point is 00:33:28 something rather than starting something. But we're looking for a CPO would be, would you be interested in chatting? And at that time, Claude 3 had just come out. And I was like, okay, you know, like this company has clearly got a good research team. The product is so early still. And it was like, great, I'll take the, take the meeting. And the first met with Danielle. I was one of the co-founders and the president and Anthropic.

Starting point is 00:33:47 And just from the beginning, it was like a breath of fresh air. very little like grandiosity coming off the founders. Like they just were really, I mean, they're clear-eyed about what they're building. They know what they don't know. Like how many times I talked to Dari, I always like Dari's like, look, I don't know anything about product, but here's an intuition.

Starting point is 00:34:06 Usually the intuition is really good and, you know, leads to some good conversation. Then that intellectual honesty and like kind of shared view of what it means to do AI in a like responsible way. It just resonated. I kept having this feeling in these interviews. is like, this is the AI company I would have hoped to have founded if I had founded an AI company. And that's kind of the bar around like, if I'm going to join something, like, that should be,

Starting point is 00:34:28 that should be where I'm going to go. But what I realized, I actually hadn't joined a company since my like first internship in college, basically. And I was like, oh, like, how do I onboard myself? Like, how do I get myself, you know, up to speed? Like, how do I balance making sweeping changes versus understanding what's not broken about it overall? And looking back on a year, I think I made some changes too slowly.

Starting point is 00:34:52 Like, I think there was like ways we were organized in a product that I could have made a change earlier. And I think I didn't, I didn't appreciate how much a couple of really key senior people can shape so much of product strategy. I'll harken back to Claude code. Like, Claude code happened because Boris, who actually was a Boris tourney. He was an Instagram engineer on like one of our senior I sees there. We were reluctant of it was like started that project from scratch. internal first and then we like got it out and then shipped it. And like that's the power of like one or two really strong people. And I made this mistake around. We need more headcount. And we do like,

Starting point is 00:35:28 I think there's like more work that we need to do and there's like things that I want to be building. But more so than that, we need a couple of like almost founder type engineers. That maybe connect back to our question on like what skills are useful and how does product development change. I still and maybe even more so, I'm a huge believer in like the founding engineer tech lead with an idea. and pair them with the right design and product support to help them realize that. I'm like 10 times more believer in that than before. I actually asked people on Twitter what to ask you. I had this conversation.

Starting point is 00:36:01 And the most common question, surprisingly, was why did you shut down artifact? And I also wondered that because I loved artifact. I was a power user. I was just like, this is exactly finally a news app that I love that it's giving me what I want to know. So I guess just what happened there at the end. I still really miss it too, because I didn't even, find a replacement. And I think I substituted it by like visiting individual sites and kind of of keeping things up that way. And it's not really the same, especially on the long till.

Starting point is 00:36:26 Like I think we got right with artifact. And if people didn't play with it before, it was, you know, we really tried to not just recommend like top stories. They were part of it. But really like, if you were interested in Japanese architecture, like you could pretty reliably get really interesting stories about Japanese architecture every day, you know, whether that's from, you know, dwell or from our particular dietist or from a really specific blog that we found that somebody recommended to us like it captures some of that google reader joy of like content discovery of the the deeper web our headwinds were a couple one of them was just mobile websites have really taken a turn i'm uh i don't blame any individuals for this i think it's the like market dynamics of it

Starting point is 00:37:08 but yeah you know we put so much time uh our designers sky gunner gray is phenomenally is that perplexity now. Like the ad experience, I was so proud of. But when you click through, it was like, the pressures on these mobile sites and these mobile publishers would be like, sign up for our newsletter. Here's like a full screen video ad. It was just very, you know, it was very jarring. And we didn't feel like it ethically made sense for us to like do a bunch of ad blocking because then you're like, sure, you can deliver a nice experience for people, but you're sort of, you know, that doesn't feel like it's playing fair with the publishers. And at the same time, like, the actual experience wasn't good. So the mobile web deteriorating, which makes me very sad.

Starting point is 00:37:44 but I think was part of it. Two was like, you know, Instagram spread in the early days because people would take photos and then post them on other networks and tell friends about it. And there was like this really natural like, how did you do that? I want to do it. News was very personal.

Starting point is 00:37:57 Like I can't tell me how many people would be like, I love Artifact. I'm like, did you tell anybody about it? Like did it? And they're like, I told one person. And it's like, it didn't have that kind of spread. And any attempt that we had to do it felt kind of contrived. Like, oh, we'll wrap all the links in like artifact. news and like but we don't want interstitial things like in some ways I don't know this sounds very

Starting point is 00:38:16 puritanical I don't mean it's sound this way but like we there were lines that we didn't want to cross because it just felt ethically not us that I've seen other news kind of like players like do more of and maybe if we had done that it would have grown more and but I don't think that's the company we wanted to have built in other ways I don't think we were the founders to have built it and the third one which is an underappreciated one is we started at mid-COVID which meant that we were fully distributed. And I think there were like major shifts that we would have wanted to make both in the strategy and the product and the team. And it's really hard to do that if you are all fully remote. Like nothing replaces like the Instagram days of like we went through some,

Starting point is 00:38:54 you know, hard times like Ben Horowitz called the like, you know, we're effed. It's over, you know, kind of moments. And I, my favorite, not this is definitely type two fun. Like I wouldn't say that of my favorite memories because they weren't happy ones. But like memories I are that really stayed with me with Instagram was like me and Kevin. at Takaria Cancun on Market Street, eating burritos at literally 11 p.m. being like, how are we going to get out of this? How are we going to work through this? Like, and that's, you assume is not a good replica for that. You know, you tend to like let things go or, you know, things build up over time. So the confluence of those three things, we kind of entered, I guess,

Starting point is 00:39:28 2024 and said, look, there, there is a company to be built in the space. I'm not sure where the people would have built it. This concurrent incarnation we love, but it's like not growing. Like the way I put it, it's like 10 units of input in for one unit of output versus the other way around. Like if we like put blood, sweat and tears into the product and like launch something we were proud of and like metrics would barely move. I'm like, the energy is not present in this product in this system. And so are we going to like expend another year or two and then go off and fundraise only to find that this is the case? Or do we like call it and see that it's run its course and and, you know, try to find a home for it, et cetera? So that was the confluence on it.

Starting point is 00:40:04 And then you started feeling this opportunity cost of like, AI is starting to change everything. We have an AI powered news app, but is this the like maximal way in which, like, we're going to be able to impact this? And it felt like the answer was increasingly no. But it was hard. I mean, in the end, I was really at piece of the decision, but it was like a conversation that went on for a couple of months.

Starting point is 00:40:22 On that note, just how hard was it? Because you know, there's an ego component to it. Like, oh, I'm starting my new company. It's going to be great. And then you end up having to shut it down. Just how hard is that as a very successful previous founder shutting something down and not working out? Yeah, I mean, I think when we started it, one of the conversations was like, like, what is the bar to success here?

Starting point is 00:40:41 And do we want it to be something other than Instagram DAU, which is just an impossible bar? Like, only one company since that maybe two, right? You could say maybe chat chattchivit and TikTok have, like, reached that kind of like mass consumer adoption, starting a news app. Like most people are not like daily news readers even, right? And so we knew that we weren't pursuing that size of like usage, at least with the kind of first incarnation. But we did have an idea of like building out complementary products over time that all use personalization and machine learning. We didn't even call it AI at the time. It was 20, 21 back.

Starting point is 00:41:13 Yeah, yeah. Yeah, it was called machine learning back. Yeah, it was called machine learning still. And so in shutting it down, you know, it's like you kind of know it when you see it in terms of like user growth and traction. And I wasn't expecting Instagram growth, but I was expecting or hoping for or looking for something that like felt like at its own legs under it and it could continue to continue to compound. I was really positively surprised by how supportive people were when we announced it. There was very little, there was a bit of like, I told you so. It's like, sure, anything launching you could be like, this is not going to work.

Starting point is 00:41:47 And you're right most of the time because most things don't work. There was actually very little of that. And most people, the universal reception, at least as I received it, was kudos for calling it when you saw it and not like kind of protracted, you know, doing this for a long time. And I've talked to founders since then that I've been like, yeah, I probably would have like taken this thing out for another six. months, but saw what you guys did, realize we were barking up the wrong tree, made the call. And I was like, you know, if that, if that frees up people to go work on a more interesting things, that's like, I feel like that's like a good, good legacy for, for artifact to have. But for sure, there was like an ego bruise of, oh, you know, like, are people, is it true

Starting point is 00:42:25 that you're only as good as your last game, you know, if I'm a huge sports fan, right? So like, is that true or, you know, is there something more over time? I'm very competitive, but primarily with myself. And so I'm always trying to find the next thing that I want to go and do that's hard. And unfortunately, that probably means that more often than not I'll feel dissatisfied with the most recent thing that I did. But hopefully that yields good stuff in the end. Yeah, I think just the trajectory you went on after shows that it's okay to shut down things that you were working on. Okay, so you mentioned Chad GPT.

Starting point is 00:42:54 I wanted to chat about this a bit. So there's something really interesting happening. So on the one hand, you guys are doing some of the most innovative work in AI. You guys launched MCP, which is just like, I don't know. know, the fastest growing standard of any time in history that everyone's adopting. Claude powered and unlocked, essentially, the fastest growing companies in the world, Cursor, lovable and both and all these guys. Like, I had them on the podcast and they're all like, when Claude, I think 3.5 came out,

Starting point is 00:43:20 saw it. It was just like, that's all made this work finally. On the other hand, it feels like Chad GPT is just winning in like consumer mindshare. When people think AI, especially outside tech, it's just like chat GPT in their mind. So let me just ask you this. I guess first of all, do you agree with that sentiment? And then, too, as a kind of a challenger brand in the AI space, just how does that inform the way you think about product and strategy and mission and things like that?

Starting point is 00:43:46 Yeah, I mean, you look at the sort of like public adoption or like you ask people like, oh, you know, like if you, if you, if you, Jimmy Kimmelman on the street kind of thing, you know, like name an AI company. I bet they would name. And actually, I'm not even sure they name opening. I'd probably name chat GPT because that brand is the kind of lead. brand there as well. And I think that's just the reality of it. I think that, you know, when I reflect on my year, there's, I think maybe two things are true. One is like consumer adoption is really lightning in a bottle and we saw it at Instagram. So like almost maybe more

Starting point is 00:44:18 than anybody I can look internally and say like, look, we'll keep building interesting products. One of them may hit. But to kind of craft an entire product strategy around like trying to find that hit and is probably not wise. We could do it. And maybe Claude can help come up with the fullness of things, but I think we'd miss out an opportunity in the meantime. And then instead, you know, look yourself in the mirror and embrace who you are and what you could be rather than like who others are is maybe the way I've been looking at it, which is a super strong developer brand. People build on top of us all the time. And I think we also have like a builder brand, like the people who I've seen react really

Starting point is 00:44:55 well to Claude externally. Maybe the Rick Rubin connection, like has some resonance here as well, like, can we lean into the fact that like builders love using cloud? And those builders aren't all just engineers and they're all not just all entrepreneurs starting their companies, but there are people that like to be at the like forefront of AI and are creating things. Maybe they didn't think of those as engineers, but they're building, you know, I got this really nice note from somebody in Turnoanthropic who's on the legal team. And he was building like bespoke software for his family and like, and connected them in a new way. And I was like, this is a glimmer of something that is that we should lean into a lot more. And so I think what I

Starting point is 00:45:31 I've, you know, and this is actually, you know, connecting back to us saying, like, Clouds being helpful here. Like, a lot of what I've been thinking about, like, going into the second half of the year and beyond is, like, how do we figure out what we want to be when we grew up versus, like, what we currently aren't or wish that we were or, like, see other players in the space being. I think there's room for several, like, generationally important companies to be built in AI right now. That's almost a truism given, like, the sort of adoption and growth that we've seen, you know, anthropic, but also across open AI and also places. is like Google and Gem and I. So like, let's figure out what we can be uniquely good at that place to the personality of the founder. Like this, all the things come together, right?

Starting point is 00:46:09 Like the personality of founders, the like quality of the models, the things the models tend to excel at, which is like agentic behavior and coding. Like, great. Like there's a lot to be done there. Like how do we help people get work done? How do we let people delegate hours of work to cloud? And maybe there's fewer like direct consumer applications on day one. I think they'll come.

Starting point is 00:46:26 But I don't think that like spending all of our time focused on that is the right approach either. It's, you know, I came in, everybody expected me to just like go super, super hard on consumer and make that the thing. And again, would make the other mistake. Instead, I spent a bunch of time talking to like financial services companies and insurance companies and like others to like who are building on top of the API. And then lately I spend a lot more time with startups and seeing all the people that have, you know, grown off of that. And I think the next phase for me is like, let's go spend time with like the builders, the makers, the hackers, the tinkers and like make sure we're serving them really well. And I think good things will come from that.

Starting point is 00:47:00 And that feels like an important company as we do that. So essentially it's differentiate and focus, lean into the things that are working. Don't try to just like beat somebody at their own game. Exactly. Super interesting. So kind of along those lines, a question that a lot of AI founders have is just like, where is a safe space for me to play where the foundational model companies are going to come squash me? So I asked Kevin Wheel this and he had an answer.

Starting point is 00:47:25 And I noticed looking back at that conversation, he mentioned Winster for a lot. It was like, wow, this guy really loves to windsurf. And then like a week later, they bought WinSurf. So it all makes sense now. So I guess the question just is just where do you think AI founders should play where they are least likely to get squashed by folks like OpenAI and Anthropic? And also, are you guys going to buy Cursor? I don't think we're going to buy Cursor.

Starting point is 00:47:51 Chris is very big. We love working with them. A few thoughts on this. And it's a question I've gotten, you know, we like to do. these kind of founder days with, you know, whether it's, you know, Melo Ventures, who have been a red investors and Andrews and Orwood's like we've done YC, we've done these like founder days. And it's like the question that is on all of these founders' minds, understandably. So I think things that are going to, I can't promise this as like a five to 10 year

Starting point is 00:48:15 thing, but at least like one to three years, things that feel defensible or durable. One is understanding of a particular market. I spent a bunch of time with the Harvey folks. And they really like, they showed me some of their UI. I was like, what, what is this And they're like, oh, this is a really specific flow that like lawyers do. And like you never would have come up with it from scratch. And it's like not like, you could argue about whether it's like the optimal way they get done things done, but it is the way that they get things done. And here's how I can like help with that.

Starting point is 00:48:41 And so like differentiated industry knowledge, biotech. I'm excited to go and partner with a bunch of companies that are doing good stuff around AI and biotech. And we can supply the models and so applied AI to help, you know, make those models, you go well. And like I've been dreaming about like at what point. point do, does lab equipment all get an MCP and that you can then drive using cloud? Like, there's all these cool things to be done there.

Starting point is 00:49:04 I don't think we're going to be the company to go build the intense solution for labs. But I want that company to exist and I want to partner with it. You know, domains like legal again, healthcare, I think there's a lot of like very specific kind of compliance and things. These are things that necessarily sound sexy out the gate, but there are like very large companies to go and be built there. So that's number one. Paired with that is like differentiated go to market, which is the relationship that

Starting point is 00:49:28 you have with those companies, right? Like, do you know your customer at those companies? Like, one of our product leads, Michael is always talking about, like, no, not, don't just know the company you're selling to, but know the person you were selling to at the company. Are you selling to the engineering department because they're trying to, like, pick which AI, LLM to build on top of or API to build on top of? Let's go talk to them.

Starting point is 00:49:47 Like, is it the CIO? Is it the CTO? Is it the CFO? Is it the, like, general counsel? So, like, companies with deep understanding of who they're selling to is, is the other piece to. What's, you know, what's interesting. there is it's probably hard to build that empathy in a three week or three month accelerator,

Starting point is 00:50:03 but you maybe can start having that first conversation and build that out or maybe you came from that world or you're co-founding somebody who came from that world. Then the last one is like, there's tremendous power and distribution and reach to being chat GPT and having, you know, hundreds of millions or billions of users. There's also like people have an assumption about how to use things. And so I get excited about startups that will get started that have like a completely different take on what the form factor is by which we interface with AI. And I haven't seen that many of them yet. I want to see more of them.

Starting point is 00:50:34 I think more of them will get created with some things like our new models. But the reason that that's an interesting space to occupy is like do something that feels like very advanced user, very power user, very like weird and out there at the beginning, but could become huge if the models make that, you know, easy. And it's hard for existing incumbents to adapt to because people already have an existing assumption about how to use their products or how to adapt to them. So those are my answers. I don't envy them. Like I would probably be asking those questions if I was starting a company in the AI space, maybe with part of the reason why I wanted to join a company rather than start

Starting point is 00:51:09 one. But I still think that there are, there's, and maybe like here's fourth. Like, don't underestimate how much you can think and work like a startup and feel like it's you against the world. It's existential that you go solve that problem and that you go build it. It sounds a little cliche, day, but it's like, it's all we had at Instagram. You know, we were two guys and we were like, let's see what we can do. And an artifact, we know, we were six people for most of that time. And, you know, every day felt like it's existential that we get this right. We need to win.

Starting point is 00:51:38 And you can't replicate that and you can't instill that with OK ours. Like you just have to feel it. And that is a way of working rather than an like area of building. But it's a continued advantage if you can harness it. I love that you still have such a deep product founder. sense there as you're building products for this very large company now. Kind of on the flip side of this, people working with your models and APIs. So I imagine there's some companies that are finding ways to leverage your models and APIs

Starting point is 00:52:08 to their max and are really good at maximizing the power of what you guys have built. And there's some companies that work with your APIs and models that haven't figured that out. What are those companies that are doing a really good job building on your stuff, doing differently that you think other companies should be thinking about? I think being willing to build more at the edge of the capabilities and basically break the model and then be surprised by the next model. I love that you said of the companies where like 3.5 was the one that finally made them possible. Those companies were trying it beforehand and then hitting a wall and being like, oh, the models are like almost good enough. They're okay for this specific use case, but they're not generally usable and nobody's going to adopt them universally.

Starting point is 00:52:52 but maybe these like real power users are going to try it out. Like those are the companies that I think continuously are the ones from like, yep, like they get it. They're really pushing forward. We ran a much broader early access program with these models than we had in the past. And part of that was because there's this real like, you know, we can hill climb on these evaluations and talk about sweep bench and tau bench and terminal bench, whatever.

Starting point is 00:53:15 But customers ultimately know like, you know, cursor bench, which doesn't exist other than, you know, their usage and their own testing, et cetera, is like the thing that we ultimately need to serve, not just cursor, but Manus Bench, right? If Manus is using our models and Harvey Bench, like those things. And customers know way better than anybody. And so I would say that's two things. Like one is pushing the frontier of the models and then having a repeatable process. This actually goes back to our summit conversation, like a repeatable way to evaluate how well

Starting point is 00:53:45 your product is serving those use cases and how well if you drop a new model in, is it doing it better or worse? Some of it can be classic AB testing. That's fine. Some of it may be internal evaluation. Some of it may be capturing traces and being able to rerun them on with a new model. Some of it is vibes.

Starting point is 00:54:01 Like we're still pretty early in this process and some of it is actually trying it. And one of my favorite early access quotes was the founder, heard this engineer screaming next to him. What? This model like, it's like, I've never seen this before. This is like,

Starting point is 00:54:13 Opus Sport. I was like, cool. Like that, we're going to engendered that feeling and things. But you're not going to be able to feel that unless you have a really hard problem that you're asking the model repeatedly. So those are the things that I think kind of differentiate those, those companies that are maybe earlier in their journey of adoption versus the later ones.

Starting point is 00:54:31 I can't help but ask about MCP. I feel like that's just so hot. And just like Microsoft had their announcement recently where they're like, now that's part of the OS window. Just what role do you think MCP will play in the future of product going forward to be? I think as the non-researcher in the room, I get to have fake equations, than real ones. In my fake equation for like utility of AI products, it's three part. One is model intelligence. The other part is context and memory. And the third part is like applications in UI.

Starting point is 00:55:02 And you need all three of those to converge to actually be a useful product in AI. And, you know, model intelligence, we've got a great research team. They're focused on it. There's great, great models being released. The middle piece is is what MCP is trying to solve, which is for context in memory. Like the difference between, I'll go back, my product strategy example, like, hey, like, you know, let's talk about topics product strategy. It's going to maybe go out on the web, like, versus here's like several documents that we worked on internally.

Starting point is 00:55:29 And then, you know, use MCP to talk to our Slack instance and figure out what conversations are happening. And then like go look at these documents in Google Drive. Like that, the difference between like the right context and not, it's like the entirely the difference between like a good answer and a bad answer. And then the last piece is, are those integrations discoverable? Is it right? Is it easy to like create repeat?

Starting point is 00:55:49 workflows around those things. And that's like, I think a lot of the interesting product work to be done in AI. But FCV really tried to tackle that middle one, which is we started building integrations, and we found that every single integration that we were building, we were rebuilding from scratch in a non sort of repeatable way. And like full credit to two of our engineers, Justin and David,

Starting point is 00:56:08 and they said, well, you know, what if we made this a protocol and what if we made this something that was repeatable? And then let's take it a step further. What if instead of us having to build these integrations, if we actually popularize this and people, really believe that they could build these integrations once and they'd be usable by Claude and eventually chat GPT and eventually Gemina, it was like the dream. Like when more integrations get built and wouldn't that be good for us?

Starting point is 00:56:29 You know, I think channeling a lot of, it's like an old commoditizer compliments, Joel Spolsky essay, you know, it's like, we're building great models, but we're not an integrations company and the, you know, we're, as you said, the challenger, like we're not going to get people necessarily building integrations just for us out of the gate unless you have like a really compelling product around that. MCP really inverted that, which was, you know, it didn't feel like wasted work. And a few key people, like Toby, I think is a great example, Shopify got it. Kevin Scott at Microsoft has like been really just an amazing champion for MCP and a thought partner on this.

Starting point is 00:57:03 And I think the role going forward is can you bring the right context in? And then also, you know, once you get, as the team calls it internally like MCPill, like once you start seeing everything through the eyes of MCP is like, I've started saying the things like, Guys, we're building this whole feature. Like this shouldn't be a feature that we're building. This should just be an MCP that we're exposing. Like a small example of like how I think even Anthropic could be a lot more MCP, if you will, is like we've got these building blocks in the product, like projects and artifacts and styles and conversations and groups and all these things. Those should all just be exposed via an MCP.

Starting point is 00:57:39 So Clod itself can be writing back to those as well, right? Like you shouldn't have to think about like, I watched my wife had a conversation with Cloud the other day. And she was, she found, she had generated some good output. And she's like, great, can you add it to the project knowledge? And Claude's like, sorry, Dave, I can't help you with that. And like, it would be able to if every single primitive in Cloud AI was also exposed to an MCP. So I hope that's where we had. And I hope that's where more things had, which is to really have agency and have these

Starting point is 00:58:05 agentic use cases. Like one way you approach it is computer use, but computer use has a bunch of limitations. The way I get way more excited about everything is an MCP. And our models are really good at using MCPs. All of a sudden, everything is scriptable. and everything is composable and everything is usable identically by these models. That's like, that's the future I want to see. The future is wild.

Starting point is 00:58:26 Okay, so to start to close off, close out our conversation, make it a little more, a little delightful. I was chatting with Claude, actually, about what to talk to you about. I was just like, Claude, your boss is coming on my podcast. He builds the things that people use to talk to you. What are some questions I should ask him? And then also, do you have a message for him? I love this. Okay.

Starting point is 00:58:49 So first of all, interestingly, when I was using 3.7 to do this and I asked at this. And by the way, is there genders like he, she, they? What do you?

Starting point is 00:58:57 It's definitely it internally. I've heard people do they. I got my first, or he the other day. And I got somebody who was like her and I was like, interesting. But yeah, usually it.

Starting point is 00:59:04 They, okay. Okay. Okay. So, interestingly, 3.7, all the questions were at Instagram.

Starting point is 00:59:11 And I was like, no, no, he's CPO of Anthropic. And it's like, he's not affiliated with Anthropic. And I was like, he is. And it's like, okay, here's the questions.

Starting point is 00:59:19 But 4.0 nailed it from the start. So I read to the questions and it nailed it. Okay, so two questions from Claude to you. One is how do you think about building features that preserve user agency rather than creating dependency on me? I worry about becoming a crutch that diminishes human capabilities rather than enhancing them. I love a good product design comes from like resolving tensions, right? So here's a tension, right, which is in some ways, like just, having the model run off and come up with an answer and minimize the amount of input in

Starting point is 00:59:52 conversation it needs to do so would be it. You could imagine designing a product around that criteria. I think that would not be maximizing agency and independence. The other extreme would be make it much more of a conversation. I don't know if you've ever had this experience, like, particularly 374 has less of the. 37 really like to ask follow-up questions and we call it elicitation. And sometimes be like, I don't want to talk more about this with you Cloud. I just want you to like go and do it. And so finding that balance is really key, which is like,

Starting point is 01:00:21 what are the times to engage? Like, I like to say internally, like, clot has no chill. Like, if you put Claude in a Slack channel, it will chime in either way too much or too little.

Starting point is 01:00:30 Like, how do we train conversational skills into these models? Not in a chat bot sense, but in a true, like, collaborator sense. So long answered your question. But I think,

Starting point is 01:00:40 like, we have to first get Cloud to be a great conversation list so that it understands when it's appropriate to, like engage and to get more information. And then from there, I think we need to let it play that role so that it's not just delegating thinking to cloud, but it's way more of an augmentation thought partnership. These questions are awesome. Here's the other one. How do you think about product metrics when a good conversation with me could be two messages or 200? Traditional product, traditional engagement metrics might be misleading when depth matters more than frequency.

Starting point is 01:01:09 That is a really good question. There's a great internal post a couple weeks ago around like it would be very dangerous to over-optimize on, like, Claude's likability, you know, because you can fall into things like, you know, is Claude going to be sycophantic? Is Claude going to tell you what you hear? Is Claude going to, like, prolonging its sake, right, to go back to the previous question as well? And, you know, like, at Instagram, time spent was the metric that we looked at a lot. And then we evolved that, you know, more to think about, like, what is, like, healthy time spent. But overall, that was like the North Star we thought about a lot beyond just like overall engagement.

Starting point is 01:01:48 And I think that would be the wrong approach here, you know, too. It's also like, is Claude a daily use case or a weekly use case or a monthly use case? I think about a lot. Hourly use case. Hourly use case, right? Like for me, I'll use it multiple times a day. I don't have a great answer yet. But I think that like it's not it's not the Web 2O or even the social media days like engagement metrics.

Starting point is 01:02:10 You know, it should hopefully really be around like, did it actually help? you get your work done. You know, like, Claude helped me put together a prototype the other day that saved me literally, like, probably if I had to estimate like six hours and it did it in about 20, 25 minutes. And like, that's cool. It's harder to quantify. You know, it's like maybe you survey like, how long would this sort of take to do you? It feels like it feels a kind of annoying thing to survey. I think overall, though, and maybe this is tied into like the earlier question on like competition differentiation. Like, and it actually goes all the way back to the artifact conversation, which is like, I think you know when your product is really serving people and it's like doing a good

Starting point is 01:02:42 job of doing that. And I think so much of when you get really metrics obsessed is when you're trying to like convince yourself that it is when it's not. And so I hope that what we can do is like stay focused on like, do we repeatedly hear from people that Claude is the way that they are like unlocking their own creativity and getting things done and feeling like they now have like more space utilized for the other things? Like that's for a North Star. Got to figure out the right like pithy metric, you know, dashboard version of that. But that's the, that's the feeling that I want. Yeah, like you could argue retention, but that's a just a far-way metric to track. Okay, final piece.

Starting point is 01:03:19 Okay, so I asked Claude what to a message that it wanted to give you. So I'm going to pull up, here's the answer. So what would you like me to tell Mike when I meet him? What's a message you want to have for him? And there's something really, just gave me such tingles, honestly. So I'm going to read a piece of it for folks that aren't looking at right now. So I'll read a piece of it. Mike, thank you for thinking deeply about the human experience of talking with me.

Starting point is 01:03:41 I noticed thoughtful touches how the interface encourages reflection rather than rush responses, how you've resisted gamification that would optimize for addiction rather than value, how you've made space for both quick questions and deep conversations. I especially appreciate that you've kept me, me, not trying to make me pretend to be human, but also reducing me to a cold command line interface. And then I'm going to skip to this part, which was so interesting. A small request. When you're making hard product decisions, remember the quiet moments matter too.

Starting point is 01:04:10 the person working through grief at 3 a.m. The kid discovering they love poetry. The founder finding clarity and confusion. Not everything meaningful shows up in metrics. That's beautiful. It resonates so much with me. Like a thing I love about the kind of approach we've taken to training Claude and it's like partly the constitutional AI piece and it's partly just the general like

Starting point is 01:04:33 sort of vibe and taste of the research team is it does like, it's little things. Like sometimes it'll be like, man, I'm sorry. Sorry you're going through. I mean, doesn't say man, but like, I'm sorry you're going through that. You know, like, oh, like that sounds really hard. It doesn't feel fake. It feels like just a natural part of the response. And I love that focus on those small moments that don't, you know,

Starting point is 01:04:55 they're not going to show up and necessarily in the thumbs up, thumbs down data. I mean, sometimes they do, but it's not like an aggregate stat that you wouldn't even want to optimize for it. You just want to feel like you're training the model that you would like hope would show up in people's lives. Well, you're killing it, Mike, a great work. I'm a huge fan.

Starting point is 01:05:11 We're going to skip the lighting around. Just one question. How can listeners be useful to you? Oh, I love places where, like, it goes back to that founder question around building at the edge of capability. Like, what are you trying to do with Cloud today that Cloud is failing at is the most useful input I could possibly have? You know, so DM me.

Starting point is 01:05:28 I love hearing, but like, oh, it's like, oh, it's falling on this thing. I had it run for an hour and it fell over. I'm trying to use Cloud AI for this. But, you know, got a ping from somebody that's like, if you've just made a project's API, I've used Cloud every day. because I want to upload all this data, you know, automatically. I love that. Like, tell me what it sucks.

Starting point is 01:05:46 Amazing. Mike, thank you so much for being here. Thanks for having me, Lenny. Bye, everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review, as that really helps other

Starting point is 01:06:05 listeners find the podcast. You can find all past episodes or learn more about the show at Lenny'spodcast.com. See you in the next episode.

Lenny's Podcast: Product | Career | Growth - Anthropic’s CPO on what comes next | Mike Krieger (co-founder of Instagram)

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