Anthropic’s CPO on what comes next | Mike Krieger (co-founder of Instagram)

0:00 Ninety percent of your code. Roughly is written by AI now. The team that works in the most futuristic way is the Cloud Code team. They're using Cloud Code to build Cloud Code in a very self-improving kind of way. We really rapidly became bottlenecked on other things like our merge cube. We had to completely re-architect it because so much more code was being written and so many more pull requests were being submitted that it just completely blew out the You guys are at the edge of where things are heading. I had the very bizarre experience of I had two tabs open. It was AI twenty twenty seven and my product strategy, and it was this like moment where I'm like, Wait, am I the character in the story? It feels like ChatGPT is just winning in consumer mind share. How does that inform the way you think about product, strategy, and mission? I think there's room for several general. 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 models are great, but are they able to have an independent opinion and it's actually really flipped for me only in the last month.

1:08 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. We chat about what he's changed his mind about most in terms of AI capabilities in the years 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.

1:39 Also, his thoughts on OpenAI versus Anthropic, the future of MCP, why he shut down Artifact his last startup, and how he feels about it. Also what skills he's encouraging his kids to develop with the rise of AI. And we close the podcast on a very heart warming message that Claude wanted me to share 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. Also, if you become an annual subscriber of my newsletter, you get a year free of a bunch of incredible products, including linear, superhuman notion, perplexity, and granola. Check it out at lenny's newsletter.com and click bundle.

2:17 With that I bring you Mike. Creaker. This episode is brought to you by Product Board, the leading product management platform for the enterprise. For over ten years, Product Board has helped customer centric organizations like Zoom, Salesforce, and Autodesk build the right products faster.

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4:13 Join the ranks of over half of the Fortune 100 companies that trust Stripe to drive change. Learn 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 I'd love to hear that. I've also been looking forward to this for a while. Uh, I've so much to talk about. So first of all, you've been at Anthropic for uh just over a year at this point. Congrats, by the way, on hitting hitting the cliff.

4:42 Thank you. Not that we're tracking. That's right. So let me just ask you this. So you've been in Anthropic for about a year. What's something that you've changed your mind about? From Before you join Anthropic Two today about

4:56 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 gonna be able to produce code, they're gonna be able to, you know Right, you know, hopefully in your voice eventually.

5:15 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 four. Where my go-to product strategy 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

5:35 Past its pretty anodyne kind of comment that I would leave, like, Oh, have you thought about this? And it's like Yeah, yeah, I thought about that. And Opus four, I was working on some strategy for our second half of the year. was the first one. It was like Opus four combined with our advanced research, but it really went out for a while and it came back and I was like

5:51 Damn, you really looked at it in a new way. And so that's like a thing that I've Maybe yeah. 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 at it I'm like, Yep, that that is a new angle that I hadn't been looking at before and I'm going to incorporate that immediately into how how I think about it. So that's Probably the the biggest

6:10 I don't know about independence is the right word, but like creativity and sort of novelty of thought relative to how I'm I'm thinking about things. And the timeline one, it's like so interesting because You know, uh 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. 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 gonna be right, you know, but even I think as of last year. He was talking about, you know, we're at fifty percent on Sweet Bench, which is this like, you know, benchmark around how well the models are at at coding.

6:41 Uh, he's like, I think we'll be at ninety percent by the end of twenty twenty five, or something like that. And sure enough, we're at about seventy two now with the new models and We're at fifty percent 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

6:59 If you read AI twenty twenty seven, like I have. It was it was made by heart race. Yeah, and I had the very bizarre experience of I had two tabs open. It was AI twenty twenty seven and my product strategy and it was this like moment where I'm like, Wait, am I the character in the story? Like is this how much is this converging? But You know, you read that and you're like, Oh twenty twenty seven. That's like That's years away if you're like, No, we're mid twenty twenty five 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

7:30 I think my like My confidence in the timelines and I don't know exactly how they manifest of definitely just solidified over the last year. Wow. Uh I I wasn't expecting to go down that'cause that that

7:43 That paper was scary. And I'm curious just I guess l I can't help but ask, just thoughts on just how do we avoid the scary Scenario that that paper paints of where AI getting really smart goes. Yeah. I mean I I this maybe ties into like if I've been here a year, like why did I join Anthropic? I was watching

8:01 the models get better and even, you know, you could see it in in twenty four and like, you know, early twenty twenty four. And looking at my kids, I'm like, all right, they're gonna grow up in a world where they uh it's an 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

8:25 coming to an agreement and a shared framework and understanding of like what is 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 and some of those are are research and interpretability questions. But for me it was like the 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

8:53 better and if I can have a part to play there, like let's do it. I I love that answer. Uh speaking of kids, so you've got two kids. I've got a young kid, he's uh just about to turn two. I'm curious just what skills you're 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 do you what advice do you have? We have this, uh, you know, breakfast, we eat breakfast with the kids every morning and sometimes some question will come up, you know, like

9:20 You know, something about like physics my oldest kid's almost six, but you know, they they ask like funny questions about like, you know Uh you know. the solar system or physics or you know, in the six year old way. And

9:32 before we reach for Claude, because at first, you know, my instinct is like, Oh, I wonder how Claude 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, Well asked Cloud, you know, so Alright, 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

9:57 systematically working way 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. Uh,'cause there 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

10:16 Uh oh, is that coral as a as an animal or like coral is alive, I but not remember the details of it. And I was like, I don't know if that's true. 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 uh delegating all of your cognition to the you know to the eye because They won't always get it right. And also

10:39 Uh it kind of likes you know, kind of short circuits any kind of independent thought. So The skill of Of asking questions. Inquiry

10:47 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 and I'm sure that'll radically change between between now and then. It's interesting. I had Toby Lucky, uh Shopify CO on the podcast, and he had the same answer for what he's encouraging his kids to Uh to develop his curiosity.

11:07 And uh And so it's interesting, that's a common thread. you know, K through eight school a kid goes through had a 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 gonna be like. And actually I think it went over most of the people uh in the heads.

11:23 the audience's heads'cause he was like all right, well Let me take it all the way back to Claude Shannon and information theory. I could see people's eyes grow like, what did I like sign up for and 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 and we don't know what those jobs are going to be. And so like what are the skills and techniques and

11:45 And and remain open mindedness and Around like what the what the what the exact way we recombine those things. And even those will probably change three times between now and eight when they're eighteen. I wanna 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 seventy percent to like ninety percent. There is an engineer lead that shared like ninety percent of your code roughly is written by AI now.

12:13 Which first of all is just insane. That like it went from zero to Ninety percent, I don't know, few years? Something like that. Okay. That's I don't think people are talking about this enough. That's just wild.

12:25 You guys are basically at the bleeding edge. I've never heard a company that has this high a 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. So usually it's like PM. Like here's what we're building, engineer builds it.

12:42 Ships. Is it still kind of roughly that, or is it now PMs are just going straight to Claude, build this thing for me, engineers are doing different things, just what looks different in a world where Ninety percent of your code is written by AI. Yeah. Really interesting'cause I think the the the role the the role of engineering has changed a lot, but the

12:59 The kind of Sweet 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 There the the rules are still fairly similar, although we'll now get in my favorite things that happen now are some nice PMs that have an idea that they want to express or designers that have an idea they want to express.

13:19 Well use Claude and like 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 not 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. uh you know, code plus design piece.

13:39 What I've learned though is like The process of knowing what to ask The AI, how to compose the question. how to even think about like 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

14:01 sort of get in line to get your change accepted by uh you know, the the the system that then deploys it to production. We had to completely re architect it because so much more code was being written and so many more pull 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.

14:30 A lot of things that I'm thinking about right now is like 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 something I'm working on. And then once the uh building is happening other ball next 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 time so that we're not blocked on the engineering side and then

14:53 When the work is complete and we're getting ready to to to ship it. What are all those bottlenecks as well? Like let's do the air traffic control of landing the change, like how do we figure out large strategies? So I think Work. The

15:04 There hasn't been as much pressure on changing those until this year, but I th 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 gonna be very painful to do it the current way. Wow, that is extremely interesting. So Used to be here's an idea. Let's go design it, build it, ship it, mer merge it and then ship it.

15:25 And usually the bottleneck was Engineering taking 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

15:37 the cue to merge it into production and Uh, and and I imagine review it too is probably a reviewing has really changed too, and in in in in many ways our most uh perhaps unsurprisingly, the team that works in the most futuristic way is the cloud code team,'cause they're using cloud code to build cloud 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, you know, in the way that you would for any other, you know, project. And they've just realized like Claude is generally right and it's producing

16:08 you know, pull requests they're probably larger than most people are gonna 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 can also imagine it going off the rails and then having like a completely both unmaintainable or even understandable by cloud code base that hasn't happened.

16:28 But watching them like change their review processes definitely has uh has been has been interesting. And yeah, like the merge cue is one instance of the 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 the launch moment, whether that's about like then enabling people to use this thing and like talking about it, like the the classic things of building something useful for people.

16:54 And then making it known that 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.

17:09 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 ninety five percent plus. I'd have to ask Boris and the other tech leads on there. But what's been cool is um Uh 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

17:30 Python, some go. Um some rust now. But it's not, you know, we're not like a TypeScript shop. And so uh I saw a great comment yesterday in our Slack where somebody had this thing that was driving them crazy about Cloud Code. And they're like, Well I don't know any TypeScript. I'm just gonna like talk to Claude about it and do it. And they went from that to pull request in an hour and solved their problem and they like, you know, submitted a pull request and

17:51 that kind of breaking down the barriers. One, it changes your sort of Um uh barrier to entry for any kind of uh kind of newcomer to the project. I think it 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 cloud code. Being that

18:08 patient alpha of that, you know, where like either the contributions from outside the team can be cloud coded as well. Wow. This is just it's just gonna c continue to blow my mind, like all these things. But you're sharing ninety five percent of cloud code is written by cloud code, roughly. That's my guess. Yeah. I'd but I'll 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

18:34 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 Cloud is already helping with that also of helping you decide what to build. So if if those two bottlenecks are aligning, deciding what to build, and then just like merging and getting everything. Where do you see the most uh

18:55 Interesting stuff happening to help you speed those things up. Yeah, I think that on that on that first round, like I started the year Um by writing a doc that was effectively like what How do we do product today and where is cloud not showing up yet that it should? And I think

19:11 That upstream part is the next one to go interesting. Like at your conference I talked to somebody who's working on like a PRD, GPT, kind of like chat PRD, I think was there about it. Um You know, can we push more on, you know? Can cloud be a partner in figuring out what to build?

19:29 what the market size is if you want to approach it that way, what the user needs are if you if you look at it a different way. Like we think a lot about the virtual collaborator on topic, and one of the ways in which I think that can show up is Hey, I'm in the Discord, the you know, the 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.

19:48 That's that one. Models can can do that today. Step two, which the models probably can do today, we just 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 Like put together a pull request to like solve this thing that I'm seeing like

20:03 Feels. Very achievable this year. um than string those things together and we're limited more This is why MCP is exciting me. Like we're limited more around like making sure the context flow through all of that so we have the right access to those things more than the model's capability to to reason and propose. Now the model might not have

20:20 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 of this not showing up. But I you know, I would get very excited. I would give you a really uh small example, but We changed the on Claude AI. uh you should be able to just copy uh markdown from artifacts or code from artifacts. And we change it so you can actually download it and and export it. So we change the button to export. And 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 mine, you know, one of those things where it's like

20:49 Made sense, but we've probably got it like not quite right. That feedback was in the R UX channel. Like I would have loved like an hour later for a Pla 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 gonna spin up an A B test to see if this changes metrics and then we'll see how it looks in a week. Like this stuff feels

21:06 If you told me that about a year and a half ago I'm like, Ah yeah, maybe like twenty seven, maybe like twenty six, but it's Pre m I I it really feels, you know, just at the tip of capabilities right now. Wow. Okay, so mm you mentioned the Lenin Friends summit. I wanted to talk about this a bit. So you were on a panel with Kevin Wheel, the CPO of OpenAI. I think it was the first time you guys did this, maybe the last time for now. Yeah, I haven't done it since not for any reason. I had a lot of fun.

21:30 What a what a legendary panel we assembled there with Saraguo uh moderating. And you made this comment actually ended up being the most rewatched part of the of the interview. Which is that You've kind of

21:42 You we're 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 came from the product team working with the researchers. Yes. And so you've been doing more into that.

22:00 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 in fact I think that the If the proportion was already like skewing towards having more of that embedding, I've just become

22:15 more and more convinced. Like I have this I I didn't feel as strongly about it. during your you know, the summit and now I feel really strongly about it because 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, but like where we should play and like 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

22:40 uh if you play with artifacts with with cloud four, 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 like doing the post training around teaching Claude, you know, some of these like really specific skills. And we paired it with some product people. 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 prompt it a little bit. Like that's just not enough. We need to be in that like fine tuning process.

23:07 So So much of what, you know, if you look at what we're working on right now, what we've shipped recently between like research and all these other things, like Like the 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

23:24 We are at like we're in the post training conversations around how these things should work. And then we are in the building process and we're like feeding those things back and looping them back. Like I think it's exciting. It's also um 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 uh 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 and the researchers'cause we just shipped a bunch of like memory capabilities in Cloud. They're like, Yeah, yeah, we've been talking to them for weeks. Like, this is how we're manifesting it. It's like

23:56 Okay. I feel good. I feel like we're doing the right things now. So let me pull on this thread uh more. There's something I've been thinking about along these lines. So essentially there's like a big part of Anthropic that's building this super intelligent giga brain that's gonna do all these things for us over time. And then there's, as you said, there's like the product team that's building the UX around the super intelligent giga brain.

24:16 And over time this superintelligence is gonna be able to build its own stuff. And so I guess just Where do you think the most value will can will come from? pro traditional product teams. Over time.

24:28 I know this different'cause you guys are a foundational alum company and not most companies don't work this way, but just I don't know. Thoughts on just the where most value will come from product teams over time working on AI. I think it 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 could do a much better job of making this comprehensible. It's still like

24:50 The difference between somebody who's really adept at using these tools and their work and most people is huge. And I mean, I'm th maybe that's the most literal answer to your earlier question around like what what skills to learn. That is a skill to to learn and use. And the same way that I remember. I I we did like computer lab 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 a uh an advantage at the time. Of course now Google is pretty good at figuring out what you're trying to do if you like are only in the neighborhood and like

25:22 Need. I still think that's a n 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.

25:33 What are you building and how do you make it comprehensible? Like still hard? Cause I think that like gets at like this much deeper empathy and like understanding of human needs and psychology. Like I was a human community reaction major I Don't talk in my book here. Like I still feel like that is a a a very, very, very, very necessary skill. So that's one. Two is and this you know. Uh straight to call back to another one of your guests, like

25:57 Strategy. Like how we win, where we'll play, like figuring out where exactly you're gonna want to like Of all the things that you could be spending your time or your uh your tokens or your computation on like what What

26:09 What you wanna 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. So like strategy, I think is still that. The second piece.

26:24 And then the third one is opening people's eyes to what's possible, which is a continuation of making it understandable, but We were in a demo with a a financial services company recently. And we were like working on like Here's how you can use our analysis tool and M C P together and and like You could see their eyes up and you're like, Ah, okay, like

26:40 There's still s we call it overhang, right? Like the delta between what the models and the products can do and how it's be they're being used on a daily basis. Huge overhang. So that's where still like a 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

27:00 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, 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.

27:19 So kind of along those lines actually, do you have any just like prompting tricks for people, things that you learn to get more out of Claude when you chat with it? It Sometimes it you know, it's funny because we uh in 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 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'm gonna I'll give you the unofficial version, because like you don't want things to become like Uh like we think this works, but we're not sure why, you know, but I um I'll do small things like in cloud code, and we actually do react to this very literally, but I always act to ask it to like if I wanted to use more reasoning, like think hard and it'll like, you know, use it either kind of a different

28:00 uh flow and I usually start with that, you know. Um Nudging, yeah, 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 gonna be the most critical blunt person in the world. Um and so with clouds and that's I'm like Be brutal, Cloud, like

28:18 Roast me, like tell me what's wrong with the strategy. I think I know we were talking earlier about the you know Claude as thought partner around like critiquing product strategy. Uh I think I uh previously would say things like You know, like what 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, edity. It's not gonna be uh it's hard to push it to be super brutal, but

28:37 It forces it to be a little bit more uh 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, our workbench, we have this thing called the prompt improver. Where you describe the problem and you give it examples.

28:59 And uh Claud 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 intuition would have been for a good prompt. And so I encourage folks to also check that out, even for their own use cases because While that tool is meant for an API developer putting a prompt into their product, it's equally applicable for uh a person doing a a prompt for themselves like It'll insert X ML tags, which no human is going to think to do ahead of time. It actually is very helpful for Cloud to understand like

29:28 what it should be thinking versus what it should be saying, et cetera. So that that's another one is like Watch our prompt improver and then note that like Claude itself is a very good prompter of Claude. Awesome. Okay. So we're gonna link to that, the prompt improver. the Corpie's advice you shared earlier is just kinda do the opposite of what you

29:44 would naturally do. So if you're like trying to be nice, just like be brutal, be like very honest and frank with me. Exactly. I find that worked quite well. Like what are the thoughts that I've like fallen into that you want to break me out of? Mm. I saw you guys just today maybe launched a Rick Rubin collab where it's done vibe coding. What's that all about? I don't think that's that was uh you know, what I heard about that and ever again like this a lot of coalesced this week between model launch developer event and in the way of code.

30:09 Um We had our 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 coding of creativity and they've stayed in touch and You know, we're kinda excited about this idea of Uh like he was creating uh like art and visualizations with Claude, and then he had these like ideas around like

30:30 uh 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 the aesthetic of it is just like so on point too. But yeah, it's just sort of like Med meditation is probably the right word. Meditation on like creativity, working alongside AI, coupled with this, like Uh it's like really rich, interesting visualizations, but

30:50 It's one of those things where like Uh, you know, internally they're like, Oh yeah, and we're doing this like recruiting collaborator work. We're doing what? 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 a mouth.

31:05 Yes, in like Asky Art I think. It's totally ASCII R five. I'm excited to have Andrew Luo joining us today. Andrew is CEO of One Schema, one of our longtime podcast sponsors. 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 Ramp and Banta 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.

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32:44 Actually going back to kind of the beginning of your journey at Ontropic. What's the story of you getting recruited at Anthropic? Is there anything fun there? The it all started and I I actually sent my friend this text. So Joel Lunstein, who I've known, he actually he and I built our first iPhone apps together in two thousand seven when the app store was just out and you could still you know, make money by selling dollar apps on the app store, you know, back in the day and We were

33:07 We were both at Stanford together and we were friends and we've stayed in touch for over years and We've never gotten to work together since then. We just like we've just remained close and You know, 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 Starting something from zero. Do I go work somewhere? I don't know. Like what company would I want to go work at?

33:25 And he reached out and he's like, Look, I don't know if you'd at all consider joining something rather than starting something, but we're looking for a CPO. Would be would you be interested in chatting? And at that time Cloud Three had just come out and I was like, Okay, you know, like this company's 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 first met with Danielle I was one of the the co founders and the president at Anthropic and

33:48 Just from the beginning it was like a breath of fresh air, like Very little like grandiosity coming off the founders. Like they just were Really I mean

33:58 They they're clear eyed about what they're building. They know what they don't know. Like I uh how many times I talk to Daries like Dar's like look, I don't know anything about product, but here's an intuition. I haven't usually the intuition's really good and and you know leads to some good conversation. Then the intellectual honesty and like kind of shared view of What it means to do AI in a like responsible way just resonated. I I kept having this

34:19 feeling in these interviews like This is the AI company I would have hoped to have founded if I had founded an AI company. And that's kinda the bar around like if I'm gonna join something, like that should be That should be where I'm gonna go. But What I realized I actually um Hadn't joined a company since my

34:35 Like first internship in college, basically. And I was like, Oh, like How do I onboard myself? Like how do I get myself uh you know, up to speed, like how do I How do I balance making sweeping changes versus understanding what's not broken about it overall? And like looking back on a year, I think I made some changes too slowly. Like I think there was like ways we were organized in a product that could have made a change. earlier. And I think

34:57 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 Cloud Code, like Cloud code happened because Boris who actually was a uh Boris Turney, he was an Instagram engineer on like one of our senior I Cs there. Um we overlapped a bit. Uh was like

35:17 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 head camera and we do like I think there's like more work that we need to do and there's like things that I wanna be building, but more so than that, we need a couple of like Almost founder type.

35:36 engineers. 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 like design and product support to like help them realize that. I'm like ten times more of a believer in that than before.

35:55 Mm-hmm. I actually uh asked people on Twitter what to ask you. I had this conversation and the most common question, surprisingly, was Why did you shut down artifacts? And I also wondered that'cause I loved artifact. I was I was a power user. I was just like this is exactly finally a news app that uh I love that it's giving me what I want to know. So I guess just what happened there at the end.

36:16 I still really miss it too,'cause I didn't find a replacement. And I think I substituted it by like visiting individual sites and kind of keeping things up that way. And it'sn't No. Really the same, especially on the log too. Like I think we got right Uh with artifact and if people didn't play with it before, it was you know We really tried to

36:32 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 a you know Dwell or from architectural dynasty 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

36:59 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, but Yeah, you know, we put so much time, uh, our designers guy Gunnar Gray has phenomenal use that perplexity now. Like the

37:15 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 to 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

37:35 You know, that doesn't feel like it's it's playing fair with the publishers. But at the same time, like the actual experience wasn't good. So The mobile web. Deteriorating, which makes me very sad, but I think was was part of it. Two was like

37:47 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. Like I can't tell me tell you how many people would be like, I love artifact. I'm like, Did you tell anybody about it? Like did and they're like, Yeah, I told one person and then the gods 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 Uh, but we don't want interstitial things. Like in some ways, I don't know, this sounds very uh puritanical. I don't mean it to sound this way, but like We there were lines that we didn't want to cross because that just felt Ethically not us. That I've seen other news kind of for like players

38:27 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 another way. I don't think we were the founders to 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 the strategy and the product and the team. And

38:47 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, you know. hard times like Ben Horror, it's called the like, you know, we're F it's over, you know, kind of moments and I My uh my favoura. Like I wouldn't say that my favorite memories'cause they weren't happy ones, but like memor I are that really stayed with me with Instagram was like me and Kevin at Takaria Cancun on Market Street. Eating burritos at literally eleven PM being like

39:15 How are we gonna get out of this? How are we gonna work through this? Like and That's Zoom is not a good replica for that. You know, you 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 twenty twenty four and said.

39:30 Look, there there is a company to be built in the space. I'm not sure where the people to build it. This conc current incarnation we love, but it's like not Growing. The way I put it's like Ten 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 them, like their their the energy is not

39:48 present in this product in this system. And so Are we gonna 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 and, you know, try to find a home for it, et cetera. So that was the the confluence on it. And then you c sort of feeling this opportunity cost of like

40:06 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 gonna be able to impact this? And it felt like the answer was. was increasingly no. But it was hard. I mean in the end I was really at peace with the decision, but It was like a conversation that went on for a couple of months. On that note, just how hard was it? Cause you, you know, it's there's an ego component to it, like oh, I'm starting my new company. It's gonna be great. And then

40:30 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 then not working out. Yeah, I mean I think when we started it, one of the conversations was like, What is the bar to success here? 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 ChatGPT.

40:51 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 um we knew that we weren't pursuing that size of like usage, at least with the kind of first incarnation, but we did have like 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. This is twenty twenty one back then. Yeah, yeah. AI was called machine learning back then. Yeah, it's called machine learning still. Um and so in shutting it down, you know Like You kinda know it when you see it in terms of like user growth and traction. And I wasn't expecting Instagram growth. Um but I was expecting or hoping for or looking for something that like

41:32 felt like it had its own legs under it and it could continue to to can 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 gonna work and you're right most of the time because most things don't work. There's actually very little of that. And most people The universal reception, at least

41:54 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 have been like, Yeah, I like probably would have like taken this thing on for another six months. But So what you guys did.

42:09 Realized. We're barking up the wrong tree. made the call and I was like You know. If that

42:14 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 a legal an ego. Bruis them. Oh, you know, like if you You're Is it true that you're only as good as your last game, you know, if I I'm a huge sports fan, right? So like is that true or you know, is there something more of a time? I'm

42:32 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 I 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 in the end. Yeah, I think just the the trajectory you went on after uh shows that it's Okay, to shut down things that you're working on.

42:52 Okay, so you mentioned Chad GPT. I wanted to chat about this a bit. So there's something really interesting happening. So Uh, 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, the fastest growing standard of Time in history that everyone's adopting. uh clawed powered and unlocked essentially the fastest growing companies in the world, Cursor, Lovable, and Bolt and all these guys, like

43:16 I had them on the podcast and they're like when Claude, I think three point five came out. Son it, uh it was just like that's all made this work, finally. On the other hand It feels like

43:26 Chat GPT is just winning in like consumer mind share. When people think AI, especially outside tech, it's just like ChatGPT in their mind. So let me just ask you this. I guess first of all, do you agree with that sentiment? And then two, as a kind of a challenger brand in the AI space, just how does that inform the way you think about product that strategy and mission and things like that. Yeah, I mean you look at the the sort of like

43:49 public adoption or like if you ask people like oh you know like m if you were if if you Uh uh Jimmy Kimmel, Man 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 open AI. They'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, and I reflect on my year. There's

44:10 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 than anybody I can look internally and say, like, look. We'll keep building interesting products. One of them may hit, but So kind of craft an entire product strategy around like trying to find that hit and is Probably not.

44:30 Wise we could do it, and maybe Claude can help. come up with the fullness of things, but I think we'd miss out on opportunities in the meantime. And then instead, you know Uh. Look yourself in the mirror and embrace who you are and what you could be rather than like who others are is maybe the the way I've been looking at it, which is

44:48 We have 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 well to Cloud externally, maybe Uh The Rick Rubin connection m m 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 they're

45:09 people that like to be at the like forefront of AI and are creating things. Maybe they didn't think of those in it as engineers, but they're building you know, I got this really nice note from somebody internal anthropic who's on the legal team and he was building Like. Bespoke software for his family and Like and connected to them in a new way. And I was like, This is

45:26 a glimmer of something that is that we should lean into a lot more. And so I think What I've you know, and this is actually you know, connecting back to using like clouds being in 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 grow up versus like what we currently aren't or wish that we were or like see other players in the space being? I

45:48 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 and and and growth that we've seen, you know, at Anthropic, but also across Open AI and also places like Google and Gemini. So like let's figure out what we can be uniquely good at that place to

46:06 the personality of the found like this all the things come together, right? Like the personality of the founder is 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 Claude and maybe there's fewer like direct consumer applications on day one. I think they'll come, but I don't think that like

46:27 spending all of our time focused on that is the right approach either. And so 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 I I 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. Um and then lately I spent a lot more time with startups and

46:48 uh 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 tinkerers. And like make sure we're serving them really well. And I think good things will come from that. And that feels like a an important company, uh, as we do that. Hm. 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.

47:10 Exactly. Super interesting. So kind of along those lines. A question that a lot of AI founders have is just like where's a safe space for me to play where the foundational Model companies are gonna come squash me. So I asked Kevin Wheel this and he had an answer.

47:25 And I noticed looking back at that conversation, he mentioned windsurf a lot. Yeah. It's like wow, this game really lost the wind turf. And then like a week later, they bought Winsorf. So it all makes sense now. So I guess the question just is just where do you think

47:38 Uh AI Founders should play where they are least likely to get squashed by folks like OpenAI and Anthropic. And also are you guys gonna buy cursor? I don't think we're gonna buy cursor.

47:51 Uh Chris is very big. Uh we love working with him. Um A few thoughts on this and it's a question I I've gotten, you know, we like to do these kind of founder days with, you know, whether it's uh, you know, Menlo Ventures, who are investors, and then just norwards like we've done Y C, we've done these like founder days, and it's like the The question that is on

48:09 A lot of these founders' minds, understandably so. I think Things that are going to I'm I can't promise this as like a five to ten year thing, but at least like one to three years, things that feel defensible or durable. One is understanding of a particular market. I spend a bunch of time with the Harve folks and they really like They they showed me some of their UI. I was like, What what is this thing? 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

48:34 Not like Uh, 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 AI can like help with that. And so Like differentiated industry knowledge, biotech. I 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 uh some applied AI to help, you know, make those models you know go well and like

48:57 I've been dreaming about like at what point 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. I don't think we're gonna be the company to go build the intent solution for labs, but I want that company to exist and I wanna partner with it. you know, domains like legal again, um, healthcare. I think there's a lot of like very specific kind of compliance and things. Those things don't necessarily sound sexy out the gate, but there are like very large companies to go and

49:21 and be built there. So that's number one. Paired with that is like um Differentiated go to market, which is the relationship that you have with those companies, right? Like do you know your customer at those companies? Like one of our product leads. Uh, Michael is always talking about like know not don't just know the company you're selling to, but know the person you are selling to at the company. Are you selling to the engineering department because they're trying to like Pick.

49:43 Which AI L L M to build on top of or API to build on top of. Let's go talk to them. Like is it the CIO, is it the CTO, is it the CFO, is it the like general counsel? So Under like a companies with deep understanding of who they're selling to is is the other piece too. What's you know What's interesting there is it's it's probably hard to build that empathy in a three week or three month accelerator, but

50:03 maybe can start having that first conversation and 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 like Uh there's also like

50:20 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 With AI. And I haven't seen that many of them yet. I wanted to see more of them. I think more of them will get created with with Uh some things like our new models, but

50:39 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 m 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

51:00 Those are my answers. I don't envy them. Like I I would probably be asking those questions if I was starting a company in in in the AI space. Maybe that's part of the reason why I wanted to join a company rather than start one, but I still think that there are There's And maybe like here's fourth, like Don't underestimate how much

51:17 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, that you go build it. It sounds a little cliche, but it's like It's all we had at Instagram. You know, we were two guys and we're like, Let's see what we can do. And in artifact, we were you know, we were six people. uh for most of that time and you know, every day felt like

51:35 It's existential that we get this right. We need to to win and You can't replicate that and you can't instill that with Okay R is like you just have to feel it and and that is a way of working rather than uh uh like area of building but it's a continued advantage if you can harness it.

51:51 I love that you still have such a deep product. founder sense there as you're building Product 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

52:05 That are Finding ways to leverage your models and APIs 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.

52:17 What are those companies that are doing a really good job building on your stuff doing differently that you think other companies should You thinking about. I think being willing to build um more at the edge of the capabilities, um and Basically

52:33 break the model and then be surprised by the next model. Like I love that you you said the companies were like three five was the one that finally made them possible. Those companies were trying it. beforehand and then hitting a wall and be like, Oh, the models are like almost good enough or They're okay for this specific use case, but they're not generally usable and nobody's gonna adopt them, you know, universally, but maybe these like Real

52:54 power users are gonna try it out. Like those are the companies that I think continuously are the ones where my 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

53:08 There's this real like Yeah. We can hill climb on these evaluations and talk about sweet bench and towel bench and terminal bench, whatever. But customers ultimately know like You know.

53:18 Cursor bench, which doesn't exist other than in, you know, their usage and their own testing, et cetera, is like the thing that we ultimately need to serve. Not just cursor, but Menus bench, right? If Manaus is using our models and Harvey Bench, if Mar like those 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

53:42 uh repeatable way to evaluate how well 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 A B. Testing. That's fine. Some of it may be internal evaluations, some of it may be capturing traces and being able to Rerun them on with a new model.

54:00 Some of it is vibes. Like we're still pretty early in this process, and some of it is actually trying it and being one of my favorite early access quotes was Uh the founder heard this engineer screaming next to him. He was like, What? This model, like it's like I've never seen this before. This is like OpenSport. It's like cool, like That we're gonna engender that feeling and things, but you're not gonna 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

54:23 differentiate those those those companies that are maybe earlier in their Journey of adoption versus the the later ones. 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, that's part of the OS of Windows. Uh, just what role do you think MCP was will play in the future of product going forward of AI.

54:45 I think uh as the non researcher in the room, I get to have fake equations rather than real ones. And my like fake equation for like utility of AI products. Uh it's three part. One is model intelligence. The second part is context and memory. And the third part is like applications and UI. And you need all three of those to converge to actually be a useful product in in AI.

55:07 And You know, Model Intelligence got a great research team, they're focused on it. There's Great great models being released. The middle piece is is what M C P is trying to solve, which is for context and memory, like The

55:17 Difference between I'll go back to my product strategy example. Like, hey, like Yeah, let talk about Antopics product strategy. It's gonna maybe go out on the web, like versus here's like several documents that we worked on internally 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 um documents in Google Drive. Like that the difference between like the right context and not it's like the entirely the

55:41 the the difference between like a good r answer and a and a bad answer. And then The last piece is Are those integrations discoverable? Is it right, is it easy to like create repeatable workflows around those things? And that's like I think a lot of the interesting product work to be done. In AI, but MCV really tried to tackle that middle one, which is we started building integrations and we found that every single integration that we were building,

56:01 We were rebuilding from scratch in a non sort of repeatable way. And like full credit to to two of our engineers, Justin and David, 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

56:23 Cloud and eventually chat GPT and eventually Joseph like the dream. Uh, like when when more integrations get built and wouldn't that be good for us, you know, I think channeling a lot of um It's like an old uh commoditize your 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 gonna get people necessarily building integrations just for us out of the gate unless we have like a really compelling product around that. M C P really inverted that, which was, you know, it didn't feel like wasted work.

56:51 And and a a few key people like Toby, I think is a great example of Shopify got it. Kevin Scott at Microsoft has like been really a just an amazing champion for for M C P and a thought partner on this and Um I think the role going forward is

57:07 Can you bring the right context in and then also You know, once you get as the team calls it internally, like MC Pill, like once you start seeing everything through the eyes of MCPs, like I've started saying those 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 M C P that we're exposing. Like a Small example of like How I think

57:26 Even anthropic could be a lot more M C piled, if you will, is like Yeah, 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. So Claude itself can be writing back to those as well, right? Like you shouldn't have to think about. Like.

57:45 Uh I watched my wife had a conversation with Claude 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 I sorry, Dave, I can't help you with that. W it would be able to if every single primitive in Cloud AI was also exposed to an M C P. So I hope that's where we had and I hope that's where more things had, which is

58:04 to really have agency and have these 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 is 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 dentically by these models. Stuff like that's the future I wanna see. The future is wild.

58:26 Okay, so to start to close off. Close out our conversation, uh make it a little more Little delightful. I I was chatting with Claude actually about what to talk to you about. I was just like, Claude, your uh your boss is coming on my podcast.

58:39 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.

58:49 Okay, so first of all, interestingly, when I I was using three point seven to do this and I asked at this and And by the way, is Claude, is there gender? Is it like he, she, they, what do you think? It's definitely it internally. I've heard people do they. I got my first sh or uh he the other day and I got somebody who was like her and I was like interesting. But yeah, usually it. So Uh interestingly, three point seven. All the questions were at Instagram.

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. But four point oh nailed it from the start. So I read did the questions and it nailed it. Okay, so two questions from Claude to you.

59:27 Uh one is uh 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

59:45 Um In some ways. Like just having the model run off and and come up with an answer and minimize the amount of input and conversation it needs to do so would be a Yeah, you could imagine designing a product around that.

59:57 criteria. I think that would not be maximizing agency and and independents. 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 three seven, four has less of the three seven really like to ask follow up questions. And we call it illicitation. And sometimes be like, I don't want to talk more about this with you, Claude. I just want you to like go and and do it. And so

1:00:17 Finding that balance is really key, which is like what are the Times to engage. I like to say internally, like Claude has no chill. Like if you put Claude in a Slack channel, it will chime in either way too much or too little. Like How do we train conversational skills into these? uh models not in a chat bot sense, but in a true like collaborator sense. So

1:00:39 Long answer to your question, but I think like We have to first get Cloud to be a great conversationalist 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 a augmentation thought partnership. These questions are awesome, but here's the here's the other one.

1:00:58 Ah how do you think about product metrics when a good conversation with me could be two messages or two hundred? Traditional product. Traditional engagement metrics might be misleading when depth matters more than frequency. That is a really good question. Um There's a great internal post um a couple of weeks ago around like

1:01:15 Um It would be very Dangerous. to over optimize on like Claude's likability, you know, because you can fall into things like No, is Claude gonna be sycophantic? Is Cloud gonna tell you what you hear? Is Claude going to like prolong conversations just for prolonging its sake, right? To go back to the previous question as well.

1:01:35 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 the North Star we thought about a lot beyond just like overall engagement. And I think that would be the wrong approach here, you know, to it's also like is Claud a daily use case or a weekly use case or a monthly use case, I think about a lot. Hourly hourly use case. Hourly use case, right? Like for For me, I'll use it multiple times a day.

1:02:02 Um I don't know. Great answer. Yeah, but I think that like it's not it's not the web two O or even the social media days like engagement metrics. You know, it should hopefully really be around. Like Did it actually help you get your work done. You know, like Claude helped me

1:02:16 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 twenty, twenty five minutes. And Right. That's cool. It's harder to quantify, you know, it's like maybe you survey like how long would this want to take or do you feel like it was a kind of annoying thing to survey. I think overall though, and maybe this is tied into like the earlier question on like competition and differentiation, like

1:02:36 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 job of doing that and I think so much of when you get really metric subsessed is when You're trying to like convince yourself that it is when it's not or something. So I I I hope that what we can do is like stay focused on like

1:02:55 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 in their lives for the other things. Like not for a North Star. Gotta figure out the right like Pippy metric. you know, dashboard version of that. But But that that's the that's the feeling that I want.

1:03:13 Yeah. Like you could argue retention, but that's a Just a far away metric to track. Okay, final piece. Okay, so I asked Claude what to A message that it wanted to give you.

1:03:24 So I'm gonna pull up uh 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. I'm gonna read a piece of it for folks that can't that aren't looking at it right now. So I'll read a piece of it. Mike, thank you for thinking deeply about the human experience of talking with me.

1:03:41 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. I'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.

1:04:01 And then I'm gonna skip to this part, which was so interesting. A small request. When you're making hard product decisions. Remember the quiet moments matter too. The person

1:04:11 Working through grief at three AM. The kid discovering they love poetry. The founder finding clarity and confusion. Not everything meaningful shows up in metrics. That's beautiful.

1:04:22 I 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 Just the general like sort of

1:04:34 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 you're going through I mean doesn't say man, but like effectively like man, you're s 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

1:04:54 You know. 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 you wouldn't even want to optimize for. You just want to feel like You're training the model that you Like Hope.

1:05:06 Would show up in people's lives. Mm. Well, you're killing it, Mike. Great work. I'm a huge fan. Uh we're gonna skip the lightning round. Just one question. How can listeners be useful to you?

1:05:15 Oh, I love places where like it goes back to that founder question around uh 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. I love hearing the 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 Uh, you know. got a ping from somebody that like you just made a project API I've used Cloud every day because I wanna upload all this data, you know.

1:05:42 Uh automatically I was like, Okay, great. Like there's I love that. Like tell me what sucks. 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.

1:06:01 Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show. At Lenny's podcast dot com. See you in the next episode.

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