Lenny's Podcast: Product | Career | Growth
00:29:54 · Amol Avasare, Lenny Rachitsky
And so if I think about that, and it's like there's so much value on offer, you need to shift more towards, "Okay, we need to take larger bets and we need to not miss the forest for the trees." And so that's why we still do all the smaller optimizations. It really matters, and no one else is going to do some of these things and so we need to do it. The compounding value is not immaterial, but we do take on much larger core productey type of swings as well. So you mentioned the Chrome Extension. That is now the thing that underpins a number of use cases on Cowork and Claude Code as well. And that's something that the growth team built. That's a very AI-peeled product that is a very research-heavy product that we were just bullish on it. We had an engineer who was very bullish on it and we were just like, "Hey, no one else is doing it. We're going to do it." And so that's the sort of thing that I wouldn't have done at another company. [00:30:53] S0: Oh, wow. I did not know that. So one takeaway here is there's stuff to extract from your advice that is only true in Anthropic and then there's, what can other companies learn from this experience that you've had? So one is, is your sense that if you're working in AI, shift more of the pie chart towards bigger bets because in the future, the opportunity is so large, you want to find those as soon as possible versus micro-optimize?
00:30:53 · Lenny Rachitsky, Amol Avasare
[00:30:53] S0: Oh, wow. I did not know that. So one takeaway here is there's stuff to extract from your advice that is only true in Anthropic and then there's, what can other companies learn from this experience that you've had? So one is, is your sense that if you're working in AI, shift more of the pie chart towards bigger bets because in the future, the opportunity is so large, you want to find those as soon as possible versus micro-optimize? [00:31:25] S1: To be more specific there, I would say that if the primary value that your product delivers is underpinned by AI as a central element of it, then I think you should operate this way. So I don't know, companies like Lovable, Cursor, all these great businesses that are, as the exponential rises, their value props are also going to continue to rise significantly. If you're building a product where it's an AI-first product, then I would definitely operate in this way. I think if you're building a product where it's not necessarily an AI- first product and you have some AI features that are on the side, but it's not the core of your value, I don't know that I would operate this way. It would depend on how is the rest of the product org staffed and how is the growth org staffed in relation to that.
00:31:25 · Amol Avasare, Lenny Rachitsky
[00:31:25] S1: To be more specific there, I would say that if the primary value that your product delivers is underpinned by AI as a central element of it, then I think you should operate this way. So I don't know, companies like Lovable, Cursor, all these great businesses that are, as the exponential rises, their value props are also going to continue to rise significantly. If you're building a product where it's an AI-first product, then I would definitely operate in this way. I think if you're building a product where it's not necessarily an AI- first product and you have some AI features that are on the side, but it's not the core of your value, I don't know that I would operate this way. It would depend on how is the rest of the product org staffed and how is the growth org staffed in relation to that. [00:32:14] S0: Okay, awesome. And then in terms of the way you are structured, I thought that was really interesting. It's a combination of different sorts of things. So there's the API growth, there's Claude Code growth, but then there's also personas, like a vertical of knowledge workers and B2B. Is that intentional, some specific bets and one just broad market opportunity?
00:32:14 · Lenny Rachitsky, Amol Avasare
[00:32:14] S0: Okay, awesome. And then in terms of the way you are structured, I thought that was really interesting. It's a combination of different sorts of things. So there's the API growth, there's Claude Code growth, but then there's also personas, like a vertical of knowledge workers and B2B. Is that intentional, some specific bets and one just broad market opportunity? [00:32:35] S1: When you have one product, it's easier to have a growth team that's more purely on the funnel. It's like you have the conversion, you have activation, you have monetization. But as you start to get into having multiple products, I think that's harder because if you do that, then if you just have one activation team, for example, but then you have Claude Code, you have Cowork, you have all the other things. They're very different audiences and they're very different sets of cross-functional stakeholders internally. So we're kind of looking at, what is the thing? And all org structures are not perfect and they're right for a point in time, but we're looking for what is the structure that allows us to have as much focus. Focus is a really big thing, and on audience and problems. And also, the tie-ins to cross-functional partners is really, really important. The folks on our Claude Code growth team, they work extremely closely with Cat and Boris and the others on that team. And so that tie-in is really important as well.
00:32:35 · Amol Avasare, Lenny Rachitsky
[00:32:35] S1: When you have one product, it's easier to have a growth team that's more purely on the funnel. It's like you have the conversion, you have activation, you have monetization. But as you start to get into having multiple products, I think that's harder because if you do that, then if you just have one activation team, for example, but then you have Claude Code, you have Cowork, you have all the other things. They're very different audiences and they're very different sets of cross-functional stakeholders internally. So we're kind of looking at, what is the thing? And all org structures are not perfect and they're right for a point in time, but we're looking for what is the structure that allows us to have as much focus. Focus is a really big thing, and on audience and problems. And also, the tie-ins to cross-functional partners is really, really important. The folks on our Claude Code growth team, they work extremely closely with Cat and Boris and the others on that team. And so that tie-in is really important as well. [00:33:34] S0: So you've done growth at a lot of different companies, a lot of basically, let's call it traditional growth before Anthropic. How else is growth as a function and as a skillset changing with the rise of AI, AI products, and AI startups?
00:33:47 · Amol Avasare
[00:33:47] S1: I think if your core product value is very backed by AI, then it is shifting where you're skewing more towards larger bets as opposed to smaller, medium experiments. I think in other things, it may be a big thing related to this that I think will accelerate this, which I am really interested to see how it will play out, is we are starting to look at how do we automate growth, which I think is a really interesting area. So our growth platform team, we're very lucky we have Alexey Komissarouk who teaches growth engineering at Reforge, and he's just the guy on the team. And so he is driving this effort. The name is, it's a little cringey. I didn't come up with it. And it's called CASH, which is Claude Accelerates Sustainable Hypergrowth. I did not come up with that, but really it's an effort to look at how can we use Claude to automate growth experimentation? And it is still very, very small. It's still very, very early. We kicked it off only, I think, a couple of months ago. I think before Opus 4.5, it wasn't really possible. We were just not seeing the results. And more recently with Opus 4.6 we're like, "Okay, this is headed in the right direction." And so this, I think, it will happen more and more across the industry, where basically if you think about, okay ... I think this can happen all across product, but growth teams in particular because there's this whole body of work that is very small optimizations, I think are just more inherently suited to tackle this earlier. I think if you think about the lifecycle of shipping, there's four parts to it. One is identifying opportunities. How good is Claude at actually identifying opportunities based on different trends, based on previous trends that Claude has seen in the past? Second is then building the actual feature and getting it ready to ship. Third is testing and ensuring that it meets your quality bar and your brand bar. And then fourth is then once you've actually shipped the thing, analyzing the data, gathering the learnings. If you think about that as the loop of, "Okay, these are four things that you can eval and hill climb on each of these areas and understand how good is a model doing for you there." And we basically think about it in that four ways and we are scoring how good is Claude doing in each of those areas. And so we've been testing this at pretty small scale right now.