Lenny's Podcast: Product | Career | Growth
00:08:36 · Amol Avasare
[00:08:36] S1: Yeah, I'd say it's very much a company-wide effort. So yes, we are the growth team. We have done great. I think we've driven a lot of impact. But honestly, man, we can't claim too much credit for the success of the company. We as Anthropic are really a model company and an intelligence company first and foremost. And so the lion's share of what has driven our success is our research team. We have, I think, the best research team in the world. We have great teams on inference and compute, and then there's many other teams like Claude Code, go to-market, et cetera, who I think deserve much more credit than us. I think just zooming out, going to some of what you said earlier, that the growth trajectory's just been insane. That 10X year-on-year revenue growth trend has been there since the beginning. I think 2023 was 0 to 100 mil, 2024 was 100 to 1, last year was 1 to roughly 10. And I look back to when I joined in 2024, revenue was in the hundreds of millions and just that trajectory through the end of 2024 and 2025, week two of when I joined, we're going into 2025 revenue planning and we have these base case and aggressive case scenarios and Dario's pushing the aggressive case scenario and people are freaking out being like, "How the hell are we going to hit that?" And Dario's like, "I think we can actually go much higher than that." And I'm coming in, this place is crazy, there's absolutely no way. And that happened. And then you get to the end of 2025 and it's like, okay, law of large numbers, there's going to be a pretty big slowdown here based on your baseline rate of 10 billion, how are you going to keep growing at this rate? And it just has not slowed down. And those numbers are public. The 19 billion number you quoted is from the end of Feb, so that is also out of date. And look, it's absolutely insane. The funniest thing is something that I've noticed internally is linear charts are just not cool. No one cares about linear charts. Everything is log linear. You have to show me at log linear scale, and that's a scale we think. And I think overall, we're just really hanging on by the seat of our pants. So we're trying to manage the growth and do the best that we can for our users.
00:10:46 · Lenny Rachitsky
[00:10:46] S0: I was talking to somebody at Anthropic about you, and they said that basically anytime they want something to grow, they ask you to help and it works. So you talked about just things are magical and amazing and Claude and all the tools you all built are amazing innately, and that's a big part of the reason they grow. I think many people listening to this will be like, "What do you even do, Amol, with a magical microgod that just can do anything for you? Why do we need a growth person? What do you even do?" Talk about just the stuff that you focus on and maybe a couple of the wins that your team has shipped that has helped accelerate growth.
00:11:21 · Amol Avasare
[00:11:21] S1: I would say they're not fully wrong. We're very, very lucky to have the best models in the world. We're very lucky to have products like Claude Code and Cowork. It certainly makes life a lot easier. Having said that, I would say this is the hardest job I've had in my life, and that's having been a founder, having been an investment banker and other things like that, it's tough. And if I look at what do we do as a growth team here, I think ultimately it's the same categories of things that you would think about at a normal company. So we care about acquisition, how are we getting more people in the door, the intent of the people coming through the door. We care about activation, the signup flow, funneling people to the right products, making them successful. We care about things like monetization, free-to-paid conversion, pricing and packaging, all of that stuff that the categories of work is the same. I think then probably the big differences is I would say that roughly 70% of what I spend my time on is what we internally refer to as success disasters. And that is where things have gone so well that other things are breaking now. And I think anyone who's worked at companies that have gone through rapid growth, you think like Facebook, or Uber, or DoorDash early on, they understand this viscerally where scaling this much just brings a lot of challenges. So if you think about each of those categories on acquisition, on activation, on monetization, there's just a ton of firefighting jumping from one urgent thing to another. And it's often extremely painful. And it's funny because you look at all the charts, all the charts are green, fully up onto the right, and everyone's just like, it can be quite tough emotionally still. And so you need to step back and just realize we're very lucky to have these problems. But that's 70% of my time, I'd say, is just these firefighting of success disasters. And I think the 30% remaining is just much more standard bread and butter growth work where it's more proactive. So you think about, okay, if we have limited resources, we have many different products, which of the products do we want to put some juice behind? What does our long-term pricing and packaging look like, especially given that the technology's changing a lot and behavior and engagement trends are shifting.
00:13:35 · Amol Avasare, Lenny Rachitsky
And then things like we have a lot of new products coming up like, okay, you ship Cowork, now what? When is the right time that we should lean in as a growth team to start optimizing the core adoption funnel for Cowork? So it's probably 70% just crazy firefighting, 30%, more bread and butter stuff. [00:13:53] S0: Okay. I'm going to dig into a lot of that stuff. One of the cleverest growth moves you all made recently was this idea of importing memory from ChatGPT where you just made it really easy and jumped on this trend of people getting really excited about Anthropic. Is there anything you could share about the behind the scenes story of that feature? [00:14:11] S1: We're just thinking about what can we do to improve the cold start problem and improve the new user experience. I think that activation is a really big challenge in AI. And so that's one example of something that we shipped that was very specific to a moment in time. But ultimately that you zoom out, it's like, okay, how do you really make it easier for people who are signing up to have Claude understand who they are and understand how Claude can help them and get them to the right place?
00:14:11 · Amol Avasare, Lenny Rachitsky
[00:14:11] S1: We're just thinking about what can we do to improve the cold start problem and improve the new user experience. I think that activation is a really big challenge in AI. And so that's one example of something that we shipped that was very specific to a moment in time. But ultimately that you zoom out, it's like, okay, how do you really make it easier for people who are signing up to have Claude understand who they are and understand how Claude can help them and get them to the right place? [00:14:43] S0: I want to follow that thread, activation. That's something that comes up a ton when I talk to people leading, driving growth on AI products. It's just there are so much stuff trying to get your attention these days for people to get to a place where they, okay, wow, this is really going to be something I want to keep using. It proves to be really hard. And it's also just unreliable sometimes, it's not going to be magical. It's AI, it's non-deterministic. I guess, one, is just how important is focusing on activation, getting people to that aha moment with AI products? And two, what are some things you've learned about how to do that well with Claude or AI in general?
00:15:19 · Amol Avasare
[00:15:19] S1: Yeah, it's a good question. I think that activation, it's critical and defining that as early activation, call it day zero, day one product experience. I think that historically anyone who's been in growth or been in product understands that that's usually one of the highest levers that you have to actually even increase longer term retention. And I think that the importance of that has just gotten exponentially higher. Now, zooming out, I feel like one of the biggest problems in the industry is capability overhang where the models are just getting better so quickly. And the real challenge is on the product side of how do we start to diffuse those benefits to people where even internally, there's new models coming internally and you're sitting there, you're so busy. And when a new model's available, you need to really carve out time to be like, "What can this do? How do I need to update my priors?" And if you think about more broadly for most people, you may have a model that is like, you may have AGI or some model that can do all sorts of crazy things, but if people's instinct is to come there and be like, "Hey, what's the weather in SF?" Then they're not going to get the most out of the product. And so I think that it's tough because the model capabilities are rising so much. So if I think about, okay, back when we had, I don't know, I want to say Opus 4, there's a series of things the model can do at that point, and Opus 4.5 unlocked a whole bunch of new things. You think about, okay, we sit there, we've got this new model, Opus 4. The time to then go and run a bunch of tests, figure out, okay, there's the capabilities from this new model, what are the right on ramps to guide people to those features? You run tests, you get the learnings, you then ship a new flow. By then, you may already have the next model, which unlocks newer capabilities that makes all these learnings irrelevant. So it's actually just a really difficult problem to stay on top of. I think that many of the same things, same old trends in growth and activation remain, I think, accurate where to me it's like ultimately some of the highest leverages from finding the right product or the right feature for the right user.