Lenny's Podcast: Product | Career | Growth
00:13:27 · Josh Woodward, Lenny Rachitsky
[00:13:27] S1: Yeah, I mean, I remember still the first time I heard the first NotebookLM podcast, exactly where I was standing. It was right outside this room and it was probably at 6:30 PM on a Tuesday or something. And there were two people on the team who came over and were like, "You got to listen to this." And it was the proceedings of the British Parliament debates, not known for its riveting material, but these two AI hosts were talking about it in a way that was literally interesting. And that was one where we're like, "We got to just do something with this." And that was one. I remember the first time when I saw something come out of Google Flow, we had a project before that called Whisk, where you could whisk together multiple images and then start to animate them. And it was a level of control and creative kind of channeling that had never been possible that it was that accessible. So maybe those are two examples. We also have other examples where we get excited, we try to build some product that's just not good. So that's the other thing is a little bit of a game of numbers in this as well. [00:14:37] S0: Let's talk about product market fit and signs that something's actually working. I feel like you've seen more ideas thrown at the wall and tried and prototyped than maybe anyone else. I don't know if that's true, but it feels true. What are signs to you that this is something we need to double down on and what's something that might surprise people about what product market fit looks like?
00:15:00 · Josh Woodward
[00:15:00] S1: Yeah, I think one of the maybe trickiest parts of it is it is way more art than science. I wish it was science. We know how to run science experiments, make data-driven decisions, set OKRs, all this process that helps. Sometimes in the early days, Lenny, it's literally a couple of people on the team who might just have conviction and they're just like, "I think the future is going to look like this" and I can't explain it. That advice is just not that useful or helpful, but I would say that's some of it. I think maybe a little more useful, we talk a lot about how do we get outside the building and just show people prototypes. And when you're showing people early prototypes, what I'm always doing in those is you're looking at people's eyes and that is the metric. It is not a DAO, it's not a DAO over a MOU. It's not a D7 [inaudible 00:15:57], it's none of that. It's literally like, "Hey, Lenny, did you see this? What do you think about this?" And if your eyes kind of light up, if you lean in, these are the kind of signals you're looking for the early days. Maybe the third thing I would say, and this one's also maybe on the flip side of your question, is the people who get excited about things, usually we try to think about what's the problem we're falling in love with, not the product. Because what I found is it takes three, four, five pivots before you either got something or you're just like, "That's it. Didn't work." So I think when people fall in love with an actual product solution first, it's going to, at least in my experience, lead to a bad time. So that's the other thing I think we try to think a lot about. And usually people talk about PMF, you sort of know it when you have it. I think that's true. In the last, I don't know, year or so when Nano Banana went viral on Gemini, when the NotebookLM podcasts have gone, when stuff's happened on Flow or Stitch or other kind of projects that have had these mini moments, you're literally just swarming it and holding on for dear life. And it is both so exhausting and so fun. But yeah, you almost have to take a lot of shots, I would say. And in the early days, again, it feels a little more art passion driven than science, unfortunately.
00:17:25 · Lenny Rachitsky, Josh Woodward
[00:17:25] S0: Those are actually very concrete, specific things to look for. The eyes, eyes light up a way I've heard someone else describe this as their pupils dilate. [00:17:35] S1: Yes. Yeah, that's right. And you know too, when you're talking to someone, if they're having a good time, if they're into it or if they're just bored out of their mind and you're showing them some dumb product. So it's really like, can you hit a nerve with a pain point in their life where they're just like, "I want that." And I mean, that's what makes this job so fun is trying to figure out how many of those nerves you can hit. [00:17:58] S0: So having that context of it's clear when things are product market fitting and their eyes are lighting up, what are signs that it's time to kill an idea?
00:18:09 · Josh Woodward
[00:18:09] S1: Yeah. One of the things on this one, and I talked a lot to the team about this too, is sometimes a team is looking for, "Hey, the leader makes the decision," and that's kind of where it comes from. What I've found is the opposite. The team usually knows before the leader knows, and the signs are like, "Does the passion start to run out? You've tried everything you can think of and it's just not working." And people know when a project's not working or a relationship's not working, that's something that people see. And so I think as a leader, what I try to do say in Labs or even some of our features in Gemini is how do you create an environment where people can ask those uncomfortable questions and be able to say, "Hey, I don't think this is working." And yesterday, just as an example, we had a feature we were really excited about in Gemini we were going to launch and we got some of the early data back. People went out, looked in the eyes, and it just wasn't there. And so I was so proud of the PM on the team because she was like, "I don't think we should launch this. It's not good." And so I replied all, I was like, "This is great. Thank you for doing this." And so it's like, how do you create a culture where that team can call both good things and bad things out? And I guess in that way as a leader, in my case, I trust the team a lot because they're the ones every day trying to make this thing go. And I think if they're like, "Hey, it's not going," then it's like, "All right, that's okay. Let's pivot. Let's do something different."
00:19:36 · Lenny Rachitsky
[00:19:36] S0: I have to ask you this question. I was going to ask it later, but I'm so curious. It's so interesting that you oversee Labs, which is zero to one experiments, try stuff that's totally new. And then the Gemini app, which is one of the most popular apps in the world with probably a billion users. How did that come to be? Why is that the structure under your team?
00:19:56 · Josh Woodward
[00:19:56] S1: Yeah. Yeah. Well, I guess in some ways they are very different ends of the scale. You're right. Gemini over a billion users, it's global. Labs, we joke on Labs. Sometimes our early projects, zero to one stuff, we'll get so excited about 10,000 mouse and most other products dashboards don't count that low. So it's kind of the joke on the team. But I think maybe there's a few common pieces though, even though the scale is different. I think one, the type of people that are attracted to these things, even Gemini, despite its size, is very much got a zero to one heartbeat all over it. And so I think part of it is the type of builder, the type of person you're attracting, these blurring lines between what it takes. Two, we're all AI kind of nuts and maniacs. We love this stuff. We can't get enough of it. We're building all the time. The third one though I would say is a little bit of everyone on the team, we talk a little bit about the order of how we think about our priorities and the mantra on the team is users first, Google second, and our product third. And so Labs third or Gemini third. And I think that unites us too. And that's like we don't try to do things for our own team. We're very much part of team Google, but also ultimately it's like, is someone loving this on the other end of this product? And so that does tie it all together, but they are on different ends of the spectrum for sure on terms of size, like conventional numbers.