Lenny's Podcast: Product | Career | Growth
00:50:19 · Dianne Penn
[00:50:19] S0: I think maybe specifically for folks who might be mid-career or folks who have been more in a managerial product leadership seat. One thing that I think I feel pretty strongly about is in order to be good managers of teams and PMs working with this technology, you have to be really hands-on yourself and have spent not just time tinkering, but actually shipping with this technology. And again, being in the details and sweating the tokens along with your PMs and your engineers and your teams. And so even for folks that I hire who have more tenured PM experience, the onboarding plans are exactly the same as somebody who is more early career. And it's around understanding users, reading consent and user feedback, talking to customers. I think there's something around being able to understand what to do with this, what good looks like, and having developed that in a very hands-on manner that's important. It's not necessarily easy for someone to agree or be able to see what a good or great AI product or AI feature could look like if they haven't kind of experienced building themselves. So I do feel pretty strongly that if you're a manager, you have to be hands-on; you have to spend a portion of your time actually shipping. You have to kind of walk in the shoes of your teams. And I always try to carve out a portion of time to actually own one to two work streams when we have models in order to keep my theory of mind, keep my sense of how the models are moving, how quickly it's improving, so I can help the team make decisions and make better decisions.
00:52:35 · Lenny Rachitsky, Dianne Penn
[00:52:35] S1: So what I'm hearing here is no matter where you are in the ladder of hierarchy at a company, if you're not building yourself, if you're not actually talking to cloud, talking to Codex, building stuff, you're not going to make it. [00:52:46] S0: And you should have fun working with this technology. I think that's the other piece. I think the folks that would be most successful, regardless of their level, are people who love working with AI and are exploring and experimenting. And carving out the time, not just for the experimentation, but actually hands-on shipping end-to-end, getting the user feedback, I think has to be fundamental for everyone.
00:52:46 · Dianne Penn, Lenny Rachitsky
[00:52:46] S0: And you should have fun working with this technology. I think that's the other piece. I think the folks that would be most successful, regardless of their level, are people who love working with AI and are exploring and experimenting. And carving out the time, not just for the experimentation, but actually hands-on shipping end-to-end, getting the user feedback, I think has to be fundamental for everyone. [00:53:12] S1: I 100% know what you mean there. Just me sitting on my newsletter and this podcast just talking about stuff and like, yeah, yeah, that sounds great. Every time I actually build something, and I tinker with all kinds of little projects, you're just like, okay, I see what's happening here. And you just get so much more. It's hard to exactly describe what you experience actually working with the models and building stuff, but it's a whole different world of like, okay, I see. Here's what they're talking about computer use; here's what they're talking about with this limitation of this UX situation. And you made this really interesting point that you have to have fun with it, which is not easy for a lot of people because they're pushed to use AI or they just don't know exactly what to do with it. For people that are just like, I don't know, it's just so annoying. I just have to do this. I hate this frigging thing. Why do I have to work with this? Things are changing so much. I'm tired. Advice for helping people find that joy in this work.
00:54:06 · Dianne Penn
[00:54:06] S0: I think maybe I'll reemphasize something I said earlier around just that experimentation is not an individual sport. Some of the moments where I think I've touched practically every version of research models across 20-plus versions of Production Claude at this point. And I think part of the joy comes from seeing other people discover use cases too. And so maybe one idea here would be pairing with somebody who is excited and seeing what on a use case that you care about and working together versus identifying or trying to figure out the perfect use case yourself because that might feel like work. Working with others feels like joy a lot of the time, and is there more that we could do to bring other people along? That's something a lot of times internally we have somebody who is very curious, and them sharing an idea of a new prototype actually brings a ton more people who are like, oh, I didn't know this could work now with Claude. And so there's just some virtuous cycles here and ways of continuing to have joy with this technology.
00:55:22 · Lenny Rachitsky, Dianne Penn
[00:55:22] S1: That's such a good point. I think that's also why Twitter is so useful for a lot of this is you see other people sharing what they've done, and it inspires you to come up with your own little ideas. And also it's just fun to share your own thing that you've done. So that's a really good point. Just find other people to play around with and look for use cases. The thing I've also heard a lot is just find a problem you want to solve in your life or work and just open up Claude, Claude Code, tell it, here's what I want to do. And it's incredible how far you can get just with a vague idea of a problem you want to solve. [00:55:53] S0: Yeah. I think it gets hard in that there's so many different things that you could try. And so you just narrowing in on either pairing with someone, working with somebody who have a lot of joy about this technology, or figuring out something that you could immediately find value. Either of those things allow you to go deeper rather than more high-level about too many things. I find it hard to keep pace with the number of prototypes or products that are out there. And so my lens has been: how do I go deep in one to two of them myself?
00:55:53 · Dianne Penn, Lenny Rachitsky
[00:55:53] S0: Yeah. I think it gets hard in that there's so many different things that you could try. And so you just narrowing in on either pairing with someone, working with somebody who have a lot of joy about this technology, or figuring out something that you could immediately find value. Either of those things allow you to go deeper rather than more high-level about too many things. I find it hard to keep pace with the number of prototypes or products that are out there. And so my lens has been: how do I go deep in one to two of them myself? [00:56:31] S1: That's so interesting you say that because that's exactly it. We just had the survey that I ran with my colleague Noam asking my readers just how they're feeling about all the things going on in the tech right now and AI. And one of the most interesting takeaways we had was to find that happiness is exactly what you said is go deep in a couple things versus trying to just ton of little things. Find a couple things to really solve well and then go deep. And that is a source because a lot of the happiness people feel is when they finally unlocked their way for AI to actually make their lives better versus just a couple messed up, broken, half-working things.