Lenny's Podcast: Product | Career | Growth
00:46:41 · Lenny Rachitsky, Tulsee Doshi
[00:46:41] S1: Yeah, I did this survey of how tech workers are feeling. And one thing that's clear is more hours does not equal burnout. A lot of people are working the hardest they've ever worked, but they're having the best time they've ever had in their career. So fun. They're building. They're able to do a lot more, especially PMs. You don't have to sit around and wait for engineers as much as you do. [00:46:59] S0: You're just like you're building, you're prototyping, you're like talking to interesting customers, you're thinking about interesting problems. And I think a lot of the team I like to think is here. Because they want that, you know? They're like really motivated and excited by that. And so for me, it's about like, okay, then how do we pull out the friction parts of their work that are likely to induce burnout, right? And what does that look like? [00:47:20] S1: And also find work that is meaningful and that they value and are excited to spend lots of hours on versus they have to. [00:47:27] S0: And that's different for every PM too. Like if you pick two PMs on my team, I think they would tell you meaningfully different things about why they come to work every day and what they find value out of. And so I feel like part of it for me is like, An org leader also is to think about what motivates the individual and how do we make sure that they are set up for success in that setup too.
00:47:27 · Tulsee Doshi, Lenny Rachitsky
[00:47:27] S0: And that's different for every PM too. Like if you pick two PMs on my team, I think they would tell you meaningfully different things about why they come to work every day and what they find value out of. And so I feel like part of it for me is like, An org leader also is to think about what motivates the individual and how do we make sure that they are set up for success in that setup too. [00:47:45] S1: Another challenge people have these days is AI slop. Basically lots of docs being sent to them that are written by AI and it becoming very, very annoying to have to sift through endless docs. Used to be a lot of the issue was spending time writing docs and now it's like reading docs is the problem because you're just all this AI written stuff. Do you have any kind of, I don't know, processes or policies you've put in place in your team to help avoid this rise of AI written content across the teams?
00:48:13 · Tulsee Doshi
[00:48:13] S0: One of the things that we talk about a lot is where where are these docs useful? Right. So, like, for example, I think where AI generated docs are actually quite helpful, for example, is after you launch something and let's say two weeks later, you want to pull the latest of like who's using it? How are they using it? What's the feedback? That's the kind of document that AI is fantastic for. Right. Because it will actually do that just so much faster than you. Right. It will go and pull from various dashboards and metrics. It'll give you an update. It'll pull kind of strengths and weaknesses. It'll pull from Twitter analysis and Reddit or whatever. And so I think one of the things we talk about is like be intentional about like where is AI helping you drive content in the doc that is helpful versus where is it content that should come from you? Right. Where is it insight? And so what you should be providing is the insight. What the model should be providing is the data. And so I think if you can separate those two things, then hopefully you find the right balance of the benefit here. I would say the other thing is we're investing a lot more as a result of that in how do you create the right skills to get the right data so that we can structure these artifacts really well. That then hopefully creates more space for the creative thinking and the creative writing that we want more people to spend time doing.
00:49:31 · Lenny Rachitsky, Tulsee Doshi
[00:49:31] S1: That's actually something I was wanting to ask about. So in terms of the PM role, AI has clearly made a big dent on prototyping. You can prototype till the cows come home now versus having a designer or an engineer build a thing. So that's clearly a place the role has changed with AI. The other area, which you just mentioned, is data. PMs can do a lot more data analysis. What would it be like behind prototyping? What are the biggest areas AI has most changed the job of a PM in the past couple of years, would you say? [00:50:04] S0: Yeah, prototyping is a great one. I think you're right that like Data science is probably like, I think you are more empowered to have the data at your fingertips to make data driven decisions. I also think actually, like to our earlier conversation, you know, you see these on social a lot, these kind of posts of like, oh, blank is the new PRD, whatever blank is, a prototype is the new PRD, evals are the new PRD, like. Data is the new PRD. But I think actually what that meaningfully shows is you've moved the PM from being someone who just writes a set of requirements down and then assumes that somebody else, whether that is a data scientist or an engineer or a researcher or designer, will figure out the meat behind it. You've moved that one step further to say your job is no longer just writing down a set of requirements. Your job is actually going one step further to have some heft to that, whatever format that heft takes.
00:50:04 · Tulsee Doshi, Lenny Rachitsky
[00:50:04] S0: Yeah, prototyping is a great one. I think you're right that like Data science is probably like, I think you are more empowered to have the data at your fingertips to make data driven decisions. I also think actually, like to our earlier conversation, you know, you see these on social a lot, these kind of posts of like, oh, blank is the new PRD, whatever blank is, a prototype is the new PRD, evals are the new PRD, like. Data is the new PRD. But I think actually what that meaningfully shows is you've moved the PM from being someone who just writes a set of requirements down and then assumes that somebody else, whether that is a data scientist or an engineer or a researcher or designer, will figure out the meat behind it. You've moved that one step further to say your job is no longer just writing down a set of requirements. Your job is actually going one step further to have some heft to that, whatever format that heft takes. [00:51:00] S1: Awesome. Okay. That aligns with a lot of what I'm hearing. Tulsee, is there anything else that you wanted to share? Is there anything else you think you want to double down on that we've talked about that you want to say before we get to our very exciting lightning round? [00:51:13] S0: No, I think that's great. [00:51:16] S1: Okay. We did it. Well, with that, Tulsee, we reached our very exciting lightning round. I've got five questions for you. Are you ready? There's some music that plays over this that's added in post-production. [00:51:27] S0: You mean does it sound like this?
00:51:27 · Tulsee Doshi, Lenny Rachitsky
[00:51:27] S0: You mean does it sound like this? [00:51:28] S1: It's exactly that. We should align it with your drum playing. OK, first question. What are two or three books that you find yourself recommending most to other people?