Lenny's Podcast: Product | Career | Growth
00:56:53 · Dan Shipper, Lenny Rachitsky
[00:56:53] S1: Which is a problem and it means that, and the same thing is happening with engineers and it means that you need more, like you actually need that engineers for this and you need data scientists and it means that you haven't set up the appropriate systems or agents to help you with this. So the way that it works inside of the big model companies, for example, at least one of them has literally a data science bot that every single person in the org can query that is hooked up to their data warehouse that knows who's who so that it knows at the warehouse level who has permission to access what. And so all of the basic questions, because there's a team that sets up this bot, all of the basic questions that people might want to ask that it sometimes that might get wrong, that they're constantly making sure it's getting it right. And so the data science team doesn't have to answer all the bullshit questions because there's another team building an agent that is set up to do that really well. But if the team didn't exist, the data scientists would hate their lives. [00:57:56] S0: Yeah. It does though make the job maybe less fun because you're just sitting there gardening people's sloppy work versus- [00:58:04] S1: Well, that's what I think is like it can actually make the job better because for the data scientists, you are now not dealing with all the silly requests. You're dealing with the deeper questions that are harder for the team who's dealing with all the basic requests and building an agent to do that. It's like filtering all that stuff out so you can focus.
00:58:04 · Dan Shipper, Lenny Rachitsky
[00:58:04] S1: Well, that's what I think is like it can actually make the job better because for the data scientists, you are now not dealing with all the silly requests. You're dealing with the deeper questions that are harder for the team who's dealing with all the basic requests and building an agent to do that. It's like filtering all that stuff out so you can focus. [00:58:24] S0: Here's a question I've been thinking about. I was not planning to talk about this, but it's something that I've been thinking about. So the question is which product tech role is the least changed now? So engineers, 100% of code AI now. It's like a completely different job. Product management, a lot of the PRDs are, you don't have to write as much, you can ship code, you don't have to wait for people. Design, the whole design process dead according to recent guests, just like there's no time to do the whole design process, very different role. Data science, very different work now. There's marketing, there's sales. So here's the question, what do you think is the least fundamentally changed role so far?
00:59:06 · Dan Shipper, Lenny Rachitsky
[00:59:06] S1: Well, on interesting thing is, I don't know if this counts, but CEOs and investors, it seems still very, very optional whether or not they use this stuff. It seems that way. I think the opposite is actually true. My experience, and we do a lot of this with senior executives and senior leadership teams, my experience is that your company's only going to go as far as your CEO goes in AI and it's not something you can delegate. You have to have your hands in it because otherwise you don't have an intuition for it. But for a long time it has seemed like, yeah, that's something that the people who are doing the work have to do, but I don't have to do that. I'll just tell them what to do. And so I think if you're a CEO, you kind of can get away with your day looking very similar. I think that will change rapidly at some point where it'll be like, "Oh no, I'm way behind." But for now, or maybe even middle managers, those kinds of people, I think it's fairly similar. I think maybe sales because it's so in person. [01:00:11] S0: Yeah, that's my vote.
01:00:11 · Lenny Rachitsky, Dan Shipper
[01:00:11] S0: Yeah, that's my vote. [01:00:14] S1: It's sort of creeping up in the kind of BDR. We can deal with a lot of BDR type queries. You're only talking to people who actually want it. For sales, it's so useful to do research. My favorite Codex, one of my favorite Codex experiences is we're hiring a head of L&D and we always put out a job post, whatever, but I was like, "I feel like there's this company called General Assembly in New York and they've done really good technology education for a long time." And so I was like, "I feel like someone who worked at General Assembly and is now into AI would be really good." And I just literally typed it into Codex and then went off and was doing something else and I came back and it found this the perfect guy. It was like worked at General Assembly, was an instructor, is super AI-pilled and follows me on Twitter. So I just DM'd him and then I had dinner with him and it's like, that's crazy. That would've taken so long before and super valuable for sales, for recruiting, all that kind of stuff.
01:01:27 · Lenny Rachitsky
[01:01:27] S0: Yeah. Sales is where my mind went. The top of funnel AI is helping a lot with sourcing and qualifying things like that. It feels like the work of a salesperson is not fundamentally different and Customer support's fundamentally changed. So that's interesting. Sales so far so good for those folks. Okay. So maybe just summarizing some of the predictions in this bucket of just the shape of the work, how it's going to change. What I'm hearing so far is just going to be a lot more reviewing of other people's output as a part of the work. And then two, there's going to be a lot of like almost babysitting of AI agents to make them do the thing you want them to do for deploying and then just gardening them along the way, make sure they continue to do their work. Anything else before we get into our third bucket?
01:02:11 · Dan Shipper
[01:02:11] S1: I would sort of split it into less babysitting agents and more your forward deployed team is trying to build a whole system that makes it so that people who have less knowledge can use that system without doing something dumb. And that's like a really interesting engineering challenge. I think babysitting kind of makes it feel like it's, yeah, you're just kind of waiting for it to fuck up and then fixing it or whatever. And that can be the case, but I think a lot of it is this extremely interesting engineering challenge of building a system to enable everybody else in your organization to do what used to be a technical job. And then if you're not one of those people, like you're the data scientist or whatever, you can go a lot deeper with AI into really important questions that eventually probably filter into the work that the forward deployed engineering team is doing, but is like more generative and more new and you're dealing with harder questions. One last thing that I think is really interesting is I think that we will be reading way more AI generated writing in documents and emails and we will like it. And I think we are already doing this in coding where we read plan documents. I don't want an engineer to hand write a plan document. That would be very silly. It would be obviously silly. And I think the same is true when we did our quarterly planning for every at the end of 2025, we did it all with Notion Agents and we just had a bunch of Notion agents and we had really one Notion agent and then we had a top level company strategy and then we had everybody in the company just talked to an agent and it asked them about what happened last year, how did it go, what were your goals? What do you want to do this year? What are your metrics? It pushed back and then it was like, how does this relate to the overall company idea? All that kind of stuff. And then I got these incredibly good AI generated strategy reports or quarterly plans for each part of each team. And then I could go in and be like, "Okay, who needs to talk to each other? Which teams need to talk to each other that don't know they need to talk to each other? And which one of these is actually low quality or which one of these is high quality?" All that kind of stuff, it makes it a lot easier to process. And I see that all the time now.