Lenny's Podcast: Product | Career | Growth
00:53:15 · Dan Shipper
[00:53:15] S1: I mean, it's for real. And one of the things that I think is special about every is... Everyone is sort of a generalist and really loves having their fingers in a lot of different pots or whatever the metaphor is. I think that'll probably settle down at some point and it'll feel more normal. Marketing people are still going to do marketing even if they're touching the website. That's just part of marketing now. But I also think that you can get a lot further being a generalist now and that's like really cool, especially for smaller companies. The other thing that I think is interesting is there are definitely some new job roles that are a thing. And the thing that is becoming really clear is the whole forward deployed engineer concept I think is for real and it comes out of every agent needs a human. You go to the big model companies, they have these agents that run internally, they have like teams of people that run these agents and I don't think those teams are going away. The models are going to get more powerful, the agents are going to get more powerful and the number of agents is going to grow, but people are still going to manage them. And so that looks like a very specific kind of person. And we have a couple of those people internally here and it's like the people who are in charge of making sure your agents are working and doing the right thing. We also do consulting. So we lend that out to people and I think that's a big thing that people want. And it's another one of those places where you're like, hmm, automation was supposed to take away jobs, but it looks like it just created one or many. And there's a specific type of engineer that really loves... Nitesh, who fits this, he's an AI engineer and he fits this sort of forward deployed category and he's on our team. He spends most of his time actually talking to one of our agents in Slack. We have an agent internally called Claudy, which runs our whole consulting practice and he spends a lot of time in Slack. There is code and he is using Claude Code and other things like that, but a lot of it is just talking to it and being like, "Why did you do this dumb thing?
00:55:28 · Dan Shipper, Lenny Rachitsky
Let's fix that." And so there's certain kinds of engineers that I think love that and love having their hands on the latest thing and also love making this being that's in a workspace and it looks a bit different than more traditional, building more traditional software. [00:55:45] S0: And your sense there is we're not near a place where these agents don't need a human. You've said that so many times now that agents need a human and there's kind of like the setup part and then there's the maintaining it forever part and feels like both are important. Is what I'm hearing like this is going to be a job for a long time. AI is not going to get smart enough to just be fully automated for a while. [00:56:06] S1: Yes. I'm simultaneously extremely AI-pilled, extremely, and very bullish on humans and the role of humans and making sure that AI is working well. [00:56:16] S0: Interesting. Okay. So the two kind of buckets here that you're talking about, one is like the way I think I hear what you described earlier is just the pace of shipping software and everything is just increasing, which also means there's so much more work reviewing all this sloppy output. I was just talking to a data science friend and he was saying how his team is just, his data science team is just, their job used to be do analysis, answer questions, see if this experiment was a good, was positive. Now it's just everyone's doing that and they're sharing their results and they're like, "No, this is not correct." And most of their job is now reviewing bad data science work.
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.