Lenny's Podcast: Product | Career | Growth
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.
01:04:43 · Dan Shipper, Lenny Rachitsky
I consistently get AI generated stuff and there's a difference between an AI generated document that's slop and not. And the slap one is it took them less time to make it than it takes me to read it and they don't stand behind every line. So my expectation is if you send me an AI generated document, I think that's great. And if we talk about it and it's clear you have no idea what's in it, big no-no, not allowed to do that. And I think we have this aversion to AI-generated stuff that will go away because the kind of strategy document that GPT 5.5 can write when it's directed well by someone on my team is way better than them just like dinking and dunking their fingers on the keyboard. [01:05:29] S0: Right. Most people are really bad at writing strategy documents. The bar is low.
01:05:29 · Lenny Rachitsky, Dan Shipper
[01:05:29] S0: Right. Most people are really bad at writing strategy documents. The bar is low. [01:05:33] S1: Yeah. And same thing with email. Most of my email is written by GPT 5.5 in Codex right now. And I honestly would prefer it to say that it's coming from GPT 5.5 and I may change it to do that. But I had this experience the other day where I had to send an email to one of our investors and I asked Codex to go do it. And Codex knows to ask me and it usually does, but this time it didn't and it just sent the email and I didn't look at it at all and I was like, "Fuck." And so I went to my sent and looked at it and I was like, "Oh, this is exactly what I would've sent." And so it's pretty close to that a lot of the time. It can be a little overformal and there's a couple of things that... It's just when you really think about it, most of your email is kind of... It's kind of wrote. It's kind of prosaic. I definitely want to be the one to think about what it should say, what it should say, but the actual sentences don't matter that much to me usually. Sometimes I do a lot and this is coming from a writer. I care a ton about writing. I think that human writing is incredibly important and I expect we only publish human writing. Well, actually we publish a mix of human and AI writing, but we always label it. Sometimes it's nice to have an AI co-author on certain things. I absolutely think that human writing is important and I think that the reaction or the aversion to AI writing is silly.
01:07:10 · Lenny Rachitsky, Dan Shipper
[01:07:10] S0: It's such an interesting lens on that because when people think about AI writing, I think about social media and videos and your point is internally, if you're just working on planning and documents and email and things like that, that is much less scary that it's AI written. And to your point, people are already doing this, you almost prefer it a lot of times because people are really bad at doing anyway. [01:07:30] S1: We have this too for external stuff. We publish all these guides and the guides are often agent, they're agent assisted and the agent is a co-author and they're intended to be read both by humans and by agents. And that's because if you're writing a huge informational thing, I mean, you do this all the time. In order to really apply it, the best way to do that is just have your agent ingest it and remember the next time I'm doing pricing to remind me of this guide and we'll go through it together or whatever. It allows you to operationalize the ideas much better and it allows you to go much deeper because agents can read like 10,000 pages in like a second. And so you talk to the human about the story and the stuff that matters and the core ideas and the agent has all the details that it can then apply for you when you need it. [01:08:21] S0: Awesome. Anything else in this category before we get into our final category? [01:08:26] S1: No. [01:08:27] S0: Okay, let's do it. So the final bucket is just who will be successful in this AI future that we are approaching/what should people be working on to be successful in this next year or two?