Lenny's Podcast: Product | Career | Growth
00:32:00 · Lenny Rachitsky, Boris Cherny
[00:32:00] S1: I don't know if you saw this, but Elon was saying that, "Why isn't the AI just writing binary straight to binary? Because what's the point of all this programming abstraction in the end?" [00:32:12] S0: Yeah. It's a good question. It totally can do that, if you want it too. [00:32:15] S1: Oh, man. So, what I'm hearing here is in terms ... There's always this question, "Should I learn to code? Should people in school learn to code?" What I heard from you is your take is in a year or two you don't really need to.
00:32:27 · Boris Cherny
[00:32:27] S0: My take is I think for people that are using Claude Code, that are using agents to code today, you still have to understand the layer under, but, yeah, in a year or two, it's not going to matter. I was thinking about what is the right historical analog for this? Because somehow we have to situate this thing in history, and figure out when have we gone through similar transitions? What's the right mental model for this? I think the thing that's come closest for me is the printing press. And so, if you look at Europe and in the mid 1400s, literacy was actually very low. There was sub-1% of the population, it was scribes, that they were the ones that did all the writing. They were the ones that did all the reading. They were employed by lords and kings that often were not literate themselves. And so, it was their job of this very tiny percent of the population to do this. And, at some point, Gutenberg and the printing press came along, and there was this crazy stat that in the 50 years after the printing press was built, there was more printed material created than in the thousand years before. And so, the volume of printed material just went way up. The cost went way down. It went down something, like, 100 X over the next 50 years. And if you look at literacy, it actually took a while, because of learning to read and write, it's quite hard, it takes an education system, it takes free time. It takes not having to work on a farm all day, so, that you actually have time for education, and things like this. But over the next 200 years it went up to 70% globally. So, I think this is the thing that we might see is a similar kind of transition. And there was actually this interesting historical document where there was an interview with some scribe in the 1400s about, "How do you feel about the printing press?" And they were actually very excited, because they were like, "Actually, the thing that I don't like doing is copying between books. The thing that I do like doing is drawing the art in books, and then doing the book binding. And I'm really glad that now my time is freed up." And it's interesting. As an engineer, I felt a parallel with this. Like, this is how I feel where I don't have to do the tedious work anymore of coding, because this has always been the detail of it.
00:34:43 · Boris Cherny, Lenny Rachitsky
It's always been the tedious part of it, and messing with [inaudible 00:34:51], and using all of these different tools. That was not the fun part. The fun part is figuring out what to build, and coming up with this. It's talking to users. It's thinking about these big systems. It's thinking about the future. It's collaborating with other people on the team, and that's what I get to do more of now. [00:35:07] S1: And what's amazing is that the tool you're building allows anybody to do this, people that have no technical experience can do exactly what you're describing. I've been doing a bunch of random little projects, and it's just, like, "Any time you get stuck just help me figure this out." And you get unblocked. I was an engineer earlier in my career for 10 years. And I just remember spending so much time on libraries and dependencies and things, and just like, "Oh my God. What do I do?" And then looking on [inaudible 00:35:34]. And now it's just like, "Help me figure this out," and, "Here's a step-by-step, one, two, three, four. Okay. We got this." [00:35:39] S0: Yeah. Exactly. Exactly. I was talking to an engineer earlier today. They're like, "They're writing some service [inaudible 00:35:44], and it's been a month already, and they built up the service. It's working quite well." And then I was like, "Okay. So, how do you feel writing it?" And he was like, "I still don't really know Go." And I think we're going to start to see more and more of this. It's, like, if you know that it works correctly and efficiently, then you don't actually have to know all the details.
00:35:39 · Boris Cherny, Lenny Rachitsky
[00:35:39] S0: Yeah. Exactly. Exactly. I was talking to an engineer earlier today. They're like, "They're writing some service [inaudible 00:35:44], and it's been a month already, and they built up the service. It's working quite well." And then I was like, "Okay. So, how do you feel writing it?" And he was like, "I still don't really know Go." And I think we're going to start to see more and more of this. It's, like, if you know that it works correctly and efficiently, then you don't actually have to know all the details. [00:36:02] S1: Clearly, the life of a software engineer has changed dramatically. It's a whole new job now as of the past year or two. What do you think is the next role that will be most impacted by AI? Either within tech, like, product managers, designers, or even outside tech, just what do you think? Where do you think AI is going next?
00:36:23 · Boris Cherny
[00:36:23] S0: I think it's going to be a lot of the roles that are adjacent to engineering. So, yeah. It could be product managers, it could be design, it could be data science. It is going to expand to pretty much any kind of work that you can do on a computer, because the model is just going to get better and better at this. And the Cowork product is the first way to get at this, but it's just the first one. And it's the thing that I think brings Agentic AI to people that haven't really used it before, and people are starting just to get a sense of it for the first time. When I think about the engineering a year ago, no one really knew what an agent was, no one really used it, but nowadays it's just the way that we do our work. And then when I look at non-technical work today, so, like ... Or maybe semi-technical, like, product work, and data science, and things like this, when you look at the concept of AI that people are using, it's always these conversational AI. It's, like, a chatbot or whatever. But no one really has used an agent before, and this word agent just gets thrown around all the time, and it's just so misused. It's lost all meaning. But agent actually has a very specific technical meaning, which is it's an AI, it's an LLM that's able to use tools. So, it doesn't just talk. It can actually act, and it can interact with your system. And this means it can use your Google Docs, and it can send email, it can run commands on your computer, and do all this kind of stuff. So, I think any kind of job where you do use computer tools in this way, I think this is going to be next. This is something we have to figure out as a society, this is something we have to figure out as an industry. And I think, for me, also this is one of the reasons, it feels very important and urgent to do this work at Anthropic, because I think we take this very, very seriously. And so, now we have economists, we have policy folks, we have social impact folks. This is something we just want to talk about a lot, so, as a society we can figure out what to do, because it shouldn't be up to us.
00:38:19 · Lenny Rachitsky
[00:38:19] S1: So, the big question, which you're alluding to is jobs and job loss, and things like that. There's this concept of Jevons paradox of just as we can do more, we hire more, and it's not actually as scary as it looks. What have you experienced so far I guess with AI becoming a big part of the engineering job? Are you hiring more than if you didn't have AI? And just thoughts on jobs.