Lenny's Podcast: Product | Career | Growth
00:40:17 · Simon Willison
[00:40:17] S0: So I'm going to refactor that to 95% of their code. I don't think we'll get to it. But yeah, it's very difficult to say worldwide, partly because there are cultural differences. I spend way too much time on hack and use, and something I've noticed about hack and use is a conversation that starts at midnight Pacific time and goes until 8:00 AM, very different tone because it's the Europeans. And the Europeans are a lot more AI skeptic than the Americans are generally. So I think different countries are going to have different cultures around this. At the same time, I think it's become undeniable this year that this stuff produces good code. It used to be that you could say, "I don't use this stuff because the code is bad." And that was a justifiable position. That's not justifiable anymore. The code is now good. It's good code, for my definition of good code at least. So we're saying 50% of engineers, let's say 15 engineers, majority of their code, it could happen by the end of this year. It could, because the technology is good enough now. And I feel like the challenge now is getting people to learn how to use this stuff, which is difficult because using this stuff, everyone's like, "Oh, it must be easy. It's just a chatbot." It's not easy. That's one of the great misconceptions in AI is that using these tools effectively is easy. It takes a lot of practice and it takes a lot of trying things that didn't work and trying things that did work. But yeah, I expect by the end of this year, it will not be uncommon to have an engineer say that almost all of that code is written by AI.
00:41:56 · Lenny Rachitsky, Simon Willison
[00:41:56] S1: That was the same rough idea I had. And how crazy is that, how quickly- [00:42:00] S0: It's wild. [00:42:00] S1: ... this job has changed and what is possible. And I think people, this is a good example of people underestimate how quickly things can change. I think Dario was predicting this a year or two ago, just, "Oh, 100% of code's going to be written by AI," and we were just like- [00:42:14] S0: We all laughed at him. Yeah. [00:42:15] S1: Right. Exactly. Like, "What are you talking about? It's so bad. It's so bad at writing code." And this might come for other jobs that people don't see coming, which is scary and interesting and exciting. [00:42:27] S0: Honestly, I'm not an AI doomer in the slightest. The economics of it do make me nervous. Are we really going to wipe out a tenth of white collar knowledge work jobs in the next few years? I really hope not, because I don't know how the economy adapts to that. So yeah, that's complicated. [00:42:46] S1: Yeah. Actually, I'm doing a report that's coming out, it'll come out ahead of this episode, looking at the job market in tech. And surprisingly, just at tech companies, we're at the highest number of open engineering roles, open PM roles. [00:43:00] S0: Interesting. [00:43:02] S1: Except for during the crazy peak during COVID. So it's kind of coming back to that. Basically, it's the highest number of open roles in three and a half-ish years for engineers and PMs at tech companies globally. [00:43:16] S0: That's very interesting. It's funny, isn't it? Because you get all of these headline grabbing-
00:43:16 · Simon Willison, Lenny Rachitsky
[00:43:16] S0: That's very interesting. It's funny, isn't it? Because you get all of these headline grabbing- [00:43:21] S1: Layoffs. [00:43:22] S0: Yeah. Was it Block that laid off 4,000 people recently? [00:43:26] S1: Yeah. Yeah. [00:43:26] S0: But the question there is always how much of that is AI and how much of it is overhiring during COVID and re-corrections and all of that kind of thing? It's always very difficult to tell. So the number of open jobs, on the one hand, maybe that's a better signal, but on the other hand, the recruitment market has been driven completely crazy by all of this stuff. All of the job ads are written by AI, the resumes are AI. People in recruitment are saying that it's never been this hard to filter through and hire people, and people who are hiring jobs say they applied to 200 things and got nobody hearing back. So it's hard, right? The macroeconomic indicators for this stuff are lagging. And at some point we should start getting more confident numbers about what the impact actually is. [00:44:13] S1: Yeah. Interestingly, the number of recruiter open roles is also approaching record numbers- [00:44:19] S0: Hilarious. That's so interesting. [00:44:20] S1: ... which is an interesting leading indicator of demand for hiring. So there's interesting trends in spite of the layoffs. So yeah, what a wild world. So you've mentioned this book you're working on. This is the agentic engineering pattern stuff, right? [00:44:33] S0: Yes.
00:44:33 · Simon Willison, Lenny Rachitsky
[00:44:33] S0: Yes. [00:44:34] S1: Okay, cool. So I want to talk about this. So you point it out. People think it's easy to build with AI. It's like, "Oh, it's going to do all these things for us. What are we going to do all day?" To your point, it's actually not. There's a lot of very specific skills you need to do this well, and you're putting them together on your blog. We'll point to it. I want to talk through a few of them to help people do this better. So one is this idea of just writing code is cheap now. You touched on this a bit. Maybe just share why this is such an important thing to know and keep in mind.
00:45:01 · Simon Willison
[00:45:01] S0: So I think this is the single biggest shock in all of this. The reason that we have to rethink how we build, how we work as software engineers is that the thing that used to take the time takes way less time. It's never been the case that programmers spend 90% of their typing code into a computer. There's so much additional work around that, but it still used to be... People talk about how important it is not to interrupt your coders. Your coders need to have solid two to four hour blocks of uninterrupted work so they can spin up their mental model and churn out the code. That's changed completely. My programming work, I need two minutes every now and then to prompt my agent about what to do next, and then I can do the other stuff and I can go back. I'm much more interruptable than I used to be. But yeah, so the thing that used to take the time is now the thing that takes way, way less time. What does that mean for everything else that we do? And that doesn't just affect programmers. It affects entire teams around software development. But as an individual programmer, you have to start thinking, okay, I can churn out 10,000 lines of code now in the time that it would take me to write 100. How do I make that code good? How do I make sure that I'm not just churning out total slop that adds up to technical debt that slows me down? How do I take the fact that code is now cheap and use that to produce better code? Because I don't just want cheap code. I want really good code that does what I need it to do that I can extend in the future that's got all of those characteristics of code that's useful and can be used in production.
00:46:35 · Lenny Rachitsky, Simon Willison
[00:46:35] S1: The point you made earlier I think is a really important one along these lines, which is when you start a project, you fire off three different versions of it and that helps you pick a direction. And that's only possible because code is so cheap now, right? [00:46:45] S0: Right. Prototyping is almost free, I think. And that really impacts me because throughout my entire career, my superpower has been prototyping. I've been very quick at knocking out working prototypes of things. I'm the person who can show up at a meeting and say, "Look, here's how it could work." And that was kind of my unique selling point, and that's gone how anyone can do what I could do. But you still have to learn when it's appropriate to prototype, how to think about prototyping, how to get the tools to build useful prototypes that you can use to explore things.