Lenny's Podcast: Product | Career | Growth
00:46:32 · Marc Andreessen, Lenny Rachitsky
If it doesn't work, or if it's not doing what you expect, or it's not fast enough or whatever, you need to be able to understand the results of what the AI is giving you." In the same way that somebody who's writing scripting language code does need to understand ultimately how the microprocessor works. And so again, it's kind of this up leveling of capability where you actually want the depth to be able to go down and be able to understand what the thing is actually doing, even if you're not spending your day actually doing that by hand. And again, I look at that and I'm like, "Okay. Now, programmers are going to be 10 times or 100 times or a thousand times more productive than they used to be." And that is, overwhelmingly, a good thing. The tasks are definitely changing. The nature of the job is changing. But are human beings going to be involved in the coding process and overseeing the AI coding and all of that? And the answer is, of course, absolutely 100%. No question. [00:47:22] S1: So you're in the camp of still learning to code is still a valuable skill?
00:47:24 · Marc Andreessen
[00:47:24] S0: Oh yeah, totally. Again, if you want to be one of these super... Look, if you just want to put yourself on autopilot, and like, "I can't be bothered. I'm just going to have AI write the code, and it's going to generate whatever it does and that's fine. And I'm going to be..." If the goal is to be a mediocre coder, then just let the AI do it. It's fine. The AI is going to be perfectly good in generating infinite amounts of mediocre code. No problem. It's all good. If the goal is, "I want to be one of the best software people in the world, and I want to build new software products and technologies that really matter," then yeah, you, 100%, want to still... You want to go all the way down. You want your skillset to go all the way down to the assembly, to assembly and machine code. You want to understand every layer of the stack. You want to deeply understand what's happening at the level of the chip, and the network, and so forth. By the way, you also really deeply want to understand how the AI itself works, because you want to... If people understand how the AI works, they're clearly able to get more value out of it than somebody who doesn't understand how it works. You're always more productive if you know how the machine works when you use the machine. And so the super-empowered individual on the other end of this that wants to do great things with the new technology, yes, you 100% want to understand this thing all the way down the stack because you want to be able to understand what it's giving you. And when something doesn't work or when something isn't right, you want to be able to really quickly understand why that is. By the way, again, this goes back to education. AI is your best friend at helping you learn all of that because it's like, "Oh, I need to understand. I don't know, this isn't fast enough." I need to figure out... As a coder, I need to figure out how to do a different approach to memory management or something. And you can be like, "Well, shit. I don't quite know how to do that. Okay, AI, let's spend 10 minutes. Teach me how to do this. Teach me what this all means." So all of a sudden, you have this incredibly synergistic relationship with the AI, where it's also helping you get better at the same time that's doing a lot of work for you.
00:49:08 · Lenny Rachitsky, Marc Andreessen
[00:49:08] S1: By the way, I was going to say, I was a big Perl programmer. I was an engineer for 10 years, and that was my language of choice. [00:49:14] S0: Do you remember? I don't know when you were doing it, but do you remember... At least early on, did you ever hit this where C coders were looking down their nose at you and being like- [00:49:23] S1: For sure. It was like, "This is so slow. It's not going to scale. What are you spending all your time on this thing?" [00:49:27] S0: Yeah, exactly. And of course, and again, it started this thing where they were sort of correct. Which is, at the beginning, it wasn't fast enough or whatever. By the end, they were definitely wrong, which is it got much better, much faster. And it swept the world. Most coding today happens as scripting languages. And then by the way, along the way, the people who really understood the scripting languages and the people who understood all the lower level systems, they were the ones who were able to actually make the scripting languages actually work really well. And so, that was a great example of this kind of adaptation. And again, the result of that was a far higher number of people writing code with scripting languages than were ever writing code with lower level languages. And I think this will just be a more dramatic version of that. [00:50:04] S1: I love that Perl was designed by a linguist. I don't know if you remember that part. And that's what made it so nice to code with. [00:50:10] S0: That's funny because, of course, it was so notorious for being impossible to understand.
00:50:10 · Marc Andreessen, Lenny Rachitsky
[00:50:10] S0: That's funny because, of course, it was so notorious for being impossible to understand. [00:50:15] S1: How ironic.
00:50:17 · Marc Andreessen
[00:50:17] S0: Yes. Yeah, that's right. And again, here, this is a great example. So again, the task level of, like, "Design the perfect icon," is going to be, all right, the AI's going to do that all day long. If it gives you a thousand icon designs, it's going to be great. It's going to be fantastic, whatever. And by the way, there will still be some level of human icon design or whatever, but AI is going to get really good at that. But what are we trying to do, kind of capital D design of, like, "All right, what is this thing for, and how is this going to function in a world of human beings? And is this going to make people happy when they use it? Is this going to make people feel good about themselves? Is it going to fit into the rest of their life? Is it going to, I don't know, challenge them in the right way?" All of these kinds of higher level questions that the great designers have always thought about. The job of designer will involve much more of those higher level, more important components, and then again, with AI doing a lot more of the underlying tasks. And so one way to think about it is, I don't know, you think of the world's best designers, Jony Ive or whatever, and you could be like, "Wow." Like, if I'm a designer today, if I'm a 25-year-old designer and I aspire to be Jony Ive in a decade, it's all of a sudden, I have a new path that I can use to get there, which is... Because Jony did everything. He did it without AI. Now, a young designer tends to be like, "Wow, if I really harness AI in a decade, I'm going to be like the best designer of the world's ever seen because it's not just going to be me. It's going to be me, plus being so super empowered by this technology to be able to do so much more. And then so much more of my time and attention is going to be able to be focused on these higher-level things that most designers never get to." I think that's going to be another great example of that.
00:53:31 · Lenny Rachitsky
[00:53:31] S1: So maybe what I'm hearing here is kind of this T-shaped strategy of if you want to be successful in any three of these roles, be very, very, very good at that specific role: product management, engineering design. And then get good enough at these other two roles.