Lenny's Podcast: Product | Career | Growth
00:16:48 · Lenny Rachitsky
[00:16:48] S1: Something I'm hearing from a few folks is AI is almost kind of resetting people's impact and success, in terms of some people that were maybe low performers pre-AI can now do ... Like things they were bad at, AI now allows them to do. And now they're thriving, building all these things, helping other people. Do you see that at all? Just AI is just lifting other people up, maybe lowering some people down?
00:17:12 · Adam Mosseri
[00:17:12] S0: Yeah, I mean the job is just different. Take engineering. Engineering used to be, maybe not majority, but a large percentage, 40%, 50%, 60% writing code. It's not now, especially if you talk to anybody at these labs. They're spending most of their time planning and reviewing code. That is a very different job. You might hate that, and you might have loved just writing code. Or you might love that, and you might not have been that fast at writing code. So who succeeds is a function of whose strengths are aligned with the tools' needs and the business' needs. And so this is definitely happening. Another thing is you've had people who had good ideas about how to contribute it to other functions, but didn't have the mechanical or technical skills to do so. And AI reduces the boundary to do that, and then all of a sudden they can. For me, it's kind of funny because when I got hired at Facebook, all the designers had to be able to program. I went through a technical loop. We gave up on that because it was too hard to hire people. But I now get to program again for the first time in maybe 10 years. And, you know, I am not a good engineer. I'm a mediocre engineer on a good day. But now, I can write code responsibly, which is just an amazing thing. You're seeing this across all sorts of levels in seniority and functions. Designers who are programming, engineers who are pulling data and doing strong analyses, data scientists who are putting together proposals for designs. The tools aren't all great, by the way. I think too often we have this really polarized binary outlook on the state of AI. Like, are you AI-pilled or are you anti-AI? It's like, people are binary. I said that to the team yesterday. And the state of the tools isn't binary either. They're amazing at some things and remarkably bad at others. And the people who I think are going to make the most of it are the ones who are clear-eyed about what AI is good at and what it's not good at, and also have an instinct or a nose for what it will be good at and not good at next month or in a couple months from now.
00:19:38 · Lenny Rachitsky, Adam Mosseri
[00:19:38] S1: You mentioned that AI writes all our code now. Someone tweeted this idea that stuck with me for months now of just like, "Remember, we used to be able to just write code for free?" [00:19:52] S0: I think you'll still be able to write code for free, it'll just be with a smaller model, but yes. That is [inaudible 00:19:58]. [00:19:57] S1: I guess that's true. There's a model that are close to free, but it's like, yeah, that's crazy. Now it's just like totally- [00:20:04] S0: But just think about the cost, think about what you pay for a model now and what the level of intelligence you're getting from that model is. And then at that same price point a year ago, what were you getting? At some point, the incremental value won't matter. We're getting there I think with small projects and programming. I think the models will matter even beyond ... This week you've got Fable and, obviously, Mythos from Anthropic, but I spent a lot of time with that this week. For the first time, I'm like, "Oh, I'm just talking to a much more technical, much smarter engineer than I am." The next version, a year out, of that model, do I need to pay for frontier tokens for whatever Anthropic model 6.0 is? Or is Fable just fine for all of my side projects? Probably just fine. Probably pretty cheap by then too.
00:20:04 · Adam Mosseri, Lenny Rachitsky
[00:20:04] S0: But just think about the cost, think about what you pay for a model now and what the level of intelligence you're getting from that model is. And then at that same price point a year ago, what were you getting? At some point, the incremental value won't matter. We're getting there I think with small projects and programming. I think the models will matter even beyond ... This week you've got Fable and, obviously, Mythos from Anthropic, but I spent a lot of time with that this week. For the first time, I'm like, "Oh, I'm just talking to a much more technical, much smarter engineer than I am." The next version, a year out, of that model, do I need to pay for frontier tokens for whatever Anthropic model 6.0 is? Or is Fable just fine for all of my side projects? Probably just fine. Probably pretty cheap by then too. [00:21:05] S1: Yeah. When Kevin Weil on the podcast when he was CPO at OpenAI, he famously said, "This is the worst the model will ever be." It's still hard to comprehend that. It's only going to get better. So on this point of tokens, spend, ROI, and things like that, Meta is famous for this leaderboard of token spend. [00:21:25] S0: It's a terrible idea. No leaderboards for token spend. [00:21:28] S1: Okay. Okay. Talk about that, and just how do you think about just budgets for engineers and product teams at this point? Just spend as much as you want? Is it like there's a cap we have? Is there any sort of thing you've figured out that works well?
00:21:39 · Adam Mosseri
[00:21:39] S0: Right now, we've managed to get the costs reigned in a little bit by shutting down the silly things that we were doing. It's not that hard to build a token incinerator and that doesn't create a lot of value. And as soon as you actually look at the dollars in and value out, you might just be like, "Oh, that's just a bad idea." And so right now, we don't have token limits for our engineers. Actually, I think for anybody, really. I think that'll eventually have to happen, particularly if costs go up before they go down. I think they'll eventually go down for the reasons that we just talked about. But I think of it as any other resource, like I have to decide how to deploy capacity to my different teams, because I have a limited number of GPUs and CPUs and storage and RAM, et cetera. I have to decide how to deploy OPEX for labeling budgets across my teams. I have to decide how to deploy payroll for headcount across my teams. I think that you can imagine, at least in a year or two coming, that the burn rate of a strong engineer might be the same as their salary or their cost of employment. And in that world, you're going to probably need to put in some caps. The caps should probably be portional to the company's trust in your ability to use them in an ROA positive way. But I can imagine caps being healthy. Right now, we're not there. I think costs will go up because we'll just be using more tokens, not because prices will necessarily go up. But then I think prices will come down because all of these frontier models are going to be in a bit of a pricing war. So we'll see. I think it'll be a bit of a rollercoaster.
00:23:23 · Lenny Rachitsky
[00:23:23] S1: So coming back to this idea that, as you said, we've evolved from, we used to write all our code, to now we're approaching all code will be written by AI. And it feels like now the transition is it's not just written by AI, but it's one-shotted by AI. Coding now is steering AI, and it's like how often you have to correct it is coding now. So it's like the software development lifecycle slowly being eaten by AI. It'll start helping us come up with ideas, I imagine, more and more. The question I like to ask people is, where do you think human brains will continue to be most valuable as AI continues to eat more and more of that product development lifecycle?