Lenny's Podcast: Product | Career | Growth
00:00:00 · Adam Mosseri, Lenny Rachitsky
[00:00:00] S0: No, I think taste matters a ton. In a world where it's easier to build things, it's more important to make sure that your time is spent figuring out what you should be building in the first place. 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. [00:00:23] S1: What's something that the Instagram algorithm knows about human behavior that people may not realize? [00:00:30] S0: I think people assume that there's a much more detailed semantic understanding of everybody's interests and preferences in the algorithm than there is. [00:00:38] S1: Is the rise of AI content a headwind or a tailwind for Instagram versus other platforms? [00:00:45] S0: I think it's going to be a tailwind, but I think it's going to be a challenge. In a world where there's an abundance of synthetic content, I actually think people are going to seek out creativity and authenticity and people. I don't think we should filter out AI content. I think we should let you know if content is AI content or not. That's hard, by the way. [00:01:04] S1: Where do you think human brains will continue to be most valuable as AI continues to eat more and more of that product development life cycle? [00:01:12] S0: That's a great question, so- Thank you for having me. Excited to be here.
00:01:12 · Adam Mosseri, Lenny Rachitsky
[00:01:12] S0: That's a great question, so- Thank you for having me. Excited to be here. [00:02:15] S1: You've been doing product for a long time. You get to see how a lot of teams operate across Meta, within Instagram. What does just the canonical product team look like in 2026? What's kind of most different today in how teams operate/should operate versus, say, a couple years ago?
00:02:33 · Adam Mosseri
[00:02:33] S0: It's changed a lot this year. So for the longest time at a big company like ours, the canonical team was something like two or three Android engineers, two or three iOS engineers, two or three server engineers, maybe a generalist, a PM, a designer, a data scientist, a researcher, if you were lucky, and maybe that's about it. So on the order of a Baker's dozen. And that is a function of you want to have, for anybody who's running code, someone who can review their code and who's familiar with that code base, and having these different functions that are more specialized. I think it's very different at a startup. But this year, it's changing. We've adopted what we call pods, which are just mini teams, where it's, call it, four to six engineers who are a bit more generalists. One, we call product staff, which is sort of an evolution of the PM, so a PM who can do some of what a designer does, and some of what a data scientist does, and some of what a research does, leveraging the latest tools that we have for them. And then whatever specialist they need. If they're doing something that requires a pricing strategy, you need a senior data scientist. If you're doing something that is really novel from an experience standpoint, you need a very senior product designer. So we try to build a team based on the needs of the work a bit, but then end up with a much smaller core, which is more on the order of six or seven usually. And that is a very big shift that's just happening to us this year. But just by virtue of having less people to coordinate, they can often move faster and make better decisions, a little bit less designed by committee. So we talk a lot about AI adjusting and improving productivity, and that's part of it. But I think another part of it is just the small teams, I think, often are just more effective. You just have less specialists, right? So you might not have any. You might be four engineers and a product staff, and there's no data scientist, there's no designer, there's no researcher, there's no content designer. The product staff is the generalist that supports all of those things. I mean, what's clearly happening is all of the functions are starting to bleed into each other, and the whole industry's wrestling with what that means. A lot of what a data scientist does at a big company, for instance, is relatively mechanical.
00:06:00 · Adam Mosseri, Lenny Rachitsky
And so there's stuff that they do that is really more like art and science and there's stuff that's really more like just pulling data, data management. So some of the tools that we're building internally to understand ... For instance, a traditional data science question would be a waterfall. So if you wanted to look at people creating reels, you would look at all the steps and then how people fall off on each step, and try to figure out where there might be opportunities to improve things. That kind of basic waterfall analysis is much easier now to use some of our internal tools to just pull automatically, as opposed to having to have a data scientist do a bunch of bespoke work for that. So our product staff might be able to do that now, and they couldn't do that a year ago. So you just end up with these people who have more generalist shapes. And then when you need it, when you really need it, you have a more senior, ideally, or just more creative specialist. So a phenomenal product designer or just a genius data scientist or researcher. [00:07:32] S1: This is so interesting. It's exactly what I just heard. I had Fiona Fung, the head of engineering for Claude Code and co-work on the podcast. She's Boris Cherny's manager, and she described the people she hires now are, one, builders with great taste that can take an idea from end-to-end, and people with deep expertise in a very specific domain. [00:07:53] S0: The taste thing matters a lot. I really agree with that. Boris used to work at Instagram. [00:07:58] S1: Oh, that's right.
00:07:58 · Lenny Rachitsky, Adam Mosseri
[00:07:58] S1: Oh, that's right. [00:07:59] S0: Yeah, he was a senior IC at Instagram for a while. I love seeing him. He's all over Threads now. It's like he's sort of like the face of Claude Code, which is amazing. Yeah, he's crushing it. [00:08:05] S1: He's killing it. He's a celebrity now. [00:08:08] S0: He really is. He's his own little ... Yeah, for sure. In a world that is on fire right now. No, I think taste matters a ton. So in a world where it's easier to build things, it's more important to make sure that your time is spent figuring out what you should be building in the first place. Actually, so a lot of designers right now are very anxious about their roles. You know, I've got these other generalists doing design, you've got engineers doing design, product staff doing design. But I'm actually pretty long on design or designers because they tend to have taste. And I think that is something that is much more difficult to imagine being automated away. And so there's other challenges with design sometimes, but I'm pretty long right now on designers. [00:08:59] S1: I've always felt that too. As it is so easy to build and all the work that AI produces is so ... You can tell this was Claude designed. I know what you did here. This is Codex. [00:09:10] S0: Well, they all have their vibe. When you vibe code your apps, we call it vibe coding, and you're all like, "Oh, that's a Codex app," or, "Oh, that's a Claude app."
00:09:10 · Adam Mosseri, Lenny Rachitsky
[00:09:10] S0: Well, they all have their vibe. When you vibe code your apps, we call it vibe coding, and you're all like, "Oh, that's a Codex app," or, "Oh, that's a Claude app." [00:09:17] S1: Right. And that's Replit, that's Lovable. You can predict these things. I've always though that too, that design should be thriving, or, you know, for some reason, it hasn't yet. If you look at jobs for designers, they're kind of flat lining. I feel like the missing piece is like the PME piece of deeply understanding the business, and what will grow it, and what success, you know, all that stuff. The business side of it versus the taste side of it.