Lenny's Podcast: Product | Career | Growth
00:32:13 · Lenny Rachitsky, Adam Mosseri
[00:32:13] S1: I love that. I so agree. I feel like everyone's always joining a team, and they just want to do vision strategy, just not actually hands-on work. "No AI's coming in here, let me do the strategy." [00:32:23] S0: Yeah, exactly. [00:32:27] S1: And I love this point that there's so much power and value, and people underestimate just the need for just a really good curator of the team's ideas.
00:32:27 · Lenny Rachitsky, Adam Mosseri
[00:32:27] S1: And I love this point that there's so much power and value, and people underestimate just the need for just a really good curator of the team's ideas. [00:32:35] S0: Yeah. Sometimes it's not just who's good or what idea's good. It's also what is going to work given the broader context. So for instance, on team building, a huge thing that I'm always considering is not just like, is this person a really strong candidate for this role? It is how does this person fit into their leadership team? So for an area like trust and safety, I have an engineering lead, I have a product staff lead, I have a data science lead, I have a design lead, I have a research lead. I need to make sure that those five compliment each other. And that's about what skills each one has, what weaknesses each one might have. I also need to make sure that this is more art than science, have a good vibe. You need trust and rapport. A leadership team with strong trust and rapport can work through most anything. A leadership team without trust or rapport, anything can become an issue. And so that chemistry bit is, like I said, much more art than science. But that also matters. And so I think some of the best leaders and product leaders specifically also either do that instinctively or consciously, but they have a nose for building teams that are going to have good energy and good collaboration. [00:33:59] S1: Warm and fuzzy stuff.
00:33:59 · Lenny Rachitsky, Adam Mosseri
[00:33:59] S1: Warm and fuzzy stuff. [00:34:01] S0: Yeah. Well, the flip side is also true. I've had many times in my career where I've had two people who I think are amazing, and I even adore them and love them, and they just can't get along. And you're like, "This isn't a competency issue. This is just a personality issue," and you just have to just sometimes call it, and split them. [00:34:23] S1: I want to transition to talk about Instagram, the product, the platform, things you guys have learned there. Let me start with this question. What's something that the Instagram algorithm knows about human behavior that people may not realize?
00:34:38 · Adam Mosseri
[00:34:38] S0: One of the most common misconceptions is actually in the opposite direction. 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. Most of what's really driven the progress in the world of recommenders over the last 5, 10 years have been these large embedding models and these other techniques that basically produce artifacts that cannot be read by people. They're not legible. They're giant vectors. It's like, sure, I can show you the vector, but it's just going to be a bunch of numbers in a seven-dimensional space. And so when we talk about, does the algorithm know something? Usually we think in these more semantic terms, it knows I like surfing. And it's like, "Eh." It doesn't. It just has this big-ass number that happens to correlate with surfing. That said, I think that is starting to change. I think that one of the things that LLMs are enabling is they can describe in words, English or whatever language you prefer, what some of those previously-illegible artifacts are at least proximate to, if not mean directly. So this is like ... The thing I haven't really ... I posted about this this week, this thing called Your Algorithm. Basically the idea is we take a look at all of the stuff that you've interacted with, and then all of that is in an embedding space. You can think of embedding space as a map. You map a bunch of videos into the same map, and so videos that are close or similar. And now, we can just have an LLM just be like, describe that part of the map. And it can be like, "Oh, that is deep pour over coffee snobbery." And that's kind of amazing.
00:36:29 · Lenny Rachitsky, Adam Mosseri
[00:36:29] S1: That is so cool. You can ask the LLM to look at these numbers, and extrapolate here's the topic that you're interested in. That's awesome. [00:36:36] S0: Yeah. Or look at the videos. [00:36:37] S1: And the videos.
00:36:37 · Lenny Rachitsky, Adam Mosseri
[00:36:37] S1: And the videos. [00:36:38] S0: And so the way you ... Both. You can also embed concepts into that same space. And so I mean, embeddings are really the underlying technology underneath LLMs. That's how all the whole thing works. And so what we do now is we let you "see your algorithm." You can see what topics we think you're interested in and you can adjust it, you can add and remove things. But the idea here, giving people some agency back in a world where these social media apps are getting taken over by recommendations. But we can't do a lot of other things yet, but we will be able to do. There's things that aren't topical that you might ask for, "I want more fun content. I want to see my friends more. I don't want to see my high school's friends' kids' photos." I don't know, we can come up with a ... "I don't want to see seven photos in a row, but I'm happy to see six photos in a row." Whatever your mind can come up with. So we have a lot of work to do, and so I'm excited about that. But I think a misconception historically is, until recently, we don't really know as much about you as you think. We're just like, "Oh, you liked these photos. These people also like those same photos, and they like these other photos, so you might like those other photos." I'm oversimplifying, but that's Kind of how it worked. Now, only now, are we actually getting as sophisticated as I think people have assumed we've been for many years.