Adam Mosseri: AI is a tailwind for authenticity

Speaker A: No, I think taste matters a ton. In a world where it's easier to

Speaker B: 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.

Speaker A: The people who I think are going

Speaker B: 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.

Speaker C: What's something that the Instagram algorithm knows about human behavior that people may not realize?

Speaker A: I think people assume that there's a

Speaker B: much more detailed semantic understanding of everybody's interests and preferences in Yoglin than there is.

Speaker C: Is the rise of AI content a headwind or a tailwind for Instagram? Um, versus other platforms?

Speaker A: 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.

Speaker B: 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.

Speaker A: Um, that's hard.

Speaker C: By the way, 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?

Speaker B: That's a great question.

Speaker A: So

Speaker C: today my guest is Adam Mosseri, head of Instagram. Over 3 billion people use Instagram monthly. That's one in every three people alive. It boggles the mind. Prior to Instagram, Adam designed and led the early Facebook news feed. He also ran the team that built a Facebook ranking algorithm. And eight years ago he took over Instagram from its founders Kevin Systrom and Mike Krieger. He's a designer turned product manager turned leader of Instagram. Adam is also famous for being the

Speaker D: face of all of the controversy and

Speaker C: changes that come with evolving Instagram as a product, which we talk about before we get into it. Don't forget to check out lennys product pass.com for a free year of the most interesting and well crafted AI products in the world. Available exclusively to Lenny's newsletter subscribers. With that, I bring you Adam Mosseri. Adam, thank you so much for being here. Welcome to the podcast.

Speaker B: Thank you for having me. Excited to be here.

Speaker C: You've been doing product for a long time. You get to see how a lot it's teams operate across meta within Instagram. What is just kind of like the Canonical product team look like in 2026? What's kind of most different today in how teams operate, slash should operate versus say a couple years ago.

Speaker A: It's changed a lot this year. So for the longest time at a

Speaker B: 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,

Speaker A: a uh, PM, a designer, a data

Speaker B: scientist, a uh, researcher if you were lucky. And maybe that's about it. So you know, on the order of a baker's dozen and that is a function of, you know, you want to have for anybody who's writing code, someone who can review their code and that's who's familiar with that code base and having these different functions that are more specialized. You know, I think it's very different at a startup.

Speaker A: But this year it's changing. We've adopted what we call pods, which

Speaker B: are just mini teams where it's call it four to six engineers who are a bit more generalists, uh, 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 the, some of, of what a research does, leveraging, um, the latest tools that

Speaker A: we have for them and then whatever specialist they need.

Speaker B: If they, 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.

Speaker A: But they just by virtue of having

Speaker B: less people to coordinate, they can often move faster and make um, better decisions, ah, a little bit less design 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.

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Speaker C: I love this. So on this team of six to seven, what's the makeup again and which role are you finding you have less of? Uh, if you're going from the kind of the, if you're going 50% size,

Speaker A: you just have less specialists, right? So you might not have any.

Speaker B: 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 sort of supports all of those things.

Speaker A: I mean, what's clearly happening is all

Speaker B: of the functions are starting to bleed into each other and the, and the whole industry is wrestling with what that means.

Speaker A: You know, a lot of what a

Speaker B: data scientist does at a big company, for instance, is relatively mechanical. And so, you know, there's stuff that they do that is really more like, you know, art and science and the stuff that's really more like just pulling data, data management, you know. So some of the tools that we're building internally to understand, for instance a traditional data science question would be a, uh, 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 whether there might be opportunities to improve things. That kind of basic waterfall analysis is like much easier now to use some of our internal tools to just pool automatically as opposed to having to have a, uh, data scientist do a bunch of bespoke work for that. So a product staff might be able to do that now and they couldn't do that a year ago.

Speaker A: So you just end up with this general, these people have more generalist shapes and then, and then when you need it, when you really need it, you

Speaker B: have a more senior ideally or just more creative specialist. Um, so you know, a phenomenal product, uh, designer or Just a. Just a genius data scientist or researcher.

Speaker C: This is so interesting. It's exactly what I just heard. I had a, uh, Fiona Fong, the head of engineering for Claude Code and cowork on the podcast. She's Boris journeys 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 deep expertise in a very specific domain.

Speaker A: The tasting matters a lot.

Speaker B: I really agree with that. Boris used to work at Instagram.

Speaker A: I love him.

Speaker C: Oh, that's right.

Speaker B: Yeah.

Speaker A: He was a senior I see at

Speaker B: Instagram for a while.

Speaker A: I love seeing him.

Speaker B: He's all over threads now. It's like he's sort of like the face of Clark.

Speaker C: He's killing it. He's. He's a. He's a celebrity now.

Speaker A: He really is. He's his own little.

Speaker B: Yeah, for sure.

Speaker A: In a world that is, like, on fire right now. No, I think taste matters a ton. Uh, so in a world where it's easier to build things, it's more important

Speaker B: to make sure that your time is spent figuring out what you should be building in the first place.

Speaker A: Um, actually, so a lot of designers

Speaker B: 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.

Speaker A: But I'm actually pretty long on design or designers because they tend to have taste.

Speaker B: 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.

Speaker C: I've always felt that too, as it is so easy to build and all the work that AI produces is so, like, you can tell this was Claude design. I know what you did here. This is Codex.

Speaker A: Well, they all have their vibe, right? You Vibe code, your apps, we call it Vibe code. And you're like, oh, that's a Codex app.

Speaker B: Or, oh, that's a cloud app.

Speaker C: Right. And that's replit. That's lovable. Like, you can. Yeah, you can predict these things you like. I've always thought that, too, that design should be thriving right now. For some reason, it hasn't yet. If you look at jobs for designers, they're kind of flatlining. 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, like all that stuff. The business side of it versus the taste side of it.

Speaker A: Yeah. I think you're gonna see, like, you know, um, we have A senior designer, uh, at Instagram, uh, called Nate, who

Speaker B: just transferred into product stuff.

Speaker A: So I think, I think some of what you'll see is, you know, it

Speaker B: will be harder to talk about design roles and who's a good designer because they, they're not going to just stay in traditional design roles.

Speaker A: You know, if you're an amazing designer,

Speaker B: you might, you probably have strong opinions outside of just the interaction and visual design. You probably have strong opinions on product strategy, even on the business on the go to market. And so I actually think some of our strongest product staff are going to be converts from design and from data science who are just looking to expand their reach. And they were influential across functional boundaries before, but this world where those functional boundaries are just wildly blurred, just allow them just to jump in and. So sure, they'll be technically a generalist on paper, but they're, they're clearly have a uniquely, you know, strong ability in one, one type of craft. But um, they've got the ability and strong opinions to make informed decisions across other parts or other crafts.

Speaker A: And so, you know, I don't know

Speaker B: that all the strongest designers I have will all be in design.

Speaker A: They, they probably will be the majority,

Speaker B: but I can imagine a bunch of really strong ones, you know, moving roles.

Speaker A: But I mean, I should also check

Speaker B: my own bias here though because I started as a designer at Facebook way back when and I switched roles.

Speaker C: Um, no, designers are great. Um, I'm a big fan. So this is really interesting. There's always been this like GM model where different types of functions can become GMs. It's like this product staff role feels like a similar situation where different functions can become product staff.

Speaker A: Yeah, yeah. And that was true of PM before, but it's just so much more true now. Um, and it'll, I mean in some ways it's probably the age of the

Speaker B: generalist, but I, I still think there's going to be a real important role for these really amazing specialists who are

Speaker A: just, they're all about going, I wish

Speaker B: I was like that. I always had this like I romanticized like the phenomenal machine learning engineer or AI researcher or shoemaker. Like I think that's the coolest thing in the world. But it's never been my shape. I've always been um, I've never been great at anything. I've always just had range. That's always been my strength.

Speaker C: Same. Okay, so this idea of product staff, so the idea is on these new pods. So this is like a new thing you guys are doing. So there's these POD teams, product staff, engineers, and maybe one specialist that's going deep on say, pricing algorithm or something like that. So what this tells me is there's these, like, adjacent roles that are maybe, uh, more in trouble over the years. Uh, data science, for example, user research, for example. You talked about designers being anxious. Is there, is there anything there? Just like, oh, these. Maybe folks in these groups should think about shifting to other roles.

Speaker A: I mean, there's anxiety everywhere. I mean, I've talked to a lot

Speaker B: of people at a lot of other companies and it just seems like this is a lot of concern right now about competition, about job displacement, about unintended or unforeseen consequences of all this technology and all this moving so quickly.

Speaker A: So that's definitely happening. I think that you will see the

Speaker B: functional lines continue to blur, but I still think there will be room for functions. They'll just be shaped differently.

Speaker A: They'll be more.

Speaker B: They won't all be senior ICs necessarily, but they'll all be either senior or on their way to being senior. You can't just have a bunch of super senior data scientists and no new ones because then who's going to be the new superseding data scientists in the future? So you need to basically hire and mentor and grow talent.

Speaker A: You know, maybe the team is smaller

Speaker B: overall and then those who aren't on their way to being super senior move into more of a generalist role. I think that's like a reasonable soft landing.

Speaker A: But I do think you're going to

Speaker B: want to make sure you're investing not only in today's senior talent for each specific function, but in tomorrow's, uh, otherwise I think you're going to regret it in a couple years.

Speaker A: My take that said, who knows what

Speaker B: the world looks like in a couple of years. Um, so my big thing and is generally like, don't over.

Speaker A: Don't be overly confident in whatever your

Speaker B: predictions are because there's just too much flux right now.

Speaker C: The Benedict evidence was on the podcast recently said the same thing. We don't know anything about what's going on.

Speaker A: Yeah, yeah, I like, I like, I like him a lot.

Speaker B: I, um, I'll make sure I listen to the pod.

Speaker C: Yeah. So you talked about taste. This makes me think about. So you're interviewing a lot of people, hiring a lot of people. What are some traits that you're, that are like trending up in things that you look for more and more now in this world? And what are some traits that are trending down maybe less Important to you?

Speaker A: I mean, there are some things that

Speaker B: are the same, right.

Speaker A: So for the longest time, almost no

Speaker B: matter what the function, I always look for three things.

Speaker A: Do you have sort of grit, like,

Speaker B: you know, you're kind of like you're really going to, you've got some drive, some fire in your belly.

Speaker A: Are you a quick learner?

Speaker C: And

Speaker A: are you, you know, are you

Speaker B: reasonably, ideally very self aware so that you can actually take feedback and know what you're good at knowing?

Speaker A: Good, because if you are those three things, if you got fire in your

Speaker B: belly, you learn quickly and you're self aware, you can kind of get good at anything eventually. Um, and, but if any of those things are missing, there's usually an issue. So that's sort of like the baseline

Speaker A: right now for hiring, but just for, I think people who are going to

Speaker B: be more successful over these next five or ten years as things change so significantly. I think two things that I, I'm continuing to encourage myself to do are to stay curious and to put yourself out there. I just think you got to try things, right? This is like, you know, to that point before that, uh, no one really knows what's going on.

Speaker A: You just have to be willing to try things. It's almost, I don't know if. Do you speak another language?

Speaker C: Uh, Russian? Yeah.

Speaker B: Yeah.

Speaker A: So when you learn another language, I think one of the most important things, one of the best predictors, this is

Speaker B: my guess, I don't have any research on this, about, you know, are you going to get good at speaking is

Speaker A: are you willing to sound like an idiot?

Speaker B: Are you willing to say it and be corrected and not be offended and then just get better and better. You just have to put yourself out

Speaker A: there and with all of these new tools and models and technologies, I think

Speaker B: you just have to be willing to try stuff. Um, so if you're curious and you try stuff, I think that'll, you know, you'll learn, you'll adapt. But if you're not curious or you're not willing to make mistakes or try things, I think you're in a ton of trouble. Or, uh, at least I think it's gonna be a really difficult time.

Speaker A: So those I think are premiums, not

Speaker B: just for hiring at a company like Meta or a team like Instagram, but I just think across the industry and multiple industries over the next 10 to 20 years.

Speaker A: Is this something that maybe we're looking for less of for some of these functions? Um, I think that there's some that

Speaker B: are still going to be very Large teams. And so you need people who are really good at managing large organizations. Large organizational leadership is its own craft and skill. It's actually different than management.

Speaker A: Um, but I do think there'll be

Speaker B: less of those roles. I think we'll have more smaller teams and there'll be less people who manage thousands of people. Um, and so that's. Not that that job will go away, but that will be less of what I'm looking for in hires, because I'm going to have less roles like that.

Speaker C: 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 or, uh, 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 like AI is just like lifting other people up, maybe lowering some people down.

Speaker A: Yeah, I mean, the job is just different. I mean, take, take us, take, um, take engineering. Engineering used to be maybe not majority,

Speaker B: but a large percentage, 40, 50, 60% writing code.

Speaker A: You know, it's not now. Especially if you talk to anybody at these labs, they're spending most of their

Speaker B: time planning and reviewing code.

Speaker A: That is a very different job. You might hate that and you might

Speaker B: have loved just writing code, or you might have, you might love that and you might not have been that fast at writing code. So who succeeds is a function of

Speaker A: whose strengths are aligned with the tools

Speaker B: needs and the businesses needs.

Speaker A: And so this is, uh, definitely happening. Another thing is you've had people who had good ideas about how to contribute

Speaker B: 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

Speaker A: can like, you know, I, it's. For me, it's kind of funny because

Speaker B: when I get hired at Facebook, we all the designers had to be able to program. That was like our. I had, I went through a technical loop.

Speaker A: We gave up on that because it

Speaker B: was too hard to hire people.

Speaker A: Um, but I now get to program

Speaker B: again for the first time in maybe 10 years. And you know, I am not a good engineer.

Speaker A: I'm a mediocre engineer on a good day. But now I can write code responsibly

Speaker B: is just an amazing thing. You're seeing this across all sorts of levels in seniority and functions. You know, designers who are programming, uh, engineers who are pulling data and doing strong analyses, data scientists who are putting together proposals for Designs, you know, 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 aren't binary. I said that to the team yesterday. And the state of the tools isn't binary either. You know, they're amazing at some things and, uh, remarkably bad at others.

Speaker A: And the people who I think are going to make the most of it

Speaker B: 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 that, you know, not, you know, next month or in a couple months from now.

Speaker C: You mentioned, um, that AI writes all our code now. Someone tweeted this. That's this idea that stuck with me for like months now of just like, remember we used to be able to just write code for free?

Speaker A: I think just, I'll be able to

Speaker B: write code for free, just be with a smaller model. But yes, I guess that's true.

Speaker C: Like there's a model that are close to free, but it's like, yeah, that's crazy. Now it's just like.

Speaker A: But just think about, think about the cost. Think about what you pay for, um, a model now and how and what the level of intelligence you're getting from that model is. And then at that same price point

Speaker B: a year ago, what were you getting?

Speaker A: At some point they will just the

Speaker B: incremental value won't matter. Like, you know, we're getting there. I think with small projects and programming. Like, I think the models will matter even beyond, you know, this week you've got Fable and obviously Mythos from Anthropic, but I spent a lot of time with that this week. I'm, it's for the first time I'm like, oh, I'm just talking to a much more technical, much smarter engineer than I am. You know, the next version, you know,

Speaker A: you know, a year out of that model, do I need to pay for

Speaker B: Frontier Tokens, you know, for whatever, you know, 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.

Speaker C: Yeah. When Kevin Whale was on the podcast when he was CPO at, uh, OpenAI, he famously said, this is the worst the model will ever be. Yeah, it's still hard to comprehend that, wow, that's only going to get better. So on this point of token, uh, spend ROI and things like that. Uh, Meta was famous for this, like, leaderboard of token spend.

Speaker B: Uh, it's a terrible idea. No leaderboards for tokens.

Speaker C: Okay, talk about that. And just how do you think about just like budgets for engineers and product teams at this point? Just like spend as much as you want? Is it like there's a cap we have? Is there any sort of thing you've kind of figured out that works?

Speaker A: Well, right now we've managed to get

Speaker B: the costs reined in a little bit by like shutting down the silly things that we were doing. And so, you know, 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 ide up.

Speaker A: And so right now we don't have,

Speaker B: um, token limits for, for, for our engineers, actually, I think for anybody really.

Speaker A: Uh, I think that'll eventually have to

Speaker B: happen, particularly, um, if costs go up before they go down. I think they'll eventually go down because for the reasons that we just talked about.

Speaker A: But I think of it like, as

Speaker B: any other resource, right? 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.

Speaker A: I think that you can imagine at

Speaker B: least in a year or two coming that uh, the burn rate of a strong engineer might be the same as their salary or their cost of employment.

Speaker A: And in that world you're going to

Speaker B: probably need to put in some caps. The cap should probably be like, uh, a proportional to your sort of, uh, you know, the company's sort of trust in your ability to use them in an roa positive way. But, uh, I can imagine caps being healthy right now. We're not there.

Speaker A: Uh, I think costs will go up

Speaker B: 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. Um, so we'll see. I think it'll be a bit of a roller coaster.

Speaker C: 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 like one shotted by AI. Like coding now is steering AI and it's like how often you have to correct it is coding now and then there's. So it's like the software development life cycle slowly being eaten by AI. Uh, 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 life cycle taste

Speaker A: like we talked about, um, judgment particularly around strategy, right?

Speaker B: Like you're not, you might get feedback from an AI on a strategy but you're not asking an AI to come up with a strategy anytime soon.

Speaker A: Or if you are then it's within

Speaker B: the context of boundaries you set. So here's my goal, here's my vision, here are my constraints, here's my job, here's my budget.

Speaker A: I think that it looks more like management, right?

Speaker B: Like you are trying to define what success looks like, decide how prescriptive you want to be about the path to success and then giving feedback along the way. And that is its own craft.

Speaker A: Uh, you know, and you, it'll be

Speaker B: interesting to see how Matt, you know,

Speaker A: you know, if some of the same dynamics come up. Like I believe that if you are too prescriptive as a leader with a

Speaker B: team, you end up stifling good ideas. But if you're too open ended sometimes teams just waste time um, going in the wrong direction. And so that level of autonomy you give a team, like maybe that applies to agents in the future. Um, particularly when we're talking not just about building something but deciding what you build in the first place.

Speaker A: Um, but I think of vision as

Speaker B: an articulation of the world or the state of the product you want to get to. And I think of strategy as an opinionated path to achieve that vision.

Speaker A: Strategy can't be like be the best or be amazing.

Speaker B: It has to be controversial that you have to be uh, a reasonable person should be able to disagree with it because otherwise you're probably just trying to compete on raw execution.

Speaker A: And I think there both vision and

Speaker B: strategy I think are going to be where our brains are spinning a lot of our more and more of our cycles and I think less on execution.

Speaker C: Something I, I've always thought is AI should be incredibly good at strategy because you would think here's the market, here's all the information on the market, our competitors, our metrics our numbers, our growth, all these things help me figure out how to win. You think AI knowing all that would be really good at this?

Speaker A: I think it could be. I have found it's not unless you

Speaker B: steer it pretty aggressively.

Speaker A: And I don't mean towards an answer. I mean, based on the constraints. It turns out when you're trying to come up with a strategy, there's a

Speaker B: lot of things to consider. Right. You need to consider the state of the technology, the personnel on the team, and what's motivating them and what you can get. You know, sometimes coming up with an idea that is on the bubble, you know, it's going to actually attract some of the best talent. And so that kind of like, that kind of the push then goes to the idea, uh, obviously the competitive landscape, the regulatory landscape for companies as large as ours, and the compliance landscape, the identity and reason to exist for the brand.

Speaker A: Um, you know, you have to consider

Speaker B: all of these things.

Speaker A: I think if you ask an AI

Speaker B: just for a strategy lazily, you're not going to get something great. You're going to get something pretty predictable that probably the competition would expect you to do.

Speaker A: I think if you want a really more effective one, you need to think

Speaker B: long and hard about what are all of the different inputs that need to be considered. Make sure you steer, uh, the AI in a way that it's considering those as well, and it needs to be a conversation in the back and forth.

Speaker A: But I think if you're willing to put in the work and the time,

Speaker B: it can definitely be helpful and definitely be clarifying, um, particularly if you tell it to be critical.

Speaker A: Different models have very different vibes, though,

Speaker B: on how willing they are to be pushed back.

Speaker A: So I. I recommend picking one that likes pushing back.

Speaker C: Yeah, M. Uh, Mythos has gotten really good at being like, I can't do this. Let's. Let's move on. Like, uh, there's all these.

Speaker A: Claude has always been a little bit of a jerk in a way that I actually appreciate.

Speaker C: I tr.

Speaker A: I appreciate it. I really do, because I don't want one that's just like, oh, you're so right. I'm so sorry I said that.

Speaker B: It's like, no, no, hold on.

Speaker A: I want. I want, you know, I want the

Speaker B: real, real sort of intelligence. I don't want to please her.

Speaker C: This point you made about people being excited about the strategy is such an interesting one. There's this idea that I read. I think Corey Doctor wrote this. There's this kind of concept of a centaur and A reverse centaur. So centaur is a human body horse. Uh, this is going somewhere, I promise. Human, Ah, body horse. Ah, sorry. Human upper part, horse lower part, horse body.

Speaker B: Yeah, yeah, yeah.

Speaker C: Horse body. Where the human is in charge. And that's kind of, we prefer that we want to be in charge. Reverse cent chart, which is what we want to avoid with AI is where the AI is controlling us and we're just doing its bidding.

Speaker A: It's a horse head on a human body.

Speaker C: Yeah, exactly. So like in a sense, like Uber drivers and doordash people kind of, this is their life, which is not great. And this is the danger thing for a lot of people is like, like if it's giving us the strategy and telling us here's what we're like, no one's going to want to do that. So that's a really interesting counterpoint to we don't want AI to be telling us the strategy. Almost.

Speaker A: Yeah, no, I, I, I think there's a lot of things to be careful

Speaker B: about right now and I would certainly not just assume that because you might be able to outsource some workflow to AI that you should, um, There are certain ones where I think it's really just a win win. There are certain ones where I think is the risk outweighs the benefits.

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Speaker C: Okay, going back to Product leadership, things you've learned along your journey. Uh, we were chatting ahead of this about just things you've learned. And one thing that you said about some of the product, the best product leaders you've worked with is that they're less visionary and more curators. I'd love to hear more along these lines.

Speaker A: Yeah, I mean you do sometimes find

Speaker B: amazing product leaders who are, who are just like idea machines, just prolific idea machines.

Speaker A: But I do think a lot of

Speaker B: the best have taste,

Speaker A: have something about them that really makes, once makes really

Speaker B: strong talent, want to work with them, but end up being curators. Curators of people, curators of ideas, curators of technologies, curators of strategies.

Speaker A: Because I don't really care if I'm

Speaker B: hiring a, uh, strong lead for an area if the strategy comes from them or comes from somebody else. I just care that there is an amazing strategy and everyone has bought into it. Uh, and then we're executing against that strategy well. And so I think that some of the best product leaders, yes, have ideas. It's hard to be a great curator if you don't have some of your

Speaker A: own ideas, but are that embrace the

Speaker B: reality that they can't come up with everything themselves. And so they need to create an environment in which great ideas bubble up and are, uh, chosen or decided upon. And so, you know, I think it's not just about curating ideas, but it's sometimes about curating teams and people.

Speaker C: I love that. I so agree. I feel like everyone's always joining a team and they just want to do vision strategy, just like not actually hands on work. Now AI is coming in here, Let me do the strategy.

Speaker A: Yeah, exactly.

Speaker C: And I love this point that there's so much power and value and people underestimate just the, uh, need for just like a really good curator of the team's ideas.

Speaker B: Yeah.

Speaker A: Sometimes it's also, sometimes it's not just who's good, uh, or what idea is 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

Speaker B: not just like, is this person a really strong candidate for this role, it is, how does this person fit into their leadership team? You know, so if I, you know, first an area like trust and safety, you know, 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.

Speaker A: You know, I need to make sure

Speaker B: that those five complement each other. I need to make and that's, you Know about what skills each one has, what weaknesses each one might have.

Speaker A: I also need to make sure that

Speaker B: they, um, this is more art than science. Have a good vibe, right? You know, you need, you know, trust and rapport.

Speaker A: A leadership team with strong trust and

Speaker B: rapport can work through most anything. A, uh, leadership team without trust or rapport, like anything can become an issue.

Speaker A: And so that, that chemistry bit is,

Speaker B: is, 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 you know, they have a, they have a nose for, for building teams that are going to have good energy and um, good collaboration.

Speaker C: Warm and fuzzy stuff.

Speaker A: Yeah, yeah. Well, this, the flip that is also true, right? Like I've, 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

Speaker B: they just can't get along.

Speaker A: You're like, this isn't a competency issue,

Speaker B: this is just a personality issue. And you just have to just sometimes call it and split them.

Speaker C: I want to transition, talk 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?

Speaker A: One of the most common misconceptions is

Speaker B: actually in the opposite direction.

Speaker A: I think people assume that there's a

Speaker B: much more detailed semantic understanding of everybody's interests and preferences in the algorithm than there is.

Speaker A: Most of what's really driven the progress

Speaker B: in the world of recommenders over the last five, ten years have been, you

Speaker A: know, these large embedding models and these

Speaker B: other techniques that basically produce artifacts that cannot be read by people. They're not legible, they're like giant vectors. It's like, sure, I can show you the vector, but it's just going to be a bunch of numbers in like a seven dimensional space. It's like.

Speaker A: And so when, when we talk about,

Speaker B: does the algorithm know something? Usually we think in these more semantic terms. It knows I like surfing.

Speaker A: And it's like it doesn't, it just

Speaker B: has this big ass number that happens to correlate with surfing.

Speaker A: Um, that said, I think that is starting to change.

Speaker B: Right.

Speaker A: I think that what one of the things that LLMs are enabling is they

Speaker B: can describe in, you know, words, you know, English for, or whatever language you

Speaker A: prefer, what some of those previously illegible

Speaker B: artifacts, um, are at least proximate to, if not Mean directly. Right? So this is like the thing I haven't really. I posted about this this week, this

Speaker A: thing called your algorithm.

Speaker B: Basically the idea is we take a look at all of the stuff that you've interacted with and then, you know, we. All of that is in an embedding space. You can think of embedding space as a map. You can map a bunch of videos into the same map. And so videos that are close are similar.

Speaker A: And now we can just have an

Speaker B: LLM just be like, describe that part of the map. And it can be like, oh, that is like deep pour over coffee snobbery. Um, and that's kind of amazing.

Speaker C: That is so cool, um, that like you can ask the LM to look at these numbers and extrapolate. Here's like the topic that you're interested in.

Speaker A: Yeah. Ah.

Speaker B: Or look at the videos.

Speaker A: And so the way you, um, both. So you can also embed concepts into that same space. And so, I mean, embeddings are really

Speaker B: the underlying technology underneath LLMs. Right? That's how the whole thing works.

Speaker A: And so, you know, so what, what we, what we, what we do now is we let you, you know, quote,

Speaker B: unquote, see your algorithm. You can see what topics we think you're interested in.

Speaker A: Um, and you can adjust it, you

Speaker B: can add and remove things. But the idea here, giving people some agency back in a world where, you know, these social media apps are getting taken over by recommendations.

Speaker A: Um, but we can't do a lot

Speaker B: of other things yet, but we will be able to do, you know, you could. 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 kids friends, kids photos.

Speaker A: You know, I don't know we can come up with.

Speaker B: I don't want to see seven photos in a row, but I'm happy to see six photos in a row. Whatever your heart can, you know, whatever your mind can come up with. So we have a lot of work to do. And so I'm excited about that.

Speaker A: But, um, I think a misconception historically is until recently, we don't really know

Speaker B: as much about you as you think. We were 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. Like, that's kind of how I'm oversimplifying. That's like 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.

Speaker C: That is really interesting. One that comes to mind is kind of this transition everyone eventually goes through to this algorithmic, broad global feed. Uh, everyone. It always feels like people think, I just want to see chronologically everyone I know and follow, and that's going to be my favorite feed. And it continues to be proven wrong. No, you actually engage, uh, a lot more when it's this algorithmic feed of things we think you will love.

Speaker A: Yeah, it's tough because, I mean, I

Speaker B: get, I mean, I posted this week this thing about agency, and I just got destroyed in the comments, which is just part of the job.

Speaker A: I get it.

Speaker B: Right.

Speaker A: Um, but there are a couple issues

Speaker B: with the algorithm, with the chronological feed.

Speaker A: So one is, and some of this is the tension between an individual's interests

Speaker B: and what works when you scale it up. Right.

Speaker A: So if you do a pure chronological feed, the incentive for everybody is to just post as much as possible, uh, because it will always be at the

Speaker B: top of everyone who follows you feed as soon as you post.

Speaker A: So what ends up happening is that the feed gets overwhelmed with professional content,

Speaker B: with usually large company content and publishers, because they get, you know, the New York Times can pump out 50 things a day.

Speaker A: Your, your best friend won't. You know, you might get one thing a week from that, and so your feed just gets taken over.

Speaker B: Um, so part of it is the incentives that emerge because when you design

Speaker A: these systems, it's almost like designing a city. You need to think about, okay, here's

Speaker B: how the mechanics work. What are the incentives that arise? How are people going to act within those incentives? And then what happens?

Speaker A: And the other thing is, uh, sometimes

Speaker B: the most interesting thing was just not the most recent thing.

Speaker A: Recency is an important input into relevance,

Speaker B: but it's not the only one.

Speaker A: My sister got engaged last night.

Speaker B: And, you know, she's in Germany. No, she didn't. If she did, she's married.

Speaker A: She got married last year.

Speaker B: That's why it was top of mind.

Speaker A: But if she got engaged, you know, and I missed it because she lives

Speaker B: in Europe and, you know, we're different time differences.

Speaker A: Like, do I really want to see

Speaker B: a picture of, like, my brother's pobo

Speaker A: sandwich, you know, po boy sandwich, or do I want to, like, see my sister's things first? So I, uh.

Speaker B: It's tough, it's tough.

Speaker A: I'd love to figure out a way

Speaker B: to find the right balance. I want to give people agency over the experience, but I think it needs to Be in a way that creates a system that makes sense, not just for us as a business which matters, I'm not pretending that's not an issue, but also for the overall community because we've done chronological by default and where you can make it default. And you see, not only does usage go down, overall sentiment goes down.

Speaker A: The individual who made that choice might

Speaker B: be happy at the moment, but when you just get pummeled with stuff you're less interested in over the course of months, we ask, we run surveys at massive scales. We just see people start to become less and less satisfied with Instagram.

Speaker C: Kind of along these lines, uh, everybody asks you about this these days, uh, AI and content and how that all impacts everything that's going on. I want to ask you something I haven't seen someone ask you. Is the rise of AI content, uh, a headwind or a tailwind for Instagram versus other platforms? Do you think this helps or hurts you guys?

Speaker A: I think it's going to be a tailwind, but I think it's going to be a challenge and not just because it's more content.

Speaker B: Obviously we're an attention business driven business. We're an advertising business. More content means potentially more attention.

Speaker A: That's not for free though.

Speaker B: Like, I don't think we're very good at ranking AI, uh content yet. There's great AI content, there's crap AI content. You should just see the stuff you're interested in and not any of the stuff you're not interested in.

Speaker A: But I do think that in a world where, or for years now, and

Speaker B: I've said this many times, power shifting from institutions to individuals across industries. The easiest example is sports where players are more relevant than teams now. And that was not the case when I was a kid.

Speaker A: In that world, you're gonna. I think it m behooves us to

Speaker B: invest in individuals and to invest in specifically for Instagram and creators. And I mean creators broadly.

Speaker A: I don't just mean influencers who are

Speaker B: promoting branded content and making, you know, native only videos.

Speaker A: I mean anybody who's using platforms, uh,

Speaker B: like Instagram to help do what they do, right, it could be, you could be a journalist, you could be an artist, you could be selling scarves, you. So but like you're out there as

Speaker A: yourself pro creating and sharing content that

Speaker B: helps you achieve whatever it is you're trying to do.

Speaker A: So we've been leaning in that direction

Speaker B: for many years now. That's been our, you know, one of our two or three most important audiences for as long as I've been on Instagram.

Speaker A: In a world where there's an abundance

Speaker B: of synthetic content, I actually think people are going to seek out creativity and authenticity and people more, not less. And I think that that will help us.

Speaker A: That doesn't mean that we won't have

Speaker B: AI content on our platform. There's going to be bad and good AI content, and we're going to try and handle that, you know, the way we normally handle content. So unsafe goes away. Interesting versus not interesting is based on ranking and personalization.

Speaker A: Um, but I think people are going

Speaker B: to really seek out other points of view, because Instagram is never just about the content. It was always about, to a certain degree, the person behind the content, the point of view, the reason they're sharing it, their perspective. And I think that's going to become more important, not less.

Speaker A: And I think given that we are not the best at a lot of

Speaker B: things, but we are the largest, uh, creator platform. Uh, if you look at how we define creators and how many creators use us versus other platforms, I think it'll be a tailwind for us because I think people are going to seek out people.

Speaker C: And this connects to your earlier point that, uh, companies, uh, like say New York Times, can pump out a bunch of AI content versus a, uh, creator. And so you're saying you kind of want to protect against that too, Allow individuals to continue to perform well in spite of just all this AI content.

Speaker A: If you just love AI content, great. Like, you should be able to have a feed that's just like AI Town. And if you don't, then you shouldn't

Speaker B: have it in your feed. Like, you know, uh, to me it's

Speaker A: like, I don't think we should.

Speaker B: I mean, I understand why people are right. There's a. I'm not oblivious to the overall paradigm shift instead of revolution we're sitting in.

Speaker A: But I don't think we should judge

Speaker B: content based on the tool that made it. Um, I think we should judge it based on the content, the point of view, the person behind the content.

Speaker A: Like, I don't.

Speaker B: I don't think we should filter out AI content. I think we should let you know if content is AI content or not. I think we should let you know more about the person who posted anything so that you can make informed decisions about whether or not to believe or trust them based on, you know, knowing who they are or where they are or how many times they've changed their profile or, you know, if their profile is three days old or three years old.

Speaker A: Um, but I don't think we should

Speaker B: be making value judgments based on what tool you used.

Speaker C: Is there an AI, uh, content creator? You love that. You're just like, this is so good. I love watching these AI Videos.

Speaker A: Yeah.

Speaker B: What is she called?

Speaker A: Plastic. Plastic Dream sequence. Is that what it is? Um, I think.

Speaker C: Check it out.

Speaker A: Check the Plastic Dream. Um, Plastic Dream sequence. I had it on my phone.

Speaker B: I'll double check.

Speaker A: Um, it's these like, uh, like sort of dolls, Barbies, but they're like singing songs and these little tiny, um, silhouettes and snippets. And it's just. It's just amazing. It's like a little weird, but, like, also kind of amazing. And it's. It's very clearly AI it's not pretending not to be, but it's has a very clear creative and aesthetic point of view. And every time I come by one, I'm like, yep, we're doing this now. I'm going to watch this for 30 seconds.

Speaker C: I have it pulled up here, and I don't. I want to watch it, but I'm not going to. That's awesome.

Speaker A: If only. That AI that's another one. He's out, uh, of. He's in France.

Speaker B: I think he's in Paris. He uses multiple different tools and models, but he kind of tries to create these dreamscapes and animate them. So he create. Uses one model to create the image, another one to create the video, music, et cetera. Uh, he's like, very clearly got his own aesthetic.

Speaker A: Um, uh, and he's just like.

Speaker B: You can think of him as a painter, but this is his tool.

Speaker C: Is there kind of a vision of AI versus human in the feed? Do you think it'll. Like you said, you maybe want to market. How do you think about people? Are they going to be like, AI count, non AI count. How do you think about? Or is that still kind of a work in progress?

Speaker A: Maybe we'll end up in the same place. But there's a difference between marking content and.

Speaker B: And marking accounts, and they're both useful and interesting.

Speaker A: So if content was created with AI

Speaker B: I think you should be able to know that.

Speaker A: Uh, that's hard, by the way, because

Speaker B: we can detect that right now. But as these models get better, we might lose the ability to detect that. So we should also be very careful, to be honest with you, about how confident we are in our own sort of assessment. Um, but I think you should be able to just ask. Be like, hey, is this AI? And we should be able to tell

Speaker A: you we think it probably is.

Speaker B: Or we're not sure or we decide definitely not or definitely is.

Speaker A: Um, I actually think we might be,

Speaker B: might m be more practical to, to label camera captured content like basically non AI content as opposed to labeling AI content long term for a couple of reasons.

Speaker A: But then at the account level I think it also matters. There is definitely an, a new spam

Speaker B: vector which is these fake accounts which

Speaker A: by the way an AI creator, that's

Speaker B: fine, there's nothing wrong with that necessarily.

Speaker A: But there is, there are these spam

Speaker B: vectors which are trying to abuse that and you know, they're selling like, you know, bogus supplements and it's like an AI monk and it doesn't present inside obvious that it's an AI and it's just trying to like take advantage of you know, a certain aesthetic or a certain sort of stereotype that we need to figure out how to crack down on that. And so I do think, I do think we should be making sure that

Speaker A: you know, basically you just need to know and then you can make your own informed decision.

Speaker B: Is the account a real person or not? Is the content a real piece of content or not?

Speaker C: When you think about other platforms, uh, in the space social, you know, content platforms, are there any um, features or just, or like ways of approaching stuff that they do well that you're kind of jealous of or really impressed by?

Speaker A: Yeah, there's a bunch, everybody.

Speaker B: So many people do so much because

Speaker A: I mean for me like one of the things that we are finally catching

Speaker B: up with, but I've been always very impressed with is TikTok and their recommenders ability to break small talent. Um, in the world of ranking recommenders,

Speaker A: um, you can talk about exploitation based ranking.

Speaker B: That sounds terrible but it just means like using the data you have and then you can talk about exploration based ranking going and trying to figure out what someone might be interested in that they might even not uh, know they're interested in yet.

Speaker A: And it is much easier to move

Speaker B: engagement by showing people stuff that you know they'll probably like because lots of people like it. It's much harder to go and figure out how to essentially test content so that we can see like hey, maybe you sure you like Bieber, but you might also like Afropunk.

Speaker A: And so we're just going to like

Speaker B: show you some Afropunk and see what happens. If you do the latter, this exploration based ranking, you can, I think it's really good for niche creators and small creators because you give them a chance to find an audience that either wasn't going to see them before or didn't even know that they were interested before.

Speaker A: So we've invested a lot over the

Speaker B: last couple years in ranking. Not just increasing engagement, but increasing originality, increasing the number of pieces, uh, of content that break out, increasing uh, recency to stay culturally relevant. And so a lot of that has been inspired by TikTok and ByteDance.

Speaker A: I think we're catching up.

Speaker B: Um, there's actually a couple of those areas where we, I think by best we can tell, we're ahead of them. There's a couple where we're still behind but we have line of sight to I think being the best in class at recommendations for the first time, um, during my tenure. Uh, so that's, I think they get a lot of credit for inspiring a lot of that work.

Speaker C: Nice job.

Speaker A: Well, we'll see. Not there yet. They call me disappointed dad.

Speaker B: My team is always like, can you

Speaker A: ease up on the disappointed dad vibe?

Speaker B: So I'm trying to be a little bit more, um, generous about giving people their flowers.

Speaker C: Like you say that. But that's an interesting common thread across really successful leaders is just never being satisfied.

Speaker B: Yeah, it's a blessing and a curse.

Speaker C: Here's all the problem it is. Yeah, on this creator piece, I think that's also, you know, people, uh, complain about this global algorithm not showing them all their friends. But I feel like this is a benefit of what happens when you do this now that you can break new creators into a wide audience. If you have this kind of global algorithmic feed, which is really great for a lot of people.

Speaker A: I mean, I'm out there talking about

Speaker B: a lot of these contentious issues and I get beat up a lot in the comments, which is fine.

Speaker A: My main thing here is just to

Speaker B: try to communicate that there's almost always trade offs. Right? There's, you know, you can't just have all of the things, unfortunately, you know,

Speaker A: you want to have, um, you know, you want to never see something you're not interested in. Then you're also just going to see

Speaker B: like the most basic general, lowest common denominator stuff all the time.

Speaker A: You know, you want to discover new and interesting things.

Speaker B: You're occasionally going to see stuff that was just a miss, you know.

Speaker A: You know, but this isn't just true about ranking.

Speaker B: These all, all these major debates have trade offs, right. You know, uh, privacy and safety, those two things are intention.

Speaker A: You know, do you, do you want a company scanning your messages or not?

Speaker B: There's some really significant trade offs on both sides of that debate.

Speaker A: Um, and so generally Speaking. When I argue and engage in debate

Speaker B: with people who feel really strongly about things, I'm not usually trying to convince them. They usually. Their mind is usually made up.

Speaker A: I'm just trying to enumerate all of

Speaker B: the different puts and takes for the rest of the people watching the conversation.

Speaker C: Speaking of getting torn apart in the comments. Like, you're so in the middle and thick of all of these really hairy, uh, situations. Changing the feed. You're like, in the Cambridge Analytica lawsuit. All this just like, you're in the center of so much controversy. And, uh, is that something? Yep. Oh, man. Is that just like you. I will lean into this. This is the thing I need to do. Or was it like Zuck being like, adam, you gotta be the front face of all the stuff and get in there? Like, where does that come from?

Speaker A: It started on newsfeed. So I used to run newsfeed at Facebook, and I. My take was that the debate was gonna happen with or without us, uh,

Speaker B: so we might as well participate.

Speaker A: And so I started being really active on Twitter specifically.

Speaker B: Cause that's where journalists really lived at the time. And I thought it'd be. Show some humility to show up on their turf, so to speak. Um, my Twitter ended up being, like, the most. The darkest place in my life because I just followed all of our biggest critics. That's not a dig on Twitter. That was just what I did.

Speaker A: Um, and that's where it started. And it kind of slowly built from there. For better or for worse, we've become a really important part of daily life

Speaker B: for a lot of people. We touch a lot of people. We have a lot of responsibility, and there's a lot of change. And there's. With change means there's gonna be anxiety and stress and scrutiny. We've made great decisions. We've made mistakes. Um, we've been criticized for things that I think, um, we've been criticized unfairly, we've been criticized fairly.

Speaker A: And so we just need to accept

Speaker B: that this debate is gonna happen broadly. So I just think it's better for us to talk about it, um, and just be clear about what we're doing, why we're doing it, what the traitors are. If people disagree, that's okay. We're not necessarily, you know, winning over friends when we talk about what we do. But I think over the long run, people are fundamentally more afraid of things that they don't understand. Um, and about things where people are more secretive and less accessible. And so I've been tried.

Speaker A: I've tried to show up in an

Speaker B: accessible and authentic way. Um, and I've made mistakes and I have enjoyed it at times and hated it at other times.

Speaker A: Um, but that's kind of how it started. There was also kind of a fun

Speaker B: debate in, um, Mark's sort of senior leadership team a long time ago where we were just talking about how we're a social media company where we had like a very sort of conventional approach to communication and like press releases and say, why don't we just use our platform? So, um, I was not in that debate, but I stuck myself into that debate trying to mediate it. And I think, um, but that was also a reason why I ended up getting sucked in. Because Mark was like, all right, well, let's see. Like, why don't you try it, See how it goes.

Speaker C: What's something that helps you deal with the hate that flows at you? Every time you say something that people

Speaker A: disagree with, you try to put it in perspective. Right? So it started with I did the

Speaker B: redesign of newsfeed in 2009. We launched it March of 2009 for Facebook.

Speaker A: I was a designer, I was an

Speaker B: IC designer, front entry level designer.

Speaker A: And the first comment that came in

Speaker B: was, uh, something pretty derogatory.

Speaker A: I think it was like, it was like homophobic and anti semitic. It was just like, literally we're all sitting there, we launched this thing and

Speaker B: we're just looking at the stream of comments and it's like the first one and it was specifically about they don't

Speaker A: know me, but it was like what? Expletive, expletive, um, sensor, sensor, uh, designed this shit. And I was like, oh, it was me. Um, and I was like devastated.

Speaker B: I was like 25 year old kid.

Speaker A: And I don't know, I thought about it and I came, I came to this idea that if you spent 30,

Speaker B: 40, 50 minutes a day at a, uh, at your desk and you organize

Speaker A: your photos there and you wrote letters to your friends there and you read there.

Speaker B: And then I just came and I rearranged your desk and I didn't tell you, I didn't warn you, I didn't even explain why.

Speaker A: Like, you would be pissed and that would be reasonable. Um, and that was what was happening

Speaker B: just, you know, with millions of people.

Speaker A: Um, so I try to put things in perspective.

Speaker B: Um, and then I try to step away from it. Um, get time with my kids, get time outside. Uh, there.

Speaker A: There are months where it's really not hard at all.

Speaker B: And there are months where it's really,

Speaker C: really grinds on me along Those lines. There's a famous kind of reversal when you redesigned the feed into this kind of video scrolly experience. There's this whole protest. The world protested.

Speaker A: Yeah, that was pretty rough.

Speaker C: Uh, what was kind of like, okay, wow, we actually not right and we should go back. What was kind of what helped you decide, okay, let's change course.

Speaker A: So actually that one got that one. Um, three or four things got conflated. We had a redesign of feed that

Speaker B: went to the video viewer.

Speaker A: That was a test to 4% of users on iOS. It was a not, it was not

Speaker B: going to roll out. It was just like an early test to get us some sense and feedback on the idea.

Speaker A: We were also leaning into reels a lot. We were also leaning into recommendations, so posts from accounts you don't follow a lot. And there were also creators who were upset about the fact that their reach

Speaker B: was going down and they were blaming ranking changes on that.

Speaker A: Those four things got all conflated. We had some pretty big name creators

Speaker B: publicly like slap us.

Speaker A: Then the press covered that creator sort of backlash which then got more creators doing it. So we ended up with this little

Speaker B: bit of like a multiplier effect or

Speaker A: echo between the creator community and the

Speaker B: press back and forth.

Speaker A: But we were never going to launch that.

Speaker B: That was an early test. We were all. We knew it was going to needed work.

Speaker A: Um, we actually have continued to grow

Speaker B: video and invest in creative tools and invest in ranking and invest in recommendations. And that's driven in the Met most of our growth in the years since.

Speaker A: Um, I think we were pushed. Well, I don't know. It doesn't feel like, but like it. I think we were. I think my real takeaway wasn't that

Speaker B: we should have not tested that design necessarily. I think we could have been, we could have done a bunch of things better to explain and maybe move a little fast, move a little slower.

Speaker A: I think we were just pushing things

Speaker B: a little bit too fast.

Speaker A: And when you are responsible for uh,

Speaker B: a platform like Instagram, you need to

Speaker A: be reasonable and realistic about how much you can evolve it now.

Speaker B: I would much rather have backlashes like that every couple years, but continue to evolve and continue to stay relevant than the alternative which would have been like we didn't have video, we didn't have DMs, we didn't have stories, we didn't

Speaker A: have ranking and we wouldn't be on

Speaker B: having this podcast right now.

Speaker A: Um, um, but the cost of leaning

Speaker B: in is that you're gonna occasionally like make a mistake and you're gonna Definitely pay, um, for it.

Speaker C: It's interesting how running experiments now is like, very risky for companies at your scale. One person spots it and I go, shit.

Speaker A: You kind of need to have a press. You don't need to be proactive about

Speaker B: communicating it, but you need to have a calm strategy.

Speaker A: Like, we can't for any, for any design change or any test that could be controversial. We, we talk about it beforehand and

Speaker B: be like, okay, not if it leaks. When it leaks, what are we saying?

Speaker A: You know, are we, you know, should we talk about proactively?

Speaker B: Should we talk about reactively? Either way, what's the message?

Speaker A: Because you can't, you can't. You can't launch something to 3 billion

Speaker B: people and not test it first.

Speaker A: But you can't test something at our

Speaker B: scale and not expect people to cover, uh, it and not.

Speaker A: And be.

Speaker B: And so you have to be ready

Speaker A: to talk it before you even know

Speaker B: you want to launch it.

Speaker A: Um, so it's, um, it makes the

Speaker B: development cycle more complicated than it used to be.

Speaker C: Yeah. Ah. The, uh, head of growth at Anthropic launched an experiment with pricing and it just went crazy. On Twitter. He's like, 1% of people were just trend stuff like, no Anthropic pricing, particularly.

Speaker A: That one is a real. You got to be real careful with that one. I, I've learned. We've all learned these lessons. We should all share notes more.

Speaker C: That's right. Here's one. How to, how to avoid the Internet hating you for the day.

Speaker A: Yeah, yeah, I'm happy to talk to that and grow.

Speaker C: I think he's all right. He's all right. Okay. I'm going to take us to two recurring corners on the podcast. Fail Corner and Hot Seat Corner. Fail Corner. What's, uh, what's something that you worked on that was just a huge failure that helped you become better?

Speaker A: Oh, bunch, um, so many. So I'll give you two, maybe. So before Instagram, my first project as

Speaker B: a PM was on a project called Facebook Home, which was a sort of fork of Android, um, at the operating system level and a piece of hardware with htc. It was a spectacular failure. I learned way more in that year, year and a half, than I did in any year, I think probably in my career because I was just a design manager. Before that, I declared myself a PM because the PM on the project quit and I just threw myself head first in understanding carriers and OEMs and, uh, certification as well as Android and operating systems and just lend a ton. Um, so.

Speaker A: And I'm happy I brought that project

Speaker B: to an end because it had been going on for a long time. And sometimes you, the best thing you can do is execute an idea that doesn't have market fit well, just to decide whether or not the idea was a good idea in the first place.

Speaker A: Another big mistake I made, um, during

Speaker B: my Instagram tenure was the first version of Reels was built on top of Stories. Stories had a ton of momentum. This was, I think, 2019 and we were trying to build reels into stories because we were trying to build on the thing that was growing the fastest, but it was not a strong foundation.

Speaker A: Most, you know, the read through rate

Speaker B: on Stories is relatively low. There's way more stories than most people have time to consume. So most of the reels were never seen and then they disappeared.

Speaker A: Um, and if we had the version

Speaker B: of reels that we launched in like mid maybe I think it's like the summer of 2020. In the summer of 2019, I think, I don't think TikTok is in. I think TikTok is still big and important, but I don't think it's as

Speaker A: big as it is now because when they really took off was when the

Speaker B: pandemic hit and a bunch of people had a lot of time at home and were looking for a little bit of joy. And we're totally fine with our phone having sound on. And so if you look at the numbers, the 2020 is when they exploded and we were out of position. And um, on one hand, you know, I'm a designer, I'm trying to not add new things to the product. I'm trying to extend existing primitives. And that was the idea. On the other hand, I was wrong

Speaker A: and it's a pretty big fork in

Speaker B: the road if you just look at the overall business over the last eight years.

Speaker C: We create a lot of economic opportunity in the world allowing TikTok to, to grow.

Speaker B: So there's a lot.

Speaker A: I'm glad they exist.

Speaker C: Okay, final question. Um, I'm curious just about your screen time policy with your kids. I know you have three kids. Uh, there's a lot of concern these days about Instagram not being great for people, not for kids. A lot of tech executives don't let their kids use devices while they're building the product. As head of Instagram, how do you think about screen time with your kids?

Speaker A: The key thing for me is boundaries. Um, it's also about education and being

Speaker B: and having conversations with them.

Speaker A: But my kids are too young to use social media.

Speaker B: They're 10, 8 and 6. Um, but they each have an iPad.

Speaker A: They get, um, they have to earn their time.

Speaker B: Um, so they have different ways they earn that time. It's usually about like sitting down to do your homework three times for half an hour each gets you a total of 90 minutes on the weekend. And then they can use that time on the weekend. Um, but you kind of have to set that boundary where it's like you can't just like, you know, we can't just be who they ask for it and you give it to them. I think that matters a lot.

Speaker A: And then I'm pretty opinionated about what

Speaker B: they do on it. Like, I approve what apps that they have. I think parents should be approving what apps kids specifically are downloading, um, onto their devices. We've been advocating for this at a policy level for a long time at Meta.

Speaker A: I think those things help a lot. Um, there are some exceptions. Um, one is planes.

Speaker B: It's just like about surviving. I don't know if you've ever, for those of you who are parents, I'm

Speaker A: going to be, yeah, yeah.

Speaker B: It's like you just like, that's just like, all right, you know, we're flying. You know, it's a ten hour flight or eight hour flight. It's like, yeah, just, just, you just need to get through it.

Speaker A: Um, the other one that I'm starting

Speaker B: to experiment with, my 10 year old

Speaker A: with is, is so schools are interesting because I think I'm pretty supportive of a no phone in classrooms.

Speaker B: Um, that's happening more and more. I think that's just probably good for education.

Speaker A: And I do also know in the

Speaker B: world of AI that there's concern about kids using AI and not learning critical thinking skills. And I think that's a valid concern.

Speaker A: But I also am worried about kids not learning how to leverage AI and

Speaker B: then being sort of at a disadvantage. So that's a balance. I think you need both.

Speaker A: So with, with my eldest, we started

Speaker B: um, Vibe coding recently together. Um, he just loves video games. So I was like, all right, let's make a video game. And so he's made this 19 level platformer game that kind of looks like an 8 bit version of Super Mario from when I was a kid. But like each level has its own theme, its own types of monsters. There's a store where you can buy different skins or weapons and there's like, uh, like all. It's unbelievable what a 10 year old who still types with 3 fingers can do, um, with just, you know, a couple hours of sitting together.

Speaker A: But that is more of like a.

Speaker B: I want you to learn how to make um, things I want you to be thinking, not just playing games. And I'm um, going to sit with you and do that. We're going to do this together.

Speaker A: Um, so to me these are the things that matter. Boundaries, um, scoping it down to the

Speaker B: activities you think are healthy for your kid. Every kid is different.

Speaker A: Um, but I do think you want

Speaker B: your kids to be digitally literate, um, AI literate. Because I think if they're not, they're going to be at a disadvantage. But you also don't want it to be a free for all.

Speaker C: This is selfishly useful for me as a, as a. I have a three year old and I'm trying to figure all this stuff out. So this is useful?

Speaker A: Oh yeah, no, for me to figure out a strategy. It's amazing. It's a thing. And you're, you're, you're not that far off.

Speaker B: You're really just not that far off. It's going to happen in a couple of years.

Speaker C: Coding next year. Let's do it.

Speaker A: I couldn't believe I tried to do it six months ago and it just totally didn't work.

Speaker B: And then now with the newer models it's been amazing.

Speaker C: What's their platform of choice? Are they cloud coding? Uh, person.

Speaker A: Yeah, my 10 year old is using, is using cloud code right now.

Speaker C: Amazing.

Speaker B: Um, but um, we will see, we'll see how that goes.

Speaker C: Adam, uh, I'm going to let you go. Thank you so much for being here this year. You're just like such a gem of a person. It's just so obvious how clear like how authentic you are and just like how deeply you think about everything. Uh, so I really appreciate you being here.

Speaker A: I appreciate you bringing me on.

Speaker B: I've um, been a fan for a long time. It's nice to finally get down. I appreciate that.

Speaker C: I really appreciate that. Uh, let me just ask you this final question. Ask everyone what's the way that listeners can be useful to you?

Speaker A: I just think you don't even have

Speaker B: to tell this to other people.

Speaker A: But just remember that this world and

Speaker B: technology is complicated and there are almost always trade offs. Um, and you can totally disagree with the decisions I, or we make.

Speaker A: Um, but just remember that we are

Speaker B: people here trying to make these decisions, just trying to do the best we can. And I actually do invite the criticism and the critique and the feedback. But um, know that none of these contentious debates are nearly as simple as most people pretend to make them out to be.

Speaker C: Wise words. Adam, thank you so much for being here.

Speaker B: Pleasure.

Speaker C: Thank you Danny Bye everyone.

Speaker D: Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify or your favorite podcast app. Also, please consider giving us a rating

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