Adam Mosseri: Building Instagram for an AI world

[00:00] 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. [00:30] I think people assume that there's a much more detailed semantic understanding of everybody's interests and preferences.

[00:37] in the Agra than there is. [00:38] Is the rise of AI content a headwind or a tailwind for Instagram versus other platforms? 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 [00:54] creativity and authenticity [00:57] and [00:58] people. [00:58] I don't think we should filter out AI content. I think we should let you know if content is AI content. [01:03] not. That's hard, by the way.

Where do you think human brains will continue to be most valuable as AI [01:09] continues to eat. [01:10] more and more of that product development life cycle. That's a great question. So [01:15] Today my guest is Adam Masseri, head of Instagram. Over 3 billion people use Instagram monthly, that's 1 in every 3 people alive. [01:26] it boggles the mind. Prior to Instagram, Adam designed and led the early Facebook newsfeed. He also ran the team that built the Facebook ranking algorithm. [01:34] 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. [01:43] Adam is also famous for being the face of all of the controversy and changes that come with evolving Instagram as a product, which we talk about. Before we get into it, don't forget to check out com for a free year of the most interesting and well-crafted AI products in the world, available exclusively to Lenny's newsletter subscribers. [02:04] With that, [02:05] I bring you Adam Masseri. [02:06] Adam, thank you so much for being here. Welcome to the podcast. [02:13] Thank you for having me.

Excited to be here. [02:15] You've been doing product for a long time. You get to see how a lot of teams operate across meta within Instagram. [02:21] What is just kind of like the canonical product team look like in 2026? What's kind of most different? [02:29] today in how teams operate/should operate versus, say, a couple years ago. [02:33] 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.

[02:45] maybe a generalist, a PM, a designer, a data scientist, [02:49] or research if you're lucky. [02:51] And maybe that's about it. So you know on the order of a baker's dozen and I [02:57] That is... [02:59] a function of [03:02] 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... [03:17] what we call pods, which are just mini teams where it's, [03:22] call it four to six engineers who are a bit more generalists.

[03:27] one... [03:30] 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 [03:41] 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 [03:59] build a team based on the needs of the work a bit, but then end up with a much smaller [04:03] 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 they... [04:12] you [04:13] Just by virtue of having less people to coordinate, they can often move faster and make more [04:20] 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 [04:33] The small teams, I think, [04:35] often are just [04:36] More effective. [04:38] This episode is brought to you by our season's presenting sponsor, WorkOS.

What do OpenAI, Anthropic, Cursor, Vercel, Replit, Sierra, Clay, and hundreds of other winning companies all have in common? They are all powered by WorkOS. If you're building a product for the enterprise, you've felt the pain of integrating single sign-on, SCIM, RBAC, audit logs, and other features required by large companies. WorkOS turns those deal blockers into drop-in APIs with a modern developer platform built [05:08] specifically for B2B SaaS. Literally every startup that I'm an investor in that starts to expand up market ends up working with WorkOS. And that's because they are the best.

Whether you are a seed stage startup trying to land your first enterprise customer or a unicorn expanding globally, WorkOS is the fastest path to becoming enterprise ready and unblocking growth. It's essentially Stripe for enterprise features. Visit com to get started or just hit up their [05:38] WorkOS allows you to build faster with delightful APIs, comprehensive docs, and a smooth developer experience. Go to com to make your app enterprise-ready today. [05:48] 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?

[05:56] if you're going 50% size. [05:59] 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, [06:10] The product staff is the generalist that sort of 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 is wrestling with what that means. [06:21] You know, a lot of what a 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. [06:40] 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 pool automatically as opposed to having to have a 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. [07:10] So you just end up with these people who have more generalist shapes and then – [07:16] And then when you need it, when you really need it, you have a more senior, ideally, or just more creative specialist. So, you know, a phenomenal product designer or just a genius data scientist or researcher. [07:32] This is so interesting. It's exactly... [07:34] What I just heard, I had Fiona Fong, the head of engineering for Cloud Code and co-work on the podcast.

She's Force Journeys Manager. [07:41] And she described the people she hires now are [07:44] One, two, three. [07:45] builders with great taste that can take an idea from end to end and People deep expertise in a very specific domain the taste thing matters a lot. I really agree with that Boris used to work at Instagram Oh, that's right. Yeah, he was a senior I see it in stream for a while. I love seeing him. He's all over threads now It's like he said like the face of Clark. He's killing it.

He's a celebrity now. I [08:08] He really is. He's his own, yeah, for sure. In a world that is like... [08:12] on fire right now. No, I think taste matters a ton. So in a world where it's easier to build things, [08:20] 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 this. [08:32] 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, you know, [08:43] They tend to have [08:44] And I think that is something that is... [08:49] much more difficult to imagine being automated away. And so, [08:54] There's other challenges with design sometimes, but I'm pretty long right now on designers. [08:59] I've always felt that too. [09:01] 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 well they all have their vibe right like your vibe code your apps we call it vibe code and you're like oh that's a Codex app or oh that's a Claude app right and that's replete 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 already 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 [09:31] of [09:31] deeply understanding the business and what will grow it and what successful, you know, like all that stuff, the business side of it versus the taste side of it.

[09:40] Yeah, I think you're going to see like, you know, we have a senior designer at Instagram called Nate who just transferred into product stuff. So I think, I think. [09:51] Some of what you'll see is, you know, [09:53] it will be harder to talk about design roles and who's a good designer because they're not going to just stay in traditional design roles. You know, if you're an amazing designer, you might you probably have strong opinions outside of. [10:07] 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 [10:27] 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 [10:35] a uniquely strong ability in one type of craft, but they've got the ability and strong opinions to make informed decisions across other parts or other crafts.

And so [10:48] I don't know that all the strongest designers I have will all be in design. They probably will be the majority, but I can imagine a bunch of really strong ones moving roles. I should also check my own bias here, because I started as a designer at Facebook way back when, and I switched roles. No, designers are great. [11:06] 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.

[11:17] Yeah, yeah. And that was true of PM before, but it's just so much more true now. And it'll, I mean... [11:25] in some ways it's probably the age of the generalist but i still think there's going to be a real important role for these really amazing specialists who are just they're all about going i wish i was like that i always had this like [11:37] I romanticized the phenomenal machine learning engineer or AI researcher or shoemaker. I think that's the coolest thing in the world. [11:45] But... [11:46] 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. [11:54] 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. [12:06] and maybe one specialist that's going deep on, say, pricing algorithm or something like that. [12:11] So what this tells me is there's these adjacent roles that are maybe more in trouble over the years. [12:16] data science, for example, user research, for example.

You talked about designers being anxious. Is there anything there of just like, oh, these, maybe folks in these groups should think about shifting to other roles? [12:26] I mean, there's anxiety everywhere. I mean, I've talked to a lot 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. So that's definitely happening. I think that [12:46] I think that you will see the functional lines continue to blur, but I still think there will be room for functions.

They'll just be shaped differently. They'll be more. 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 functions. [13:03] data scientists and like no new ones because then who's going to be the new [13:07] should proceed with data scientists in the future. So you need to... [13:11] basically hire and mentor and grow talent. You know, maybe the team is smaller 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. But yeah. [13:25] I do think you're going to want to make sure you're investing not only in today's senior talent for each specific function, but in tomorrow's. Otherwise, I think you're going to regret it in a couple of years. My take. That said, who knows what the world looks like in a couple of years? So my big thing is generally like don't overthink. [13:43] Don't be over the... [13:45] confident in whatever your predictions are because there's just too much. [13:49] Flux right now.

[13:51] there benedict evidence was on was on the podcast recently said the same thing we don't know anything about what's going on yeah i like i like i like him i'll make sure i listen to the part [14:00] 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 are trending up in things that you look for more and more now in this world? [14:12] And what are some traits that are trending down and maybe less important to you?

[14:17] I mean, there are some things that are the same, right? So for the longest time, almost no matter what the function, I always look for three things. Do you have sort of... [14:25] Grit, like, you know, you're kind of like, you're really going to, you've got some drive, some fire in your belly. [14:29] Are you a quick learner? [14:32] And... [14:35] are you, you know, are you reasonably, ideally very self-aware so that you can actually take feedback and know what you're good at, know what you're not good. Because if you're those things, if you got fire in your belly, you learn quickly and you're self-aware, you can kind of get good at anything eventually.

And if any of those things are missing, it's usually an issue. So that's sort of like the baseline. [14:53] Right now for hiring, but just for, I think people who are going to be [14:59] more successful over these next five or 10 years as things change so significantly. I think two things that 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 [15:16] you know, to that point before that no one really knows what's going on.

You just have to [15:21] be willing to try things. It's almost, I don't know, do you speak another language? [15:24] Russian, yeah. [15:26] Yeah. So when you learn another language, I think one of the most important things, one of the best predictors, this is my guess. I don't have any research on this about, you know, are you going to get good at speaking? [15:35] is are you willing to sound like an idiot? Are you willing just to say it and be corrected and not be offended and then just get better and better?

You just have to put yourself out there. And with all of these new tools and models and technologies, I think you just have to be willing to try stuff. Um... [15:50] So if you're curious and you try stuff, I think that 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 these are things that can be a really difficult time. [16:03] Those, I think, are premiums, not 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.

[16:13] Is there something that maybe we're looking for less of? For some of these functions, [16:18] I think that... [16:20] There's some that 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. [16:30] But I do think there'll be less of those roles. I think we'll have more smaller teams and there'll be less people who manage thousands of people. 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.

[16:48] Something I'm hearing from a few folks [16:50] is AI is almost kind of resetting people's impact and success. [16:55] in terms of some people that were maybe low performers pre-AI can now do like things they were bad at or AI now allows them to do. [17:03] 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. [17:12] Yeah, I mean, the job is just different. I mean, take engineering. Engineering used to be, maybe not majority, but a large percentage, 40, 50, 60% writing code.

[17:24] You know, it's not. [17:26] Now, especially if you talk to anybody in 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 have... [17:38] You might love that and you might not have been that fast at writing code. So, you know, who [17:42] who succeeds is a function of whose strengths are aligned with the tools, needs, and the [17:50] businesses needs. And so this is definitely happening. Another thing is you've had people who had good ideas about how to contribute to other functions, but didn't have the mechanical [18:02] or technical skills to do so.

And AI reduces the boundary to do that. And then all of a sudden they can't. [18:11] 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. [18:23] I now get to program again for the first time in maybe 10 years. And, you know, I am not. [18:29] I [18:29] 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 [18:41] levels of seniority and functions. You know, designers who are programming, engineers who are [18:49] 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 [19:02] 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 [19:13] State of the tools isn't binary either.

You know, they're amazing at some things and remarkably bad at others. And the people who I think are going to [19:23] 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. [19:38] You mentioned [19:39] that AI writes all our code now. Someone tweeted this, this idea that stuck with me for like months now.

Just like, remember we used to be able to just write code for free? [19:48]. [19:52] I think you'll still be able to write code for free. Just do with a smaller model. But yes. I guess that's true. Like there's a model that are close to free, but it's like, yeah, that's crazy. Now it's just like. [20:02] - Totally. - But just think about the cost, think about [20:06] what you pay for a model now and how and [20:11] 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, they will just the incremental value will be won't matter, like [20:24] you know, we're getting there, I think, with small projects and programming. I think the models will matter [20:30] Even beyond, you know, this week you've got Fable and obviously Mythos from Anthropic, but... [20:36] I... [20:37] 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. You know, the next version, you know, a year out of that model is...

[20:50] Do I need to pay for Frontier tokens, you know, for whatever, you know, anthropic [20:56] model 6.0 is. [20:58] Whereas Fable, just fine for all of my side projects? Probably just fine. Probably pretty cheap by then, too. [21:04] Yeah, when Kevin Whale was on the podcast when he was CPO at OpenAI, he famously said, "This is the worst the model will ever be." [21:12] Yeah. [21:13] It's still hard to comprehend that. Wow, that's only going to get better. So on this point of tokens, spend, ROI, and things like that, [21:22] Meadow is famous for this like leaderboard of token spend.

It's a terrible idea. No leaderboards for token. Okay. Okay. We 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 a [21:37] then you've kind of figured out that works well. - Right now we've managed to get 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 idea." And so right now we don't have token limits for our engineers. [22:00] 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 like as 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. [22:30] across my teams. I think that you can imagine, at least in a year or two, [22:36] coming that the burn rate of a strong engineer might be the same as their salary or their cost of employment. And if in that world, like you're going to probably need to put in some caps, the caps should probably be... [22:54] like a portion to your sort of, you know, the company's sort of trust in your ability to use them in an ROI positive way.

But I can imagine caps being healthy. Right now we're not there. I think costs... [23:08] 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 [23:17] going to be [23:18] in a bit of a pricing war. Um, so we'll see, I think it'll be a bit of a rollercoaster. [23:23] So coming back to... [23:25] This idea that, as you said, we've evolved from, we used to write all our code, to now [23:31] we're approaching all code will be written by AI.

And it feels like now the transition is [23:35] It's not just written by AI, but it's like one-shotted by AI. Coding now is steering AI. And it's like how often you have to correct it is coding now. [23:43] And then there's so it's like the software development lifecycle slowly being eaten by AI. [23:49] uh it'll start helping us come up with ideas i imagine more and more [23:54] The question I like to ask people is, where do you think human brains will continue to be most valuable? [23:59] as AI [24:00] continues to eat more and more of that product development lifecycle.

[24:04] Taste, like we talked about, judgment, particularly around strategy, right? Like you're not... [24:11] You might get feedback from an AI on the strategy, but you're not asking an AI to come up with a strategy anytime soon. Or if you are, then it's within the context of bounds you set. So here's my goal. Here's my vision. Here are my constraints. Here's my job. Here's my budget. [24:26] I think that it looks more like management, right? Like you are trying to define what success looks like. [24:32] 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. You know, and you it'll be interesting to see how Matt, you know, [24:48] you know, some of the same dynamics come up. Like I believe that if you are [24:52] too prescriptive as a leader with a team, you end up stifling good ideas. [24:59] But if you're too open ended, sometimes teams just waste time going in the wrong direction. And so that level of autonomy you give a team, maybe that applies to agents in the future, particularly when we're talking not just about building something, but deciding what you build in the first place.

But I think of I think of vision as an articulation of the. [25:21] 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. Strategy can't be like, [25:32] be the best or be amazing. It has to be [25:36] controversial that you have to be just a reasonable person should be able to disagree with it because otherwise you're probably just trying to compete on raw execution and [25:46] I think both vision and strategy, I think, are going to be where our brains are spending a lot of our more and more of our cycles.

And I think less on... [25:55] on execution. [25:56] Something I... [25:57] I've always thought is AI should be incredibly good at strategy because you would think, [26:02] Here's the market. Here's all the information on the market, our competitors. [26:06] our metrics, our numbers, our growth, all these things. [26:10] Help me figure out how to win. [26:11] You think AI? [26:12] Knowing all that would be really good at this. [26:14] I think it could be. I have found it's not unless you steer it. [26:18] pretty aggressively. 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 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 [26:35] on the bubble [26:37] you know it's going to actually attract some of the best talent. And so that kind of the push then goes to the idea. 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, and the brand.

[26:56] You have to consider all of these things. I think if you... [27:00] Ask an AI just for a strategy lazily. You're not going to get something great. You're going to get something pretty predictable that pop up with the competition would expect you to do. I think if you want a really more effective one, you need to think long and hard about what are all of the different inputs that need to be considered. Make sure you... [27:17] Right. [27:19] steer the AI in a way that it's considering those as well.

And it needs to be a conversation in the back and forth. But I think if you're willing to put in the work and the time, [27:28] it can definitely be helpful and definitely be clarifying, particularly if you tell it to be critical. [27:34] Different models have very different vibes, though, on how willing they are to be pushed back. So I recommend picking one that likes pushing back. [27:43] Yeah. Mythos has gotten really good at being like, I can't do this. Let's move on. Like... [27:48] Claude has always been a little bit of a jerk in a way that I actually appreciate.

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. It's like, no, hold on. I want... [28:02] You know, I want the real... [28:04] Real sort of intelligence. I don't want a pleaser. This point you made about people being excited about the strategy is such an interesting one. [28:12] 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. [28:18] So centaur is a human body horse.

This is going somewhere, I promise. Human body horse. Sorry, human upper part horse. Lower part horse body. Yeah, yeah, yeah. Horse body where the human is in charge. [28:32] And that's kind of we prefer that we want to be charged reverse end chart, which is what we want to avoid with AI is where the AI is controlling us. [28:39] And we're just doing its bidding. Just a horse head on a human body. Yeah, exactly. It's terrifying. [28:44] In a sense, Uber drivers and DoorDash people, this is their life, which is not great.

[28:50] 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 thing. [28:58] Counterpoint to we don't want AI to be telling us the strategy almost. [29:02] Yeah, no, I think there's... [29:04] a lot of things to be careful about right now. And I would certainly... [29:10] Not just assume that because you might be able to outsource some workflow, [29:15] to AI that you should.

There are certain ones where I think it's really just a win-win. There are certain ones where I think the risk outweighs the benefits. [29:23] This episode is brought to you by Mercury. Radically different banking loved by over 300,000 entrepreneurs and now with command. I've been a customer of Mercury's for over six years. I have never once thought about leaving. Mercury is basically what happens when banking is built by product people, not by bankers. They make it so easy, dare I say fun, to send invoices, move money around, set up virtual cards for folks on my team.

Does your bank have an API, [29:53] or an AI-ready MCP server? I don't think so. And just recently, they launched Command, a conversational interface built directly into Mercury, which acts as your financial operator. I've been using Command to transfer money around to figure out what categories I've been spending the most money in, analyze my cash flows. And just today, I used it to find out how much I've made from a specific sponsor over the past year. I just asked, how much have I made from X over the [30:23] cool. Visit

com to learn more and apply online in minutes. Mercury is a fintech company, not an FDIC insured bank. Banking services provided through Choice Financial Group and Column NA members FDIC. [30:36] Okay, going back to product leadership, things you've learned along your journey. [30:41] We were chatting ahead of this about just things you've learned. And one thing that you said about [30:45] some of the best product leaders you've worked with, [30:48] is that they're less visionary and more curators. [30:51] I'd love to hear more along these lines. [30:54] Yeah, I mean, you do sometimes find amazing product leaders who are just like idea machines, just prolific idea machines.

But I do think a lot of the best are... [31:05] have taste are... [31:09] have something about them that really makes once makes really strong talent want to work with them. [31:15] but end up sort of being curators, curators of people, curators of ideas, curators of technologies, curators of strategies. Because I don't really care if I'm hiring a 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 is bought into it. And then we're executing against that strategy well.

And so I think that some of the best product leaders are. [31:43] Yes, have ideas. It's hard to be a great curator if you don't have some of your own ideas, but are [31:51] that embrace the 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, you [32:04] chosen or decided upon. And so, you know, I think it's not just about curating ideas, but it's sometimes about curating [32:11] teams and people. [32:13] I love that. I so agree.

I feel like everyone's always joining a team and they just want to do vision strategy. [32:18] not actually hands-on work. No AI is coming in here. Let me do the strategy. Yeah, exactly. [32:25] And I love this point that there's so much power and value and people underestimate just the need for just like a really good curator of... [32:33] of the team's ideas. [32:35] Yeah. Sometimes it's also, sometimes it's not just who's good or what idea is good. It's also what [32:42] 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? [32:54] 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, data science lead, I have a design lead, I have a research lead. You know, I need to make sure 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. I also need to make sure that they, um, [33:20] this is more art than science, have a good vibe, right?

You need trust and rapport. A leadership team with strong trust and rapport can work through most anything. A leadership team without trust, [33:32] or rapport, like [33:34] 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 [33:46] Also, [33:48] either do that instinctively or consciously, but they have a nose for building teams that are going to have problems [33:55] good energy and good collaboration. [33:59] Warm and fuzzy stuff.

[34:00] Yeah, yeah. Well, the flip side is also true, right? Like I've had many times in my career, I've had two people who I think are amazing, and I even adore them and love them. [34:11] And they just can't get along. [34:14] You're like, this isn't a competency issue. This is just a personality issue. And you just have to sometimes call it and split them. [34:23] 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 [34:33] knows about human behavior, [34:35] that [34:36] people may not realize 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 five ten years have been you know these large embedding models and these other techniques that basically produce artifacts that

[35:06] giant vectors. It's like, sure, I can show you the [35:09] Vector, but it's just gonna be a bunch of numbers and like a seven-dimensional space. It's like [35:15] And so when [35:17] 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, it doesn't. It just has this big-ass number that happens to correlate with surfing. [35:30] That said, I think that is... [35:33] starting to change, right? I think that what [35:37] One of the things that LLMs are enabling is they can describe in words, English or whatever language you prefer, what language.

[35:49] some of those previously illegible artifacts, um, [35:54] are at least proximate to, if not mean directly, right? So this is like the thing I've been 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 [36:06] 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 [36:14] videos that are close or similar.

[36:16] Thank you. [36:17] 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 like, [36:23] deep pour over coffee snobbery. And that's kind of amazing. That is so cool. Like you can ask the LM to look at these numbers and, [36:33] extrapolate here is like the topic that you're interested in. Yeah. Or look at the videos. And so the way you both. And so you can also embed concepts into that same space. And so, I mean, embeddings are really the underlying technology underneath LLMs, right?

That's how the whole thing works. [36:50] And so, you know, so what... [36:53] What we do now is we let you, you know, quote unquote, 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, you know, these social media apps are getting taken over by recommendations. [37:13] But we can't do a lot of other things yet, which 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 schools. [37:26] Kids. [37:27] friends kids photos you know i don't know we can come up with it i don't want to see seven photos in a row but i'm happy to see six photos or whatever your hearts 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 but [37:39] I think a misconception historically is until recently, [37:43] we don't really know as much about you as you think.

We're just like, oh, like you liked these photos. [37:50] This 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 you [37:58] I'm oversimplified. That's like, yeah. [38:00] 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. [38:06] That is really interesting. One that comes to mind is kind of this... [38:10] transition everyone eventually goes through to this like algorithmic [38:14] broad global feed.

[38:16] Everyone 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 a lot more. [38:27] a lot more when it's this algorithmic feed of things we think you will love. [38:31] Yeah, it's tough because I mean, I 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. [38:39] I get it, right?

But there are a couple of issues. [38:44] with the algorithm with the chronological feed so one is and some of this is the tension between an individual's interests and what works when you scale it up right so [38:56] If you do a pure chronological feed, the incentive for everybody [39:00] is to just post as much as possible. [39:04] because it will always be at the top of everyone who follows you's feed as soon as you post. So what ends up happening is that the feed gets overwhelmed with professional content. [39:15] with usually large company content and publishers, because the New York Times can pump out 50 things a day.

Your best friend... [39:23] won't. You might get one thing a week from that. And so your fee just gets taken over. So part of it is the incentives that emerge. Because when you design these systems, it's almost like designing a city, you need to think about, okay, here's how the mechanics work. What are the incentives that arise? How are people going to act within those incentives? And then what happens? [39:44] And the other thing is sometimes the most interesting thing was just not the most recent thing. Recency is an important input into relevance, but it's not the only one.

My sister got engaged last night. [39:55] And she's in Germany. - Congratulations. - No, she didn't. If she did, she's married. - Oh, okay. - She got married last year. That's why it was top of my mind. - All right. - But if she got engaged, and I missed it, because she lives in Europe, and we're different time differences, like, do I really wanna see a picture of my brother's pobo sandwich? [40:12] Po-poi sandwich or do I want to like... [40:14] See my sister's things first. [40:16] So it's tough, it's tough.

I'd love to figure out a way to find the right balance. I wanna give people agency over the experience, but I think it needs to be in a way that [40:26] 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 [40:32] Because we've done chronological research [40:35] by default and where you can make it default and you see not only does usage go down [40:41] overall sentiment goes down. [40:43] The individual who made that choice might be happy at the moment, but when you just get pummeled with stuff you're less interested in over the course of months, [40:51] We ask, we run surveys on massive scales.

We just see people start to become less and less satisfied with Instagram. [40:57] Kind of along these lines, everybody asks you about this these days, 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. [41:07] is the rise of ai content a headwind or a tailwind for instagram [41:12] versus other platforms do you think this helps or hurts you guys i think it's going to be a tailwind but i think it's going to be a challenge it's and not just because it's more content obviously we're an attention business driven business we're an advertising business more content means potentially more attention that's not for free though like i don't think we're very good at ranking ai 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 [41:39] But I do think that...

[41:42] In a world where, or for years now, and I've said this many times, power is shifting from institutions to individuals across industries. The easiest example of sports where players are more relevant than teams now, and that was not the case when I was a kid. [41:55] Okay. [41:56] In that world, I think it behooves us to invest in individuals and to invest in people. [42:03] specifically for Instagram and creators. And I mean, creators broadly, I don't just mean influencers who are promoting branded content and making, you know, native only videos.

I mean, anybody who's using platforms 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 sew, but like you're out there as yourself, [42:25] So, [42:27] creating and sharing content that helps you achieve whatever it is you're trying to do. So we've been leaning in that direction 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. [42:39] In a world where there's an abundance of synthetic content, I actually think people are going to seek out.

[42:44] creativity and authenticity and, [42:48] people more, not less. And I think that that will help us. That doesn't mean that we won't have AI content on our platform. There's going to be, [42:56] 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 [43:07] But I think people are going to really seek out other points of view because Instagram was never just about the content. It was always about.

[43:15] 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. And I think... [43:25] given that we are [43:27] not the best at a lot of things, but we are the largest creator platform. If you look at [43:34] how we define creators and how many creators use us for still the platforms. I think it'll be a tailwind for us because I think people are going to seek out people. [43:42] And this connects to your earlier point that companies like say New York Jams can pump out a bunch of AI content [43:49] versus a creator and so you're saying you kind of want to protect against that to allow individuals to continue to [43:55] perform well in spite of just all this AI content.

If you just love AI content, [44:01] Great. Like you should be able to have a feed that's just like AI town. [44:05] And if you [44:07] don't, then you shouldn't have it in your feet. To me, it's like, I don't think we should. I mean, I understand why people are, right? I'm not oblivious to the overall paradigm shift and sort of revolution that we're sitting in. But I don't think [44:21] We should judge content based on the tool that made it. I think we should judge it based on the content, the point of view, the person behind the content.

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 knowing who they are or where they are or how many times they've changed their profile. [44:51] You know, if their profile is three days old or three years old. But I don't think we should be making value judgments based on what tool you used.

[44:59] Is there an AI content creator you love that you're just like, this is so good? [45:04] I love watching these AI videos. Yeah. What is she called? Plastic, plastic dream sequence? Is that what it is? [45:10] I think I'm going to check it out. [45:15] um plastic dream sequence i have it on my phone i'll double check um it's these like [45:20] like sort of doll's [45:23] Barbies, but they're like singing. I [45:26] songs and these little tiny 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 very clearly AI. It's not pretending not to be. [45:38] But it has a very clear creative and aesthetic point of view. And every time I come by one, I'm like, [45:44] Yep, we're doing this now. I'm going to watch this for 30 seconds. I have it pulled up here and I want to watch it, but I'm not going to. [45:52] That's awesome. If only that AI, that's another one. He's in France. 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 uses one model to create the image, another one to create the video, music, etc. [46:10] He's very clearly got his own aesthetic. You could think of him as a painter, but this is his tool. [46:18] Is there kind of a vision of AI versus human in the feed? Do you think it'll... [46:23] You said you maybe want to market. How do you think about people? Are they going to be like AI account, non-AI account? [46:28] How do you think about it? Or is that still kind of a work in progress?

Maybe we'll end up in the same place, but there's a difference between marking content and marking accounts, and they're both useful and interesting. So if content was created with AI, I think you should be able to know that. That's hard, by the way, because 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. [46:58] hey, is this AI? And we should be able to tell you who we think it probably is, or we're not sure, or it's definitely not, or it definitely is.

[47:05] I actually think we might be more practical to label [47:10] camera captured content, like basically non-AI content as opposed to labeling AI content long term for a couple of reasons. [47:17] But then at the count level, I think it also matters. There is definitely a new spam vector, which is these fake accounts, [47:25] Which, by the way, an AI creator, that's fine. There's nothing wrong with that necessarily. But there are these spam vectors which are trying to abuse that. And they're selling like... [47:34] Thank you. [47:35] you know, bogus supplements and it's like an AI monk and doesn't present it.

It's not obvious that it's an AI and it's just trying to like take advantage of... [47:44] 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, [47:52] You know. [47:53] Basically, you just need to know, and then you can make your own informed decision. Is the account a real person or not? [47:58] is the content a real piece of content or not. [48:01] When you think about other platforms in the space, social content platforms, are there any features or ways of...

[48:09] of approaching stuff that they do well that you're kind of jealous of [48:13] or really impressed by. [48:15] Yeah, there's a bunch. So many people do so much. I mean, for me, one of the things that we are finally catching up with, but I've been always very impressed with, is TikTok and their recommenders' ability to break things. [48:29] Small talent. [48:30] In the world of ranking recommenders, you can talk about exploitation-based ranking. That sounds terrible, but it just means 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 know they're interested in yet.

[48:47] And it is much easier to move 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. [49:06] Bieber, but you might also like Afropunk. And so we're just going to like 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 [49:23] Wasn't going to see them before or didn't even know that they were interested before.

So we've invested a lot over the last couple of years in ranking, not just increasing engagement, but increasing originality, increasing the number of pieces of content that break out, increasing recency to stay culturally relevant. And so a lot of that has been inspired by it. [49:42] by TikTok and ByteDance. I think we're catching up. There's actually a couple of those areas where we, I think, by the 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...

[49:54] in class at [49:56] recommendations for the first time during my tenure. So that's, I think, and they get a lot of credit for inspiring a lot of that work. [50:05] Nice job. [50:07] Well, we'll see. Not there yet. They call me disappointed dad. My team is always like, can you ease up on the disappointed dad vibe? So I'm trying to be a little bit more generous about giving people their flowers. [50:26] leaders is just never being satisfied. [50:29] Yeah, it's a blessing and a curse. Here's all the problem. It is.

[50:34] On this creator piece, I think that's also, you know, people... [50:38] complain about this global [50:40] algorithm not showing them all the friends but i feel like this is a benefit of what happens when you do this [50:46] now that you can break new creators into a wide audience if you have this kind of global algorithmic feed. [50:52] which is really great for a lot of people. I'm out there talking about a lot of these contentious issues and I get beat up a lot in the comments, which is fine.

[51:01] My main thing here is just to try to [51:03] communicate [51:04] that [51:05] There's almost always trade-offs, right? There's, you know, you can't just have all of the things, unfortunately, you know, you want to have... [51:13] Um... [51:15] You want to never see something you're not interested in, then you're also just going to see the most... [51:21] basic general lowest common denominator stuff all the time. You know, you want to [51:26] discover new and interesting things, you're occasionally going to see stuff that [51:30] was just a mess. [51:31] you know, you know, but this isn't just true about ranking these all all these major debates have trade offs, right?

You know, uh,