Creating Muse: The Fastest-Growing AI Product Since ChatGPT | Alexandr Wang, Meta

1. 0:00 – 1:32

   The vision behind Muse

   1. DSDavid Senra0:03

      Tell me the history of Muse.

   2. AWAlexandr Wang0:04

      The first thing to start with is probably like what our vision was when we started Meta Superintelligence Labs. And when we started it, Mark published this memo that myself and Nat Friedman and a bunch of others like worked together with him closely on, called Personal Superintelligence. And the whole idea was like, how do we make AI? How do we build powerful AI that actually makes all of our lives better? Like, how do we build something that actually like lifts the human experience, broadly speaking? You know, in that memo, we, we spoke to a bunch of the ideas, and we basically alluded to, I think, a lot of what Muse has ultimately become. But we were gesturing at it like very, very early. Like this was in, you know, June 2025, um, before really agents had happened in any meaningful way. You know, in the summer of 2025, like AI was just, you know, in a totally different spot. But that's where-- what we wanted to build towards. That was the mission of MSL, and that was really the-- where we wanted to take everything. Then we kind of just got to work building models because, you know, the end-to-end process of developing any of these frontier models is many, many, many months. Um, like you have to go through, you have to build your entire stack, you have to produce the datasets, you have to, you know, pre-train the model, then you have to do pro training the model. You have to build a little stack for that. Like it's an-- it's a very lengthy end-to-end process, um, and kind of this whole, um, production line. At the start of this year, um, you

2. 1:32 – 3:34

   Discovering the potential of personal AI agents

   1. AWAlexandr Wang1:32

      know, Opus 4.5 obviously like really caused many people including, including us, to sort of like see what the future of agents could be or was, and really brought the sort of like potential of agents to the foreground. And OpenClaw had-- was happening at the start of the year. Nat Friedman, who, you know, I worked closely with, was I think the first person within MSL to, uh, work with OpenClaw and try OpenClaw. He had an experience I would, I would describe as somewhere between terrifying and euphoric. Like, I think he-

   2. DSDavid Senra2:08

      [laughing]

   3. AWAlexandr Wang2:11

      He really like, I think, went all in. He trusted entirely. You know, he's told some of these stories at a-- he had like a, a Stripe Sessions interview where he talked about some of this, where, you know, he told his OpenClaw that he wanted to drink more water and then, you know, his OpenClaw would like watch him in security footage to make sure that he was drinking water and tell him good job. And he had like many, many crazy stories like this. And it also makes you realize like, oh, there's something quite, quite magical about what's happening here. And I remember one of the things that, that Nat told me, and this is one of the things I remember the most, is he said, "I think there really is something here." And I don't know, like early adopter Nat Friedman is like a pretty good indicator 'cause he was one of the, he was one of the first customers of Stripe back in the day and then was really ear-early on a lot of open source developer stuff. And he also, you know, helped create GitHub Copilot and many other things. So that was like a clear signal. It's like, okay, there's clearly something pretty magical about personal agents, generally speaking, but, you know, when you actually kind of trust the personal agent and, and really bring it into your life, it has the ability to do s-so many things. And after hearing all of these crazy stories, I started using it very deeply. You know, I think for many weeks, I think both of us were sort of like, every spare moment we would just spend talking to our OpenClaws and like, you know, you know, really immersing ourselves in like what personal agents could

3. 3:34 – 5:46

   Three years of therapy in one AI session

   1. AWAlexandr Wang3:34

      be.

   2. DSDavid Senra3:34

      What was your initial reaction? Euphoria or terror?

   3. AWAlexandr Wang3:38

      Nat at the time, he had this, um, he had built this prompt to do this like basically like full psycho analysis of yourself. There were like multiple waves of like deep psychological probing. He basically wrote this prompt for you to, to use in OpenClaw. And I think that was like one of the very first things I did with, with my own OpenClaw. It was maybe the equivalent of like three years of therapy all, you know, compressed into this one moment. Um-

   4. DSDavid Senra4:04

      Wait, I'm curious why when you get your first shot at OpenClaw, this is one of the first things you do.

   5. AWAlexandr Wang4:09

      You know, I downloaded it, I installed it, and then Nat was like, "Oh, you should do this." I was like-- I was like next to him. He was like, "Okay, I'll just do the thing you're telling me to do." And then-

   6. DSDavid Senra4:18

      Okay. [laughing]

   7. AWAlexandr Wang4:19

      Um, and then, you know, this crazy experience.

   8. DSDavid Senra4:21

      Wait, say more about the crazy experience, though.

   9. AWAlexandr Wang4:24

      Oh, well, it was just like very intense. I mean, it was like, again, it was like three years of therapy because it was just like successive rounds of like deep, uh, psychological probing 'cause it had other information about me. It had, you know, access to my email and access to a bunch of my photos and like it could like go and look up other information and sort of pull it all together. I was very vulnerable through that process. This was in February. Uh, both of us, I think, had come to this like joint conclusion that this is, this is clearly like, you know, there's clearly something really special happening here. And we both wrote memos at that time. I wrote a memo about, uh, and both of these we, you know, kind of like sent up through the meta board, um, 'cause, you know, for whatever reason, it was board season. I wrote a memo about how I felt that in many ways this was like the final consumer product, and that the super agent was something that was like, as far as consumer products went, like a clear endpoint in many ways. And then he wrote one called-- he wrote actually two, uh, which had very memorable names, "The Claw Is the Law" and "Trust Is a Must", which ultimately defined, um, a lot of the, a lot of the core of how we built these products. So we came to this conclusion, this was in February, there was something very important here. Um, and then we built, you know, our teams, we built a prototype within like

4. 5:46 – 9:16

   Building Muse in 7 months

   1. AWAlexandr Wang5:46

      a f- a week, maybe two weeks, um, and demoed it to the board. Like it was this like very fast cycle. And many of the decisions that made its way into the final Muse product were present in the first prototype. The Jollybot was there. You know, the early version of the Muse Charm was in that first-- of that very first meeting. Not all of the ideas and not all of the tabs were there, but a lot of them were present. Then from there, you know, obviously that was February, we launched the product seven months later, was like the really, really hard process of taking- This, you know, prototype where it, it like sometimes worked and was magical, but most of the time didn't work and would constantly break. And like going from there to something that would be highly reliable, like, you know, kind of like a beautiful crafted consumer product that we felt confident in as a contribution to the consumer world. And that, that was like this very lengthy and quite painful process. And I think-

   2. DSDavid Senra6:44

      Be- be- before we get there, I wanna go back to the idea that you wrote the prototype. You guys created a prototype in like a, a one to two weeks. You show it to the board. What's Mark's role in all this?

   3. AWAlexandr Wang6:53

      He was like experiencing the sort of like euphoria/terror of the product at the same time as all of us. I think he's talked about this in various interviews, [chuckles] but like I think he, he used it for a bunch of things around the home, and he was using it with his kids, and he used it in his, um, uh, in his MMA training. Like he had it watch videos of his, of himself doing MMA training, and like where it would give him tips on like how to improve. There were like some staff meetings. And by the way, like, you know, this kinda became a thing for, for much of the, the Meta team, um, was like having these-- having a staff meeting and saying like, "What were the, what were the crazy experiences that you had-

   4. DSDavid Senra7:34

      [laughs]

   5. AWAlexandr Wang7:34

      ... with, um, with, with your agent?" Again, this was back in February. It's very interesting to kind of, um, think back to this whole cycle because obviously the OpenClau phenomena, it peaked and then-

   6. DSDavid Senra7:48

      Disappeared.

   7. AWAlexandr Wang7:49

      Yeah. There were-- you know, it didn't, it didn't persist and, and I think a lot of people kind of had this experience, which was-- it was just like you'd have these magic moments, but for the most part, it didn't like work that well, and there were all these problems. And then, you know, it just kinda like died down. And so part of, I think, the building the product where a lot of the focus of the product was just like, how do you make this into an actual reliable consumer experience that you feel comfortable putting in front of, over time, billions of people, and you can feel confident that like most of those people have an amazing experience?

   8. DSDavid Senra8:21

      Why were you able to do that and the developers of OpenClau were not?

   9. AWAlexandr Wang8:25

      Well, first of all, I think we respect Peter Steinberger and everyone who's contributed to OpenClau immensely. Like, I think Peter is definitely a visionary. I think whatever happened in his beautiful brain that [laughs] created OpenClau is, is magical. And I think maybe the unglamorous part of what ma- made Muse so magical was just really grinding out and sanding out all of the details to make the combined model plus product experience really amazing. And we did many, many iterations on the model itself. We aimed the overall model roadmap towards building towards personal superintelligence, broadly speaking, but certainly personal agents as like-

   10. DSDavid Senra9:10

       S- say more about that and how that differentiates between your other competitors.

   11. AWAlexandr Wang9:14

       Yeah. Well, this is, this is kind of-

   12. DSDavid Senra9:15

       That's actually really

5. 9:16 – 16:51

   Why Meta bet on personal AI

   1. DSDavid Senra9:16

      interesting.

   2. AWAlexandr Wang9:16

      Yeah. It's actually useful to go back to the moment because if you go back to the start of the year, February, Opus and Claude were way ahead in coding, way ahead of anyone else, and they were exploding. It was the beginning of the sort of like, you know, Anthropic takeover in the whole industry. And it was a very scary moment, I think, generally speaking, where it's like, "Oh, wow, this is-- you know, they're so ahead on coding." And, and then obviously they had Mythos and, you know, they were-- they had like this clear lead. I think the, the conventional wisdom in the industry was that coding agents were the only form factor that mattered or maybe the only technology that mattered or only the only capability that mattered for advanced AI. You know, there's this incredible force in the industry to just like go and crowd that use case and compete with Anthropic on coding agents. Because of our experiences and our conviction that I think formed around this idea of personal agents and the sort of taking a step back, I think the reason why we did personal superintelligence in the first place was that it was also what we believed Meta was uniquely positioned to do. And we can talk more about that. But like, I think because of that-

   3. DSDavid Senra10:24

      Well, let's talk about that now.

   4. AWAlexandr Wang10:25

      Okay.

   5. DSDavid Senra10:25

      We'll just do the weave.

   6. AWAlexandr Wang10:26

      I think one of the first things I told Mark, even before the whole Scale and Meta deal happened and, and everything, one of the first things I told him was, "I think Meta is actually a really special company if we have AGI or ASI or whatever you wanna call it," because part of the promise of AI is that for all of us, we have a mix of things that we want to do and things that we have to do. And the promise of AGI is that we're gonna spend almost none of the time doing things that we have to do because the AIs will start doing a lot of that for us, and we can spend all of our time on things that we want to do. And for me, Meta's products represent the things that we all want to do. Like my use of Instagram or WhatsApp or Facebook, like these are products that represent what I... You know, I follow my interests, I follow my hobbies. I follow people that I-- like my friends and the people I care about. Like the product span of Meta encapsulates the sort of like what we want to spend our times doing sort of realm. I think on a long arc, Meta's an incredible winner through all this. That's like one of the first things that I believed about Meta and superintelligence. And then ultimately, a lot of those ideas made its way into this concept of personal superintelligence. You know, you can kind of look at it a few ways. One is what's the heritage of the company? The heritage of Meta is connecting people with their friends and family, um, helping them discover their interests. Like, these are things that are very human. And then there's also this sort of like you can take the business lines, which is Meta has distribution to three and a half billion people using the apps every day, and the natural area where Meta was going to be successful is consumer AI.

   7. DSDavid Senra12:18

      It's not work, it's not coding, it's personal.

   8. AWAlexandr Wang12:20

      Yeah, exactly. So- Personal agents and personal AI and, and consumer AI, broadly speaking, were always the zones where we always felt that Meta was going to do some-- be able to do something very special. So that brings us to, uh, okay, early twenty twenty-six.

   9. DSDavid Senra12:38

      Now you have the prototype, you have the buy-in. Everybody knows Mark's on board, and now you're doing the seven months of the difficult work of refining this product.

   10. AWAlexandr Wang12:48

       Yes. There was kind of this specter, which was like, oh, personal agents, like they came and went. And we always felt that it was because, you know, the, the experience wasn't perfect, it wasn't polished yet, you know, all that kind of stuff. But, like, we knew that that was something that we had to, like, think about as, you know, as we were developing this product. We focused our model development roadmap, the top focus, uh, or one of the top focuses, was towards building on personal agents. And we had, like, these long spreadsheets of hundreds or even thousands of different-- not thousands, but, but more than a hundred, like, specific behaviors and capabilities that we knew the model needed to have to be able to build the ideal personal agent. Like it was very, very meticulous. And then over time, we built evals for all of those. We figured out how to train the model to be really, really good at every single one of those things. And we have these reviews where we, like, look at that spreadsheet, and we figure out the things that we're bad at and make sure that we're improving on them. Like we did this like very meticulous process to slowly just keep improving and sanding out all the edges of the model so that it could power the product.

   11. DSDavid Senra13:54

       Is that really the culture of Meta, too? This, like, constant, like, sanding of the products?

   12. AWAlexandr Wang13:59

       Yeah, a lot of the culture of Meta is, like, once you know what you're measuring, there's very talented people, and you can optimize for those things. You can sand out all the details. You can make those things amazing. I think in the sort of like pre-AI consumer-

   13. DSDavid Senra14:12

       Mm-hmm

   14. AWAlexandr Wang14:12

       ... um, product era, like, a lot of this was around how do you optimize your conversion, how do you optimize the referral rates, how do you optimize, like, every part of the onboarding flow? Like, how do you make all that stuff just perfect? And then I think the AI era, I think, uh, a lot of that, especially for Muse, has been in the form of sanding out every detail of the model to be perfect for the product.

   15. DSDavid Senra14:34

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6. 16:51 – 20:15

   Mark Zuckerberg's obsession with improving products

   1. DSDavid Senra16:51

      Did you see this profile that's written, uh, on Zuck, uh, in Colossus, written by Jeremy Stern?

   2. AWAlexandr Wang16:57

      I saw it. If I'm being honest, it was like too long, didn't read. But-

   3. DSDavid Senra17:00

      It's, it's fifteen thousand words. I just had Jeremy on the podcast. He's, uh, he's my favorite writer.

   4. AWAlexandr Wang17:04

      Which I saw. I saw the-

   5. DSDavid Senra17:05

      Yeah

   6. AWAlexandr Wang17:05

      ... I saw the review.

   7. DSDavid Senra17:06

      Yeah, he's, he's just-- he-- I think he's writing the best profiles on, on entrepreneurs and investors in the world right now. I think he's literally the best in the world at what he does. But in that, Zuck says, we-- he was talking about the advantage that he has relative to, I think, Elon and, like, Sam Altman. And he's just like, "Listen, my skill set is very unsexy, but I'm really good at building teams and then just improving a product slowly over a long period of time." And relative to Elon and, and Sam Altman and others, he's like, "I'm not gonna lose control of my company, and I don't need to raise any more money. And so I can just do this for an excessively long period of time." That section of the profile is popping in my mind when you're talking about this seven-month, uh, stretch of trying to refine and build Muse.

   8. AWAlexandr Wang17:44

      I think that's exactly right. Like, I think that it took immense patience. I mean, seven months, like, you know, maybe doesn't sound like that long, but AI is, like, fierce.

   9. DSDavid Senra17:54

      Seven months in AI time [chuckles] .

   10. AWAlexandr Wang17:55

       Seven months in AI time, which may be like a decade in, like, non-AI time. And I think it, it, for all of us, um, but, but I really give Mark a lot of credit for this. Like, it took a lot of patience and restraint and a lot of trust in the process. And, like, you'll-- you can go back and look at the, you know, what Mark said in earnings calls for this entire period from February through, through, um, September. And he will allude to the fact that we were building personal agents, and we weren't really trying to hide it. Like, we were, we were building Personal agents, and we were talking about it, but I think, you know, there was a lot of doubt in the company for that entire period. Like people-- obviously the, one of the dominant Wall Street narratives was like, "Oh my gosh, Meta's just burning all this money. Like, are they even gonna pull anything off in AI?"

   11. DSDavid Senra18:43

       It's more than that. It's like Zuck cannot win AI. It's said over and over again for a long period of time. And again, going back to Jeremy Stern's profile on him, he then off, off, uh, off the record starts talking to his competitors, including one former researcher that is now at a competitor that used to work for-- w-with Zuck and is now at another lab, and he goes, "I don't wanna have to compete with him 'cause Mark's like the fucking..." I think the line is, "Mark is the fucking Terminator."

   12. AWAlexandr Wang19:06

       [laughs]

   13. DSDavid Senra19:07

       And it was like, he just does-- will, will not stop, and he's like, uh... Then he talks about, um, one of his superpowers is that he's never happy with the state of the product, so he always wants constant improvement, but he's not an asshole. Like, he's not, you know, dict- uh, he is definitely a dictator, but like he's not, you know, rude and like kinda will destroy the chemistry of a team. He actually talks about in the, in the piece that team cohesion is excessively important to him.

   14. AWAlexandr Wang19:30

       I think that's all right. I think one of the magical things about Muse was that we really like took quite a bit of time to sweat all the details before ultimately coming out with the product. Because I think that, that personal agents was one of these product areas that kind of rested on a knife's edge, which is if it can reliably deliver these magical experiences, it has the potential to be one of the greatest consumer products ever. But you're dealing with models, you're dealing with something non-deterministic, you're dealing with agents. They can break. They can be, uh, unreliable. Like, if people have these unreliable experiences, they think it's trash. It was one of these things where like getting to the product to a point of like quality and value such that it resonated with so many of the people who downloaded and

7. 20:15 – 23:32

   How Meta knew Muse was ready to launch

   1. AWAlexandr Wang20:15

      used it.

   2. DSDavid Senra20:15

      I wanna talk more about this process. I'm curious how much resources, how big the team, how many people are working on it. Do you guys have any data on like, okay, I'm using Muse, nine great experiences, two like inconsistent or, you know, shitty experiences, and then I churn out? Like, would you have any information on this?

   3. AWAlexandr Wang20:31

      You can see this from like a lot of the other agent products at the start of the year, like almost everyone churned out of those, and it's because they were unreliable. We did a lot of user testing of Muse. Like, we did many, many rounds of new people trying to like download it and, and use it from scratch, and just seeing what that experience was like. A huge part of the premise of Muse, and I think what's really landed, is that it is like a s- different thing from what most pe-people have experienced with consumer AI. Like, it is not a chatbot, it is an agent, and it can do agent things for you. And like I think to a developer, this is kinda like old news because they've had to like, you know, download agents and have been using them for a while. But for most people, like just even that graduation process, like, you know, y-it needs to be explained and needs to be onboarded on, on a very thoughtful way. And then for sure, we definitely have seen like when-- 'Cause we've done tests with like different versions of the model and various model versions and continuously do those AB tests. You know, the product experience is very, very sensitive to model quality. It's very sensitive to how reliably the models can undergo these tasks, how reliably they communicate with the user about them, how reliably they like, you know, check back with the user if there's things that they require guidance on.

   4. DSDavid Senra21:54

      But how'd you know it was ready to be released?

   5. AWAlexandr Wang21:57

      Part of it was, was pretty numerical, which is like we had set, you know, you go back to that spreadsheet of a hundred-

   6. DSDavid Senra22:05

      Hmm

   7. AWAlexandr Wang22:05

      ... plus things. We had set for each of those rows what threshold is launch blocking and above what threshold is good. And we had many checkpoints that like were like green on eighty of the rows but red on twenty of the rows. We, we knew that wasn't good enough. And we had our first model checkpoint, which was green across the board, which was a specially trained version of Muse Spark 1.3. So there was like the very numerical part of it, and then there was just the experiential part, which is we tried the model. We could tell that it was a lot better than, than anything we had iterated with before.

   8. DSDavid Senra22:38

      So going back to that seven-month period you were talking about, how many people were working on the product?

   9. AWAlexandr Wang22:41

      Between the model work, model work and the product work, like under two hundred people total, which I guess sounds like a lot, but in big company land is-

   10. DSDavid Senra22:51

       It's actually so it's a lot smaller considering-

   11. AWAlexandr Wang22:52

       It's not very many. What's kind of cool looking back on this and thinking about it is like we knew this was going to be [chuckles] the most important product that-- I-and the, the whole executive team, everyone knew that this was probably gonna be one of the most important products that Meta would ship this year. But we had the restraint organizationally, I think, to make sure that ev- like it was a really small focused team working on it, and that it didn't balloon to this like, "Oh, you have like, you know, huge swaths of the company working on it," because we knew it was a single work of art. Like it required strong point of view and strong focus to like pull that through into one beautiful

8. 23:32 – 25:56

   Why great products need a single point of view

   1. AWAlexandr Wang23:32

      experience.

   2. DSDavid Senra23:32

      Say more about this. Why did you just call it like a single piece of art and that you need a single perspective or point of view?

   3. AWAlexandr Wang23:38

      I think one, um, criticism of products that come from larger organizations, and I think this like is even now starting to plague a lot of the AI labs, is that they become kind of this Frankenstein, almost like Cronenberg amalgamation of a bunch of different people's points of view and visions and, and beliefs, and it's sort of like, it's sort of like PM hell in some sense, where like every PM has like a thing that they're trying to get into the product and jam into the product, and then all of that kinda like blends together into this like, like smoothie almost. People feel it as users and consumers. Like you fee- you can feel when a product is just like it feels like lots of different people worked on it, and there were like clearly different people who were gold differently, so they just kind of like jammed it all together. Muse had to be the exact opposite, which is like there's one clear point of view, there's one cohesive experience that we're trying to deliver, um, to the world.

   4. DSDavid Senra24:43

      I love that you said that, and it just happens to be pure coincidence. Like we're-- I don't know when this is gonna come out, but we're recording this on the fifteenth anniversary of Steve Jobs' death, and when you think of somebody like a pro- consumer products that had a single point of view, you obviously think of Steve first. I've read every single book on Steve and the history of Apple, and I've done like, I don't know, fifteen or twenty episodes on him, uh, for my other podcast, Founders. But one of my favorite lines to describe Steve's impact was it says that, um, "Apple is just Steve Jobs with ten thousand lives." The products are to his taste, his perspective, his point of view. What he wanted to see in the world is exactly what the, the, the end user got.

   5. AWAlexandr Wang25:19

      In Muse's case, like Nat Friedman deserves a lot of credit. Like his taste and sensibilities ultimately dictated and shaped what that product became.

   6. DSDavid Senra25:27

      Do you talk about how many people are using Muse? Are you guys talking about this publicly or no?

   7. AWAlexandr Wang25:31

      We have said millions, and then I have retweeted other people who have [laughs] shown graphs where w- we would imply that Muse is the fastest-growing consumer app of all time, consumer AI app of all time.

   8. DSDavid Senra25:48

      So you won't say a number though?

   9. AWAlexandr Wang25:49

      We've said millions, yeah.

   10. DSDavid Senra25:50

       Well, millions could be hundreds of millions [laughs] .

   11. AWAlexandr Wang25:53

       Yeah.

   12. DSDavid Senra25:53

       That's also millions. You're just not adding the, the,

9. 25:56 – 34:38

   Making Meta cool again with memes

   1. DSDavid Senra25:56

      the beginning to that. I wanna like go into like you've been kinda going like, uh, and I mean this in a loving way, like unhinged [laughs] on, on X. So can you talk about like your... Is this like marketing strategy? Like, what is going on? Like I feel like you're-- there's a distinct difference in your public tweeting since Muse launch. What are you doing?

   2. AWAlexandr Wang26:14

      There's a few pieces of what's happening here.

   3. DSDavid Senra26:16

      This has gotta be more fun than Scale AI. When you were building Scale AI, it has to be.

   4. AWAlexandr Wang26:19

      [laughs] We'll talk about that later, but it has to be. The past few weeks have been some of my most fun, uh, building, like yeah, certainly my career, and I think there's a few things happening. So one is it felt important that we figured out a way to make Muse break through. It's a pretty like torrential s- going back to this, like this torrential stream of updates. Like for most people, there's just so-- they're just like constantly being like, like kind of bombarded with new AI products and startups and releases and things and models, and here's another model, and here's another product, and here's another feature, and it's just like this torrential stream. We had actually a meeting, large meetings where we talked about this as a team. It was like, "We need to break through." And obviously, like Meta, we have lots of distribution, like we can make sure everyone knows about it, but that's not enough to like make it break through in an interesting cultural or fascinating way for the world to kind of like, you know, pay attention to. I separately in my life have h- happened to have met and, um, become friends with like memers and, and, uh, I have these, uh, I affectionately call them my schizo friends, but, but people who like literally they spend all their day like thinking about memes and the internet and, and understanding that.

   5. DSDavid Senra27:38

      I have people that are obsessed with Muse, and they're also obsessed with your X feed, and they're convinced that you have like a hundred Muses coming up with memes to-

   6. AWAlexandr Wang27:46

      [laughs]

   7. DSDavid Senra27:46

      ... to meme Muse. [laughs]

   8. AWAlexandr Wang27:49

      Um, no-

   9. DSDavid Senra27:50

      Or you just have a collection of schizophrenic friends that are terminally online.

   10. AWAlexandr Wang27:53

       The X feed, just for what it's worth, is like literally every one of those tweets I write. There were like a few of... Quite a period where I was like-- it was like really hard for me to pay attention in meetings because I would just like think of a meme, and I'd be like, "Oh, God, I have to get this out." And I would like-

   11. DSDavid Senra28:07

       [laughs]

   12. AWAlexandr Wang28:08

       It was like moments of inspiration. There was like, um, I was, uh, I was with one of my... Like, I was trying to have like dinner with one of my coworkers, and then during the dinner, I was like, "Wait, wait, I figured it out," and then I posted a different me-- it was like-

   13. DSDavid Senra28:21

       [laughs]

   14. AWAlexandr Wang28:22

       It was almost-- I almost felt like I was like an engineer solving bugs again 'cause it was like, you know, you could tell when something was gonna click. But yeah, so one thing is we wanted to break through. Another thing that, like I just thought was important is like, uh, how do we make Meta cool again? 'Cause like for whatever reason, despite having like these incredible pieces of cultural software, um, like Instagram and, and, uh, Facebook historically, Meta h- was not cool for a while. And so wanted to make Meta cool and interesting and kind of like funny. And then the unhingedness was something that happened kind of like naturally over time. When Muse first came out, I was just like mostly focused on promoting all the interesting use cases that I saw. I was like retweeting like tens of interesting use cases that I saw of people using Muse. And then at some point, it was like, because I was just like tweeting so much, there were like a few tweets where I was just like, didn't think that much about it and just said, "Fuck it, post." And then some of those tweets like did really well.

   15. DSDavid Senra29:25

       Have you seen this like time spent thinking about it and then banger?

   16. AWAlexandr Wang29:27

       [laughs]

   17. DSDavid Senra29:28

       Have you seen that graph?

   18. AWAlexandr Wang29:29

       I haven't seen, but I agree with that. That's like... I just... Some things I didn't spend that much time on, I like tweeted them. I didn't even check my phone for a few hours, and I was like, "Oh, wow, that one went really well." And then I kinda like remembered something about the internet, which i-- it wasn't super intentional, but I remembered like the internet rewards risk and it rewards like surprise and like things that like people don't expect. And so then I just kind of like went off. A lot of my coworkers, uh, I think were somewhat surprised at, uh, th- th... Something that, um, Nat said for, for the whole week when I was posting all the memes, he was like, "You know, I thought we would learn..." He said this like in a bunch of meetings. He was like, "I thought we were gonna learn a lot about, um, you know, Muse users and like how people were using Muse, but actually, like we're just learning a lot about Alex's mind." [laughs]

   19. DSDavid Senra30:16

       [laughs]

   20. AWAlexandr Wang30:17

       It was a lot of fun. I felt like I was drawing on all of my internet knowledge for years and years and years to like pull into those moments. And I think it actually, you know, I don't... We don't have the tracking and it's like hard to tell, but I actually think it actually genuinely made an impact on gr- growth, which is kind of hilarious.

   21. DSDavid Senra30:34

       I definitely think it did. I ju-- I can't stop hearing about it, so-

   22. AWAlexandr Wang30:37

       [laughs]

   23. DSDavid Senra30:38

       And all the people I'm hearing about it from have also downloaded the app and are now using it. Go back to what you were saying, though. It's like, hey, we have distribution, but that doesn't make-- mean that we can make something that people are gonna come back to and actually sticky. So, like, how did you do this with Muse?

   24. AWAlexandr Wang30:49

       I think it was like, how do you make this into... How do we, how do we make this breakthrough as a product? And I think there were, like, a few parts of that, but I think one is how do you get people to a match experience? Like, how do you get someone to s- to a wow moment with the product as early as possible? And this product, I think, naturally lends itself to that because I think, you know, there's-- it's like, it's AI, it can do a lot. It can-- There's like... If, if someone has never used an AI agent, there's a lot that it can do that will surprise you. So that, I think, was a big part of it. I think another part of it was like, how do you harness all of those stories of people having these wow moments and, like, use those to help make the product sing and make the product fly?

   25. DSDavid Senra31:33

       How are you using the stories, though? 'Cause you, you guys aren't running ads for Muse, are you?

   26. AWAlexandr Wang31:36

       We ran a few ads. I mean, honestly, like, I think at first it was just the fact that I would retweet so many of them.

   27. DSDavid Senra31:43

       Yeah.

   28. AWAlexandr Wang31:43

       Like, I was just-- Anytime I saw someone do something interesting about Muse, I was like, quote tweet.

   29. DSDavid Senra31:47

       I couldn't understand why both Anthropic and OpenAI's, um, ads are so bad. Because remember, Anthropic was doing this huge, like, outdoor campaign, and it's all about them. And same thing with, um, ChatGPT. It's like they'd have, like, the, the icon, which I don't even think people recognize that as a logo, and it'd just say Codex, and it'd just be two people sitting at a desk. And I'm like, "What the fuck does this mean?" Where it's like you have all these use cases. All you have to do, your ad is to show the benefit of your product.

   30. AWAlexandr Wang32:11

       Yeah. [laughs]
10. 34:38 – 38:43

    Building trust with AI agents

    1. AWAlexandr Wang34:38

       average person.

    2. DSDavid Senra34:38

       Okay, but that might get them-- raise awareness, might even get them to download the app. But then how do you get them to stay engaged once the app is-- they-- uh, the app is on their phone?

    3. AWAlexandr Wang34:47

       There's a cycle that people who, who really love the app, and, and we even, like, we back in February had, which is you try to give it a little thing, and it does it, and you're like, "Oh, wow, it did that." And then you try to give it a little bit, something like a little bit more, and then it does it, and you're like, "Oh, wow, it can do that?" It could do that first thing. It could do the second thing, and then you give it like a little bit bigger of a, of a problem. And it's kind of this like trust fall almost that you have with an AI agent, which is, at first, you, like, don't really trust it, but you're, like, kind of intrigued, so you're gonna give it a little bit nibble, and then you give it, like, something a little bigger and something a little bigger and something a little bigger. Certainly, like, when I talk to, like, college students or whatever, like, some of the ways that people use AI, it's like a full trust fall. They just, like, are recording voice notes and brain dumping to the AI, and they send it, and then they just trust the AI to, like, organize all the thoughts and give them, like, clear plans and think clear things to do, et cetera. I think for the successful users of Muse, it really is like getting them onto this trust fall process of using it for slightly bigger and bigger and bigger things and then having it work every one of those times.

    4. DSDavid Senra35:59

       I found one of my all-time favorite quotes when I was reading the book Zero to One. The quote says, "The single most powerful pattern I have noticed is that successful people find value in unexpected places, and they do this by thinking about business from first principles instead of formulas." That is exactly what AppLovin has done with their advertising platform. AppLovin connects you with over a billion potential new customers in mobile games. AppLovin allows you to capture undivided attention. AppLovin ads are full-screen videos that are watched for an average of thirty-five seconds. That is retention that blows other ad platforms out of the water. And you can launch on AppLovin in minutes. You set the goal, and AppLovin achieves it. No complex setup, no expertise needed. And AppLovin scales quickly. They can put your ads in front of over a billion potential customers. Other businesses have seen immediate results scale to hundreds of thousands of dollars of spend per day and increase their revenue by millions. So you wanna get started quickly before all of your competitors are on AppLovin. And you can do that by going to applovin.com. That's applovin.com I wonder how much of this is actually driven by word of mouth. So o-obviously talking to you, I was gonna have to download the app no matter what. But what really piqued my interest is a friend of mine was sending me screenshots of how he was using Muse. So like when you download the app, it's, it doesn't really tell you like too much of like what you can do. It's kind of like open-ended, like a, you know, like a open text box. But the, a friend of mine who's running like a hundred billion dollar company, he left his ID on a flex jet, and then he just went through and he's just like: "Well, I want to..." He was telling me, he said, "I don't wanna be involved in this process at all. You have to figure out how to get the ID, uh, like go pick up the ID, this is where it's at, and then this is where I'm at, and then figure out all the steps along the way." And it like arranged the courier, went there, got through his security in his office somehow [laughs]

    5. AWAlexandr Wang37:47

       [laughs]

    6. DSDavid Senra37:48

       ... and got upstairs to his office. Like, and it was like, holy shit, this is the best marketing. It's just three or four screenshots of Muse going back and forth and him saying, "I don't wanna be involved. You have to do everything," and it figuring it out on the way.

    7. AWAlexandr Wang38:00

       Yeah, no, this is actually one of the things about, um, uh, I dunno how int- uh, it was like somewhat intentional, but definitely like modern product marketing is like very screenshot driven. Like I think to have a modern consumer product, like it has to work on the screenshot. Like the screenshot has to, has to fly, so, so to speak, either through like group chats or word of mouth or like online. And I think that's been a big part of it. This wasn't intentional or engineered, but I think the fact that we had the little guy in the screenshot is like, was a big deal because I think having Jolly or whoever your muse is, like in the screenshot immediately communicates this is a Muse screenshot. It's just like the best marketing you could possibly

11. 38:43 – 45:22

    The future of human ambition

    1. AWAlexandr Wang38:43

       have.

    2. DSDavid Senra38:43

       So are you on your Muse like all day long? Like what is your own personal usage?

    3. AWAlexandr Wang38:47

       I mostly like set up a lot of workflows using it, and then I, um, I guess my meetings a lot of the time, but [laughs] um, so in practice, like I think in my job, quote-unquote, I'm supposed to like pay attention and, and be pretty present and interact with people. I set a bunch of workflows and like kind of treat it as like a second brain.

    4. DSDavid Senra39:08

       Yeah, you said that in the, the post that you wrote, and it was like really short and I think I saw last time it was like five and a half million views so far. But you said, uh, I love how you describe Muse. Like, well, if everyone had a second mind beyond their own.

    5. AWAlexandr Wang39:19

       I think this is, philosophically speaking, one of the most interesting questions about like, what does it mean to be human when you have like powerful AI and what is that relationship like and, and how exactly does that look? And different people have different takes on what exactly that looks like. I think the sort of like fear cases obviously that, you know, AI is above us and that we're just sort of like doing what they do, and there's like other worlds where that relationship is different.

    6. DSDavid Senra39:47

       Be-before you go on, what is your own personal view though?

    7. AWAlexandr Wang39:49

       I really believe in Muse. I believe in the personal agent as like a long-term form factor of just it is human nature to have wants and desires and dreams, and I think like AI and personal agents like Muse will be the mechanism and the bridge that enables us to accomplish those things and continue accomplishing them. And then like humans will dream bigger and bigger and bigger and bigger. Kids will dream of like having their own star systems or whatever, and like Muse will help them do that. Like, there's this thing that like, um, Bezos, uh, wrote in like, well, I think it was one of his shareholder letters, which is like the beautiful thing about, uh, consumers is that they're like always beautifully unhappy or something like that, or like beautifully unsatisfied with the options they have. That is like one modality of our relationship with AI. I think that there's other modalities, like I think that AI will start doing more and more of the scientific discovery process. I think AIs will start inserting more and more into various parts of the economy. But I do believe in this mode- in this like form factor long term of everyone having an AI that supports them and what they want.

    8. DSDavid Senra40:57

       So you see AI as kinda like removing all like the kludge of life, right? The stuff that we don't wanna do. I think in the, the piece you said something like, "The world is full of like gatekeepers and obstacles, and like Muse can get around this for you without spending any more of your time, so you can actually focus on stuff you wanna do."

    9. AWAlexandr Wang41:12

       I think Muse can give everyone the adulthood they dreamed of in childhood. Everyone when they're kids, they have like big ideas and big dreams and when they like imagine their life and they sort of like, you know, play it out, they, they imagine this life where they can like be an astronaut or they can save the planet, or they can change the world, or, you know, whatever it might be. It's like a big-- They, they think big. And then for, you know, a variety of reasons, by the time people finish school, they enter the workforce, by the time they get to their, you know, have been in a job for a while, it's that like all that hope and ambition and, and ability to dream has been sucked out of them.

    10. DSDavid Senra41:57

        No, it's been beaten out of them.

    11. AWAlexandr Wang41:58

        [laughs]

    12. DSDavid Senra41:59

        Life has beaten them, beaten out of them.

    13. AWAlexandr Wang42:01

        I think as adults, I don't think we like, just like appreciate the degree to which we're all zombies. We lost our agency and we lost our... I think ambition is really a good word for it. One of the promises of AI, broadly speaking, and I think through products like Muse, is to, um, keep it going from when you're a kid. Like you're a kid, you have big dreams, use AI, help make those things happen. Then like dream bigger, make those things happen, dream bigger, make those things happen, and kind of like experience this like escalator of agency, an increase in agency throughout your life versus a sort of like you have lots of agency and then it gets like crushed.

    14. DSDavid Senra42:39

        You know what I was thinking of when you were speaking just now? You, you mentioned earlier this, this experience that you and Nat Friedman went through where you had this like disturbing, uh, psychological audit [laughs] in like way- coming in waves from this AI. It's like, yeah, what if we had that? But it's like the opposite of what you're saying. It's like, it actually gave you more self-confidence, like gave you under- understanding like the world is malleable, and if you push on it hard enough, you go after it with enough energy and drive, you can actually change it around you.

    15. AWAlexandr Wang43:02

        Yeah. I think this is something that like certainly the most impressive entrepreneurs exhibit, which is like, I think they like dream big- They accomplish that, then they dream bigger, and then maybe they work on that for like a decade. But then like, you know, if they accomplish that, then they dream bigger. Like Elon is obviously a great example of this. Um, I think Mark is a great example of this, I think. But I think a lot of like the iconic entrepreneurs, this is like, like kind of like their lived experience. And I think there's a version of that that should be true for every person. And I think what's kind of like tragic is, I think for most people, you have like big dreams, big ideas. You enter the corporate workforce, you're, you kind of become a zombie. Maybe at some point you want to go tackle your dreams, but then it's like really, really hard 'cause you have all these commitments. You maybe have a family, maybe have... Whatever it is, it's just like y-it's almost you become trapped in this sort of like zombiehood.

    16. DSDavid Senra44:00

        There's one more thing on Muse before I wanna get to Scale AI and like the, the partnership that you do, that you did with, uh, Meta in making that decision. But in this essay that you wrote, it was like short post that you wrote, I love what you said. You said, "The world until now has been shaped by the small number of fanatics who have somehow found a way to make their wants real. But we've never seen humanity with every single person's agency fully switched on."

    17. AWAlexandr Wang44:22

        I think the promise of this world that I'm describing, where every single person has this like agency increase throughout their lifetime, and they have the ability to accomplish their wants and their dreams. I think that world looks crazy in a very good way. Like, I think it will be very interesting. It'll be artistic and cool, and you'll like go into different pockets of the world, and it will be very like diverse, that it'll be kind of insane to think about what exactly that looks like, where literally billions of people have, you know, all of a sudden, because of abundant intelligence, like the resources to make incredible things happen. I think that's part of the promise. Like, I think in my head it's almost like the Rick and Morty interdimensional cable-

    18. DSDavid Senra45:08

        [laughing]

    19. AWAlexandr Wang45:09

        ... but, but just like somehow manifested into like reality for humans. Like, it could just be really awesome.

    20. DSDavid Senra45:15

        So I keep hearing, uh, this line from that show where it's like the universe eats smart people. [laughing]

12. 45:22 – 52:13

    The call from Mark Zuckerberg that changed everything

    1. DSDavid Senra45:22

       Um, okay, so I wanna go back. What I'm personally curious, uh, about is you're, you founded Scale AI, you're running Scale AI, and then one day Mark Zuckerberg reaches out and says, "Hey..." I assume he says, "Hey, I wanna talk." Can you tell me about this?

    2. AWAlexandr Wang45:36

       Yeah, I think the exact message was something like, "Hey, do you have time for a call?"

    3. DSDavid Senra45:40

       Did you have a relationship with Mark previously? Did you spend any time with him? Like, what, what was... what's the background there?

    4. AWAlexandr Wang45:44

       I have a friend who, uh, Al- this guy, Alex Schultz, uh, who I've known actually for many, many years, um, from when Scale was maybe a year old. I met him in San Francisco. He's currently the chief data officer at Meta, but was longtime Meta executive lieutenant, and he introduced me to Mark, I wanna say in about like I think it was '2021 or '2022 to talk about AI. Because I think at that time, Mark was getting a lot more interested in AI. Scale, we were doing a lot of stuff in AI. My memory is that I think it took like one year to schedule that first meeting. Like from the [laughs] , from the intro to the meeting being scheduled, I think it was like a full year.

    5. DSDavid Senra46:24

       Why?

    6. AWAlexandr Wang46:25

       Mark obviously has like an insane calendar and like has like a bajillion different things to deal with, and he's also very good at prioritization. Like, he will spend a lot of time on the things that are really, really important. In some ways, like too much time on the things that are like really, really important. The only way he's able to do that and like be a good dad and be a good partner and all this other stuff is to just like ruthlessly prioritize. I had spoken to him a few times kind of after that and had gotten his advice. As a founder to get advice from, you know, Mark Zuckerberg, obviously is like a really big deal. Um, and I actually remember at that time, like he, he wrote these like... Like, I would ask him a question on WhatsApp, and then he would write these like really long responses, and I was really confused. I was like, "How does he have time to write these like really long responses?" But now I realize he like is just like really fast and rigorous at writing. To like a lot of WhatsApp messages, he'll like, like write a very rigorous, like long response very quickly. It's like a very impressive skill of his.

    7. DSDavid Senra47:24

       Gives you a sneak peek into what's going on in his mind.

    8. AWAlexandr Wang47:26

       Yeah.

    9. DSDavid Senra47:27

       Right.

    10. AWAlexandr Wang47:27

        It's like, yeah, there's racing. Meta was a important customer of Scale. Um, and I would say like prior to this instance, maybe we spoke once every like six months or so. Maybe that was like the cadence of our interactions. And then, yeah, I think he, he had called, and I think it was... It was actually like not obvious at first what the like endpoint was gonna be because the first conversation was just like, you know, this was after Llama 4, and it, it was kind of just like asking, "What do you think we should be doing?"

    11. DSDavid Senra48:00

        Why is that an important point, that this is-- this conversation is happening after Llama 4?

    12. AWAlexandr Wang48:04

        Llama 4 was not on the trajectory that Meta wanted as a company. It definitely was like, I think internally speaking, a disappointment, and it was also at a time where AI was clearly becoming just like increasingly, increasingly important. It was clear that AI was gonna be like really critical to the future of Meta. So yeah, the first few conversations we spoke on the phone and he was just sort of ask, saying, you know, asking for advice on like, "What do you think we should be doing? What do you think we should be focused on?"

    13. DSDavid Senra48:29

        This is what I've heard about him privately, that he has this insane... It's not a board of directors like a Meta board, although there's some people on the board that he also does this with, but he's got this group of world-class entrepreneurs around him, and I've spoken to some of them. Like, you don't understand, like he asks for advice constantly.

    14. AWAlexandr Wang48:45

        Yeah.

    15. DSDavid Senra48:45

        He's like hitting us up. He's like, "Here's what's going on in my life or in the work. What would you do?" And he'll do that over and over and over again.

    16. AWAlexandr Wang48:51

        Yeah, I think this is like quite an impressive trait-

    17. DSDavid Senra48:52

        Yes

    18. AWAlexandr Wang48:53

        ... because it takes humility, obviously, like continue doing that even, you know, after all the incredible things that, you know, he's been able to accomplish. I mean, the whole, you know, timeline from when he first reached out to when we announced the deal was like five, six weeks. Like, it was like pretty quick.

    19. DSDavid Senra49:11

        Another thing that world-class entrepreneurs have in common. We had Jonathan Rauss, the founder of Groq, on this podcast. The first time we ever spoke, uh- Publicly about the $20 billion deal he did with NVIDIA was on the show, and he's like, "From the time J- first call from Jensen to money's in my bank account was three weeks."

    20. AWAlexandr Wang49:27

        Yeah. [laughs] That's amazing.

    21. DSDavid Senra49:28

        [laughs]

    22. AWAlexandr Wang49:29

        Um, I just had lots of ideas on, like, what I'd be doing if I were in his shoes, on, like, how I'd be thinking about Meta's AI strategy. One of the first things I said was what I said earlier, which is like, "Hey, I actually think because Meta is, like, heritage-wise focused on things that people want to do, is actually one of the most specially placed companies with this incredible shift to AI." I, like, had lots and lots of ideas that I sort of sent his way, and then that was kind of this, like, conversation happening in parallel with this like, "Oh yeah, maybe we should potentially consider if there's a way to work together."

    23. DSDavid Senra50:04

        So when he says that, what do you think? 'Cause this ca- you, this was not predictable to you.

    24. AWAlexandr Wang50:09

        Not predictable, no. The whole thing was like... It was like a really crazy, um, sequence. At the time, I was just like, "Oh, that's nice," but, like, there's no way that-

    25. DSDavid Senra50:18

        But you know what that means when they say that, right? They wanna buy you. [laughs] It's always the same thing, but they say, it's like, "Oh, we gotta find a way to work together."

    26. AWAlexandr Wang50:25

        Yeah, I don't think I'd had that, that kind of coaching at the, [laughs] at the time. One thing that I explicitly felt at the time was like, yeah, like, it probably doesn't make that much sense even. Like, I, I think I personally was like, yeah, like, you know, maybe he's like, you know, teasing it or, or, like, throwing it out there, but, like, does it even, like, make that much sense?

    27. DSDavid Senra50:45

        Why wouldn't you think it makes sense?

    28. AWAlexandr Wang50:47

        I mean, obviously, like, a deal ended up happening, so, you know, what do I know? But, um-

    29. DSDavid Senra50:51

        No, no, but back then. Not-- We know what happened, but, like, I'm very curious to your thinking as you're going, experiencing this. Like, the fact that you said, "Oh, yeah," you said this with like, "That doesn't make sense." Like, why wouldn't it make sense?

    30. AWAlexandr Wang51:01

        Taking a step back, obviously the, the, like, iconic Meta acquisitions have been Instagram and WhatsApp, and these were, like, very clear, you know, product logic-driven decisions. Scale is like, you know, a deep enterprise government sales, like, you know, entirely different kind of business from, from Meta. And so the, the logic that I had thought at the time would've made sense is if Meta wanted to get into those things. The-then I think, you know, it, like, you know, maybe that's, that's the industrial logic that would've made sense. But this wasn't that, 'cause I think Mark clearly was, like, predominantly interested in getting the Llama program or his overall AI program onto-
13. 52:13 – 55:19

    Leaving Scale AI for Meta

    1. AWAlexandr Wang52:13

       There was a part of it which was like, "Wow, this is, like, a really fascinating deal construct." We ultimately landed on this deal where, you know, Meta invested, uh, owns forty-nine percent of Scale. Scale continues. Myself and a few people joined, joined Meta. What I was kind of incredulous about at the time was, like, it's such a weird kind of deal, but is one that I think genuinely kind of was, like, this very interesting win-win-win-win, so to speak. It was a win for the shareholders of Scale because all shareholders of Scale got a great deal, benefited a lot. Scale continues, and I truly believe the best days of Scale are ahead of it.

    2. DSDavid Senra52:57

       My question to you is, like, or what I'm personally interested in is, like, okay, is this-- He can explain this to you and you come around to that perspective in one phone call? Is this just like, "Wait, wait, Mark, I gotta think. This is so fucking crazy. I gotta think about this for a few days." Like, explain this process as much as you can.

    3. AWAlexandr Wang53:14

       Many weeks of being like, "This is insane. Is this even real? Does this make any sense? If it does make sense, like, how do I feel about it?" The predominant emotion was more like, "This is kind of insane." And then as I thought more about, like, "Oh, I actually have to make a decision of whether or not I do this," that was it- in and of itself, there were, like, a lot of, like, conversations there with the people at Scale, with our investors. Like, there were a lot of interesting conversations in, in that, uh, on that side. I mean, it's obviously, like, really hard to, to, like, let go of or give up your baby.

    4. DSDavid Senra53:49

       'Cause you worked on Scale for how long? How many years?

    5. AWAlexandr Wang53:51

       Yeah, nine years from founding until the-

    6. DSDavid Senra53:53

       And you're still young. You're not even thirty yet.

    7. AWAlexandr Wang53:55

       Yeah.

    8. DSDavid Senra53:55

       So this is, like, a third of your life.

    9. AWAlexandr Wang53:57

       There was something that Paul Graham said for a long time, which is like, you know, "If you think of your company as your life's work, like, you will operate differently." I did really genuinely think of, of Scale as my knif- life's work for the whole time that I was, I was working on it. That was a tough emotional process. And then I think what ultimately got me was, like, a combination of, wow, this is, like, a win-win-win-win, like, kind of all, um, it's, it's a good deal for all parties. And also just this, like, I saw the potential of what could happen at Meta. And you know, this is a point at which, like, I think Meta on AI certainly looked like damaged goods in many ways and, like, was maybe not the most appealing place to work on AI, but I think w- that kind of, like, triggered my entrepreneur side, where I was like-

    10. DSDavid Senra54:51

        'Cause you can g- essentially rebuild it. It's like a refounding of the lab.

    11. AWAlexandr Wang54:54

        Yeah. Yeah, like, y- I think this is [laughs] this is one of the things that ended up being kind of fascinating, which is like, because it was, you know, it was clear that there was so much work to do, it really, like, cleared the way for me to be able to, to kind of build a lot of stuff up from scratch, set up the right principles, build the right culture. Like, it, it created, uh, the conditions for us to build something

14. 55:19 – 58:13

    Rebuilding Meta's AI lab from scratch

    1. AWAlexandr Wang55:19

       amazing.

    2. DSDavid Senra55:19

       And is that how you and Mark discussed it? It's like, "Hey, we have to hit a reset here. Like, obviously, like, we're not-- If we keep on this path, like, we're essentially gonna throw out what we have and redo it." With a completely different le- uh, s- like, level of talent. Are these the conversations that are, that are occurring between you and him? It didn't seem like he wanted, like, a minor adjustment.

    3. AWAlexandr Wang55:38

       [laughs]

    4. DSDavid Senra55:38

       You understand what I'm saying? He's just like, "Oh, this is not working at all. If it's not gonna work, then let's, you know, rip it down to the foundation and rebuild." Correct?

    5. AWAlexandr Wang55:46

       It was an evolving conversation where I think there were, like... Like, one of the things that I believe really strongly, I think Elon is, like... has, has demonstrated this in, in various times in his career, where you just have, like, a small, incredibly cracked, highly technical team that's, like, very flat, and, like, if you do that, you can accomplish a lot very, very quickly. So that was something that I had a lot of conviction in, and, like, I believed in AI. Like, that is the right approach, especially if you had to, you know, do what we had to do, which is, like, move very, very quickly. And I think early on, that was one of the things that I talked to Mark a lot about, and I think he was excited about as well. That ended up becoming one of the tentpoles of our overall strategies, like small, very flat, highly technical team, very high talent density. How quickly can you move if you have those ingredients?

    6. DSDavid Senra56:36

       From an outside perspective, it just seems like Mark, like, empties the clip, is the way I think about this, [laughs] right? Where, um, have you ever read this book called The Mind of Napoleon?

    7. AWAlexandr Wang56:44

       No.

    8. DSDavid Senra56:44

       Okay. So The Mind of Napoleon, published in 1957. Um, I found it because I saw this interview. I think Tyler Cowen interviewed Sam Altman, and, uh, I think this was 2019, and he asked him, like, "What's the most important book that you read this year?" And he said, "The Mind of Napoleon." So it's very hard to find. I buy the book. It's 300 pages of just Napoleon, his own words, organized by topic, and in that, he talks about over and over again that hesitation is fatal. But he says, "Before you engage in a course of action, there's a lot of de- deliberation." He'll, like, study from every angle, make sure he's making the right decision. I feel there's, like, a echo with the way Mark, uh, Mark operates. Does a lot of deliberation, make sure he's on the right path, but once he makes that decision, he just goes all in. He won't, like... He just empties the clip, and that's the way I felt about, like, what it was w- was occurring right at, at this point, where he comes and gets you, and then you guys start rebuilding this entire organization.

    9. AWAlexandr Wang57:29

       I think he really has internalized that failure doesn't matter. It matters, like, when you win, how big you win, and I think this overall concept, I think, enables him to operate, I think, truly without fear in a lot of circumstances because I think he is just very comfortable taking lots of risk.

    10. DSDavid Senra57:46

        He's got that great line where it's like, "In a world that's changing all the time, the biggest risk is not taking any."

    11. AWAlexandr Wang57:51

        Yeah, exactly. I think he's internalized this quite deeply. Like, there's certainly, like, versions of the world where Meta looks a lot more like Google in some sense. Like, it's just like, you know, more slow-moving and more bureaucratic and just, like, looks very different as a company, but I think he, because he's internalized some of these lessons so deeply, keeps the thing dynamic and moving and

15. 58:13 – 1:05:55

    From founder to coach

    1. AWAlexandr Wang58:13

       live.

    2. DSDavid Senra58:13

       Talk me through the difference between how, like, how you were as, like, you're, you're the founder, you're running Scale. Now you're refounding this lab working with Mark, like, the different ways that you approach your work in those two, uh, environments.

    3. AWAlexandr Wang58:29

       At Scale, one of the things, like, I really internalized, and I don't even remember where this advice came from, but it was kind of one of these ideas that, like, if you wanna be a good founder, you have to be able to do every job. Had a very strong point of view about how, like, every little thing at the company should happen and should work, and that resulted in a company that was, like, very much so a product of my effort and, like, my point of view, and it, like, expressed itself in all sorts of ways throughout the company but also made me the bottleneck in very big ways. There's, like, pros and cons of that for a company. I think, definitely I think that's, like, you know, if you were to start a company and it's your first time starting a company, you should, like, definitely take that approach because it's, like, much more likely to be successful than if you don't take that approach.

    4. DSDavid Senra59:12

       Was Scale your first company?

    5. AWAlexandr Wang59:13

       Scale was my first company.

    6. DSDavid Senra59:14

       Incredible.

    7. AWAlexandr Wang59:15

       Yeah. But at Meta, like, it was a very different assignment because we had to accomplish so much in so little time. Like, I didn't have the luxury of being able to, quote-unquote, "do every job" within the lab. I, uh, certainly am not an AI researcher. I'm also not a, like, product designer. I'm not, you know, I'm not all these things that are, like, totally pivotal to, um, the organization being successful. So at Meta, very much so I adopted this, like, philosophy around just how do we create an environment where we're gonna hire incredible people. Like, that, that was, like, job number one, hire brilliant and incredible people, like, who are at the top of their field across every discipline.

    8. DSDavid Senra1:00:04

       Why do you think you're able to do that at Meta? It's just 'cause you have more resources? Like, w- why can you do that?

    9. AWAlexandr Wang1:00:09

       I think one of the things that was char- very charismatic and attractive about the founding moment of MSL was there was an opportunity to build a lab from scratch with a really, really small team, and we had a strong point of view of where we're going, personal super intelligence, that I think resonated with people. Like, I think everyone who works on AI wants it to help people, like, wants it to ultimately, like, mean something to their mom or to their grandma or to their grandpa, wants it to, like, be something that's, like, meaningful to every single person in the world. Because of the fact that we're doing this with the resources of Meta, there was an opportunity to... We weren't gonna be bottlenecked by compute. We weren't gonna be bottlenecked by, like, infrastructure. We weren't gonna be bottlenecked by, by distribution. Like, we weren't gonna be bottlenecked by the things that, like, startups often are bottleneck, but it was an opportunity to, like, really build something that you could, like, put your stamp on and have, like, a lot of influence over and really shape. I think that was, like, quite exciting for a lot of people.

    10. DSDavid Senra1:01:11

        And so that's why he chooses to spend an insane amount of money, what looks like an insane amount of money, getting a handful of top talent because then he knows that, that those top talent will recruit, in turn, other top talent, but you need that as a starting point, correct?

    11. AWAlexandr Wang1:01:26

        Yeah, and I think one thing that, like, is maybe somewhat underappreciated about that specific part is that, like- I mean, the stock prices of OpenAI and Anthropic like ran up a lot. So it just happened to be the case that like, you know, if you looked at how much these people were making, if they stayed at [laughs] OpenAI or Anthropic, like it also was like it was a lot of money. So y- I think for a lot of the talented ended up being like kind of net neutral in many ways, um, comp-wise. But obviously, I think it's kind of like the same thing. Like oftentimes we don't, we don't-- a lot of us don't think about how much like the early employees or the early researchers at these like labs that have had immense stock, uh, stock value appreciation, you know, what their comp looks like. We started this like fully on the belief of having and hiring like most brilliant people. That's one. Setting a clear North Star, personal superintelligence, um, those two. How do we create the environment where people do their best work? At Meta and in MSL, I'm doing much more of like quote unquote, "traditional management," so to speak, 'cause these are the things I'm like thinking a lot about, is like, how do I create the environment where all these brilliant people who are exceptional and very, very special can all operate and achieve the greatest expression of their talent? I'm thinking a lot more like a, almost like a coach in some, in some ways, versus at scale it was like very different. Like I was the auteur. I was like the person whose like point of view had to be expressed in everything that we did.

    12. DSDavid Senra1:02:59

        Say more about that you're, you feel like a coach.

    13. AWAlexandr Wang1:03:02

        I think that like my job is to, A, like help set the like North Star and the like directionality of where we wanna go. Which again, personal superintelligence, winning on consumer AI, like building AI that means something to every person. And then spotting in the team special talent and then giving people the environment and resources to be able to express that maximally. Like I think one thing that big companies often mess up is that there's like exceptional people at most big companies, but they just like are not empowered to do their best work, and that's why they often leave to go to smaller companies or to start startups or whatever it might be.

    14. DSDavid Senra1:03:44

        You mentioned Jeff Bezos' shareholder letters earlier. That's something he would repeat as well. In all the books I read about him, about all his shareholder letters. Like if you have great people that can't build, they're gonna leave.

    15. AWAlexandr Wang1:03:52

        Yeah, exactly. And AI is such like a, it's like such a special thing. Like I think, you know, this term research now it al- it feels like it almost doesn't mean anything. But like by definition, humanity doesn't know the limits of what these like models are capable of and what are the things you can do with them. Like they really are these like unknown artifacts that we don't understand super well. So like it really is science and research. Like we are exploring the limits of what you can do with this technology and what are the things that can be built. And it is a process that requires lots of trial and error. It requires like brilliant people to have incredible insight. Like it's one of these things that you can't think about this process like a very operationally intense process. Like people need space and time and resources to be able to like, you know, do their best work. And I think about it in many dimensions, right? Like I think Nat Friedman is absolutely brilliant. And as I mentioned, like so much of Muse, if there were a single person whose like single point of view came through on Muse, it is Nat. It's important for him to be in a position where, where he can like fully express all of that. And then we have some brilliant researchers. They have like very, very exciting ideas. How do we create an environment where they are able to explore those? And like most of them won't work out, some of them will work out incredibly well. The cases where that works out justify all the investment. So there's two kinds of problems, so to speak, and that analogy is like diamond mining or building skyscrapers, and it depends on what the distribution of the payoff of the things that you're working on are. So there's like some areas where the distribution is super power law. Like most of the things you do will be kinda useless, but then some things you do can be like literally a million times more valuable than everything else. VC, I think, is a great like diamond mining kind of industry, to give a sense. It's just all about like being able to identify the special ideas and like see those through.

16. 1:05:55 – 1:08:04

    The future billionaires at Alexandr's 19th birthday party

    1. DSDavid Senra1:05:55

       And that's easy to do. Just look at-- Just go to your nineteenth birthday party.

    2. AWAlexandr Wang1:05:58

       Yeah. I mean, that's like unbelievable. Um-

    3. DSDavid Senra1:06:01

       To people listening that don't know what the hell I just referenced, can you talk about real quick what, what-- who was at your nineteenth birthday party and why that was-- would've been a good bet if they just invested everybody around the table?

    4. AWAlexandr Wang1:06:11

       This is like a really crazy thing to think about. My nineteenth birthday party, I was a freshman at MIT. It was in January. I was doing a winternship, a winter internship, [laughs] a winternship at a trading firm called HRT. There were like ten of us, twelve of us, um, something like that, almost all of whom were students at either Harvard or MIT. Most of us already knew each other from various math and science or computer science competitions. Some of the other people were Jeffrey Yan, who went on to start Hyperliquid.

    5. DSDavid Senra1:06:41

       Yep.

    6. AWAlexandr Wang1:06:41

       Very successful. Um, Scott Wu went on to start Cognition, very successful. Another person there was, uh, uh, Jesse Zhang, went on to start Decagon, also quite successful. Um, [laughs] another person was Vicky Yi, who is, I think, a brilliant researcher, works at Anthropic. Everyone was like incredibly talented. I actually talked to the, uh, the founder of HRT recently, and HRT itself, by the way, now is like crushing it unbelievably.

    7. DSDavid Senra1:07:09

       What does HRT do?

    8. AWAlexandr Wang1:07:11

       HRT, it's a proprietary trading firm.

    9. DSDavid Senra1:07:12

       Okay.

    10. AWAlexandr Wang1:07:13

        So they, they trade their own money. In Q two of twenty twenty, um, six had eleven billion of revenue and like I think eight billion of profit is the, the recorded number.

    11. DSDavid Senra1:07:26

        [laughs]

    12. AWAlexandr Wang1:07:26

        This is, uh, I think it's under a thousand people work at HRT. I mean, it's like unbelievable. But I, I recently, uh- Met up with the founder of HRT, and they were talking about like, "Oh yeah, we're starting to do some private investments." I was like, "Just invest into the interns."

    13. DSDavid Senra1:07:40

        [laughs]

    14. AWAlexandr Wang1:07:41

        All the interns get seed checks.

    15. DSDavid Senra1:07:44

        [laughs] That's great.

    16. AWAlexandr Wang1:07:46

        I grew up doing, uh, math and, uh, science and computer science competitions and Olympiads and got to know like all sorts of people from all around America doing this kind of stuff. It's like quite surreal to see the people that I did competitions with become so

17. 1:08:04 – 1:09:21

    Diamond mining vs. building skyscrapers

    1. AWAlexandr Wang1:08:04

       successful.

    2. DSDavid Senra1:08:04

       It's gonna be fascinating what all of you do the next few decades. Uh, before I interrupted you, did you have more to say about diamond mining and skyscraper stuff?

    3. AWAlexandr Wang1:08:11

       Basically, it's like does it have a power law payoff where some things just like pay for like literally like a million times the investment, or is it something more like building skyscrapers, which I would, I would describe data annotation or, or, or data businesses as more like this, which is like Amazon Prime deliveries is another example where the payoff is pretty linear and you have to just get... develop a process where you're like really, really amazing at doing it. If you're building skyscrapers, then it's like a very operational business and it's all about like squeezing every ounce of efficiency out of that process and you just look at it and you figure out like, I can make it like 2% more efficient this way and 1% more efficient that way and half a percent more efficient that way, and you just try to squeeze every ounce of optimization out of it. And then there's diamond mining where you're kind of like, "Hey, everyone, just like, you know, go forth, try all your crazy ideas," and like you're just nurturing the ideas that seem promising.

    4. DSDavid Senra1:09:09

       That's what you feel you're doing at Meta right now?

    5. AWAlexandr Wang1:09:10

       That's what I feel I'm doing at Meta and, and, and the building skyscraper stuff, generally speaking, is what I felt I was doing at, at Scale. And that's like this big paradigm, um, paradigm difference.

18. 1:09:21 – 1:12:16

    Why Alexandr has more than 200 direct reports

    1. DSDavid Senra1:09:21

       Going back to the coaching and the way the difference of how you're essentially managing now at Meta compared to what you did at Scale, why do you have so many direct reports? Do you have like what, 100 direct reports? How many direct reports?

    2. AWAlexandr Wang1:09:29

       Yeah. I think more than 200, yeah.

    3. DSDavid Senra1:09:31

       How? How? And why?

    4. AWAlexandr Wang1:09:33

       Going back to it, like one of the th- one of the founding thesis of MSL and, and, and what we want to do and like rebuilding this, this lab was how do you have like really, really high talent density? How do you make it like the best place for a lot of these brilliant people to do their best work? And how do you build an organization that's like technically focused where the technical people are doing their best work and, you know, have, have the opportunity to really do their life's work? And one of the design principles around this was making it very flat. You know, effectively all the researchers that we hired into this group called TBD, they report directly to me, and it's more of a statement of the fact that like this is like anti-bureaucracy. Like TBD is like literally anti-bureaucracy. The point is we're hiring brilliant people, and you all have like demonstrated clearly that you can do brilliant, brilliant work. You have like done it before in your careers. So I don't need to be micromanaging you to do brilliant work. You're gonna be able to do that on your own. We need technical leadership, so some of you are gonna be responsible for like setting clear technical directions for your groups, and we have this kind of like pod structure where we have various pods and there's various technical leads of the pods who m- who tiebreak on the technical decisions. So there's like a technical leadership structure in place, but from a, quote-unquote, "people management perspective" or from a bureaucracy perspective, we're just anti-bureaucracy. And then the other part of this is like we do make a lot of group decisions. Like we want to debate because it is like a very talent-dense group of like brilliant people. We wanna have those conversations where we're all discussing what we think the right path is. Everyone in the lab has done brilliant things. Like we wanna hear all these opinions. We wanna arrive at things that we all believe are the best path forward. So it's certainly like an unconventional way to run the team and like I would be lying if I said I was like the best manager to [laughs] more than 200-

    5. DSDavid Senra1:11:31

       [laughs]

    6. AWAlexandr Wang1:11:32

       ... um, people. But this is part of the point is that like they don't need managers. They need a great environment. They are all brilliant. They're all extremely capable. They just need the room to cook.

    7. DSDavid Senra1:11:44

       Yeah. So maybe not a coach, but you're kind of like the steward of a great environment so they can do great work.

    8. AWAlexandr Wang1:11:48

       Yeah, exactly.

    9. DSDavid Senra1:11:50

       Alex, this was awesome, man. Thanks for taking, taking the time.

    10. AWAlexandr Wang1:11:52

        Yeah. Thank you.

    11. DSDavid Senra1:11:52

        It's awesome talking. I hope you enjoyed this episode. Please remember to subscribe wherever you're listening and leave a review and make sure you listen to my other podcast, Founders. For almost a decade, I've obsessively read over 400 biographies of history's greatest entrepreneurs searching for ideas that you can use in your work. Most of the guests you hear on this show first found me through Founders. [upbeat music]

Episode duration: 1:12:16