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

Origins: Personal Super Intelligence memo

00:02Tell me the history of Muse. The first thing to start with is probably like what our vision was when we started Meta Super Intelligence Labs. And when we started it, Mark published this memo that myself and Natt Friedman and a bunch of others like worked together with him closely on called Personal Super Intelligence. 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 muses ultimately become, but we were gesturing at it like

00:44very 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 endto-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 data sets you have to you know pre-train the model then you have to do pro training the model you have to build a whole stack for that. like is an it's

OpenClaw experiences and February memos

01:23a very lengthy end to- end process. Um and kind of this whole um production line at the start of this year um you 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 was happening at the start of the year. Natt Freriedman, who you know I work 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 he really like I think went all in. He trusted entirely. you know, he's told some of

02:15these stories that uh he had like a a Stripe Sessions interview where he talked about some of this where, you know, he told his open claw 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 Freedman is like a pretty good indicator

02:52cuz 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 is the ability to do so many things. And after hearing all of these crazy stories, I start 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 open claws and like you

03:30know, you know, really immersing ourselves in like what personal agents could be. >> What was your initial reaction? Euphoria or terror? >> Nat at the time he had this um he had built this prompt to do this like basically like full psychoanalysis of yourself. There were like multiple waves of like deep psychological probing. He basically wrote this prompt for you to to use an open claw. 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 3 years of therapy all, you know, compressed into this one moment. >> Wait, I'm curious why when you get your first shot at open call, this is one of the first things you do. >> You know, I downloaded it. I installed

04:11it. 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 >> okay um >> and then you know this crazy experience. >> Wait say more about the crazy experience though. >> Oh well it was just like very intense. I mean it was like again it was like 3 years of therapy because it was just like successive rounds of like deep uh psychological probing cuz 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

04:48had 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 and both of these we you know kind of like sent up through the meta board um cuz you know for whatever reason it's 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 end point in many ways. And then he wrote one called he wrote actually two uh which had very memorable blames the clause the law and trust is a must which ultimately defined um a lot of the

05:32a 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 a 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 know the early version of the Muse Charm was in that first that very first meeting. Not all of the ideas and not all the tabs were there but a lot of them were present. Then from there, you know, obviously that was February, we launched the product 7 months later was

Prototype and board demo

06:15like 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 as a contribution to the consumer world. And that that was like this very lengthy and quite painful process. And >> before we get there, I want to go back to the idea that you wrote the prototype. You guys created the prototype in like a one to two weeks. You show to the board. What's Mark's role in all this? >> He was like experiencing the sort of like euphoria/ terror of the product at

07:00the same time as all of us. I think he's talked about this in various interviews, 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 it would give him tips on like how to improve. there were like some staff meetings and by the way like you know this kind of 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 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

07:42because obviously the openclaw phenomena it peaked and then disappeared. 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 didn't like work that well and there were all these problems and then you know it just kind of like died down and so part of I think the building the product 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. >> Why were you able to do that and the

08:23developers open call were not? >> Well, first of all, I think we respect Peter Seinberger and everyone who's contributed to OpenClaw immensely. Like I think Peter is definitely a visionary. I think whatever happened in his beautiful brain that created OpenClaw is magical. And I think maybe the unglamorous part of what made 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 super intelligence broadly speaking but certainly personal agents as like >> say more about that and how that

09:11differentiates between your other competitors. >> Yeah. Well, this is this is kind of >> that's actually really interesting. >> 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 enthropic takeover in the whole industry and it was a very scary moment I think generally speaking which is 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

Why Meta and personal agents

09:50the 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 enthropic 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 super intelligence 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 >> well let's talk about that now >> okay we'll just do do the weave. I think

10:27one 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 want to 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 going to spend almost none of the time doing things that we have to do because the AIS will just 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

11:14these are products that represent what I, you know, I follow my interests. I follow my hobbies. I follow all 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 is an incredible winner through all this. That's like one of the first things that I believed about meta and super intelligence. And then ultimately a lot of those ideas made its way into this concept of personal super intelligence. 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

11:58interests. Like these are things that are very human. And then there's also the sort of like you can take the business lens which is Meta has distribution to three and a half billion people using the apps every day. And the natural area where Meadow was going to be successful is consumer AI. >> It's not work. It's not coding. It's personal. >> 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 2026. 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

Seven months of refinement

12:46refining this product. >> Yes. There was kind of the spectre 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 road map. 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

13:29build 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. >> Is that really the culture of meta too? This like constant like sanding of the products? >> Yeah. A lot of the culture of meta is like once you know what you're measuring, there's very talented people

14:04and you can optimize for those things, you can send out all the details, you can make those things amazing. I think in the sort of like preAI consumer 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 from Muse has been in the form of sanding out every detail of the model to be perfect for the product. >> I want to tell you about the presenting sponsor of this podcast ramp. I have been reading a lot about SpaceX lately. SpaceX is one of the most valuable

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16:38corner of the world. Deal is trusted by over 40,000 companies and growing fast. Learn how they can help your business by going to deal.comra. That is deal.com/enra. >> Did you see this profile that's written on Zuck in Colossus written by Jeremy Stern? >> I saw it. If I'm being honest, it was like too long. Didn't read. But >> it's 15,000 words. I just had Jeremy on the podcast. He's he's my favorite. >> I saw I saw I saw that. He's he's just 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 Zach says he was talking about the advantage that he has relative to I think Elon and like Sam Alman and

17:18he'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 Alman and others. like I'm not going to 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 7-month uh stretch of trying to refine and build muse. >> I think that's exactly right. Like I think that it took immense patience. I mean 7 months like you know maybe it doesn't sound like that long but AI is like fierce. seven months in AI time. >> Seven months in AI time, which maybe

17:56like a decade and like nonAI 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's a lot of doubt in the company for that entire period like people obviously the one of the dominant Wall Street narratives is like

18:38oh my god Gosh, Meta is just burning all this money. Like, are they even going to pull anything off in AI? >> 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 the record starts talking to his competitors, including one former researcher that is now at a competitor that used to work for with Zuck and is now at another lab. And he goes, "I don't want to have to compete with them cuz Mark's like the [ __ ] I think the line is Mark is the [ __ ] Terminator." And it was like he just does will not stop. And he's like uh then he talks about um one of his superpowers that he's never happy with

19:14the state of the product. So he always wants to constant prove but he's not an [ __ ] Like he's not you know dict he's he is definitely a dictator but like he's not you know rude and like kind of will destroy the chemistry of a team. He actually talks about in the in the piece that team cohesion is excessively important to him. >> I think it'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 a 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

19:53one of the greatest consumer products ever. But you're dealing with models, you're dealing with something nondeterministic, 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 used it. I want to talk more about this process. I'm curious how much resources, how big the team, how many people 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 turn out like do

20:30you have any information on this? You can see this from like a lot of the other agent products at the start of the year like almost everyone turned 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 different thing from what most 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 kind of like old news because they've had to like you know download

21:17agents and have been using them for a while. But for most people like just even that graduation process like you know 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 because 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. >> But how'd you know it was ready to be

Launch readiness and team size

21:56released? Part of it was was pretty numerical which is like we had set you know you go back to that spreadsheet of 100 plus things. >> We had set for each of those rows what threshold is launch blocking and above what threshold is good. We had many checkpoints that like were like green on 80 of the rows but red on 20 of the rows. 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 Musepark 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. So going back

22:38to that seven-month period you were talking about, how many people were working on the product >> between the model work, model work and the product work? Like under 200 people total, which I guess sounds like a lot, but in big company land is >> it's actually a lot smaller. >> It's not very many. That was kind of cool looking back on this and thinking about it is like we knew this was going to be the most important product that the whole executive team, everyone knew that this was probably going to 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 like it was a really small focused team working on it and that it didn't balloon to this like oh

23:20you have like you know huge swasts of the company working on 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 experience. Say more about this. Why did you just call it like a single piece of art and you need a single perspective or point of view? 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 Croninberg 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 PM hell in some sense where like every PM has

Single point of view and Nat Friedman's taste

24:11like a thing that they're trying to get into the product and jam into the product and then all that kind of like blends together into this like like smoothie almost. People feel it as users and consumers like you feel 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 is one cohesive experience that we're trying to deliver um to the world. I love that you said that and it just happens to be pure coincidence. Like we're I don't know when this is going to come out, but

24:47we're recording this on the 15th 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 15 or 20 episodes on them 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 10,000 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. In Muse's case, like Nat Friedman deserves a lot of credit. like his taste and sensibilities ultimately dictated

25:25and shaped what that product became. >> Do you talk about how many people are using Muse? Are you guys talking about this publicly or no? >> We have said millions and then I have retweeted other people who have shown graphs where we would imply that Muse is the fastest growing consumer app of all time. Consumer AI app of all time. >> So you won't say a number though. >> We've said millions. Yeah. Well, millions could be hundreds of millions. That's also millions. So, you're just not adding the the the beginning to that. I want to like go into like you've been kind of going like uh and I mean this in a loving way like unhinged on X. So, you talk about like your is this like marketing strategy? Like what

Unhinged marketing on X

26:07is going on? Like I feel like you're there's a distinct difference in your public tweeting since Muse launch. What are you doing? >> There's a few pieces of what's happening here. This has got to be more fun than scale AI when you were building scale. It has to be. 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 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

26:46kind 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 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 happened to have met and um become friends with like memers and and uh I have these uh I affectionately call them my skitso

27:31friends but but people who like literally they spend all their day like thinking about memes in the internet and and understanding that 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 to be used. Um, turns out you just have a collection of schizophrenic friends that are terminally online. >> The X feed, just for what it's worth, is like literally every one of those tweets I write. There were like a few a 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

28:08it 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 co-workers and then during the dinner I was like wait I figured it out and then I like posted a different me. It was like it was almost I almost felt like I was like an engineer solving bugs again cuz it was like you know you could tell when something was going to click. But yeah so one things we wanted to break through. Another thing that like I just thought was important is like how do we make meta cool again? cuz like for whatever reason despite having like these incredible pieces of cultural software um like Instagram and and uh Facebook historically meta was not cool

28:49for 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 [ __ ] it post. And then some of those tweets like did really well. Have you seen this like time spent thinking about it and then banger you seen that? Great.

29:29>> I haven't se but I agree with that. That's like I just some of these 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 kind of 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 co-workers uh I think were somewhat surprised at uh 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

30:05bunch of meetings. He was like, "I thought we were going to 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." 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 know, we don't have the tracking and it's like hard to tell, but I actually think it actually genuinely made an impact on grow growth, which is kind of hilarious. >> I definitely think it did. I I can't stop hearing about it. So, and all the people I'm hearing about it from also downloaded the app and are now using it.

30:41Go 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 going to come back to and actually sticky. So, like, how did you do this with Muse? >> 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 the experience? Like how do you get someone to 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

31:14used 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. >> How are you using the stories though? Cuz you you guys aren't running ads for me, are you? >> 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. Like I was just >> anytime I saw someone do something interesting about news I was like quote tweet. I couldn't understand why both Enthropic and OpenAI's um ads are so bad because remember Anthropic was doing

31:54this huge like outdoor campaign and it's all about them and same thing with um Chet. It's like they'd have like the the icon which I don't even think people recognize that as a logo and it just say CEX and just be two people sitting at a desk. I'm like what the [ __ ] 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. That's it. >> You have millions of them. Run those ads. It's it's so like elementary that it's interesting that people make this mistake over and over again. >> Yeah. I think one thing that we came back to a lot in in both building news as well as just like building stuff in general at me was just like why would

32:29someone care? They don't care about you or your company. They care about everybody's self-interested. This is like I'm there's this very old book. It's called Scientific Advertising. It was written in the early 1900s. is now it was so valuable that the guy that made the most money as an advertising agency founder in the like 1920s and 30s he it was written by this guy named Hopkins who was this best copyriter he's like these are all the ideas how I'm making this money I'm putting this manuscript in a safe for 20 years right but essentially it's not about advertising it's about human nature which doesn't change and his whole point was just like your ads are about you your ads about your product your ads are

33:01like hey I have this thing buy people don't care about that they're self-interested and if you just all you have to do is just do the work of what this does if for you and you just lead with that and there's people that took that ideas those ideas were written in the early 1900s and went on to build massive companies advertising agency foundations in the ' 50s ' 60s7s and even to this day >> yeah 100%. I think there are a few insights. Let's say first is most people in the world, they don't give a [ __ ] about like OpenAI or the research or you know these other companies or the research or even us or our research or whatever. They care about like what it can do for them. We had this gift of the

33:38mascot which was the little jolly guy. I remember the tweet where I where I realized I was like, "Oh, wow. This is mileage." because I was basically I was um I was driving into work uh and while I was on the way I like generated a a version of the guy holding a briefcase and I tweeted MFW I go to work on Monday. I remember thinking like this is like one of my best tweets in a while and then I kind of realized like you can put him into a lot of situations. One thing I like kind of realized as we were going through that process was like how do you create surprising moments that are very different from any other marketing that you've seen of like a product. I don't like really remember the last time someone like you know

34:23leaned into racy memes to like market a product. Um I don't actually know if it's been done before but I kind of like realize like oh wow this is like a thing that we can do with this little guy. And that's something that like is interesting. That's something that like drives intrigue to like the average person. >> 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 the app is on their phone? 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. You're like, "Oh, wow. It did that." And then

Retention: the trust fall cycle

35:02you 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 going to 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 fault. They just like

35:36are 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 etc. 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. I found one of my all-time favorite quotes when I was reading the book 0ero to1. 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

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36:55to get started quickly before all of your competitors are on AppLoven. And you can do that by going to apploven.com. That's apploven.com. I wonder how much of this is actually driven by word of mouth. So, obviously talking to you, I was going to 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 was kind of like open-ended like a you know like a open text box but a friend of mine who's running like a hundred billion dollar company he left his ID on a flexjet and then he went through and he's just like

37:33well I want to he tell me I don't want to be involved in this process at all you have to figure out how to get the ID that 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 then like arranged the courier went there got to his security in his office somehow and got upstairs to his office like and it was like holy [ __ ] this is the best marketing. It's just three or four screenshots of Muse going back and forth and him saying, "I don't want to be involved. You have to do everything." And it figuring out on the way. Yeah. Know, this is actually one of the things about um uh I don't know how it was like somewhat intentional, but definitely like modern

38:08product 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 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 have. So, are you on your Muse like all day long? like what is your own personal usage? >> I mostly like set up a lot of workflows

Philosophy: agency and dreaming bigger

38:49using it and then I um I guess my meetings a lot of the time but um so in practice like I think in my job quoteunquote I'm supposed to like pay attention and and be pretty present and interact with people. I set up a bunch of workflows and like kind of treated as like a second brain. >> Yeah. You said that in the post that you wrote and it was like really short and I think I saw last time it was like 5 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. 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

39:31relationship 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. And before you go on, what is your own personal view though? >> 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

40:10bigger 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,

40:48but I do believe in this mod in this like form factor long term of everyone having an AI that supports them and what they want. So you see AI as kind of like removing all like the cludge of life, right? The stuff that we don't want to 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 the stuff you want to do. 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

41:31where 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. >> No, it's been beaten out of them. The life is beaten beaten out of them. 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

42:12promises 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. You know what I was thinking of when you were speaking just now? You you mentioned earlier this this experience that you and Nat Freeman went through where you had this like disturbing uh psychological audit and like coming in waves from this AI. It's like yeah, what

42:52if we had that but it's like the opposite of what you're saying. It's like actually gave you more self-confidence like gave you 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. >> 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

43:24entrepreneurs 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 bigger 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 cuz you all these commitments. You maybe you have a family, maybe you have whatever it is. It's just like it's almost you become trapped in the sort of like zombiehood. There's one more thing on miuse before I want to get to scale AI and like the the partnership that you do that you did with Meta and making

44:08that decision. But in this essay that you wrote or this like short postage you wrote, I love what you said. 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." 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

44:47like diverse. 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 but but just like somehow manifested into like reality for humans. Like it could just be really awesome. So I keep hearing uh this line from that show where it's like the universe eats smart people. >> Um okay so I want to 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

The Scale AI deal with Mark Zuckerberg

45:31Zuckerberg reaches out and says, "Hey," I assume he says, "Hey, I want to talk. Can you tell me about this?" >> Yeah, I think the exact message was something like, "Hey, do you have time for a call?" >> Did you have a relationship with Mark previously? Did you spend any time with him? Like what was what's the background there? I had a friend who uh 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 is currently the chief data officer at Meta but was longtime Meta executive lieutenant and he introduced me to Mark I want to say in about like I think it was 2021 or 2022 to talk about AI because I think at that time Mark was

46:11getting 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 1 year to schedule that first meeting. Like from from the intro to the meeting being scheduled, I think it was like a full year. Why? 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

46:50times kind of after that and had gotten his advice as a founder to get advice from you know Mark Zuckerberg obviously 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 write like write a very rigorous like long response very quickly. It's like a very impressive skill of his. >> Gives you a sneak peek in what's going on his mind. >> Yeah. Like yeah, there's racing. Meta

47:29was 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 end point was going to be because the first conversation was just like, you know, this was after Llama 4 and it was kind of just like asking what do you think we should be doing? Why is that an important point that this is this conversation is happening after Llama 4? >> 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

48:12where AI was clearly becoming just like increasingly increasingly important. It was clear that AI was going to be like really critical to the future of meta. So yeah the first few conversations we spoke on the phone and he was sort of as 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? 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 asked for advice constantly he's like hitting us

48:45up he's like here's what's going on in my life or the work would you do and he'll do that over and over and over again I think this is like quite an impressive trait yes 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. Another thing that world-class entrepreneurs have in common. We had Jonathan Ross, the founder of Grock, 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,

49:23"From the time first call from Jensen to money's in my bank account was 3 weeks." >> Yeah. That's amazing. Um, I just had lots of ideas on like what I'd be doing if I choose and 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 focus on the things that people want to do is actually one of the most specially placed companies with this incredible shift 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.

50:04>> So when he says that what do you think cuz this kid you this was not predictable to you. >> 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 >> Well, you know what that means when they say that, right? They want to buy you. It's always the same thing, but they say it's like, "Oh, we got to find a way to work together." >> Yeah. I don't think I'd had that that kind of coaching at the 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 think I personally was like, "Yeah, like you know, maybe he's like, you

50:41know, teasing it or or like throwing it out there, but like does he even like make that much sense?" >> Why wouldn't you think it makes sense? >> I mean, obviously like a deal ended up happening, so you know, what do I know? But um >> No, no, but back then, not we know what happened, but like I'm very curious your thinking as you're going experiencing this like the fact that you said, "Oh, yeah, you said this was like that doesn't make sense." Like why wouldn't it make sense? >> 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

51:22know a entirely different kind of business from from Meta. And so the the logic that I had thought at the time would have made sense is if meta wanted to get into those things that then I think you know like you know maybe that's that's the industrial logic that would have made sense but this wasn't that cuz I think Mark clearly was like predominantly interested in getting the llama program and over his overall AI program on >> he wanted talent. >> Yeah he wanted talent. It was kind of shrouded and unclear the whole time, you know, a lot of the way. >> When did it become clear? After the money hit the account when, you know, when we started talking numbers, I was like, "Oh, wow. Okay. All right." Um,

52:04that's when things became like more clear. >> At first, you're like, "Oh, this this will make sense." Then you guys keep talking. And how fast did he convince you like this is a good path for you? >> There was a part of it was just like, "Wow, this is like a really fascinating deal construct." We ultimately landed on this deal where you know Meta invested uh owns 49% of scale scale continues. Myself and a few people join join 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 winwin win so to speak. There's a win for the shareholders of scale because all shareholders of scale got a great deal, benefited a lot. Scale

52:52continues and I truly believe the best days of scale are ahead of it. 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 Mark I got to think this is so [ __ ] crazy. I got to think about this for a few days. Like explain this process as much as you can. 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

53:33itself. 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 >> cuz you worked on scale for how long? How many years? >> Yeah. 9 years from founding until the >> And you're still young. You're not even 30 yet. So this is like a third of your life. >> 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 scales my life's work for the whole time that I was I was working

54:11on 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 winwin 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 that kind of like triggered my entrepreneur side where I was like >> you can essentially rebuild it. It's like a refounding of the lab. >> Yeah. Yeah. Like I think this is this is one of the things that ended up being kind of fascinating which is like

55:01because 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 a the conditions for us to build something amazing. >> And is that how you and Mark discuss 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 going to throw out what we have and redo it with a completely different uh like level of talent. Are these the conversations that are that occurring between you and him? It didn't seem like he wanted like a minor adjustment. You understand what I'm

55:39saying? He's just like, "Oh, this is not working at all. It's not going to work. Then let's, you know, rip it down to the foundation and rebuild." Correct. 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

56:19on that was one of the things I talked to Mark a lot about and I think he was excited about as well that ended up becoming one of the tent poles 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 From an outside perspective, it just seems like Mark like empties the clip is the way I think about this, right? Where um have you ever read this book called The Mind of Napoleon? >> No. >> Okay. So, mind and Napoleon published in 1957. Um I found it because I saw this interview. I think Tyler Cowan interviewed Sam Alman. And uh I think this is 2018 and he asked him like what's the most important book that you

56:54read 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 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 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 was was occurring right at this point where he comes and gets you and then you

57:27guys start rebuilding this entire organization. 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. >> 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. >> 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 slowm moving and more bureaucratic and just like looks very

58:06different as a company but I think he because he's internalized some of these lessons so deeply keeps the thing dynamic and moving and live. 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 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 want to 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

58:45happen 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. >> Scale your first company. >> Scale is my first company. >> Yeah. But at Meta like it was a very different assignment because we had to accomplish so much in so little time.

Managing MSL: coach, flat structure, diamond mining

59:23Like 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 going to hire incredible people like it was like job number one hire brilliant and incredible people like who are at the top of their field across every discipline. >> Why do you think you're able to do that at Meta? Is it just because you have more resources? Like why can you do that? I think one of the things that was

1:00:13charis 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 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 going to be bottlenecked by comput. We weren't going to be bottlenecked by like

1:00:55infrastructure. We weren't going to be bottlenecked by by distribution. like we weren't going to be bottlenecked by the things that like startups often are bottlenecked, 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. >> 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. >> Yeah. Yeah. And I think one thing that like is maybe somewhat underappreciated

1:01:29about 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 Open Eye or Enthropic like it also was like it was a lot of money. So I think for a lot of the talent it ended up being like kind of net neutral in many ways. um compwise, 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

1:02:15having and hiring like most brilliant people that's one Slar personal super intelligence 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. Cuz 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 aur I was like the person whose like point of view had to be expressed

1:02:58in everything that we did. >> Say more about that you're you feel like a coach. I think that like my job is to a like help set the like north star and the like directionality of where we want to go, which again personal super intelligence, 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 startups or whatever it might be.

1:03:44You mentioned Jeff Bezos's shareholder letters earlier. That's something he would repeat as well and all the books I read about him but also shareholder letters like if you have great people that can't build they're going to leave. >> 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 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 world. So like it really is science and research. Like we are exploring the limits of what you can do with this

1:04:24technology 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 opishly 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 in many dimensions, right? Like I think Nat Freiedman 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 now 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

1:05:07brilliant 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. Like most of the things you do will be kind of useless, but then some of the things you do can be like literally a million times more valuable than everything else. VC I think is a

1:05:46great 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 >> and that's easy to do. Just look at just go to your 19th birthday party. >> Yeah. I mean that's like unbelievable. Um >> do people listening that don't know what the hell I just referenced? Can you talk about real quick what who was at your 19th birthday party and why that was would have been a good bet if they just invested everybody around the table? >> This is like a really crazy thing to think about. my 19th birthday party. I was a freshman at MIT. It was in January. I was doing a win internship, a winter internship, win internship at a

1:06:21trading firm called HRT. There were like 10 of us, 12 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 Hyperlquid very successful. Um Scott Woo went on to start Cognition very successful. Another person there was uh uh Jesse Zang went to start Decagon also quite successful. Um another person was Vicky Yei 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. Unbelievable.

1:07:09>> What does HRT do? >> HRT it's a proprietary trading firm. So they they trade their own money in Q2 of 2020 um 6 had 11 billion of revenue and like I think 8 billion of profit is the the record number. This is a 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. All the interns get seed checks." That's great. >> 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

1:07:59surreal to see the people that I did competitions with become so successful. >> It's going to be fascinating what all of you do the next few decades. Before I interrupted you, did you have more to say about diamond mining and skyscraper stuff? 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

1:08:37skyscrapers, 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." >> That's what you feel you're doing at Meta right now. >> That's what I feel I'm doing at Meta. And and and the building server stuff generally speaking is what I felt I was

1:09:15doing at at scale. And that's like this big paradigm um paradigm difference. Going back to the coaching and the way the difference of how you're essentially managing now at meta compared to way scale. Why do you have so many direct reports? You have like what 100 direct reports? How many direct reports? >> Yeah, I think more than 200. Yeah. H >> how and why? >> Going back to it like one of the 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

1:09:56focused where the technical people are doing their best work, 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 going to be able to do that on

1:10:36your own. We need technical leadership. So, some of you are going to 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 who tie break 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 want to have those conversations where we're

1:11:13all discussing what do we think the right path is. Everyone in the lab has done brilliant things. Like we want to hear all these opinions. We want to 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 more than 200 um people. But this 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. >> Yeah. And so maybe not a coach. You're kind of like the steward of a great environment so they can do great work. >> Yeah. Exactly. >> Alex, this was awesome, man. Thanks for

1:11:51making taking the time. >> Yeah. >> 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.