Alexandr Wang: “This is a Once-in-a-Civilization Opportunity”

[MUSIC PLAYING] All right, full rock star treatment for Alexander Wang, everyone. All right. [APPLAUSE] So why don't we start out backstage. We're saying, one of the cool ways to think about this event is this room is actually full of people who are just like us. But when we were 18 or 20, there's 16 year olds in this audience. Let's jump to your story. I mean, it came up always really smart, like, math-alumpiate, like jump us to the Alex of that time. What were you feeling, what were you thinking, and what drove you down this road? Yeah, well, I grew up in New Mexico. Well, Southwest New Mexico, which now, often, are famous, but it really was the middle of nowhere. And I remember-- I did all these math competitions, all these computer science competitions. But then I knew I wanted to do really big things, and it was like, not exactly clear how or what the exact path to do that would be. And I had a friend who was really into programming. And after high school, I got an internship in the Valley. I think his first internship was a volunteer. And he was kind of this influence for me. And so after I finished high school, I ended up working at Cora, here in Silicon Valley. And then I worked there for a year. I took a gap here to work there. And then I went to MIT. And this was-- I was 19 when I worked at Cora. I was 18 when I went to MIT. And there was 19 when I started scale. And I remember this period from 17 to 19. It was-- I felt like I was constantly changing. You know, exactly what I wanted to do was constantly changing. I was learning so much just from the people around me. And it was just like-- I felt like I was drinking from the fire hose pretty constantly during that time. And I would definitely recommend-- the two things that were really important. One is, I think working at a company was really valuable. Because I think from the outside in, you have no idea how companies work. You have no idea what it looks like to actually build something. You have no idea what it looks like to iterate on something. You have no idea what it looks like for groups of people who make decisions. And so I thought that was really important. And then going to school at MIT was actually really important, because it just gave me a lot of opportunity to explore what was interesting. And so it was at MIT that I started training my first models, and that I played around with TensorFlow, which had just come out that year at MIT. And ultimately came up with the idea of scale. And then after one year of MIT, I applied to YC. You know, I felt like kind of like a miracle to get in at that time. And YC was really critical to my entrepreneurial journey. Like I don't think-- YC is this amazing blend of-- you know, they're very supportive. And they obviously want you to succeed. But they also give it to you very real. And they tell you when you're being a dumbass, which I think is, you know, this we all need in life. So yeah, that was, I think, the story till there's 19-start scale and the rest of history. I guess you work with Jared Friedman at the time. And you came in with actually a very different idea than what ended up becoming scale. Yeah, so we wanted to build like an AI agent, for people like get medical care. And it was like the right-- it was a great example of an idea that I think will ultimately exist. Like I think we're even seeing it now. Like AI agents to help people get medical care are very real. But it was the wrong timing. And we worked on it for about a month or two before Jared pulled us aside and we're like, guys, this is-- I don't know if this is going to go anywhere. And that's exactly what we need to hear. And it was at that time when like, you know, I studied AI to my tea, I had like trained models. And we thought we sort of went back to the drawing board, thought deeply about where the opportunity was, and came up with scale. I guess selling data at the time, you know, large language models were not even had not really come to the fore yet. But self-driving cars were sort of coming up. And computer vision suddenly became-- so that was sort of the first market, is that right? Yeah, so the story here is that like, when I was at MIT, I did a bunch of projects like train models of various forms. And these were like, you know, by comparison today, they're like little toy models. And I remember the train model. I needed three things. I needed a GCP account, like I needed an account on some cloud service to get compute. I needed the code to run to actually train the model. And I needed data. I needed a data set. And for two out of these three things, you could just press a button online and get them. And then for the last one, for data, there was no effective way to get data for training these models. And so it felt incredibly obvious that this was going to be the future. That there was going to be a way to press a button, so to speak, and get data. And it was very funny, because in the years that we followed, like in the first many years of scale, data was very unsexy. So every time we would go out to fundraise, even though our numbers were great and we had great revenue, DCs and investors would always be very skeptical. They'd be like, oh, I don't know if this is a good business. Does that want Jeviti? Is it durable? And it was really weird to me. But none of the investors had ever trained a model. So I guess they didn't really get it. And fast forward to today, we managed to raise money. We managed to keep going, managed to keep growing the business. But the very same investors who passed on us and were very dour on the potential of AI are writing think pieces today about how data is so critical and is one of the biggest business opportunities in AI. So it's very funny to see that whole thing come full circle. It seems like that's actually a real good case study in first principles thinking. Like you can't start a company by opening the pages of the Wall Street Journal and saying, well, this data is hot. Like we're going to go work on that. You literally couldn't have started scale that way. You had to start from, I think, like simple statements that are about the world that you know to be true. And then sort of building something for that. Yeah, I think the key thing is you need to develop conviction in a set of beliefs that nobody else agrees with. Like I think if you look at all the most successful companies in the world, they were started at a time long before the sort of like core idea was popular. And they work on that. They toil in obscurity for years and years before the idea or the space or the concept of the business becomes consensus. And the only way you're going to be successful is if you're able to identify these truths about the world early, long before everyone else. And I think that like, I mean, one of the most surprising things, like scale we've been working on AI for a decade, you just, you can't base your business decisions based on what everyone else is saying around you. Like if you go too much with the herd, you will get immensely confused and you will end up nowhere. And so you have to develop your own compass of what you think the future is going to look like. Because everyone else will just confuse you. It seems like one of the things you got incredibly great at was you start with this kernel of like we believe that X and nobody else believes it. But then the mechanics of building the business are talking to investors and convincing them and not letting them demoralize you, talking to customers who I mean should just get it. And then especially like convincing people to come work for you. Yeah, I think that the, these early mechanics of building a company, like these are things that I think you might have some predisposition to be good at. But like nobody is good at starting a company when they start a company. And I remember talking to a lot of the investors who I met very early on and they, you know, a lot of them would say like, oh, like, you know, you just grew so quickly and you changed so quickly. And like I didn't, you know, I didn't see it at the time. And I think that's probably true for literally everyone who starts a company. Like nobody is, nobody is good at something they've never done before, right? And so I think for all entrepreneurs, you start out pretty shity at everything. And the whole game is how do you develop yourself to continuously improve to get better and learn quickly? Backstage we were talking about this is actually a really lucky time to start a company because, you know, obviously you can come to YC, you know, the people in this room have each other, which is kind of wild. But not only that, now you have a ideal personal AI that's going to tell you, you know, hey, these are some ways to do it. Do you think that would have helped you like accelerate even faster? Like, you know, talk to them. What do you think it's like to start a company today with AI in the age of AI? Yeah. I mean, I really think, I think we're at this like, an amazing moment in the world where the bottleneck is not the progress of the AI models. The bottleneck is diffusing that through the rest of the world and helping the world adapt to this amazing technology that already exists. Like, I think if the models didn't improve it all from today, there would still be like, decked. decades and decades of total upheaval and change in the economy and how the world operates and everything around us. So I think as a result, it's like one of the most incredible, it's probably a once in a civilization opportunity to be a dreamer and to have a vision and to have ambition and to impose a view of how the future world should look by building something amazing. One of the things that we were chatting about backstage is when I started scale, 10 years ago, if you started a company, you had to be, it was like David versus Goliath and you had to be clever and you had to find an angle into the market and you had to figure out a way to compete even though you had much fewer resources. And now I actually think with the power of agents and AI broadly speaking, it's much closer to Goliath for Scoliath. Like I think, but maybe the startup is like a mecca Goliath that is like vastly enhanced by the power of agents and AI and you know the large companies are the sort of like more traditional Goliath so to speak. But I think that startups now like if you properly embrace AI agents and figure out the way to leverage their strengths in the most like ambitious ways, you can easily outcompete and convince. So let's talk about super intelligence because that's clearly, that's even in the name of your lab. What does super intelligence mean operationally inside meta right now? Yeah, I think that you know, we a year ago, Mark wrote this memo about personal super intelligence. I think actually it's very similar to your concept of personally GI, but you know, we believe that everybody in the world, you know, all the billions of people in the world are going to have a super intelligence that is adapted and tailored to them that is, enables them to accomplish their goals, knows their context and ultimately is an expander of their own agency. Like I think the thing that we think a lot about is is agents expansion. How do we help people accomplish things that they couldn't have ever dreamed of before? And what would everyone in the world do if everything was just easy? And we think about this in an ecosystem way as well. I think Patrick mentioned it, but you know, we don't believe in this totalizing, you know, totalitarian view of, you know, AI's that control the world. We believe that these are going to enhance this very broad ecosystem. So, you know, we believe in billions of people all around the world all having their own personal super intelligence and we also believe in, you know, an explosion of entrepreneurship. There's 200 million businesses that are on Metas platforms today. We think that numbers should go to billions with this explosion of creativity and using AI tools. And ultimately we think that, you know, there's going to be this like dynamic ecosystem of business agents working with, you know, personal agents and developing this sort of like complex ecosystem that is fully AI supercharged. So I was really psyched to see Metas Spark 1.1. My open claw absolutely loved it. How has running a frontier lab been? The Metas Spark level is sort of the opus level. What's coming down the pipe? And also I think that you're increasingly looking at open source, which I think this audience really loves. Yeah, yeah. So I think it was, it's been, you know, I've been at Meta for about a year now and it's been quite a year. I think, you know, getting in and, you know, Meta we've talked about it publicly like Lama IV wasn't on the trajectory that was needed for Meta. And so I got in there and we kind of did a zero-based build of how do you, you know, build an entire frontier lab, you know, in some ways kind of from scratch, obviously using a lot of what we had and move as quickly as possible. And so within nine months of that moment we launched Meta Spark 1 and then two months later we launched News Image and Meta Spark 1.1. And you know, there's a few things that I think have really struck me about this. You know, the first is talent density was incredibly important. That was the core thing to bet on and like talent density is something that compounds naturally. Like the more talented people you have, the more of the most talented people want to join you. You know, and I think it's kind of amazing to see on the inside, but you know, frontier AI work is research. Like we are, it is scientific work. We are exploring what can you do with these models? How can you push these models? What are the reaches of what to be accomplished with these models, which requires a totally different mindset and operating model than, you know, existed for internet companies or internet products and whatnot. There's a lot more about experimentation, about science, about scaling. And everything ultimately is about how do you develop a lab and operating model system that will just be able to compound with all of the exponential growth that will happen in the ecosystem. Both the exponential growth capabilities, the exponential growth compute, the exponential growth in adoption and usage. Like these are all, we are on this like very, very steep exponent across maybe every dimension of the ecosystem. And it's important to develop like an organism, that's how you think about the lab. is able to grow with that. No, it's been very exciting and we're going to be shipping a lot more. So I think we just launched Mooseport 1.1, which was a great model. We're going to continue to have updates on the Moose Spark line. We'll also have bigger models on the way that I think will be much more competitive with even the very best models that are out there today. We're going to be launching a harness soon and have been working on a harness to help empower all the developers and agentic developers out there. And then we're also, as you mentioned, we're working on open source models. And we want to kind of as I described before, we believe in a decentralized world of AI capability and progress and development. We want to empower the broader ecosystem and everyone in the world to be able to build and develop using this technology. And so we have a lot of exciting things on the way. And I think we want to empower the ecosystem and developers as much as humanly possible. I mean, it sounds like one of the ways-- I mean, certainly when I was using Moose Spark with my open claw, it became clear that it was as good as Opus, especially for that sort of agentic flow with skill files. But it was like 8x cheaper, actually. Yes. Well, I think this goes to it. I don't believe in a world where these models are so expensive that they get rationed only for the most wealthy of developers and companies. It's important for everyone to be able to use the technology and to build whatever they want to build with it. And I think that we take a view-- I think the best AI products have even been developed yet. If you look at the AI ecosystem and everything that's happened, every wave is 10 times bigger than the past wave. So when I started scale, the first wave was maybe self-driving cars. Self-driving cars are really awesome. They're really, really cool. But that was like pales in comparison to large-language models and chatbots. And chatbots became this thing that was probably 10 times bigger even than self-driving cars. And then there were coding agents, which came a few years later. And coding agents were probably 10 times bigger than chatbots. And I think we're just on this steep curve. We're going to keep seeing these new modalities, inform factors, and developments of the AI paradigm that will each be dramatically bigger than the last. And so our point of view is like, let's unleash the ecosystem, let's explore and let's see what's build the future of the world together. So what's the best way to actually take advantage of the coding model for MUSE Spark? It's open code, right? Yeah. Today, the easiest way is to use open code. We have onboarding on the website. And then soon we'll have a harness of our own. And ultimately, I think we want great models that plug into all of the available harnesses and empowers much sort of combinatorial innovation the ecosystem is possible. Yeah, I know the harness is underwrap still, but can you tease us with-- I still use OpenClaw. I still use Hermes agent. These things are-- I call them ferraris that break down on the side of the road all the time. Is this a Ferrari that won't break down? Tease, that's a little bit. Yeah, hopefully it doesn't break down. I mean, I think we're really focused on speed. I think speed is-- for anyone that uses these tools, speed is probably one of the most critical things. I think also reliability, like you mentioned, we want to be extremely reliable. We want to be very extensible and to scale to, as complex and interesting of a multi-agent setup that you want to have. I think there's so much innovation that will occur even above the harness, frankly, in terms of how to orchestrate and set up loops and develop like very complex ecosystems of these agents working together. We want to be really extensible. And ultimately, we want to just empower people to harness this technology, because harness-- [LAUGHS] No pun-- actually, pun not intended. But there's-- I truly believe these models are already just incredibly powerful. They should be so powerful to fuel many, many points of expansion of GDP growth. And I think it's up to smart people with vision and ambition to make all that happen. Let's see. So one question. I mean, when you look back on the decade, what do you think those say was obvious in hindsight about AI that people are just missing in real time right now? You know, so much of the debate that happens these days is around, oh, how good are the models actually getting and can the models actually bridge this issue and, you know, when are we going to get superintelligence? Is that in like two years or five years? And, you know, are we going to hit a wall? And, you know, so much of that debate is like, I think, in some ways, a little bit of a waste of time because, you know, I think it's inevitable that we're going to have very powerful models. And, you know, rather than, I think we'll look back and say, oh, all this arguing around like when exactly was going to happen was sort of was short-sighted because the reality is we are just as a entire human civilization and this incredible exponential. Like, you cannot look at the progress of AI over the past decade and not just be totally awestruck by how far it's come. Like a decade ago, the best AI models could recognize cats in YouTube videos. And now, you know, we're talking to, you know, a digital god that can, you know, we've all seen some of the hacks and some of the things these systems are capable of and you just can't help with the awestruck. And, and I think this trend will just continue. Like these, these models are going to become more and more powerful. And so, I think a decade looking back, it'll be obvious that intelligence became abundant and that agency became abundant. Like, the current trends we're on are just going to keep continuing. And, this will be very strange. I mean, I think for the history of humanity, you know, groups of smart people getting together towards a shared goal was the bottleneck of progress. You know, the United States of America, in some sense, was an example. It's like the United States of America is formed from a group of very smart people getting together and having a vision for the future that they wanted to enact. And that's the story of nearly every company in America and story of every YC company. And that's going to change. Like all of a sudden, the scarce resource isn't going to be intelligence or agency. I really think it's going to be vision and ambition. It's like, do you have a clear view of what you want the world to look like in the future? What is the like one way in which you want to put your finger on the scale for how the future of the world will develop and how the world will look like in five to ten years, but it does not look like today. And you have the ambition and drive to like go through all the crap to make that happen. And AI will make that easier. Like agents and AI makes that maybe ten times or a hundred times easier than it was a decade ago. But the flip side of that is that you almost only can dream bigger. Like I think, and the world is like, you know, there's so many things that need to evolve for us to be able to fully embrace this technology. You know, the world is like really just, you know, barely even ready for this technology day. And I think, you know, as a builder, we have a responsibility to prepare the world, right? Like we have to help enterprises and governments, you know, adapt to this new technology. We have to help figure out how we secure the world from a biosecurity perspective or a cybersecurity perspective. We'd figure out how we, how we're going to manage all these risks that we see with this new technology. But on the flip side, it's also the time of like, you know, unprecedented opportunity for humans. Like we can develop new sciences. We can solve problems in health and biology that have been forever unsolved. We can build new businesses that you couldn't have even imagined before. There's like new creative opportunities that couldn't have existed before. So it's like, it's just this incredible cradle of opportunity and risks that I think makes it like no better time to be someone who's a builder and has a strong view of how the world should change. Do you think the path has changed? I mean, one of the things I saw, I think Stanford, the amount of computer science majors actually dropped by some double digit percentage. People sort of worried like, which is sort of insane to me. Like you still sort of need those skills to even create agents that are that good. Maybe that won't be true. I'm not really sure. How, you know, have you changed, you know, what would you say to people in this audience right now? Like this is sort of a real question that people are sort of facing. Like should they become more word sell unless shape rotator? Like what, you know, what's the move? And, you know, has that changed the kind of people you're looking to hire and how you manage your teams right now in that out? I think systematic and rigorous thinking are still incredibly important because, you know, the abstraction layer, I mean, I didn't use to believe that this is how this is going to play out, but it really has like the abstraction layer just keeps changing. So, you know, when I started a company back in my day, we wrote code. And now, you know, I'm sure nobody here writes code anymore. That's ridiculous. But now it's about how do you orchestrate the agents together? And then it's like, how do you develop these organizations of agents? Like how do you get like a million agents to work together well? And then it'll be, how do you get like a trillion agents to work together? Like, I think that there's going to be this continued need to figure out how you structure workflows at the abstraction layer that we're going to be operating on. And that form of like rigorous, systematic thinking, I mean, traditionally the way this would work like in my era of certain companies is you would start by writing code and then you would have organizations of humans and you'd figure out how to organize those humans. And that requires a system thinking. And now maybe it's like much more, much closer to first you, you orchestrate the agent that you figure out how to orchestrate like these armies of agents. But I think systems thinking is never going to go to style. So I think it's definitely a mistake to go all in on word cell. Like I think you need to, you need to shape rotate. But then I think the sort of like much more of I think what's necessary going in the future is having a deeper sort of compass and philosophical view on how the world should develop. Because I think there are many, many lessons from human history around how we think civilization go through this period. And you know, humanity will change more in the next decade than it has in the past 100 years probably. And so I think like the imperative for us to have positive visions for that and have coherent articulations of how that should develop are more important than ever. Let's get a little more concrete. I mean one of the things I'm curious about is like are there sort of applications of AI that you're seeing among your friends or internal the meta that you can talk about that are, you know, they're sort of obvious in your term. Maybe people haven't figured out yet. I mean, give us some alpha. I mean, I think there's still just like astronomical opportunity in agentic looping and figuring out how you develop systems that enable you to spend like 1000x more or 1 million x more on tokens to drive an outcome in a continuous feedback group. Like if you think about most companies, companies are just these like large skill feedback groups where humans are operating each of the edges like, you know, companies they get customers and they figure out how to make those customers happier and if the customers are happier then they spend more and if they spend more, they can hire more people who can then go figure out how to get more customers and make those customers happier. And that's like this, you know, that in some sense is the feedback loop of every startup or every business. And you know, these within that there are micro feedback loops that exist. And I think developing agentic systems that can operate and optimize these feedback loops is there's like just huge amounts of alpha there. Like I think we've seen internally in meta cases where if you can develop the right agentic loop and you have the right eval of the right metric for the agents to optimize, you can have a swarm of agents accomplish more than like a team of 100 engineers in, you know, very, very handily actually, very, very easily. And so I think figuring out what the world looks like with lots of sort of this like these agentic coordination problems. I think that is like one of the most interesting problems today. So mechanically speaking, I mean, Markdown files, cron jobs is, I mean, is it that's, and then basically pointing the agent at enough data so that it can figure something out that, you know, maybe is an indistribution. Yeah, like, yeah, mechanically figuring out what the metric is. And then yeah, it just comes down to skills, Markdown files, cron jobs, slash goal. Yeah, slash goal. Like I think I think it's always funny how mundane everything is once you really dig into it. But, um, so it's not magic, you know, I mean, some people put a lot of magic. There's like some LinkedIn threads out there about some magic stuff. Top advice, ignore LinkedIn. LinkedIn is where you get customers. So I like to end on this, which is, you know, you get a telegram to send to the 18 year old version of yourself. You know, what do you say to that person right now, given all, you know, I mean, thank you for coming back and sharing your wisdom with this audience. I mean, you know, what would you send in a message and a bottle to the 18 year old version of yourself right now? Yeah, I think that I think it really boils down to develop your own internal compass for how you think the future will develop and have strong conviction in it because is you will get so you will get inundated with noise and people telling you shit. And like you'll be very confusing and it'll be very hard. And especially when you're young and you don't have experience like it can feel very difficult to have true conviction in what you believe and what you wanna do. But I think that's the most important thing. Kind of as we talked about, you know, it took a deep, deep conviction in what we're building to be able to weather the sort of storms of many years of chaos in the market, in the industry, in the people around us. And so, and the other piece of advice I would have is try to identify what is the exponential in the world that has both the steepest curve and will go the longest. And you know, many decades ago this curve was more as long and that probably was, you know, that was, yeah, at the time, like clearly the right thing to invest on. I think right now it's AI progress but there will be more of these very steep curves in the future. And it's fine if these curves start, you know, the starting point is very boring or like it doesn't even seem that interesting. Like, you know, when we started, when I started working on scale, you know, we had cap detectors in YouTube videos and that felt, you know, it's hard to say that you explain the story that that's like the most important technology of our time but it was on just this like unbelievable exponential. And I think in one last thing I gotta say, yes. Which is, we are meta is proud to offer everyone in this room, a thousand dollars of free credits for the new Spark API. (audience applauds) And we're gonna keep making the models better. And right now, new Spark is, I think, ADEX cheaper than Opus. So if you can buy that to Opus Dollars, (laughs) a lot more. But no, everyone here will work to get everyone the details and how to get how to get these credits. And we're really excited to see what everyone builds. Alexander Wang, everyone. (audience applauds)