Y Combinator Startup Podcast - Alexandr Wang: “This is a Once-in-a-Civilization Opportunity”

Episode Date: July 31, 2026

Alexandr Wang's advice to his 18-year-old self: develop your own internal compass for how the future will unfold, and hold conviction in it against the noise. At Startup School 2026, the Scale AI (YC S16) founder — now leading Meta's Superintelligence Labs — talks with Garry Tan about rebuilding a frontier lab from scratch, why talent density compounds, and how to spot the exponential worth betting your twenties on.

Full rock star treatment for Alexander Wang, everyone. All right. So why don't we start out? Backstage we were saying, you know, one of the cool ways to think about this event is like, you know, this room is actually full of people who are just like us, but when we were 18 or 20 or, you know, there's some 16-year-olds in this audience, you know.

Let's jump to your story. I mean, you got, it came up always really smart, like Math Olympiad. Like, jump us to, you know, the Alex of that. 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, Salamis New Mexico, which now Oppenheimer 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 not exactly clear how or what the exact paths to do that would be.

And I had a friend who was really into programming, and, you know, after high school, got an internship in the Valley, I think his first internship was at Palantir. And he, you know, he was kind of this influence for me. And so after I finished high school, I ended up working at Quora here in Silicon Valley. and then I worked there for a year. I took a gap year to work there, and then I went to MIT.

And I was 19 when I worked at Quora, I was 18 when I went to MIT, and there was 19 when I started scale. And I remember this period from like 17 to 19. It was, I felt like I was constantly changing, like, you know, exactly what I wanted to do was constantly changing. You know, 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, you know, the two things that were really important. One is, I think working at a company was really valuable, because, like, 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 to 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 where I ultimately came up with the idea of scale.

And then after one year of MIT, I applied to YC, you know, it 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, like, Y.C. 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, that's what we all need in life. So, yeah, that was, I think, the story till there's 19, start scale and the rest is 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 doc, to help 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 had studied AI at MIT, I had 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 floor 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, I was, when I was at MIT,

I did a bunch of projects to train models of various forms. And these were, like, you know, by comparison today, they're like little toy models. And I remember to train a 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 dataset. 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 like 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, you know, press a button so to speak and get data. And it was very funny because in the years that followed, like in the first many years of scale, data was very unsexy still. Every time we would go out

to fundraise, even though our numbers were great and we had great revenue, you know, VCs and investors would always be very skeptical. They'd be like, oh, I don't know if this is a good business, does this have longevity, is this durable? And it was really weird to me, but you know, none of the investors had ever trained a model.

So I guess they didn't really get it. And fast forward to today, you know, 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 dower 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. I mean, it seems like that's actually a real good case study and first principles thinking, right? Like, you can't start a company by opening the pages of the Wall Street Journal and saying, well, this data's hot, like, we're going to go work on that. You literally

couldn't have started scale that way. You had to start from 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, you know, the idea or the space or the concept of the business, you know, 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, you know, scale, we've been working on AI for a decade. You know, 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 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 change 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 shitty at everything, and the whole game is

how do you develop yourself to continuously improve, to get better, and learn quickly? Backstage we're talking about, this is actually a really lucky time to start a company, because, you know, obviously you can come do 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? 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, 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. If the models didn't improve it all from today, there would still be 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.

You know, one of the things that we were chatting about, you know, backstage is, you know, when I started scale or, you know, 10 years ago, if you start a company, you had to be, you know, it was like David versus Goliath. And you had to be clever and you had to find like an angle into the market and you had to sort of like, you know, 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 versus Goliath. 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 ambitious ways, you can easily out-compete incumbents.

So let's talk about superintelligence, because clearly that's even in the name of your lab. What does superintelligence mean operationally inside meta right now? Yeah, I think that, you know, we, a year ago, Mark wrote this memo about personal superintelligence, which I think actually is very similar to your concept of personal AI. 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 superintelligence that is adapted and tailored to them that 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 agency 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, you know, Patrick mentioned it, but, you know, we don't believe in this totalizing, you know, totalitarian view of, you know, AIs 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 METIS platforms today. We think that number should go to billions with this explosion of creativity and using AI tools. And ultimately, we think that, you know, it'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 MetaSpark 1.1. My OpenClaw absolutely loved it. How has running a frontier lab been?

The MetaSpark 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 4 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 Muse Spark 1, and then two months later, we launched News Image and Meese 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 more of the more of the the most talented people want to join you.

And I think it's kind of amazing to see on the inside, but Frontier AI work is research. 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 we accomplished with these models, which requires a totally different mindset and operating model than 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, an operating model, a system that will just be able to compound with all of the exponential growth that will happen in the ecosystem. Both the exponential growth in capabilities, the exponential growth in compute,

the exponential growth in adoption and usage. Like these are all, we are on this very, very steep exponent across maybe every dimension of the ecosystem. And it's important to develop like a, an organism that's how I think about the lab that 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 will, we just launched MewSport 1.1, which was a great model. We're going to continue to have updates on the Mew Spark line.

We're also a 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, you know, as you mentioned, we're working on open source models. And we want to kind of as I described before, like 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 be, we want to empower the ecosystem and developers as much as humanly possible. And it sounds like one of the ways, I mean, certainly when I was using Mew Spark with my OpenClaw, like it became clear that it was as good as Opus, especially for that sort of agenetic flow with skill files,

but it was like 8X cheaper, actually. Yes. Well, I think this goes to it. Like, you know, we don't believe in a world where these models are so expensive that, you know, 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, you know, we take a view, I think the best AI products haven't even been developed yet.

If you look at the AI ecosystem and everything that's happened, like every wave is 10 times bigger than the past rate. So, you know, when I started scale, the first wave was maybe self-driving cars. Selfering cars are really awesome. They're like really, really cool. But that was like tails in comparison to large language models and chat bots. And like, you know, chatbots became this thing

that was like probably 10 times bigger even than, you know, self-shroned 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. Like we're going to keep seeing these new modalities and form 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, let's build kind of the future of the world together. So what's the best way to actually take advantage of the coding model for Mew 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 empower as much, you know, sort of combinatorial innovation in the ecosystem as possible. Yeah, I know the harness is, you know, under wrap still, but like, can you tease us with, you know, I mean, I still use OpenClawe. I still use Hermes agent, you know, it's, you know, these things are, I call them Ferraris that break down on the side of the road all the time. Like, is this a Ferrari

that won't break down? Like, you know, tease us a little bit. Yeah, hopefully, hopefully it doesn't, it doesn't break down. I mean, I think we're really focused on speed. I think speed is, um, You know, for anyone that uses these tools, speed is probably the, you know, 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. Like, I think there's so much innovation that will occur, even above the harness, frankly, in terms of, like, how to orchestrate and set up loops and develop, like, you know, 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.

No pun, actually, a pun not intended, but there's, like, I truly believe these models are already just incredibly powerful. Like, they should be so powerful to fuel, you know, many, many points of expansion of GDP growth. And I think it's like 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 they'll 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 when are we going to get superintelligence?

Is that in like two years or five years? And are we going to hit a wall? And 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 an entire human civilization

on this incredible exponential. 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, I mean, we've all seen some of the hacks and some of the things these systems are capable of. And you just can't help to be awestruck. And I think this trend will just continue.

Like 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 human beings. 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 of this. 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 the story of every YC company. And that's going to change. Like all of a sudden, the scarce resource isn't going to be in touch. 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 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 that 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 10 times or 100 times easier than it was a decade ago. But the flip side of that is, all of a sudden you 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 today. 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, to 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 would figure out how we, how we're going to manage all these risks that we see with this new technology. But 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 even imagine 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, 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 cell and less 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, you know, how you manage your teams right now at Meadow. I think systematic and rigorous thinking are still incredibly important because the abstraction layer, I mean, I didn't used to believe that this is how this is going to play out, but it really has. 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. 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 well? 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 at. And that form of like rigorous systematic thinking, I mean, traditionally the way this would work, like in my era of starting companies, is you would start by writing code.

And then you would have organizations of humans, and you figure out how to organize those humans. And that requires systems thinking. And now maybe it's much more, much closer to, first, you orchestrate the agent, then you figure out of orchestrate these armies of agents. But I think systems thinking is never gonna go out of style. So I think it's definitely a mistake to go all in on word cell.

Like I think you need to shape rotate. But then I think the sort of like much more of I think what's necessary going to 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 humanity will change more in the next decade than it has in the past 100 years,

probably. And so I think 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, there's sort of obvious near term. Maybe people haven't figured out yet.

I mean, give us some alpha. I mean, I think there's still just like astronomical operative. opportunity in agentic looping and figuring out how you develop systems that enable you to spend like 1,000 X more or 1 million X more on tokens to drive an outcome in a continuous feedback loop. If you think about most companies, companies are just these like large-scale feedback loops where humans are operating each of the edges. Like you know, companies they get customers and they figure out to make those customers happier.

And if the customers are happier, then they spend more. And if they spend more, then you can hire more people, 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 microfeedback loops that exist. And I think developing agenic 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 a meta,

cases where if you can develop the right agentic loop and you have the right e-val or the right metric for the agents to optimize, you can have a swarm of agents accomplish more than 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? I mean, is it that, and then basically pointing the agent at enough data so that it can figure something out that, you know, maybe isn't in distribution.

Yeah, I think figuring out, yeah, mechanically figuring out what the metric is. And then, yeah, it just comes down to skills, markdown files, prong 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. So it's not magic. 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. What would you send in a message

in a bottle to the 18-year-old version of yourself right now? Yeah, 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, you know, 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 experiences, like it can feel very difficult to have true conviction in what you believe and what you want to 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 were building to be able to weather the sort of storms of many years of chaos in the market, in the

industry, and 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 Moore's Law. And that probably was, you know, that was 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 when I started working on scale,

we had cat detectors and YouTube videos. And that felt, it's hard to say, you explain the story that that's the most important technology of our time, but it was on just this unbelievable exponential. And I think one last thing I gotta say. Yes. Yes, which is, we are, Meta is proud to offer everyone in this room $1,000 of free credits for the new Spark API.

Fantastic. And we're going to keep making the models better. And right now, New Spark is, I think, 8x cheaper than Opus. So if you can borrow that to Opus dollars, a lot more. But no, everyone here will work to get everyone the details on how to get these credits. And we're really excited to see what everyone builds. Alexander Wang, everyone.