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

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.

• 0:07Speaker 00

  All right, full rock star treatment for Alexander Wang, everyone. All right.

• 0:16Speaker 00

  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 are some 16-year-olds in this audience, you know.

• 0:33Speaker 00

  Let's jump to your story. I mean, you came up always really smart, like math Olympiad. Like, jump us to, you know, the Alex of that time. Like, what were you feeling, what were you thinking, and what drove you down this road?

• 0:47Speaker 01

  Yeah, I, um, uh, well, I grew up in New Mexico, Los Alamos, 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 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, you know, after high school, got an internship in the valley. I think his first internship was at Palantir. And, um, and he, you know, he was kind of this influence for me. And so after I finished high school,

• 1:32Speaker 01

  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 this was, I was 19 when I worked at Quora. I was 18 when I went to MIT. And I was 19 when I started Scale. And I remember this period from like 17 to 19. It was, um, uh, 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.

• 2:16Speaker 01

  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 like played around with TensorFlow, which had just come out that year at MIT. And where I like 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.

• 3:00Speaker 01

  And, uh, and YC was, was really critical to my entrepreneurial journey. Like, I don't think, um, like YC is this amazing blend of, uh, 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, uh, you know, that's what we all need in life. So, um, yeah, that was, I think, the story till then. I was 19, started Scale, and the rest is history.

• 3:25Speaker 00

  I guess with, uh, you worked with Jared Friedman at the time.

• 3:28Speaker 01

  Yes.

• 3:29Speaker 00

  And you came in with actually a very different idea than what ended up becoming Scale.

• 3:35Speaker 01

  Yeah, so we wanted to build like an AI agent, funnily enough, for, uh, for Doc, for 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, uh, and we worked on it for about a month or two before Jared pulled us aside and were like, guys, this is, I don't know if this is gonna go anywhere. And, uh, and that's exactly what we needed to hear. And it was at that time when like, you know, we're, I had studied AI at MIT, 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.

• 4:21Speaker 00

  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?

• 4:36Speaker 01

  Yeah, so the, the story here is that like, I was, when I was at MIT, I did a bunch of projects like train, train models of various forms. And these were like, you know, by comparison today, they're like little toy models. And, um, and I remembered to train a model, I needed three things. I needed a GCP account, like I need 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 like no effective way to get data for training these, training these models.

• 5:19Speaker 01

  And so it felt incredibly obvious that this was going to be the future. That there was going to be a way to, um, you know, press a button, so to speak, and get data. And, uh, 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, we managed to raise money, we managed to keep going,

• 6:03Speaker 01

  managed to keep growing the business. But, um, the very same investors who passed on us and were, um, were very dour on the potential of AI are writing think pieces today about how data is so critical and one of the biggest business opportunities in AI. So it's very funny to see that whole, whole thing come full circle.

• 6:23Speaker 00

  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 is hot, like, we're gonna go work on that. It's like, you literally couldn't have started Scale that way. You had to start from, uh, think like, simple statements that are about the world that you know to be true, and then sort of building something for that.

• 6:49Speaker 01

  Yeah, I think the, 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, the idea or the space or the concept of the business, you know, becomes consensus. And the only way you're gonna be successful is if you're able to identify these truths about the world early, long before everyone else. And I think the like, I mean, one of the most surprising things, like, you know, Scale, we've been working on AI for a decade.

• 7:33Speaker 01

  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 gonna look like. Because everyone else will just confuse you.

• 7:53Speaker 00

  It seems like one of the things you got incredibly great at was, you know, 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.

• 8:17Speaker 01

  Yeah, I think that the, the, these early mechanics of building a company, like, the, 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, uh, 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, um, I think for all entrepreneurs, you start out pretty shitty at everything. And, um, the whole game is how do you develop yourself

• 9:02Speaker 01

  to continuously improve, to get better and learn quickly?

• 9:06Speaker 00

  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. Uh, you're, 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 gonna 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, what do you think it's like to start a company today with, with AI, in the age of AI?

• 9:36Speaker 01

  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, and helping the world adapt to this amazing technology that already exists. Like, I think if the models didn't improve at all from today, there would still be like decades and decades of like total upheaval and change in the economy and how the world operates and, and everything around us. And, um, so I think it's, as a result, it's like one of the most incredible, it's probably a, like, once in a civilization opportunity to be a dreamer and to have a vision and to have ambition

• 10:20Speaker 01

  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, we were chatting about,

• 10:30Speaker 01

  you know, backstage is, you know, when, when I started Scale or, you know, 10 years ago, if you start a company, you had to be, um, 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 mecha Goliath that is like vastly enhanced by the power of agents and AI. And, you know, the, the large companies are the sort of like more traditional Goliath, so to speak.

• 11:12Speaker 01

  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 incumbents.

• 11:27Speaker 00

  So let's talk about superintelligence, because that's clearly, that's even in the name of your lab. What does superintelligence mean operationally inside Meta right now?

• 11:38Speaker 01

  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 AGI. 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.

• 12:23Speaker 01

  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 superintelligence, and we also believe in, you know, an explosion of entrepreneurship. There's 200 million businesses that are on Meta's 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

• 13:04Speaker 01

  of like complex ecosystem that is fully AI supercharged.

• 13:10Speaker 00

  So I was really psyched to see Meta Spark 1.1. My OpenClaw absolutely loved it. How, you know, how has running a frontier lab been? You know, the Meta 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.

• 13:33Speaker 01

  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 Llama4 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 MuseSpark 1, and then two months later, we launched MuseImage and MuseSpark 1.1. And, you know, there's a few things that I think have really struck me about this.

• 14:18Speaker 01

  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, 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 is the, what are the reaches of what we accomplish 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.

• 15:03Speaker 01

  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 like very, very steep exponent across maybe every dimension of the ecosystem. And it's important to develop like a, like an organism, that's how I think about the lab, that is able to sort of grow with that. No, it's been, it's been very exciting, and we're going to be shipping a lot more. So I think, you know, we will, we just launched MuseSpark 1.1, which was a great model. We're going to continue to have updates on the MuseSpark line.

• 15:48Speaker 01

  We're 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, 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, 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

• 16:32Speaker 01

  to empower the ecosystem and developers as much as humanly possible.

• 16:35Speaker 00

  I mean, it sounds like one of the ways, I mean, certainly when I was using MuseSpark with my OpenClaw, like, 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.

• 16:50Speaker 01

  Yes. Well, we, I think this goes to it, like, I don't, 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 to, 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. You know, if you look at the AI ecosystem and everything that's happened, like, every wave is 10x bigger than the past wave. So, you know, when I started scale, the first wave was maybe self-driving cars. Self-driving cars are really awesome. They're like really, really cool.

• 17:32Speaker 01

  But that was like pales in comparison to large language models and chatbots. And like, you know, chatbots became this thing that was like probably 10x bigger even than, than, you know, self-driving cars. And then there were coding agents, which came a few years later. And coding agents are probably 10x bigger than, 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, you know, our point of view is like, let's unleash the ecosystem, let's explore, and let's see, let's build, you know, kind of the future of the world together.

• 18:09Speaker 00

  So what's the best way to actually take advantage of the coding model for MuseSpark? It's open code, right?

• 18:15Speaker 01

  Yeah. Today, the easiest way is to use open code. We have like 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.

• 18:33Speaker 00

  Yeah, I know the harness is, you know, under wraps still, but like, can you tease us with, you know, I mean, I still use OpenClaw, 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.

• 18:51Speaker 01

  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, 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, 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, and develop like, you know, very complex ecosystems of these agents working together. We want to be really extensible and, and ultimately, we want to just empower people

• 19:32Speaker 01

  to harness this technology because harness, no pun, actually, pun not intended, but, but there's, like, I truly believe these, these models are already just incredibly powerful. Like, they should, 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.

• 20:00Speaker 00

  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?

• 20:14Speaker 01

  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 it was going to happen was sort of, was short sighted.

• 20:53Speaker 01

  Because the reality is, we are just, as a entire human civilization, on 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. I mean, we've all seen some of the hacks and some of the, some of the things these systems are capable of. And you just can't help but be awestruck. And, and I think this trend will just continue. Like, these, these models are going to become more and more powerful.

• 21:32Speaker 01

  And so, I think a decade looking back, it'll, 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, was the bottleneck of progress. You know, US, the United States of America, in some sense, was an example. It's like the United States of America is formed from a smart, 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.

• 22:16Speaker 01

  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, how the world will look like in five to 10 years that it does not look like today? And do 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, then you, all of a sudden, you can dream bigger.

• 22:56Speaker 01

  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 have to figure out how we, how we're going to, 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.

• 23:41Speaker 01

  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, of opportunity and risks that, uh, that I think makes it like no better time to be someone who's a builder and, and has a strong view of how the world should change.

• 24:06Speaker 00

  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 do you, 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 Meta?

• 24:47Speaker 01

  I think systematic and rigorous thinking are still incredibly important. Because, you know, 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. Like, the abstraction layer just keeps changing. So, you know, when I started a company, back in my day, we wrote code. And, and now, you know, I'm sure nobody here writes code anymore. That's ridiculous. But, 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 well? Like, I think that there's going to be this continued, uh, need to figure out how you structure workflows at the abstraction layer that we're going to be operating at.

• 25:32Speaker 01

  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'd figure out how you, how to organize those humans. And that requires system thinking. And now, maybe it's like much more, much closer to, first, you orchestrate the agent, then you figure out how to orchestrate like these armies of agents. But, but I think systems thinking is never going to 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, you need to shape rotate. But then I think the sort of like, much more of, I think, what's necessary going into

• 26:12Speaker 01

  the future is having a deeper sort of compass and philosophical view on how the world should develop. Because I think there, there are many, many lessons from human history around how, 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, 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, are more important than ever.

• 26:51Speaker 00

  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 to Meta that you can talk about that are, you know, that's sort of obvious near term, maybe people haven't figured out yet? I mean, give us some alpha.

• 27:10Speaker 01

  I mean, I think there's still just like astronomical opportunity in agentic looping and, and figuring out how you develop systems that enable you to spend like 1,000x more or 1 millionx more on tokens to drive an outcome in a, in a continuous feedback loop. Like, 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, 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, then you can hire more people and 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, of every startup or every business.

• 27:55Speaker 01

  And, you know, these, within that, there are microfeedback 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, of alpha there. Like, I think we've seen internally at Meta, cases where if you can develop the right agentic loop and you have the right eval or 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,

• 28:38Speaker 01

  these agentic coordination problems, I think that is like one of the most interesting problems today.

• 28:42Speaker 00

  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 isn't in distribution?

• 28:56Speaker 01

  Yeah, I think figuring out, yeah, mechanically figuring out what the metric is. And then, yeah, it just comes down to skills, markdown files, cron jobs.

• 29:05Speaker 00

  Slash goal.

• 29:05Speaker 01

  Yeah, slash goal. Like, I think, I think it's always funny how mundane everything is once you really dig into it, but, um.

• 29:12Speaker 00

  So it's not magic? You know, I mean, some, some people put a lot of magic, there's like some LinkedIn threads out there about some magic stuff.

• 29:18Speaker 01

  Top advice, ignore LinkedIn.

• 29:20Speaker 00

  Hey.

• 29:20Speaker 01

  LinkedIn is where you get customers.

• 29:25Speaker 00

  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, a message in a bottle to the 18-year-old version of yourself right now?

• 29:47Speaker 01

  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, 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, 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

• 30:27Speaker 01

  building to be able to weather the, the sort of storms of many years of, of chaos in the market, in the industry, in the people around us. And so, and then the other piece of advice I would have is try to identify, what is the, 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, uh, 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.

• 31:11Speaker 01

  Like, you know, when we started, when I started working on scale, you know, we had cat detectors in YouTube videos. And that felt, you know, it's hard to say, to explain the story that that's like the most important technology of our time, but it was on just this like unbelievable exponential.

• 31:28Speaker 01

  And I think I have one last thing I got to say.

• 31:30Speaker 00

  Yes.

• 31:30Speaker 01

  Yes, which is, uh, we are, Meta is proud to offer everyone in this room $1,000 of free credits for Newsport API.

• 31:40Speaker 00

  Fantastic.

• 31:44Speaker 01

  And, uh,

• 31:48Speaker 01

  and we're going to keep making the models better. And, uh, right now, Newsport is, I think, 8x cheaper than Opus. So, uh, so if you convert that to Opus dollars, uh, it's a lot more. But, um, no, everyone here will, will work to get everyone the details on how to get, uh, how to get these credits. And, uh, we're really excited to see what everyone builds.

• 32:06Speaker 00

  Alexander Wang, everyone.