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All right, [music] full rockstar treatment for Alexander Wang, everyone. All right.
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>> [cheering] [applause] >> So, why don't we start out uh backstage we're 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.
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Let's jump to your story.
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I mean, you got it came up always really smart like math olympiad like jump us to, you know, the Alex of that time.
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Like, what were you feeling? What were you thinking?
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And what drove you down this road?
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>> Yeah, I am uh Well, I grew up in New Mexico, Los Alamos, New Mexico, um which now Oppenheimer famous, but um it really was the middle of nowhere and uh I remember I did all these math competitions, all these um computer science competitions, but then um 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.
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Um and I had a friend who was really into programming um and, you know, after high school got an internship in the valley.
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I think his first internship was at Palantir.
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And um and he, you know, he was kind of this um influence for me and so after I finished uh high school, I ended up working at Quora um here in Silicon Valley.
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And then um I worked there for a year.
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I took a gap year to work there um and then I went to MIT.
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Um and this is I was 19 when I worked at Quora, I was 18 when I went to MIT and I was 19 when I started um Scale.
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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 want to do was constantly changing.
2:02
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 firehose pretty constantly during that time.
2:09
Um and um I would definitely recommend you know the two things that were really important.
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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.
2:22
You have no idea what it looks like to actually build something.
2:25
You have no idea what it looks like to iterate on something.
2:27
You have no idea what it looks like for groups of people to make decisions.
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And so I thought that was really important.
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And then going to school at MIT was actually really important because it just gave me a lot of um opportunity to explore what was interesting.
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And so it was at MIT that I started training my first models and um 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.
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And then after one year at MIT I applied to YC.
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You know, it felt like kind of like a miracle to get in at that time.
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And uh and YC was was really critical to my entrepreneurial journey.
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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 um which I think is uh you know, that's what we all need in life.
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So um yeah, that was I think the story till then I was 19 started Scale and uh the rest is history.
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>> I guess with uh you work with Jared Friedman at the time and um you came in with actually a very different idea than what ended up becoming Scale.
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>> Yeah, so we wanted to build um like an AI agent funnily enough for uh for doc for to help people like get medical care.
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Um and it was like the right it was a great example of an idea that I think will ultimately exist.
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Like I think we're even seeing it now.
3:50
Like AI agents to help people get medical care are very real.
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But it was the wrong timing.
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Um 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 going to go anywhere."
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>> [laughter] >> Um and uh and that's exactly what we needed to hear.
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And it was at that time when like, you know, where I had studied AI to my T.
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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.
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>> I guess selling data at the time, you know, large language models were not even had had not really come to the fore yet.
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Um but self-driving cars were sort of coming up and and computer vision suddenly became So that was sort of the first market. Is that right?
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>> 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.
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And these were like, you know, by comparison today, they're like little toy models.
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And um and I remember to train a model, uh I needed three things.
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I needed a uh GCP account, like I needed an account on some cloud service to get compute.
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I needed um the code to run to actually train the model. And I needed data. I needed a data set.
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And uh for two out of these three things, you could just press a button online and get them.
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And then for the last one, for data, there was like no effective way to get data for training these training these models.
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Um 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.
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And uh it was very funny because in the years that followed, like in the first many years of Scale, data was very unsexy still.
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Um 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.
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They'd be like, "Oh, I don't know if this is a good business. Does it have longevity? Is it durable?"
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Um and uh it was really weird to me, but you know, none of the investors had ever trained a model.
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So, I guess they didn't really get it.
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Um and uh fast forward to today, you know, we we managed to raise money, we managed to keep going, 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 is one of the biggest business opportunities um in AI.
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So, uh it's very funny to see that whole whole thing come full circle.
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>> I mean, it seems like that's actually a real good um case study in first principles thinking, right?
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Like, you can't start a company by opening the pages of the Wall Street Journal and saying, "Well, this data is hot.
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Like, we're going to go work on that."
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It's like, you literally couldn't have started Scale that way.
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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.
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>> Yeah, I think the the key thing is you need to develop conviction in a set of beliefs that nobody else um agrees with.
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Like, I think if you look at all the most successful companies in the world, um they were started at a time long before the sort of like core idea was popular. And they work on that.
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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.
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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.
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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.
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You know, you just you can't base your business decisions based on what everyone else is saying around you.
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Like, if you go too much with the herd, you will get immensely confused and you will end up nowhere.
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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.
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>> 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.
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>> 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 be good at, but like nobody is good at starting a company when they start a company.
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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.
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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?
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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.
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>> Uh backstage we're talking about this is actually a really lucky time to start a company cuz you know, obviously you can come do 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 going to tell you, you know, hey, these are some ways to do it.
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Um Do you think that would have helped you like accelerate even faster?
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Like you know, talk What do you think it's like to start a company today with with AI in the age of AI? >> Yeah.
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I mean I really think I think we're at this like in 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.
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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 you
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know, 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 and to impose a view of how the future world should look by building something amazing. Um you know, one of the things
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Um you know, one of the things that we were we were chatting about um uh 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 um a way to compete even though you had much fewer resources.
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And now I actually think with the power of agents um and AI broadly speaking, it's much closer to Goliath versus Goliath.
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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 the large companies are the sort of like more traditional Goliath, so to speak.
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But I think that startups now like if you properly embrace AI agents and um figure out the way to leverage their strengths in the most like ambitious ways, you can easily outcompete incumbents.
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>> So, let's talk about super intelligence because that's clearly that's even in the name of your lab.
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Um, what does super intelligence mean operationally inside Meta right now?
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>> Yeah, I think that you know, we a year ago Mark wrote this um, memo about personal super intelligence, which I think actually is very similar to your concept of personal AGI.
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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.
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Like I think the thing that we think a lot about is is agency expansion.
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How do we help people accomplish things that they couldn't have ever dreamed of before?
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And what would everyone in the world do if everything was just easy?
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Um, and we think about this in a in an ecosystem way um, as well.
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I think uh, 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.
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We believe that these are going to enhance this very broad ecosystem.
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And so, you know, we believe in billions of people all around the world all having their own personal super intelligence.
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And we also believe in, you know, an explosion of entrepreneurship.
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There's 200 million businesses that uh, are on Meta's platforms today.
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We think that number should go to billions with this explosion of of creativity and using AI tools.
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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 uh, complex ecosystem that is fully AI supercharged.
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>> So, I was really psyched to see Meta Spark uh, 1. 1.
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My my open claw absolutely loved it.
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Um, how you know, how how has running a frontier lab been?
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Um, you know, the Meta Spark level is sort of the opus level.
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Uh, what's coming down the pipe?
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And also I think that you're uh, you're increasingly looking at open source which uh, I think this audience really loves. >> Yeah, yeah.
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So, I think it was um, it's been you know, I've been at Meta for about a year now and it's been um, quite a year.
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I think uh, you know, getting in and um, you know, Meta we we've talked about it publicly like Llama 4 wasn't on the trajectory that was needed for um, for Meta and so I got in there and we kind of did a zero-based build of how do you um, you know, build an entire frontier lab uh, you know, in some ways kind of from scratch obviously using a lot of what we had um, and move as quickly as possible.
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And so within nine months of that moment we launched new Spark 1 and then uh, two months later we launched new image and new Spark 1.
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1 and um, you know, there's a few things that I think have really struck me about this.
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It you know, the first is talent density was incredibly important.
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That was the the core thing to bet on and um, like talent density is something that compounds naturally.
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Like the more talented people you have the more of the most most talented people want to join you.
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Um, you know, in and I think it's it's kind of um, amazing to see on the inside but you know, frontier AI work is research.
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Like we are it is scientific work.
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We're exploring what can you do with these models?
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How can you push these models?
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What is the what are the reaches of what can 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 what not.
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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 um, be able to compound with all of the exponential growth that will happen in the ecosystem.
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Both the exponential growth in capabilities, the exponential growth in compute, um, the exponential growth in adoption and usage.
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Like these are all um, we are on this like very, very steep exponent across maybe every dimension of the ecosystem.
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And um, it's important to develop like a like an organism.
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That's how I think about the lab that that's able to sort of grow with that.
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Um, you know, it's been it's been very exciting and we're we're going to be shipping a lot more.
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So, um, I think uh, you know, we will we just launched Muse Spark 1.
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1, which was a great model.
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We're going to continue to have updates on the Muse Spark line.
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Um, we're also have bigger models on the way that I think will be uh, much more competitive with even the very best models that are out there today.
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We're going to be launching a harness um, soon and have been working on a harness to help empower all the developers and agentic developers out there.
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Um, and uh, and then we're also um, you know, as you mentioned, we're working on open-source models.
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And we want to kind of as I described before, like, we believe in a decentralized world of AI capability and progress and development.
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Like, 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.
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And so, um, we have a lot of exciting things on the way and I think we want to be um, we want to empower the ecosystem and developers as much as humanly possible.
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>> I mean, it sounds like one of the ways, I mean, certainly when I was using uh, Muse Spark with my Open Claw, like, it it became clear that it was as good as Opus, especially for that sort of agentic flow with skill files, but it was like eight x cheaper, actually. >> [laughter] >> Yes.
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Well, we I think this this goes to it.
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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 and companies.
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Um, it's important for everyone to be able to use the technology to um, and to build whatever they want to build with it.
17:09
And I think that, you know, we take a view I think the best AI products haven't even been developed yet.
17:15
You know, that if you look at the AI ecosystem and everything that's happened, like every wave is 10 times bigger than the past wave.
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So, you know, when I started scale, the first wave was maybe self-driving cars.
17:27
Self-driving cars are really awesome.
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They're like really, really cool, but that was like pales in comparison to large language models and chatbots.
17:35
And like, you know, chatbots became this thing that was like probably 10 times bigger even than um than uh you know, self-driving cars.
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And then there were coding agents which came a few years later.
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And coding agents are probably 10 times bigger than than um chatbots.
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And I think we're just on this steep curve.
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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.
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And so, um you know, our point of view is like, let's let's unleash the ecosystem.
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Let's explore and let's see um let's build, you know, kind of the future of the world together.
18:09
>> So, what's the best way to actually take advantage of the coding model uh from U Spark?
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It's it's open code, right? >> Yeah.
18:16
Today, um the the easiest way is to use open code.
18:19
We have like onboarding on the website.
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And then uh soon we'll have a harness of our own.
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And um ultimately, I think we want great models that plug into all of the available harnesses and empower as much, you know, uh sort of combinatorial innovation in the ecosystem as possible.
18:33
>> Yeah, I know the harness is uh you know, under wraps still.
18:35
But like, can you tease us with you know, I mean, I still use open claw.
18:40
I still use Hermes agent.
18:40
You know, it's uh you know, these things are I call them Ferraris that break down on the side of the road all the time.
18:46
Like, is this a Ferrari that won't break down?
18:49
Like, you know, tease us a little bit.
18:51
>> Yeah, hopefully hopefully it doesn't it doesn't break down.
18:53
I mean, I think we're really focused on speed.
18:54
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.
19:01
I think also reliability, like you mentioned, we want to be extremely reliable.
19:04
Um 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.
19:14
Like I think there's so much innovation that will occur even above the harness, frankly, um 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.
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Um Uh we want to be really extensible and and um ultimately we want to just empower people to harness this technology because harness that Oh, >> [laughter] >> uh no pun actually pun not intended, but um but there's like I truly believe these these models are already just incredibly powerful.
19:45
Like they should they should be so powerful to fuel, you know, um 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.
20:01
I mean, when you look back on the decade, um what do you think they'll say was obvious in hindsight about AI that people are just missing in real time right now?
20:13
>> 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 super intelligence?
20:25
Is that in like 2 years or 5 years?
20:27
And, you know, are we going to hit a wall?
20:29
are we going to hit a wall? And, you know, so much of that debate is like I think um in some ways uh 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 um you
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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 um was short-sighted because the reality is we are just as a entire human civilization on this incredible exponential. Like you cannot look at the
20:59
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.
21:08
Like a decade ago, the best AI models could recognize cats in YouTube videos.
21:13
And now, you know, we're talking to um you know, a digital god that can, you know, uh I mean, we've all seen some of the hacks and some of the some of the things these systems are capable of.
21:23
And you just can't help but be awestruck.
21:27
And and I think this trend will just continue.
21:29
Like these these models are going to become more and more powerful.
21:32
And so, I think a decade looking back, it'll it'll be obvious that intelligence became abundant and that agency became abundant.
21:41
Like the current trends we're on are just going to keep continuing.
21:45
And um this will be very strange.
21:45
I mean, I think for the history of humanity, um you know, groups of smart people getting together towards a shared goal was was the bottleneck of progress.
21:56
You know, US The United States of America in some sense was an example of this.
22:00
Like the United States of America was formed from a smart group of very smart people getting together and having a vision for the future that they wanted to enact.
22:08
And that's the story of nearly every company um in America.
22:09
And it's the story of every YC company.
22:11
Um and that's going to change.
22:15
Like all of a sudden, the scarce resource isn't going to be intelligence or agency.
22:19
I really think it's going to be vision and ambition.
22:24
It's like, do you have a clear view of what you want the world to look like in the future?
22:29
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 5 to 10 years that it does not look like today?
22:39
And do you have the ambition and drive to like go through all the crap to make that happen?
22:45
And AI will make that easier.
22:48
Like agents in AI makes that maybe 10 times or 100 times easier than it was a decade ago.
22:53
But the flip side of that is then you all of a sudden you can dream bigger.
22:56
Like I think And the world is like um you know, there's so many things that need to evolve for us to be able to fully embrace this technology.
23:05
Um you know, the world is like really just, you know, barely even ready for this technology today.
23:11
And I think, you know, as a builder, we have a responsibility to prepare the world, right?
23:15
Like we have to help enterprises and governments, you know, to adapt to this new technology.
23:22
We have to help figure out how we secure the world from a biosecurity perspective or cybersecurity perspective.
23:27
We have to figure out how we um how we're going to to manage all these risks that we see with this new technology.
23:33
But on the flip side, it's also the time of like, you know, unprecedented opportunity for humans.
23:39
Like we can develop new sciences.
23:39
We can solve problems in health and biology that have been forever unsolved.
23:43
We can build new businesses that you couldn't have even imagined before.
23:46
There's like new creative opportunities that couldn't have existed before.
23:49
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 um and has a strong view of how the world should change.
24:06
>> Do you think the path has changed?
24:06
I mean, one of the things I saw, I think Stanford uh the amount of computer science majors actually dropped by some double-digit percentage.
24:13
It's people sort of worried like, which is sort of insane to me.
24:17
Like you still sort of need those skills to even create agents that are that good.
24:21
Maybe that won't be true. I'm not really sure.
24:25
How you know, have you changed you know, what do you what would you say to people in this audience right now?
24:28
Like this is sort of a real question that people are sort of facing.
24:32
Like should they become more word cell and less shape rotator?
24:36
Like what you know, what's the move?
24:36
And you has that changed um the kind of people you're looking to hire and, you know, how you manage your teams right now at Meta?
24:47
>> 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 it was going to play out, but it really has, like, the abstraction layer just keeps changing.
24:58
So, you know, when I started a company back in my day, we wrote code.
25:03
Um >> [laughter] >> And now, you know, I'm sure nobody here writes code anymore. That's ridiculous.
25:09
But um but now it's about how do you orchestrate the agents together?
25:11
And then it's like, how do you develop these organizations of agents?
25:15
Like, how do you get like a million agents to work together well?
25:18
And then it'll be, how do you get like a trillion agents to work together well?
25:21
Like, I think that there's going to be this continued um uh need to figure out how you structure uh workflows at the abstraction layer that we're going to be operating at.
25:31
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.
25:46
Um and that requires systems thinking.
25:48
And now, maybe it's like much more much closer to first you you orchestrate the agent, then you figure out how to orchestrate like these armies of agents.
25:54
But um but I think systems thinking is never going to go out of style.
25:59
So, I think it's definitely a mistake to go all in on Word Cell.
26:02
Like, I think you need to you need to shape rotate.
26:04
Um but then I think the sort of like um much more of I think what's necessary going in the future is having um a deeper sort of compass and philosophical view on how the world should develop.
26:19
Because I think there are there are many many lessons um from human history around um how how we think civilization go through this period.
26:29
And um So, you know, humanity will change more in the next decade than it has in the past 100 years, probably.
26:35
And um And so, I think like the imperative for us to have positive visions for that and have coherent uh articulations of how that should develop are are more important than ever.
26:50
>> Um Let's get a little more concrete.
26:51
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, they're sort of obvious near-term maybe people haven't figured out yet.
27:04
I mean, give us some alpha.
27:08
>> [laughter] >> Um, I mean, I think there's still just like astronomical opportunity in uh agentic looping and 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 in a continuous feedback loop.
27:27
Like if you think about most companies, companies are just these like large-scale feedback loops where humans are operating each of the edges.
27:35
Like, you know, companies they um, they get customers and they figure out to make those customers happier.
27:41
And if customers are happier, then they spend more.
27:43
And if they spend more, then they can hire more people who can then go figure out how to get more customers and make those customers happier.
27:47
And that's like this, you know, that in some sense is the feedback loop of uh of every startup or every business.
27:53
And, you know, these within that there are micro feedback loops that exist.
27:58
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.
28:08
Like I think we've seen internally at Meta um 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, uh very very handily actually, very very easily.
28:29
And so, I think figuring out what the world um looks like with lots of uh sort of this like um these agentic coordination problems, I think that is like one of the most interesting problems today.
28:41
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.
28:56
>> Yeah, I think figuring out Yeah, yeah, mechanically figuring out what the metric is and then yeah, it just comes down to skills, markdown files, cron jobs, >> /goal. >> Yeah, /goal.
29:05
Like I think I think it's always funny how mundane everything is once you really dig into it.
29:10
But um >> So it's not magic, you know what I mean?
29:13
Some some people put a lot of magic There's like some LinkedIn threads about some magic stuff.
29:18
>> advice, ignore LinkedIn.
29:20
LinkedIn is where you get customers.
29:22
>> [laughter] >> So I like to end on this, which is um 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.
29:40
I mean, you know, what would you send in a message in a bottle to the 18-year-old version of yourself right now?
29:47
>> Yeah, I think the 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 and like you'll could be very confusing and it'll be very hard.
30:09
And especially when you're young and you don't have experience like it can feel very difficult to um have true conviction in what you believe and and what you want to do.
30:18
But I think that's the most important thing.
30:20
Kind of as we talked about, you know, um it took a deep deep conviction in what we were building to be able to weather the the sort of storms of many years of um of uh chaos in the market, in the industry, in the people around us.
30:36
And so um And 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.
30:47
And you know many decades ago this curve was was Moore's law and that probably was you know that was at the time like clearly the right thing to invest on.
30:58
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.
31:10
Like it 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 explain the story that that's like the most important technology of our time but it was on just this like unbelievable exponential.
31:27
Um And I think I have one last thing I got to 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. Fantastic.
31:40
[applause] And uh And we're going to keep making the models better and right now new Spark is I think eight x cheaper than Opus so so if you convert that to Opus dollars uh >> [laughter] >> it's a lot more but no everyone here will will work to get everyone the details on how to get how to get these credits and we're really excited to see what everyone builds. Alexander Wang everyone.