All right, [music] full rockstar

treatment for Alexander Wang, everyone.

All right.

>> [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.

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 time. Like, what were

you feeling? What were you thinking? And

what drove you down this road?

>> 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.

Um

and I had a friend who was really into

programming um 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 um influence for me and

so after I finished uh high school, I

ended up working at Quora um here in

Silicon Valley. And then um

I worked there for a year. I took a gap

year to work there um and then I went to

MIT.

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. 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. 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. Um

and um 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 um opportunity to

explore what was interesting. 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. And then after one year at MIT I

applied to YC.

You know, it felt like kind of like a

miracle to get in at that time. 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 um 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 uh the

rest is history.

>> 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.

>> 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. Um 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.

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."

>> [laughter]

>> Um and uh and that's exactly what we

needed to hear. And it was at that time

when like, you know, where I had studied

AI to my T. I had like trained models

and we thought we sort of went back to

the drawing board, thought deeply about

where the opportunity was, and

>> came up with Scale.

>> I guess selling data at the time, you

know, large language models were not

even had had not really come to the fore

yet. 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?

>> 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 remember

to train a model, uh I needed three

things. I needed a

uh GCP account, like I needed an account

on some cloud service to get compute. I

needed um the code to run to actually

train the model. And I needed data. I

needed a data set. And uh 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. 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. 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. 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. They'd

be like, "Oh, I don't know if this is a

good business. Does it have longevity?

Is it durable?" Um

and uh 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. 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.

So, uh it's very funny to see that whole

whole thing come full circle.

>> I mean, it seems like that's actually a

real good um

case study in 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 going to 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.

>> Yeah, I think the the key thing is you

need to develop conviction in a set of

beliefs that nobody else um agrees with.

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. 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 going to 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. 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 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.

>> 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.

And

I remember talking to a lot of the

investors who I met very early on and

they you know, a lot of them would say

like oh, like you know, you just grew so

quickly and you changed so quickly and

like I didn't you know, I didn't see it

at the time. And I think that's probably

true for literally everyone who starts a

company like nobody is

nobody is good at something they've

never done before, right? And so

I think for all entrepreneurs, you start

out pretty shitty at everything and

the whole game is how do you develop

yourself to continuously improve to get

better and learn quickly.

>> 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. Um

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?

>> Yeah.

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. 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

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

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. And now I actually think with

the power of agents um 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 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 um

figure out the way to leverage their

strengths in the most like ambitious

ways, you can easily outcompete

incumbents.

>> So, let's talk about super intelligence

because that's clearly that's even in

the name of your lab.

Um,

what does super intelligence mean

operationally inside Meta right now?

>> 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. But,

you know, we believe that everybody in

the world, you know, all the billions of

people in the world are going to have a

super intelligence that is adapted and

tailored to them, that is enables them

to accomplish their goals, knows their

context, and ultimately is an expander

of their own agency. Like I think the

thing that we think a lot about is is

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? Um, and we

think about this in a in an ecosystem

way um, as well. 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. We believe

that these are going to enhance this

very broad ecosystem. And 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 uh, are on Meta's

platforms today. We think that number

should go to billions with this

explosion of 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

uh, complex ecosystem that is fully AI

supercharged.

>> So, I was really psyched to see Meta

Spark uh, 1.1. My my open claw

absolutely loved it. Um,

how you know, how how has running a

frontier lab been? Um, you know, the

Meta Spark level is sort of the opus

level. Uh, what's coming down the pipe?

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.

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. 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.

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 1.1 and um,

you know, there's a few things that I

think have really struck me about this.

It you know, the first is talent density

was incredibly important. That was the

the core thing to bet on and um,

like talent density is something that

compounds naturally. Like the more

talented people you have the more of the

most most talented people want to join

you. 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. Like we

are it is scientific work. We're

exploring what can you do with these

models? How can you push these models?

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. 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. Both the

exponential growth in capabilities, the

exponential growth in compute, um, the

exponential growth in adoption and

usage. Like these are all um, we are on

this like very, very steep exponent

across maybe every dimension of the

ecosystem. And um, it's important to

develop like a like an organism. That's

how I think about the lab that that's

able to sort of grow with that. Um, you

know, it's been it's been very exciting

and we're we're going to be shipping a

lot more. So, um, I think uh, you know,

we will we just launched Muse Spark 1.1,

which was a great model. We're going to

continue to have updates on the Muse

Spark line. 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. 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. Um,

and uh, and then we're also um, 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.

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. 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.

>> 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. Well, we I think this 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 and companies. 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. And I think that, you know, we take

a view

I think the best AI products haven't

even been developed yet. 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. 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, 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

10 times bigger even than um than uh you

know, self-driving cars. And then there

were coding agents which came a few

years later. And coding agents are

probably 10 times bigger than than um

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, um you

know, our point of view is like, let's

let's unleash the ecosystem. Let's

explore and let's see um let's build,

you know, kind of the future of the

world together.

>> So, what's the best way to actually take

advantage of the coding model uh from U

Spark? It's it's open code, right?

>> Yeah. Today, um the the easiest way is

to use open code. We have like

onboarding on the website. And then uh

soon we'll have a harness of our own.

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.

>> Yeah, I know the harness is uh you know,

under wraps still. But like, can you

tease us with

you know, I mean, I still use open claw.

I still use Hermes agent. 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. 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. 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. 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.

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. 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. 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?

>> 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? Is that in

like 2 years or 5 years? And, you know,

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

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

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

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. 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.

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 um this will be very strange. 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. You know, US

The United States of America in some

sense was an example of this. 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.

And that's the story of nearly every

company um in America. And it's the

story of every YC company. Um

and

that's going to change. Like all of a

sudden, the scarce resource isn't going

to be intelligence or agency. I really

think it's going to be vision and

ambition. It's like, do you have a clear

view of

what you want the world to look like in

the future? What is the like one way in

which you want to put your finger on the

scale for how the future of the world

will develop and how the how the world

will look like in 5 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 in 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. 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. Um 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 cybersecurity

perspective. 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. But on the flip side, it's

also the time of like, you know,

unprecedented opportunity for humans.

Like we can develop new sciences. We can

solve problems in health and biology

that have been forever unsolved. We can

build new businesses that you couldn't

have even imagined before. There's like

new creative opportunities that couldn't

have existed before. So, it's like

it's just this incredible cradle of 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.

>> Do you think the path has changed? 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. It's 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

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?

>> 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. So, you know, when

I started a company back in my day, we

wrote code. Um

>> [laughter]

>> And now, you know, I'm sure nobody here

writes code anymore. That's ridiculous.

But um 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 um

uh need to figure out how you structure

uh 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'd

figure out how you how to organize those

humans. Um and that requires systems

thinking. 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. But um

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. 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. 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. And um

So, you know, humanity will change more

in the next decade than it has in the

past 100 years, probably. 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.

>> Um

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, they're sort of obvious near-term

maybe people haven't figured out yet. I

mean, give us some alpha.

>> [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. 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 um, they get

customers and they figure out to make

those customers happier. And if

customers are happier, then they spend

more. 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. And that's

like this, you know, that in some sense

is the feedback loop of uh

of every startup or every business. And,

you know, these within that there are

micro feedback loops that exist. And I

think developing agentic systems that

can operate and optimize these feedback

loops is there's like just huge amounts

of of alpha there. 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. 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. 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.

>> 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. Like I think I think it's

always funny how mundane everything is

once you really dig into it. But um

>> So it's not magic, you know what I mean?

Some some people put a lot of magic

There's like some LinkedIn threads about

some magic stuff.

>> advice, ignore LinkedIn.

LinkedIn is where you get customers.

>> [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. I mean,

you know, what would you send in a

message in a bottle to the 18-year-old

version of yourself right now?

>> 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. 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. But I think

that's the most important thing. 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. 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. 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. 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 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.

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. [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.