We're about to get started.

My next guest, I am so impressed by this person, three reasons.

First reason is there are only a handful of entrepreneurs, founders that started a company that literally touched the lives of billions of people around the world as part of the social fabric, invented services, and a state-of-the-art computing company.

Two, very few entrepreneurs, founders founded the company and led it to over a trillion dollars of value.

And three, a college dropout.

All three things simultaneously true.

Ladies and gentlemen, please help me welcome Mark Zuckerberg.

Mark, welcome to your first SIGGRAPH.

Can you believe this?

One of the pioneers of computing, a driver of modern computing, and I had to invite him to SIGGRAPH.

So, anyways, Mark, sit down.

It's great to have you here.

Welcome.

Thanks for flying down.

Yeah, no, this will be fun.

I hear you've been going for like five hours already or something.

Well, yeah, sure.

This is SIGGRAPH, you know, these 90% PhDs.

And so the thing that's really great about SIGGRAPH, as you know, this is the show of computer graphics, image processing, artificial intelligence, and robotics combined.

And some of the companies that over the years have demonstrated and revealed amazing things here from Disney, Pixar, Adobe, Epic Games, and of course, you know, NVIDIA.

We've done a lot of work here.

This year we introduced 20 papers at the intersection of artificial intelligence and simulation.

So we're using artificial intelligence to help simulation be way larger scale, way faster.

For example, differentiable physics.

We're using simulation to create simulation environments for synthetic data generation for artificial intelligence.

And so these two areas are really coming together.

Really proud of the work that we've done here at Meta.

You guys have done amazing AI work.

I mean, one of the things that I find amusing is when the press writes about how Meta's jumped into AI this last couple of years.

As if, you know, the work that FAIR has done, remember, we all use PyTorch.

That comes out of Meta.

The work that you do in computer vision, the work in language models, real-time translation, groundbreaking work.

I guess my first question for you is, how do you see the advances of generative AI at

Meta today, and how do you apply it to either enhance your operations or introduce new capabilities that you're offering?

Yeah.

So, a lot to unpack there.

First of all, really happy to be here.

Meta has done a lot of work and has been at SIGGRAPH for eight years.

So, we're noobs compared to you guys.

But I think it was back in 2018...

You're dressed right, but this is my hood.

I just wanted to...

I mean, well, thank you for welcoming me to your hood.

I think it was back in 2018, we showed some of the early hand-tracking work for our VR and mixed reality headsets.

I think we've talked a bunch about the progress that we're making on codec avatars, the photorealistic avatars that we want to be able to drive from consumer headsets, which we're getting closer and closer to.

So, pretty excited about that.

And also, a lot of the display systems work that we've done.

So, some of the future prototypes and research for getting the mixed reality headsets to be able to be really thin, with just pretty advanced optical stacks and display systems, the integrated system.

That's been stuff that we've typically shown here first.

So, excited to be here this year, not just talking about the metaverse stuff, but also all the AI pieces, which, as you said, we started FAIR, the AI research center.

Back then it was Facebook, now Meta, before we started Reality Labs.

We've been at this for a while.

All the stuff around Gen AI, it's an interesting revolution.

And I think that it's going to end up making all of the different products that we do different in interesting ways.

You can look at the big product lines that we have already, so things like the feed and recommendation systems and Instagram and Facebook.

We've kind of been on this journey where that's gone from just being about connecting with your friends.

And the ranking was always important, because even when you were just following friends, if someone did something really important, like your cousin had a baby or something, it's like, you want that at the top, you'd be pretty angry at us if it was buried somewhere down in your feed.

So, the ranking was important, but now, over the last few years, it's gotten to a point where more of that stuff is just different public content that's out there.

The recommendation systems are super important, because now, instead of just a few hundred or thousand potential candidate posts from friends, there's millions of pieces of content.

And that turns into a really interesting recommendation problem.

And with generative AI, I think we're going to quickly move into the zone where not only is the majority of the content that you see today on Instagram just recommended to you from stuff that's out there in the world that matches your interests and whether or not you follow the people.

I think in the future, a lot of this stuff is going to be created with these tools, too.

Some of that is going to be creators using the tools to create new content.

Some of it, I think, eventually is going to be content that's either created on the fly for you or pulled together and synthesized through different things that are out there.

So I think that's just one example of how the core part of what we're doing is just going to evolve.

And it's been evolving for 20 years already.

Well, very few people realize that one of the largest computing systems the world has ever conceived of is a recommender system.

Yeah.

I mean, it's this whole different path, right?

It's not quite the kind of gen AI hotness that people talk about, but I think it's all the transformer architectures and it's a similar thing of just building up more and more general models.

Embedding unstructured data into features.

Yeah.

One of the big things that just drives quality improvements is it used to be that you'd have a different model for each type of content, right?

So a recent example is we had one model for ranking and recommending reels and another model for ranking and recommending more long form videos.

And then it takes some product work to basically make it so that the system can display anything in line.

But the more you just create more general recommendation models that can span everything, it just gets better and better.

So, I mean, part of it I think is just like economics and liquidity of content and the broader of a pool that you can pull from.

You're just not having these weird inefficiencies of pulling from different pools.

But yeah, I mean, as the models get bigger and more general, that gets better and better.

So I kind of dream of one day, like you can almost imagine all of Facebook or Instagram being like a single AI model that has unified all these different content types and systems together that actually have different objectives over different timeframes, right?

Because some of it is just showing you, you know, what's the interesting content that you're going to be, that you want to see today.

But some of it is helping you build out your network over the long term, right?

People you may know or accounts you might want to follow.

These multimodal models tend to be much better at recognizing patterns, weak signals and such.

And so one of the things that people always, you know, it's so interesting that AI has been so deep in your company.

You've been building GPU infrastructure, running these large recommender systems for a long time.

Now you're, now you're...

I'm a little slow on it, actually.

Getting to GPUs.

Yeah.

I was trying to be nice.

I know.

Well, you know, too nice.

I was trying to be nice, you know, you're my guest.

When I was backstage before I came on here, you were talking about like owning your mistakes or something.

Right?

So...

You don't have to volunteer it out of the blue.

I think this one has been well tried.

Yeah.

It's like I got raked over the shoulder for a while, you know?

But as soon as you got into it, as soon as you got into it, you got into it strong.

Let's just put it...

There you go.

There you go.

Now, the thing that's really cool about generative AI is these days when I use WhatsApp, I feel like I'm collaborating with WhatsApp.

I love imagine.

I'm sitting here typing and it's generating the images as I'm going.

I go back and I change my words, it's generating other images, you know?

And so, the one that old Chinese guy enjoying a glass of whiskey at sundown with three dogs, golden retriever, golden doodle, and a Bernese mountain dog.

And it generates, you know, a pretty good looking picture.

Yeah.

Yeah.

We're getting there.

That's me.

Every month.

Yeah, yeah.

And then now you could actually load my picture in there and it'll actually be me.

Yeah.

That's as of last week.

Yeah.

That's me.

I know.

I'm spending a lot of time with my daughters imagining them as mermaids and things over the last week.

It's been a lot of fun.

Yeah.

But yeah, I mean, that's the other half of it.

I mean, a lot of the gen AI stuff is going to...

On the one hand, it's, I think, going to just be this big upgrade for all of the workflows and products that we've had for a long time.

But on the other hand, there's going to be all these completely new things that can now get created.

So, meta AI, you know, the idea of having, you know, just an AI assistant that can help you with different tasks in our world is going to be, you know, very creatively oriented, like you're saying.

But, I mean, they're very general, so you don't need to just constrain it to that.

It'll be able to answer any question.

Over time, I think, you know, when we move from, like, the LLAMA3 class of models to

LLAMA4 and beyond, it's going to, I think, feel less like a chat bot where it's like, you give it a prompt and it just responds, and then you give it a prompt and it responds, and it's just, like, back and forth.

I think it's going to pretty quickly evolve to, you give it an intent, and it actually can go away on multiple timeframes, and, I mean, it probably should acknowledge that you gave it an intent up front, but, I mean, you know, some of the stuff, I think, will end up, you know, it'll spin up, you know, compute jobs that take, you know, weeks or months or something, and then just come back to you when, like, something happens in the world, and I think that that's going to be really powerful.

So, I mean, I'm quite...

Today's AI, as you know, is kind of turn-based.

You say something, it says something back to you, but, obviously, when we think, when we're given a mission or we're given a problem, you know, we'll contemplate multiple options or maybe we come up with a, you know, a tree of options, a decision tree, and we walk down to the decision tree simulating in our mind, you know, what are the different outcomes of each decision that we could potentially make, and so we're doing planning, and so in the future, AIs will kind of do the same.

One of the things that I was super excited about, when you talked about your vision of creator AI, I just think that's a home-run idea, frankly.

Tell everybody about the creator AI and AI studio that's going to enable you to do that.

Yeah, so we actually, I mean, this is something that we're, you know, we've talked about it a bit, but we're rolling it out a lot wider today.

You know, a lot of our vision is that...

I don't think that there's just going to be, like, one AI model, right?

I mean, this is something that some of the other companies in the industry, they're, like, you know, it's like they're building, like, one central agent, and yeah, we'll have the meta AI assistant that you can use, but a lot of our vision is that we want to empower all the people who use our products to basically create agents for themselves.

So whether that's, you know, all the many, many millions of creators that are on the platform or, you know, hundreds of millions of small businesses, we eventually want to just be able to pull in all your content and very quickly stand up a business agent and be able to interact with your customers and, you know, do sales and customer support and all that.

So the one that we're just starting to roll out more now is, we call it AI studio, and it basically is a set of tools that eventually is going to make it so that every creator can build sort of an AI version of themselves as sort of an agent or an assistant that their community can interact with.

There's kind of a fundamental issue here where there's, there's just not enough hours in the day, right?

It's like, if you're a creator, you want to engage more with your community, but you're constrained on time.

And similarly, your community wants to engage with you.

But it's tough.

I mean, there's just, there's limited time to do that.

So the next best thing is, is allowing people to basically create these artifacts, right?

It's sort of, it's an agent, but it's, you train it to kind of, on your material to represent you in the way that you want.

I think it, it's a very kind of creative endeavor, almost like a, like a piece of art or content that you're putting out there.

And it's going to be very clear that it's not engaging with the creator themselves, but I think it'll be another interesting way, just like how creators put out content on, on these social systems to be able to have agents that do that.

Similarly, I think that there's going to be a thing where people basically create their own agents for all different kinds of uses.

Some will be sort of customized utility, things that they're trying to get done that they want to kind of fine tune and, and train an agent for.

Some of them will be entertainment.

And some of the things that people create are just funny, you know, and just kind of silly in different ways, or, or kind of have a funny attitude about things that, you know, we probably couldn't, we probably wouldn't build into meta AI as an assistant.

But I think people, people are kind of pretty interested to see and interact with.