At the end of last year, we're like, all right, we are quite

We haven't seen the models yet.

You know, we're going to write this check for like, I don't know,

like $100 billion, $200 billion.

I'm like, Alex, are you sure this is going to work?

It's like—Cheers.

Cheers. Yeah.

Cheers. Cheers.

Cheers.

So, give me the update on Muse.

If you were looking at the third-party reports,

you would see that this might be the fastest-growing agent or AI app

that I think has existed yet.

What are your guys' personal favorite Muse use cases, either yours or other people's?

I was actually talking about this at dinner yesterday.

My favorite thing is I have my Muse, whose name is Oiler,

constantly source increasingly obscure teas

from increasingly obscure vendors of tea.

So at this point he's, on a weekly basis, my Muse is buying

some random tea from some farm in Taiwan or some farm in Japan

and sourcing it over and bringing it back.

What's your favorite Muse use case?

I don't know. I mean, I use it for a ton of stuff.

I still like...

My favorite both use case and probably eval for the product

is having it coach me in MMA.

So I put cameras in my gym, and I basically...

It watches the cameras when I'm in there, and it gives me feedback in between

the rounds, which is a very hard problem, it turns out, because not

only does it have to...

It basically has to process all the video quickly enough because you only

get 1 minute in between the rounds.

Oh, it coaches you between the rounds? Yeah.

I want feedback. I want feedback on how to win.

You know what I mean? It's like, come on.

Did you have to instruct it...

I've wondered about this too, because looking at videos is

a really good AI use case.

It feels like you can't just give it a multi-gigabyte video file.

So do you tell it how to slice stills out of it?

No, it basically... Yeah, it pulls stills.

I think it kind of figured that out by itself.

Um, it turns out that Muse has pretty good native understanding

of MMA, so that's good.

So like the multi-modal—

The pre-training.

Yeah, someone on the team knew. Now...

Just kind of out there in the corpus.

I think it's a good test of the kind of coding ability that...

Because really basically what it's doing, it's like, you know,

within the agent, I'm not seeing any code, but it basically is writing this whole

video processing, image processing pipeline.

Yes.

And I'm constantly telling it like, all right...

Like, look you took 30 seconds to give me that feedback.

I only have a 60-second break. Like, you gotta go faster.

Just optimize yourself, optimize yourself.

And it's, I think, a good example of how stuff is

going to go in the future where basically everyone is actually

going to be writing tons of code.

They're just not going to know that they're writing code.

What kind of MMA steers does a model give you?

Um, you want to see? Yeah.

Basically, let me see if I can pull some stuff out.

But basically it gives me a lot of stuff about balance and, like, hip position.

Or it's like when you're kind of in side control here.

It's just like you didn't get this underhook

in this place so you're going to...

You're vulnerable to this getting rolled over here.

I'll pull up some stuff. You guys should try this.

It was definitely one of his first use cases actually of even

the initial prototypes. Yeah.

Yeah.

I mean, I did this with OpenClaw before we even had Muse working.

It was just like...

Then that's why I was saying it's kind of like become an eval for this too.

It's good.

I actually didn't know that it was an eval for the product.

So noted. Yeah.

There you go.

How is all this...

In the model's training data, like the fine-grained,

this is what an MMA fighter looks like when they're appropriately

balanced, and this is when they're like too much on the back foot.

Just, I'm wondering, how has it consumed so many images of

appropriate versus inappropriate MMA posture?

Here you can see it.

It just pulls out frames from it and it's like too much on my back leg there.

Yeah, but is this the emergent intelligence?

Like it does actually...

Again, does it know from seeing lots of images of MMA

or does it know because it understands the concept of where you're

meant to be balancing?

It's read books about MMA, and then it sees that you're

too much on the back foot, or—

Well, we did train the models with a lot of visual perception data.

Yeah.

So, I mean, I think that that's like kind of making good multimodal models.

I mean, this gets into like, I think, a core approach thing that we have,

which is we're training the models end-to-end to be good

personal agents, right?

So like the question is why would Meta want to build these models?

First of all, I think just superintelligence overall is going

to allow us to run the company better and build all kinds of different things.

But for the products that we're building, there's certain capabilities and certain

mixes of skills that basically are just much more applicable to these cases.

So I mean, this is one of the things that Alex focused on from the beginning

was like, all right, we really want to make sure that our

multimodal program is good because that's disproportionately important for the types

of things that people use all these services for.

How do you train a good multimodal model?

Is this where Meta just has an advantage because you guys have so

much image and video data? It's very helpful.

Yeah.

And I think ultimately, a lot of this, it comes down to how are

you focused on it, right?

I think because of the fact that we wanted to build personal superintelligence.

And fundamentally, I think most people in the world really

experience the world in a highly multimodal way.

They like talking to other people and listening to things and seeing videos

and filming things of their own.

I think most consumers have this highly multimodal experience.

And especially earlier this year, there was actually a point of tension

almost in the industry because I think so much of the consensus view within the AI

industry was that it was just all about coding and coding was all that mattered.

And I think that for the success of a product like

Muse and the success of a lot of these consumer use cases, it really depends on

the ability to be strong and multimodal. Yeah.

I think we're just kind of scratching the surface of that.

I mean, a lot of the initial theory that we had was that,

I mean, most people don't actually like reading that much, right?

So even the text-based interaction model, I think it's something that people do.

I mean, everyone texts.

But when you think about stuff like the glasses that we're working on,

or even our other products that are very visual,

the glasses basically will eventually just be a pure multimodal product, right?

It's like you can let it hear what you are listening to, and it can talk to you,

and it can see what you see if you let it.

And then eventually, I don't even know if we're going to

have an app store on the glasses.

It'll just be like a multimodal generation that kind of creates whatever

you need right in your vision.

And I imagine something like that will probably end up powering a lot

of the apps that we build too.

But yeah, I mean, we're still in the early days of that.

But for now, the perception work has been really impressive.

I want to ask you guys about the product space here in AI

and try something out on you.

If I was to describe to you, say, with Stripe, what I think a winning

product looks like, it would take me pages and pages to talk about because we have

the developer experience and we have to talk about all the international

heterogeneity of payment methods. Then we have to talk about stablecoins.

Like, it would take me a long time.

Whereas I notice a phenomenon in AI where it feels like the winning products

collapse very quickly onto an agreed-upon product space

that everyone is pursuing, and so transformers worked, and then

like the chat agent UI really worked.

And now we are seeing...

You mentioned OpenClaw earlier, the kind of OpenClaw persistent agent,

has a computer, able to do things.

You know, computer use out there in the world view.

We're collapsing on that product space.

But there's something about AI where the winning products

tend to be very simple, pursued by all the companies

that are taking it seriously.

You could describe on the back of a postcard...

Like you could describe very concisely.

And obviously, implementing them is massively complicated.

Like, we'll get into the new model.

There's, you know, huge amounts of frontier work

that goes into making a good version.

But the winning brand—now you're talking about the glasses or the future versions.

But somehow there's a very small number of clearly correct products in AI

as opposed to it being this, like, very broad and complicated product space.

I kind of disagree.

Sure.

Well, first of all, I think part of the theory

from the beginning was that this kind of agent or whatever form it took was

going to be quite a different thing from this text-based assistant, right?

Every company has one of those. I mean, ours is Meta AI.

It reaches a ton of people, but I mean, it's like the same thing

that all these companies have.

And we, I think,

specifically think that that is used for very informational purposes,

but people don't think about it as like a thing that gets stuff done for them.

It's retrieval, not delegation or something.

Yeah, and even like when you say it has a computer...

Yeah, but there's lots of different ways to do that, right?

You know, I think that it's only kind of this most recent wave that

gives the agent a dedicated VM, which I think is actually incredibly

important for making you feel confident that the security and privacy of your

information is—it's not just like, okay, it's not connecting to some elastic

compute that just everyone uses.

But my claim isn't that it's easy to implement, nor that it's obvious—

I think there are going to be a lot of things.

Yeah. Okay.

You think there's going to be a lot of different things?

Because again, my claim is that now that people see Muse,

everyone looks at that and says, "Oh yeah, that's clearly how you want the AI agent

product to work, and that the future AI agent products that we'll see

will be fairly Muse-shaped."

So I think...

My take on this is, I think we're just so early in the realm

of what AI products will look like that I think a lot of the interesting

differentiation about the products kind of gets collapsed in how

we view the products today.

I think the traditional way in which we wrote down and think about

product specs, that is...

Those aren't the relevant dimensions and levers and axes

to consider the important design decisions on AI-native products.

And so, Muse, for example, I think so much attention was put in the design

of the model for the product.

And there were a lot of very intentional decisions around, in all these situations,

how exactly should the model behave?

And what's the exact personality of the model in these situations?

And how should it handle these sorts of tool-use scenarios?

And these are very intentional decisions.

And probably in the same way that the full spec of how we wanted the model to behave

in all these scenarios would be an extremely long document.

But because of just how we think about products today, it's just AI.

It's kind of like how when mobile first started happening,

it was like, "Oh, it's just an app and everything is just

an app." Well, it turns out there's a lot of nuance in what exactly an app is.

And we're kind of in this phase where, oh, it's just AI, or it's just an agent,

or it's just an agent with a VM or something like that.

But the interesting differentiations we have to tease out.

Okay.

So you guys think I'm making the classic mistake, which is like,

my domain is complex and this domain is, again, from a product

point-of-view, it's simple. All payment processors seem the same.

Yeah, no, I really agree with what you're saying.

One thing that we talked about from early on, is that basically

there is this pattern where I think the products are getting thinner

and the model is getting to be more of the product, right?

So one of the things that we say internally is like,

the model is the product. Yes.

Right?

So, and when you're talking about an agent,

a lot of the complex product work that went into Muse

is basically around the privacy and security model for handling your data

and the things that get connected to it.

And that, I think, is something that we're just very proud of that we

really focused on.

But that's a lot of the product surface that we need to innovate on.

But the capabilities, the personality, a lot of the stuff is like

you're training into the model.

And whether they're the people in the lab who are working on that or the people

on Nat's team who are working on the product,

I think when Nat is thinking about what is the key stuff that we need to make

the product feel good, he's talking about post-training the model, right?

Not like...

I mean, of course the UI of the product matters too, but the most important

part of the UI is the product. Yeah, exactly.

I think there's like, there's still very viscerally, I think from a user,

there's a feeling of using Muse over time.

And maybe some meaningful portion of that is just the reliability

of Muse to handle certain tasks.

But then another meaningful portion is how is it proactive?

And in what context is it proactive?

And when it talks to you, how do you feel about how it talks to you?

And so there's clearly a lot of...

And I'm starting to see more and more of this actually show up on the X feed,

which are people are starting to tease out these, like...

They're all proactive agents, but what are the actual meaningful

differences between them?

And these, I think, are the interesting product decisions,

which, you know, the funny thing is how do you specify these product decisions?

Well, it's ultimately in model design decisions, which ultimately boil down to

training methods or data or evals or, you know, all these things.

Yes.

As we talk about product space, one notable Muse detail is

that there is no model selector.

and no effort selector.

Do you guys think that's just obviously correct in a consumer AI product, or—

I just want to make it simple.

Yeah. I think that we've been...

I think somewhat infamously, a lot of the AI products have been

designed by a highly technical group that do not have these sort of randomly

sampled American in their mental model.

And so the products...

I mean, the naming of some of the models is like so egregious.

The numbers go backwards?

Some of the times the numbers go backwards and sometimes it's just like a totally

different word and you have another set of words.

And like, I just like, how is somebody who's not

carefully reading API design specs supposed to know

what they're supposed to pick? Like, it's just far too confusing.

Okay, we're talking about models a lot. Tell me about the new model.

It sounds awesome.

Yeah, I mean, I think we're quite proud of this.

Um, yeah, the new model—Muse Wonder—it's a new class of model

that is at the frontier.

So we've basically been working on this since the beginning.

Basically, when you're doing these pre-trains, you don't just go to whatever

the biggest cluster is that you have.

You want to work your way up the ladder training models that are

maybe 4 or 5 times bigger, and then you just keep on repeating that process.

So the first set of models that we've had Muse Spark was all based on this initial

pre-training that we did in, what was it, December?

Yeah, the end of last year. Yeah.

And, and it's actually...

I think we've all just been quite surprised and impressed with how

much intelligence and capability the team has been able to build

into a model which is like frankly not that big, right?

I mean, and then after that we were working on this next model,

and the pre-training has been done for a bit, and we've been working

on the post-training, and now it is here.

So it's the next class of model.

It is significantly bigger.

So I think you you will feel that in terms of the nuance in which it can think.

And it really.

A lot of the areas that we focused on were making it the best personal

and professional agentic model.

Some of the different things that you can optimize for that's it for us, and then

yeah, a lot of the basic capabilities that we focused on building in.

Very good memory, very good ability to persist through complicated tasks.

But a ton of work went into this across the lab and the product

teams and all of this.

And I think it's also just sort of a testament to the the team being able

to methodically figure out the process to scale these models.

So I think this is also just, you know,

a milestone on the way towards the time

between each subsequently bigger class of models, I think will

now start to compress.

And that's very exciting for the type of stuff that we can build.

At Meta or in the industry generally?

At Meta.

I mean, I think you're seeing something like this everywhere else, but—

Is this an RSI effect or something else?

Well, I think at Meta, part of this is just...

We needed to...

Well, I think quite famously, we were not happy with the trajectory

that we were on with Llama 4.

We kind of like rebuilt the whole team.

That's been this kind of crazy thing over the last year and a half that we should

probably spend a bunch of time on.

Oh, we'll get to that. Yeah, yeah.

Some war stories.

But the team basically had to go build up like different tiers

of models to get to this one.

And now that we are kind of at the frontier,

now everything that we do going forward will just be pushing the frontier,

as opposed to there's a lot of background work that we needed to do before that.

I don't know, what would you say? Yeah.

Well, I think we're really excited about it.

We will have 2 models in this series that are going to be available.

So there's Muse Wonder and Muse Wonder Full.

Laugh? Yeah, all right, there you go.

Yeah, very good. Uh, which is basically a much higher—

Studio band version.

Yeah, no, a much higher-end model.

It's the full version of Muse 1.

I know, I got it. Yeah, yeah, it's not—

It's 2 words. Wonder full version.

You're clearly a father.

What's up?

This is an excellent dad joke. Yeah, there you go.

Um, and, and the performance...

I mean, we've seen like incredible performance across a slate of evaluations

on personal, professional agent tasks.

It's state-of-the-art based on our measurement.

And then there's a number of, I think, specific highlights which we're

really excited about.

Which we've designed into the model over time.

So active memory, like Mark mentioned, is one area where

we think that to build personal agents, personal professional agents,

like memory is just one of the clear requirements to build reliable agents

and is one of the areas where we've invested, I think,

disproportionately actually compared to many of the other efforts.

And we have our own internal evals of Memory Bench which demonstrate

a lot of the capabilities there.

What does memory mean in a model to a layperson?

Is it just how much stuff it can...

Like the working memory of if you're working on a task, how much stuff

the model can kind of keep in its—

Yeah. How's it different from context?

I think it's in the experience of using the model.

How well does it remember things that you mentioned to it over days, weeks,

or months, and then leverage those in the process of doing future tasks?

I see.

This is the stuff that's buggy AF in OpenClaw.

Like, I think we're all extremely fond of OpenClaw.

Very happy OpenClaw user, but it ultimately does this compaction

where it has like the long-term memory and the working short-term memory or whatever.

And I think once people use OpenClaw for a while, they find the memory pretty buggy.

Yeah.

So we've designed basically both the model and a system for the model to use

to develop a much stronger form of memory. We call it active memory.

Yeah.

In addition, as Mark mentioned on the persistence—

But so this is outside the model, like the model exists and it's—

No, no, but some of the capabilities that you're talking about,

like the compaction, are things that the model does, right?

So basically being able to do this well, both with—

It's not a harness, it's in the model itself.

Yes, it's in the model itself. Exactly, yeah.

So it's aware of its memory as a core system.

And it's about how it's able to leverage various sources to be able to

conduct tasks in the future, as well as understand its ability to do

compaction, how it should do that compaction most effectively, et cetera.

Okay.

But this is a big deal because I think a lot of people's early experience

of AI agents has been buggy memory.

And so, yeah, improving that is—

And to be clear, there's still plenty of room to improve.

Our evaluations show this, but this is something that we're quite

proud of, and we think this is a meaningful contribution.

Okay. So active memory is one thing.

Yeah. Active memory.

Active focus is another, which is effectively just the ability for the model

to, when conducting tasks,

be able to carry them through, especially as we've seen

Muse users as well as Muse Code users, give higher and higher complexity

and more and more ambiguous and more and more complex tasks,

we want the model to be able to conduct those and do those very effectively.

And this is one of the things actually in early dogfooding and testing

that a lot of people have noticed about the model that is sort of special

and stands out with respect to other models that they've tried or used.

The third one is domain expertise.

What is focus in AI terms?

Is it like putting a lot of compute on the task?

Is it not getting distracted with other—

Kind of, persistence? Yeah, like persistence.

It's basically like the ability to conduct complete tasks over very long horizons.

Okay.

And then when faced with various setbacks along the way,

the ability to identify other solutions to those problems in an aligned way.

Okay.

Then there's...

It's actually a big step up in coding capabilities,

so frontier coding capabilities.

So it is actually state-of-the-art on DeepSWE and, SWE-Marathon

and does very well on other benchmarks like Terminal-Bench 4 and many others.

So we think that users who use it for coding,

which will be a minority of all of our users, but the ones who do will see

a big improvement and a big step up.

Okay, so we should also just be using Muse Wonder Full for coding at Stripe?

Potentially, yeah.

I mean, let's go out there and—

But like if it's state of the art on SWE-bench, yeah.

We'd be missing out.

Yeah, no, it's a big improvement on coding.

I mean, obviously, we want people to try it and see how they

experience it, but we've been very pleased in our own internal testing.

Okay, so this is—

Yeah, I think people will like it for a lot of things.

Okay, so this is both just your latest frontier model, like people can come,

you know, developers can come directly to you, they can use it on OpenRouter,

like they can just consume it as a model, and it's also a big upgrade

inside of Muse that people will just feel as active users of Muse.

Yeah.

Well, what we're going to do in Muse, because it's a lot bigger and it's more

expensive to serve, we're going to start off primarily in paid subscriptions.

So what we want to do with Muse as a product, we want it to be

free for everyone to use.

So we have this huge amount of free tokens that people can use with

with Muse Spark as the model.

But if you want to use Wonder for most of the hard tasks, then

at least for now, you'll use the paid version.

But, you know, I mean, eventually we'll, you know how quickly the field moves.

Yes.

And we hope to, you know, at some point not too far from now,

have a model that is as good as Wonder, but in the Spark form factor.

And that will be a great thing to be able to serve hundreds of millions

of people around the world.

Okay, so you go to Muse, you subscribe to the—what's it

called—like the premium or pro plan, or...

Pay money. Yeah.

And just don't stop until you've taken out your credit card.

Yeah. And then you'll be able to...

You'll select it, or it'll just magically— Oh, it just gets better.

It just gets better for you. Yeah.

And then it'll also be available via API, on the Meta Model API and OpenRouter.

And then it'll also be available for Muse Code users.

And then Muse Wonder Full will be available to subscribers of Muse Code.

So it's pretty exciting.

I think we want to, as Mark mentioned, it is a much larger model.

And so we're working through how to serve that most effectively

to as many people as possible.

But we're excited to get it in the hands of as many people as possible.

It's a big step forward.

I mean, this is like, and we really with Muse Spark,

we were very happy with the performance that we were able to deliver as

we were getting up to this point.

But like, this is, the beginning of, like, MSL kind of being at the frontier.

We feel like the models are quite competitive with the best

models that are out there. And I think that this is—

Okay, so you think it's like Astra, Fable, and Wonder altogether at the frontier?

I mean, you can look at it empirically by the benchmarks.

Empirically by the benchmarks, one would conclude that.

Yes. I see.

Yes.

And I think actually one of the things that we're especially proud of is

we have taken the time, as we've mentioned, to optimize models

for the product form factor of Muse.

So this is the state-of-the-art model.

We think it really is the best model in the world for the use case

of personal professional agents.

Yeah, people inside companies like complaining about Goodhart's Law.

You know, a metric that becomes a target ceases to become a good metric.

You hear complaints that models are benchmaxed and that at a certain point,

kind of overfitting to the benchmarks actually makes the models slightly worse.

As people who actually have to train AI models, is that true?

Are the benchmarks pretty good at grading models?

Do you start to see weird edge failure cases from the—

The benchmarks have to keep moving, right?

Because it happens very quickly, but they just get saturated so quickly.

So, I mean, I do think that—

But you think non-saturated benchmarks are fine?

I mean, as we've gone on, we steer the models more towards our internal

benchmarks for the use cases that we're focused on.

So, I mean, early on when we were kind of just

doing the science of trying to climb the ladder,

we cared more about what the external benchmarks were just to make

sure we were on the right track.

And then I'd say, you know, each release that goes by,

the larger share of what we're looking at are internal ones.

And one of the things that we actually quantified in our research blog post as

part of Muse Wonder is this metric for big model smell,

which is kind of, you know, big model smell and feeling of benchmaxing

are kind of like inverses of one another.

This idea that you could just like tell when you're using a really

big model, it just... You can tell.

It just has nuance. It's got that je ne sais quoi.

Yeah.

And so we, the way we quantify that is actually almost explicitly what you're

mentioning, which is the gap between in-distribution performance

and out-of-distribution performance. So what is the...

We kind of measure how well does the model perform on tasks

that are in distribution of our RL and post-training versus tasks

that are out of distribution.

And what does that gap look like for...

What does that gap look like between the Muse Spark line and the Muse Wonder line?

And we actually see this huge step up from the Muse Spark to the Muse Wonder

line because of, as Mark mentioned, like, it's just a much better base

model powering this new line.

And there's an entire je ne sais quoi to that,

but I think we've been able to actually quantify this effect.

And it's an obviously dimension that we're excited to continue improving on.

But if you're saying that external benchmarks have ceased to become

as important, and you care much more about your internal benchmarks.

Is that kind of an argument against benchmaxing, that you think that

just trying to get the top score on the external benchmarks that everyone

talks about is maybe not going to lead you to develop a great model?

Well, yeah, but also we're focused on specific things.

I mean, part of the whole strategy is personal superintelligence and just trying

to build different types of products than other people are trying to build.

So even if we did have the exact same evals as everyone else.

It just—

Yes.

And though I would say a personal superintelligence is not like a super

niche interest, like it's probably something that other

models want to be able to do.

I think that there are things that are very different about it.

I mean, I think like the personality probably matters more.

I mean, one of the things that we talk about is this idea of discretion, right?

So if you want to like use the model to get something done,

It needs to be able to know a lot about you and get things done in the world

while disclosing as little as possible.

So, you know, if you're— you have an allergy or you're

pregnant or something, you're trying to, like, get a,

you know, like, a table at a restaurant and you don't want to, like,

have to tell them your medical condition. Yes, yeah.

You know, to make sure that it's going to be a good place for you.

Like, okay, how do you handle that?

And I mean, humans have common sense and the models so far

If you're building a coding agent that works in an environment where everyone has

access to the same context, discretion isn't particularly important.

So there are all these things that I think are just kind of more important for us.

Yes.

Aspects of coding where the code needs to work,

but you don't— the consumers who are using it may not even ever see the code.

I mean, that probably matters more in a personal superintelligence context

rather than serving software developers.

So I think these things are just a bit different.

Yeah.

Ultimately, we care about making useful models.

So we want models when you put them into people's hands,

they have a great experience using.

And I think that is the most important metric for any model that we

or other people produce.

I think that really focusing on the external benchmarks,

if you really dig into the supply chain a little bit, it's some

PhD student who made these benchmarks, a benchmark as part of their,

as the course of them doing their PhD.

And so you're kind of outsourcing all of your product thinking to that PhD

student who happened to make a viral benchmark.

Right. So clearly that's wrong.

Yeah. And so that's why we care a lot.

A huge part of what a lot of people at Meta and MSL are focused

on is crafting amazing evals.

And now that we have them, we can work on our own.

But if you're building a you're ramping up your lab, right?

It's like those public ones are not a crazy place to start, but it's

just not where you want to end up.

You're just at that point, you're just looking for signs of life.

But then when you're like really in this mode, as we are now, where it's like,

you know, we want to be very intentional about how

we're improving our model over time to build better and better, better products.

Like a lot of the cases that Mark just mentioned, like these are

actually detailed very rigorously in like the spreadsheet of all the little

capabilities and nuances of the product that we think really

matter to the end user.

And maybe each of them matters like 2% to the user, but in aggregate,

that's the obsession that will ultimately result in these models that feel magical.

Can you guys describe for me just what goes into training an RL model?

Or sorry, a leading-edge frontier model?

I'm just curious in that I think a lot of people listening

to this have a rough idea where there's there's a pre-training

and a post-training step.

So pre-training is you kind of ingest this huge amount of data.

You look at a lot of pictures and these are cats and these are dogs.

And after a while you start to develop a sense for what is catness and what is

dogness and what is fur and everything like that.

And so that's kind of the pre-training step where you just hoover up data

and maybe you can talk about the data acquisition and just kind

of what goes into that.

And then there's this RL stage, which is the newish advance

over kind of the past few years of AI.

And again, it sounds like that is where a lot of the specific nuance of this model

has come in, where you guys have chosen better domains,

better hills to climb for the model.

But just at a high level, describe how many people,

how long it takes, what the steps are, how you think about those in—

How much time do you have? Yeah, at a high level.

This happens to be What we spend all of our time on.

Exactly. And so summarizing—

YouTube courses.

Yeah, I think at a very high level, these are the stages.

It's pre-training and post-training.

Ironically, training is the superset of all of it.

But that's—

so how many people are on this whole effort, like when you

develop these models?

So this, I think you're getting into what is interesting about MSL

and the approach that we've taken, which is, I mean, From the very beginning,

I think when I first reached out to you, I think we were just looking at this

because we were like, just like, wow, okay, how far we've come.

And I was just like, I pulled up the screenshot of like

when I first reached out to Alex.

My first thing was like, hey, do you have time to catch up and talk?

And then it's like, all right, we talked for—

That's how they get you. 44 minutes or whatever.

And then after that call, 2 days later, Alex sends me this long thing,

which is like, well, here's what I think I would do if I were in your position.

And the very first thing is that you don't need a lot of people, and

talent density is the thing that matters.

So I'd say, like, from the beginning of the plan, this was,

like, the core of your strategy, right?

It's basically, like, you actually don't need

a massive number of people, but what you do need is for,

like, each of the people to be as good as possible, which kind of informed this

kind of crazy recruiting push and, like, very

intense filtering that we had to take, you know, some of the best people

from inside Meta and a very intense process to get people from outside Meta.

But I actually think like a lot of what has made MSL special

and why a lot of people have wanted to join is because I think it's in some

ways it's just like more fun to work on a talent and smaller effort.

It feels like the startup. Yes.

That's operating.

In some ways it has all the benefits of a startup

with the stability and resources of a larger company.

And this was like this environment that I think this was like point one in your kind

of plan for what, um, for what Alex was like, this is what we need to go do.

So I don't know the answer of like how big is a pre-training team, you know,

it's, it doesn't need to be that big.

But I think if you look at, you know, some of the other labs, um, it's

probably quite big in some other places.

So, um, I think part of the reason why, uh, these models are doing well is because

you You have people who we're betting on and they have a lot of resources

because we're taking our compute.

We're not peanut buttering it across a lot of competing efforts.

We're basically just investing in the key people, a lot of compute per researcher,

and then they can do their work.

Was Wonder the one big training effort?

That was where most of the training was?

It was the next pre-train.

It was climbing up this ladder.

So, I mean, we have this tiering system and then kind of Spark.

I mean, internally, like, Avocado was the code name

for the Muse Spark line, and then Watermelon was the code name for this one.

And we're, like, out of fruits now. We're out of fruits.

Yeah, so we're going to have to switch to a, to a new convention.

But, um, I think the only fruit that is technically larger is the durian,

and you don't want to go to the durian. No, no, no, no.

Yeah, yeah, yeah. No, you want to stay far away.

A durian is bigger than a watermelon?

I think it's definitely heavier. That's horrifying.

Definitely heavier. Spikier.

Spikier, yeah.

But no, yeah, I think as Mark mentioned, like, you know, each of the— I mean,

each of the stages I'll describe, like, it's tens of people working on it

at Meta versus, you know, hundreds at—

So Meta is a huge company, but this is like a really tiny effort inside of Meta.

Yeah. Oh yeah, I know.

The research team is less than 200 people.

Yeah, it's very small.

It's intentionally very, very tight. I think that the—

It's closer to 100, right?

And then there's a bunch of InfoFolks. Yeah.

It's a lot of infra work.

So I think at a very high level, to go back to the, to the, to the what is

it question, you know, how does it happen?

How does model happen? That's basically what I'm asking.

Where do models come from?

You take a huge amount of data for pre-training, and here you're—

And it's here, like, roughly, it's like all the,

you know, public internet and like all the books you can get your hands

on and just like as much data as possible. But different types.

Plus the proprietary But also different modalities, right?

So if you want it to be good at perception, then you want to make sure

it has a lot of images and audio and video and this kind of stuff.

Um, you know, if you want it to

be good at certain domains, you don't just want to RL that in at the end.

You want to have it have exposure to that from the beginning.

Yeah. Yep.

Yeah.

So, so, and in this stage, you're kind of, you're much less,

you care a lot less exactly what the data looks like or how it's formatted or

You know, all of everything about it.

But it's just lots and lots of data.

When you talk about this being the best personal agent model,

was there data you had to get for the pre-train where it's like huge

amounts of restaurant reservation, call booking?

I don't know what it is, but like—

You just kind of want to dump in large volumes of the modalities

that people are going to care about.

I mean, there were a bunch of things.

I mean, I don't actually think we talked about this when we were talking

about where Muse Wonder excels.

But some of the areas that it's like, all right, what do people care about, right?

It's, I mean, there are things like shopping that people care about a lot,

personal advice that people care about a lot.

People have personal legal questions, personal health questions,

personal tax questions.

Muse Wonder, I mean, is like quite good at those.

I don't know if there's any quantification that we want to give,

but it's, let's say it's—

It's very good on those personal advice questions.

Yeah, and so those types of things, I mean, you can RL them to some degree,

but you're like, just want to put in a large amount of data.

Like, you can imagine health is a very— there's a lot of data

out there about health.

And, um, in, in pre-training, you just wanted to like see

all these different things.

And, um, so you, you kind of just put these different corpora in.

Yeah.

So, so that— so as, um, as Mark was mentioning, like, the, uh, we,

uh, one of the highlights of Muse Wonder actually is its ability on domain

various domain expertises. Yes.

And I think our philosophy here is that, you know, the best personal agent should

feel like a team of experts in your corner.

Like, it should feel like it is your legal team.

Yes.

And your finance team, and your medical team, and, and, and, and, and.

And so, um, on a bunch of these domains, we, you know, Muse Wonder is actually

state of the art on, I think, um, a lot of the finance evals, as well

as a lot of the, um, uh, legal evals.

the state-of-the-art model.

And that, you know, these, these domain areas, that starts in pre-training.

Those, you know, we look for a lot of domain data to put into pre-training.

Yes.

But a lot of the, a lot of the art of pre-training— I mean,

pre-training is, um, there's this one Rune tweet where it's like pre-training is full

of the researchers who like chew glass and stare out into the distance

and into the, into the abyss or whatnot.

But, but I think there's a truth to that because it is like, um,

It is both the highest stakes of the research areas because you,

you know, we invested a huge percentage of our compute into training Muse Wonder,

into pre-training Muse Wonder.

And it was very high stakes.

I think probably both of us at various points in that process, you know—

You went through troughs of sadness.

But yeah, troughs of uncertainties, you know, all the 7 stages of grief,

you know, happen while you're pre-training a model.

And it is the most sort of like fundamentally scientific in an interesting

way, because, you know, you just don't have very many samples.

So, like, your ability to infer exactly what will happen scientifically

really depends on your ability to sort of like,

you know, big brain reason about everything that's

happening in this process. Okay.

So in the pre-training stage, everyone who's training frontier models

basically is trying to get as much data as possible for the pre-train

with maybe a bent towards If you want Muse Wonder to excel at legal

and finance and tax questions and things like this,

then you want to have as much of that specific corpus as

possible in the pre-train.

Yeah, you still want as much intentionality about where ultimately you

want your model to be, but again, it just, you know, benefits from a lot more data.

Yeah.

And this is sort of the massive data, massive compute stage.

Do you hit some saturation in the data you put in the pre-train, or ultimately

is it just more data is better?

Well, there's a relationship between if you want

the model to be bigger, you need more data.

Otherwise, it overfits.

Yes, but my point is, like, is there data where it's like,

we can't distract the model with that, we can't clutter up its brain attic

with knowing that stuff, or is it just more data is better?

There are some of these effects, I think.

Yeah, because this is part of the science.

Okay, we're getting into the secret sauce, sorry.

No, it's not even that, it's just like, I mean, I think that there are all these

questions about, like, okay, well, we put a lot of these tokens in,

is there any zero-sumness in a bunch of places there isn't,

or it's actually positive transfer, but then like depending on how it's set

up, like you can have some negative transfer.

Yes.

And so I don't know, there are all these interesting questions.

But in general, one of the bigger challenges of scaling bigger models

is it's not just about how many flops and how much compute.

You want to be able to scale data with that.

I mean, this is like part of what, when the original kind of scaling laws

for pre-training were written, I mean, this is sort of what came

out of that is is that.

So if the internet isn't growing that quickly

and you want to scale bigger models that can be more intelligent,

I think one of the interesting problems is, all right, where's

the data coming from? Yes.

So I think that probably gets to the end of what we're going to talk about on that.

But we could go to the other— Excellent CEO energy over here.

But so post-training, so then I think Post-training is kind of— is

a very exciting field now, I think, and is an area where

I think actually, I think almost on a continual basis,

the entire field is learning about the optimal recipes to be able

to post-train these models.

And again, for people, like, post-training is where you give the model

a task that you expect it to do, grade it on how it does.

And so if it's a chess-playing model, it plays chess games, learns the— Well,

there's actually 2 stages.

There's the SFT, So there's supervised learning.

So that's like a lot of when you take this kind of raw thing and you give it both

the question and the answer that you would want it to have, right?

If like, if you're having a chat conversation, how would you

want it to respond in this case?

So when you're training things like personality, the SFT is actually like

a very important part for molding it.

But then I think most of the advances in the recent years have been in RL.

Yeah. Yeah.

So I think one mental model that can be helpful is like, And SFT is where

you kind of initialize a lot of— SFT is supervised fine-tuning.

Supervised fine-tuning, yeah.

Where you initialize a lot of the behaviors that you want the model

to be able to exhibit later on.

And then, you know, maybe at the end of SFT,

it has those behaviors kind of baked in, but it'll only do it like 10% of the time.

And then in reinforcement learning, you really sort of give the model

reinforcement in various situations to bring the elicitation of those

behaviors dramatically up.

And I think that the— yeah, so I think there's a bunch

of interesting things that are happening in RL and post-training.

I think one is that just the scaling of RL and post-training has been

one of the clear axes, just spending more compute and gathering more data.

Just doing much more of it before you release a model.

Yes, doing much more of it, especially as the pre-trains scale up, doing much more.

It's like anything in scaling laws, like it has to go with the rest

of the factors or it doesn't work. Yeah, in general.

I mean, if I were to give, say, like one broad statement about AI,

it's just scale everything, you know?

But, um, uh, but in particular, you have to scale everything.

Like scaling one thing in isolation does not work.

You, you technically can, you can scale one dimension.

You just will get much more optimal performance if

you scale many things altogether.

Well, I mean, so one way to think about like Muse Wonder, right?

It's like, all right, so we did the pre-train.

Yep. we've done some RL, right?

When you look at it like Muse Spark, right?

So version 1 to 1.1, 1.2, 1.3, we didn't change anything about pre-training, right?

We just continued doing more RL on top of it.

So, um, so you, when you think about the early versions

of a model of a new class, they're actually relatively less,

like the RL to pre-training ratio is going to be lower for RL

than the later versions.

That is kind of what the point releases in models are.

Well, I think every company has its own convention.

For us, that's what it is meant to be.

But for a lot of companies, like the big number is the pre-train and then the—

I think it kind of lost that semantic value.

Yeah, I think now you actually can't do that.

Okay, but that is true at Meta at least.

But we haven't even done it.

We kind of decided to not even do it. It's true at Muse Spark at least.

It is true at Muse Spark and Muse Wonder.

It's true that Muse Spark and Muse Wonder are different pre-trains.

And we decided to kick the can down the road on this question for Muse Wonder

by not giving it a number yet.

But when we have our first dot release, we will have to decide that.

Yeah.

But yeah, no, I think the RL stage, part of what has been—

part of what's very interesting is first is just producing the environments

and producing the tasks.

And I think that is one of the areas where there's

a lot of intentionality in training these models.

It's also one of the areas where you notice very funny things.

Like if you— in a lot of cases, less is actually more when it comes to data in RL.

And so I think I think the sort of, like, the feel and the science of it, like,

is pretty flipped, generally speaking, from pre-training.

Yeah.

The other is that I think it can give very magnified kind of instruction

to the model, if that makes sense.

So I think that, like, a lot of safety cases or alignment cases

that we care a lot about are, like, you know, and, you know,

where you would see when you do large high-compute RL runs on frontier models,

are where you apply a lot of RL or optimization pressure in scenarios where

the only way for the model to succeed is to do something misaligned

or do something unsafe.

And so that's another thing that we had to be very careful about and thoughtful

about in training Muse Wonder is ensuring that we curated the RL environments very

thoughtfully to ensure that all of them were eliciting and driving

towards more aligned behavior from the model.

So the point that you're saying about quality, I think,

is an important one because I think a lot of the previous questions back and forth

were about the quantity and have the flavor of like, oh,

you need to scale the quantity. Yes.

But I mean, I do think like a huge part of this is just, is,

is the point that you just made of like the even a smaller amount of high quality.

Yeah, I think, I think in RL, like we, you know, as we've seen

pretty big jumps in our new Spark line, I think these have been like accompanied

with, you know, real meaningful improvements to the underlying recipe.

And each of these improvements in the recipe have been like,

I think the improvement between Muse Spark 1 to Muse Spark 1.3 feels night and day.

I think probably to most people, if you ask them,

do these feel like they came from different pretrains?

I think they probably would say yes, because I think you can—

Mm-hmm.

I think it's happening industry-wide for what it's worth.

It's just like we are learning to squeeze so much capability and so much

and elicit so much agentic capability from these existing base models.

Now, that's not to say, like, we believe scaling is very important.

That's why we have to use Wonderland in the first place.

Yes.

But, you know, this sort of like, these two things are happening at the same

time, which is also, by the way, for us, this is why we're seeing so

much progress in general.

Like, I think it's the fact that we're scaling both pre-training and scaling our

recipes in RL kind of like concurrently. At a nice clip.

Yeah.

And then there's these other scaling axes too.

I mean, like making sure that your agent harness is good and that you can

co-optimize these things is probably just another aspect of,

or dimension for scaling.

But if I'm understanding you guys correctly, are you saying that alignment

is not about coming up with some alignment-specific RL

steps and instead about making sure the entire RL process

is kind of steering The model—

You have to be thoughtful about it in the entire process of RL.

Yeah.

So I think this is a holistic effort, I think.

Yeah.

But also, I think different labs have very different theories.

I mean, alignment is a very philosophical concept, right?

Of like, who do you want it to be aligned to?

I mean, a lot of our theory is like, okay, we care more about it being aligned to,

you know, we're trying to give people agents, right?

And it needs to, if you ask it to do something,

you need to have a high confidence that it's going to do what you ask it to do.

Yes.

You know, like, you want it to do that with high reliability.

You don't want a moralizing lecture for why you're wrong

for asking it to do the thing.

Obviously, if you're trying to do something that's catastrophic,

it needs to gracefully, without pissing you off, like, not do it.

But in general, you know, we view alignment as more

of a question of instruction and values alignment with the person

who it's intending to serve.

And I think that that's kind of a different mode of thinking about it.

It's very different from, I think, how some of the others do.

But one of the more interesting things I think that is maybe a question related

to your original question is also just how long the process of training

one of these models takes.

Like Muse Wonder, I mean, in general, we've planned Muse Wonder since

last You know, August.

Yeah, so yeah, I remember the offsite when we were— so this is like when

they first presented this.

Yeah, so I think all of the stages that we just talked about are like, you know, you

really work on them for quite a long time.

And, and, um, it's not that we, we didn't start working on Muse Wonder

when we finished Muse Spark.

It's that we've actually just been planning and working on this

for quite a long time.

And we really view Muse Spark, if anything, as like I think we mentioned

this in the first blog post where we wrote about it.

That was a stepping stone to get to Muse Wonder.

And that really was how we thought about it.

Even at the time you thought about it.

Yeah, and in terms of how long it takes, I mean, we're also even just

talking about the research part.

I mean, there's also the compute part where we've been investing in building up,

first of all, a huge amount of compute overall.

But a lot of, you know, part of our philosophy on this is that

you can kind of measure the compute strength of a lab by how big

its largest single computer is, supercomputer, for being able to do these

large training runs.

So, I mean, we built this Prometheus cluster, we call it, in Ohio.

Mm-hmm. That's scaling to larger than a gigawatt.

And now we're building this Hyperion cluster in Louisiana that's

going to scale to 5 gigawatts.

And those actually, predate MSL.

Because like building a whole like data center site from scratch,

at least the plans for those, they're coming online now.

But that was—

You guys thought it might be the feed, right?

That you just have used for a lot of compute generally?

No, no, no.

This is kind of predating, even going back to the, to kind of the LLaMA program.

I see.

I mean, it was probably in 2023.

or yeah, either 2023, 2024 was when we basically started making

the plans to build those clusters.

They're now kind of fully coming online.

Well, I guess the Louisiana one will come online over a couple years.

Yeah.

But if you want to go really end-to-end on undoing this stuff, then, I mean,

obviously the kind of full stack, and I mean, Meta is a full-stack tech company.

I mean, we build our own data centers, we build our own chips,

we build our own low-level infrastructure.

And now this is kind of the next component, which, I mean,

arguably is probably the most important component in all of this for the next wave

of technology is kind of building really good models and then

delivering them to people.

But I mean, yeah, if you want to go fully end-to-end, I mean,

this is a long process.

I do think this is like a— I mean, maybe it's well appreciated by folks

in the tech industry, but It really is, you know, it feels like models

are coming out constantly.

So maybe you'd have the mental model that like, oh, a company like launches

a model and then they figure out how they're going to make the next model and,

or something like that.

But it's actually these like very long planning processes and these

sort of like very long work streams that kind of all come together at the very

end and then result in these beautiful models.

Yeah.

How do you build good products when the feedback cycle is so long?

Like ultimately you start planning the model in August and you don't

know until more than a year later. if it's good.

And, you know, Silicon Valley famously likes to run on metrics, but, like,

ultimately, do you just need someone who

is the aesthete and says, I'm sorry, I don't like it?

It's nice when you have metrics, but I think the, like,

state of being an entrepreneur is, like, how well do you make decisions

in an information-poor environment, right? So—

But this is, like, that on steroids, right?

That on mega steroids.

Yeah, I think that this is— and I think Marc gets a huge amount

of credit for this, like, Because of how long everything takes,

like, there were many points along over the past year since I've been here,

but also, you know, as you mentioned, like, long before that, where

you have to make very large investments or make very big

decisions about, you know, how you're going to invest or develop

the technology or, you know, what decisions you're going to make

long before there's proof points around whether or not even the thing is working.

And so I think this is like, in some sense, it's like

entrepreneurship on hard mode.

It's like the opposite of Lean Startup.

Like, Lean Startup is like, yeah, you like see if you get something working

and then you double down and then you see if that works, then you double down.

Like, you cannot do that in AI.

You have to like weave— I mean, we've been investing into these data

centers since, you know, for years and years and years.

We've had to—

And you'll be radically behind if you wait to see if things are working.

So we have these conversations where it's like, At the end of last year, we're like,

all right, we are quite excited about the progress.

Yeah, we haven't seen the models yet.

Like, it's like, I'm going to, like, you know, we're going to write this check

for, like, I don't know, like $100 billion, $200 billion.

I'm like, Alex, are you sure this is going to work?

It's like, um, yeah, so it was, it was so extreme that, you know,

we had, we had like the whole exec team.

I think Susan scared you a bit.

Yeah, well, yeah, no, well, it was like The joke every time we talked

about it would just be like, yeah, and we hope it's all going to work and be good.

But like, there were literally, like, at the end of last year, other than

me giving a vibe report on how our research was going and, you know,

I could sort of show some plots and whatnot, but it was like, I mean,

I was feeling good about it, but it was certainly not clear

at all that it was going to work.

Yeah.

You guys are saying that the essence of training a frontier model is you have

to push your chips all in before you've been dealt the cards.

You just have to decide that you're going to develop a frontier model

and you don't get to meter the inputs. At this scale.

I mean, if we, like, back a few years ago, the clusters didn't have to be

this big, but now they do.

So I think this is part of the excitement

of booting up a frontier lab now, right?

Is basically like the ante is extremely high.

And I think this is— and like, I kind of like viscerally remember this.

We were in Hawaii at the end of last year.

It was kind of like the whole exec team got together.

And yeah, Susan pulled me aside many times and was like,

you know, I just want to tell you, if this doesn't work, like, you know,

this is going to be really, really bad.

It's going to be very, very bad in financial terms.

I really need to impress on you that this model has to work.

it has to be good.

If it's good, it's all good. We're great.

But if it's bad, it's really, really, really bad.

And Susan, who you know— And that was useful motivation?

It was.

Well, it was like, it was one of these very funny moments

where I was like, you know, obviously the amounts of money

are like so obscenely large.

What are the amounts of money in training?

Order of magnitude, whatever you can share, but just

like how much are they costing? Well, you can do the classic math.

Yeah, no, I mean, a gigawatt cluster.

I mean, it's Like, I don't know, I think what you'd see out there would be,

you know, to operate a gigawatt a year, call it on the order of $15 billion.

Okay, so it's roughly a $15 billion effort to train a bunch of models.

To operate a gigawatt cluster for a year.

If you're talking about building the CapEx, it's probably closer to,

you know, I mean, you pay for it upfront, so it's probably, you know,

$50 or $60 billion.

So, but then that's just kind of, one cluster for one part of training.

Yeah. Um, then you have all the other parts.

Um, research isn't just doing the run, right?

It's like, that's like probably an equal amount of it

is because for each change that you make, you're testing all the ladders all

the way up to the big, the big scale.

Um, and then they're serving the stuff.

So, I mean, part of the stuff that in some ways—

So tens of billions of dollars to train a Frontier model now.

Yeah, definitely the whole, I mean, I've seen the financial rollups,

tens of billions of dollars is a good estimate for—

Yeah.

And then, but then, but then you want to do something with it, right?

So I actually think like some of the more complicated questions

that we had were like, all right, so we're training this model and we know,

we know we want to train the model cuz we, this is like key, but like

how much product demand do we think we're going to have?

Right.

Because so now it's like, all right, well the product doesn't exist.

Um, like, and as in you're trying to build data centers to serve Muse

for a product that doesn't exist yet. Yeah.

And we have to make it years in advance, right?

So this is probably where, like, when we were talking about it last year,

we were just like, huh, there was a moment where I think

a bunch of people were like, yeah, maybe, maybe we should just

like be a little more conservative.

And it's like, no, we're doing this.

It's like, it's like, we're going to make it work.

We're going to make it work. We're going to burn the boats.

Yeah, this is going to work.

And But it was like, I mean, even, I mean, I too am an entrepreneur

and founder and have, you know, at various points in my life taken risk.

I remember the visceral feeling at the time was like, this is a fuck ton of risk.

Like, I don't know if I really— Oh, it's only riskier.

Yeah, yeah, it's only—

well, I do think this is like one of the like things that I think, Mark,

is very impressive about you, which is I think you have really

internalized this concept of You know, it's about how big it is when you win,

not like, you know, failure is okay.

It's just like how big is the win when you do win?

And this was, I think—

Mark has fully internalized power laws in a way that many people have not.

I think quite deeply, yeah.

Well, I think the other piece is, I don't actually think it's

quite as risky as this.

I mean, the numbers are big, but I think—

Meta has a backstop in terms of how it can use compute.

Yeah, so I think there's 2 things.

One is if AI works overall, plenty of other people want all our compute.

Right?

So they're willing to pay, like, I mean, if like the cost is whatever,

$15 billion a gigawatt, we could get like a lot more for that.

So I think that you, in order for it not to work out,

it's not just that we have to fail, AI overall has to fail.

Yes.

And then even if that happens, we still have all these other

uses to productively use compute.

So in the grand scheme of things, we were definitely building out

this compute for inference ahead of knowing that the product

was going to be ready.

But you were AI-pilled enough that you said GPUs online and powered

will be useful to someone.

Yeah, but also like we believed in what we were building.

I mean, it's like, I think, and you can look at the kind

of the trajectory where it's like, all right, first there's ChatGPT

as the text-based assistant.

Then, you know, Anthropic got coding agents to work.

And no one had built the kind of personal consumer version of that.

And it just seemed like this is the thing that Meta should build in the world.

And I think that that was just like pretty clear to you and me and Nat,

like from the beginning, right?

That that's like, that's kind of what we're here to do.

And so I don't know.

I mean, there's always like the real question for me on the Muse launch even

was you never know when you launch something how it's going to be received.

Right?

So there's always the case where you get a home run, which in this

case it was, right? Yes.

And it's just like extremely well received right off the bat.

But the normal case would be you kind of— it's fine, and you have to like iterate

a few times to get product-market fit before you really grow.

In some ways, like, I actually think the product,

we maybe could have even grown it faster, but we didn't have all the growth stuff

really ready to go when we shipped it because we are like— our median

expectation was like, all right, we're probably going to have to like

iterate on this a bit, and like, that'll be That'll be fine.

But I don't know, I think like if it takes a few iterations,

it's like not the end of the world. That's normal.

Yes.

Um, you can have launches where it's like you

get like, it's kind of catastrophic and then it takes a while to recover,

but that's not good.

But, but it's like, but I mean, even if it took a few

iterations, it would have been fine.

So I don't know, I guess it just seemed to me like this was going to be— yeah,

the numbers were big, but I— it seemed like it was probably—

Was it bonding for the executive team to be Holding hands,

taking this risk together.

You're talking about Susan taking UXi? Bonding or bonding moment?

Is that a bonding moment? It's like, I mean, it's stressful.

I think it was—Both.

Both. Yeah.

Being in a stressful situation together is generally bonding.

Well, one thing that I think is quite notable about Meta is the degree to which,

like, first of all, most of the exec team has been there quite a long time.

Like, Nat and I were really newbies.

And And,

you know, most people have been through crazy situations many

times in the past as well. Mm-hmm.

And I think Meta as a company is like an extremely resilient company that has

been through like, you know, many situations that also at the time

would've seemed incredibly daunting.

And so everyone's kind of like knows the program, so to speak,

in terms of, you know, this obviously on paper, and especially when like

Susan Li, our CFO, is sort of like pulling me aside in the meetings, like, does,

you know, does seem like a particularly scary situation.

But I think most people were like, yeah, yeah, no, but we're Meta,

we're going to get through it.

Like, I think there was like a real camaraderie around that and sort of like

quite a nice energy around that, which really I did appreciate because I

think it would be very easy for, you know, it's the other executives at

the company who are operating and running— Who are paying the bills.

Yeah, one of the greatest businesses of all time.

Yes.

And it'd be very easy for them to be quite annoyed by this whole endeavor.

But I think everyone is also very excited about—

They are.

What the opportunities for the company with AI are.

And I think, like, you know, taking a step back,

I think one of Mark's jobs as, like,

the founder and CEO of the company is to ensure that the company is always

pushing towards the most ambitious and expansive version of what it can be.

And I think that one of the great things about this entire MSL and AI push is

that I think this is one of the rawest expressions of that is

we knew that AI certainly is a very important technology and it's one

of the bigger technologies right now.

But if we were able to build it effectively

and properly integrate it into everything that we do, as well as like, you know,

as a full-stack technology company, like build it into,

you know, these incredible products that we know will exist in the future,

this could be like immensely expansive for Meta.

And I think this is true.

Like, I think Muse is an incredibly exciting product.

It has like, you know, it can go in 1,000 different directions.

I think it's one of like probably the most exciting products to just like You know,

when like late at night and you just think about all the ways it can go.

Like, I think Nat, Mark, and I just like, you know, shoot across messages late

at night of just like, oh, we could do this and we could do this and,

you know, we can make this move and we can like build this product.

And I think it's just like, you know, very—

It's a very exciting time in technology right now.

Yeah, it's very blue ocean in that way. Yeah.

Yeah.

I'm struck by, as we talk about

Muse and just how it clearly feels like

the next platform, an agent operating on your behalf.

You've talked for a long time about not wanting to be

reliant on other platforms and how kind of the experience of building on

Android and iOS kind of burned you on being dependent on other.

And often when you would talk about, say, Reality Labs, is the importance of owning.

the platform of the future and kind of many years

working on Reality Labs with the intent of owning the platform of the future.

And now this new modality comes along that feels

pretty clearly like how you do a lot of productive work and is

the platform of the future.

And now Meta is really well set up.

Yeah, no, I mean, it's less about wanting to own a platform for its own sake.

It's about being able to build the best things.

And at some level, you know, if you look at like other social apps,

for example, I think part of the reason why Meta is so

much better is because we don't just build the app, right?

A lot of people like think about it, we're like this app company.

We're not an app company, right?

It's like we're like a deep technology company and we do this because we

are world-class at machine learning and like all the recommendation systems

are operating at a scale that basically few others are in the world.

I think it's basically like Just us and ByteDance on that,

and and then like okay we just kind of go all the way to the metal.

And to me, being able to also deliver go the last mile through the device,

it does come from a while of having these experiences where we couldn't build

the experiences that we wanted on phones.

But I just think for the future, the the models are going to be by far

the most important part of delivering the technology experience.

And I mean, this is something that I think we kind of bonded on and agreed on, like,

very early, is this notion that, like, in every generation of technology

companies, there's, like, no, there's usually not one company that

kind of is the— is, like, wins everything.

I don't think that that's how this is going to go either.

But with the internet, there were, like, okay, 5 or 6 very big companies and then

a whole ecosystem of other companies that are great.

The companies that did the best kind of had the core technology to be

able to serve people the best there.

And I think in this next era, there are going to probably be a small

handful of companies that have the ability to build frontier superintelligence.

And I think that those companies are going to be the ones that effectively drive

the agenda and can build most of the interesting, the most interesting

experiences for consumers, for enterprises, hardware,

these different kinds of things.

So, and I just look at it as, okay, like, is it even really a choice, right?

It's like, yeah, okay, Meta could, will probably exist into the future

regardless of whether we do this.

But if we want to be kind of an interesting company that is

like inventing new things that matter to people in the future,

I don't see how you can do that if you don't have the ability to build end-to-end

your own superintelligence and build them into the products that people use.

And I think that this is something that you just like believed from very

early on, Is basically, it's like, all right, there

are going to be like a handful of these.

Like, it's like there's a kind of personal and consumer-oriented version of this.

And like, this lane is open because everyone else seems to just be obsessed

so much with just automating knowledge work, which, I mean, isn't even that

charismatic version of the future, right?

Because it's like, all right, like, a big part of our belief too is

that the kind of highest use of superintelligence is invention

and creation, not automation.

And so, I don't know, it just seemed like there's like a big

place in there, but you kind of need to own the stack.

How is the Muse-mediated future going to shake

out with businesses need to— okay,

businesses variously need to build support

for

for Muse, where it might try to computer

use its way in, but that might be janky in certain ways.

So you kind of want everyone to have first-class support

for agents talking to things. Yeah.

Then the bigger question is they might actively try to block agents

from interacting with them.

And this might be because they have a misguided sense where, like,

it's actually in their interest to support agents, but they don't want to.

It might actually not be in their interest to support agents.

You can imagine where maybe they have a very thin moat and it all comes from

a business that only succeeds by consumers spending time on the site or only succeeds

by overcharging consumers, then they should rationally not want

to allow agents because the agents are like, why?

But those are going to get disrupted. Yeah.

So talk to me just through the second-order

effects of how the economy works.

Well, I think that it starts off with— I mean, we trained into the models

very good browser use, right?

So that way we want to make it so that no one No one has to do anything

to integrate with Muse.

And then, but then of course, for— we're kind of just going down

the list of the most important partners, and for people who want to work with us,

we'll just get direct API access, right?

And then it's like, okay, then it ends up being way more efficient

and faster for people to use Muse.

It's more efficient because now you don't have an agent crawling your website.

It's just going.

So anyone who wants to do that, we eventually want to get there.

Um, and then I think the fullness of time is

that, you know, just like today every business has,

um, a website and social media accounts and email addresses and all these things,

every business is going to have agents too.

Mm-hmm.

So, and we're also building that on the other side of it.

And, um, and so of course Muse agents will talk to other types of agents, but they'll

work really well with our agents, right?

Because I mean, we'll, we'll be able to integrate that Just,

I mean, so Muse using websites is just a moment in time thing.

Just to make it easy for everyone.

You guys want to have it as a backstop so it's reliable for people using it.

Yes, and easy for businesses to get bootstrapped.

But we have, we have way more businesses that are calling us asking to integrate

deeply, and very few really who have tried to block it.

And my guess is that the ones that try to block it, it will end up not being

the right strategic thing for them.

But we'll see.

This will kind of play out over time.

It saves them a lot of time. Yeah.

It saves them a lot of money.

Like, I think these are, these are, like, clearly good things for the consumer.

Yeah.

So in the long arc, you'd clearly expect that demand will

shift from current avenues more and more towards agentic demand.

And, you know, there always will be demand that lives elsewhere,

just in the same way that, like, there's still brick-and-mortar demand in commerce.

But there's a lot of ecommerce demand.

And kind of in the same way, like, I think agentic commerce demand

will grow as a portion of the pie.

The others will still exist, but will grow as a portion of the pie.

And I think there's sort of these questions for businesses in the long run,

like, do they want to participate in that slice

or that portion of the economy over time?

And I think it's an incredibly exciting area.

I think it's, like, very, very exciting to imagine what will

happen in this agentic commerce future. Yes.

I actually think Patrick wrote a great tweeticle.

Is that what we call it these days? I don't know.

Patrick has his own thing going on with tweets.

It's like they're buried amongst—

I noticed that Brother Karamazov was quite a good literary analysis.

But yeah, no, I think clearly

this is a discontinuous event for how

commerce will happen, broadly speaking, or just generally speaking, how

the entire how all the business pipes will work, maybe even in a broader sense.

But I think that, you know, you just go back to, this is clearly

a lot better for the consumers.

It's a lot better for the customers.

So there's a natural gravity towards that reality.

One of the exciting things, I'm curious what you guys see too,

but I think Muse is one of the first products where you're actually people are

fully delegating purchases to the agents.

And this is like a— this is kind of like a, you know, a step change, right?

Like, I think, you know, we've been talking a lot about agentic

shopping and like the possibility of it, but now it's like really happening.

And a lot of people are trusting the agents to shop for them.

And I think the agents are finding them, you know, in my case, like great teas, or,

you know, in whomever— whoever's case, like great products for them to purchase,

which is quite exciting.

And so I think that there's There's clearly an exciting thing happening here.

I think, I think part of the value prop— well, one, one part of the value prop

for the user is just reduced friction.

Like, hey, I don't feel like click on all these websites and do all this research

to try to find the exact products.

Like, that step is taken care of for me.

And also, like, criteria can just be automatically applied.

Like, hey, I need something by Wednesday.

So, you know, the agent will only look for things that could get to me by Wednesday.

But that's, I mean, keyword search.

It was insane that we ever relied on it because that's a very

reasonable constraint. You just want to shop by.

Yeah, exactly.

And so I think the like old way of like searching for stuff just feels so

obviously primitive now, now that you can do this.

Then there's this sort of like back part of the funnel, so to speak,

but just like the ease of friction reduction of just, yeah,

filling out the form, just like getting it ordered is quite nice.

And I think the long-term thing that we're quite excited about

is greater personalization.

Ultimately, I think one of the exciting things about

Muse is that it has sort of like this holistic context into you as a person

and all of the aspects of your life.

So it sort of like can holistically understand things about your family or

things about your coworkers or things about your friends.

And then sort of like help you co-optimize across all of those to get you the exact

right gift for your, you know, best friend in the context of your friend

group based on what it knows about your friend group.

Like, I think there's just like a lot of exciting opportunities

that are somewhat new and novel when it comes to like these

personalizations, these recommendations.

Because historically, a lot of these recommendation systems are

quite siloed and quite isolated. Mm-hmm.

And so they're sort of like, they don't see like a holistic view

of you as a person.

And Muse has an opportunity to offer much greater levels of personalization.

Yeah, I think people are already seeing that,

where if they ask, like with Muse,

if you ask it to buy something for you, quite frequently it'll add in some bit

of context that it pulled in from a different conversation.

You had a good example of this. Yeah.

Well, so my 3-year-old daughter loves baking.

She loves cooking.

And so I'm like, all right, I'll start baking stuff.

It's like I'm normally into smoking meats, but I'll do some baking in order

to To make, to make Aurelia happy.

And so we've gotten into this really cute routine where,

um, you know, we, we, I've been playing with Muse voice mode,

which isn't fully rolled out yet, but we're, we're playing with it.

And, um, it does this great thing.

It segments the, the voices.

So it knows which one is me and it knows which one is not me based on that.

And we just like, you know, throughout the week we're talking about

what we'll want to bake that weekend. Mm-hmm.

And she'll like give it ideas or like she'll kind of come

up with crazy things that are not real recipes, but like bounce it off with Muse.

And then Muse will just make sure that we,

every Saturday when it's time to bake,

I get the ingredients from Instacart and they kind of show up and they're

on time, which I mean, the biggest thing is like if I were just

starting the activity on Saturday, you'd have to get everything

and it would take some time.

But now everything's ready whenever we're going to go sit down and do it.

This weekend it knew that watermelon was launching, so it threw

a watermelon in the bag.

It was very, very sweet.

It was very heartwarming.

It deduced that the recipe did not have

enough— there was not enough, I guess,

expense in it to get the delivery, so it needed something else.

And it was like, you're going to— And it topped it up with a watermelon.

It was like, do you want this watermelon? I was like, yes.

I'm like, yes, that's very cute.

Which I think you, uh, did you— is that the watermelon you ate?

Yeah, that was the one I ate on the livestream.

Okay, but the watermelon on the livestream was—

That was just like, Muse just got it for me.

That was a Muse—yeah, those are just—

You just got it for me.

But I think there's this, um—

That was a very memeable moment of that very memeable situation.

So we have Muse to thank for it.

So, but here's another interesting thing about the whole thing is I didn't

really use Instacart that much before.

You know, it's like every once in a while, maybe twice a year, I would like like,

use Priscilla's account or something. Yes.

I went and got an Instacart account to give Muse, and now I use it like once

or twice a week because like I'm talking with Aurelia with Muse,

and we're coming up with these recipes, and it's delivering them, and now

it's like a core part of my workflow.

So I actually think that the opportunity here isn't just, okay, it's going to be

20% more efficient on the conversion rate.

Like, I am a new Instacart like subscriber, and I just

wouldn't have done this before. Yes.

So, I do think that there's this question, and then there's this question,

which I think is these sort of interesting— like,

this is maybe the more interesting economic question,

is that Instacart account really for me, or is it for the Muse?

Because I think, like, over time, I think that the question is,

is the total addressable market for all of commerce just increasing dramatically?

Yes. Because it's not just the people anymore.

Like, now agents are effectively going to have accounts.

Yes.

And I think that's where this starts to, like, become a really very exciting

opportunity where anyone who sits this out, I think, is going to end

up feeling sad over time.

And why did you have to sign up for an Instacart account?

Like, we're solving this at Stripe Projects for developer

stuff where your coding agent—

The Stripe integration, by the way, is great.

You guys did a great job with us on Muse.

I think we're very happy.

We have been very excited about agent-to-commerce for a long time,

obviously, because again, felt to us like clearly how people

are going to buy things in the future.

And that was both apparent because no one needs all that friction in their life

from the actual buying stage, and how early product research with AI had

product-market fit, even from kind of the earliest AIs.

It really feels like computer use was the last big unlock that we needed to make

it happen, because now you can ask your muse to buy anything, more or less.

And it'll just figure it out with computer use, right?

And so it's like the ultimate backwards compatibility layer.

Yeah, it's pretty—

this is a set of things that Nat really obsessed over.

Yeah, yeah.

I think, I think there was a lot of, like, I think a lot of magic of Muse is

the fact that like a lot of this stuff just works.

And so like clearly if everything was just like a little bit

less reliable, it would feel like a total pain in the ass and nobody would use

it and people just throw away the app.

And so I think the fact that so much of this has gotten to the point

of reliability where You know, it just works is like a huge part of the magic.

Yeah.

What were you saying Matt was obsessed with?

Oh, well, I mean, well, Matt's kind of obsessed with everything.

Yeah, well, that's generally a sexy character.

Yeah, no, it's great, it's great.

But this is, I mean, but yeah, I mean, this getting this experience to be good,

I think is something that he and a core group of people on that

team really focused on.

It's kind of funny.

I think I had a dinner with Patrick, like, I want to say it was 2023,

where I was, he was like, what do you think we should be doing now?

I was Oh, you're like, you're already way behind the 8-ball

on this agentic commerce stuff.

Like, you got to figure this stuff out.

Like, agents are going to be buying things and like, you're not prepared for it.

So you were saying this to Patrick or Patrick saying this to you?

I was saying this to Patrick.

Well, because Patrick was very politely asking, what do you

think we should be doing?

And I sort of had this like very like urgent spiel.

Yeah, and I think it's very, uh, it's funny how that was like—

Well, thank you for your provocation.

Well, it was directionally correct, but, uh, but like 3 years early.

2023, I think.

And so, um, no, it's very interesting to look

at the agentic commerce stats at Stripe because

we started developing all this stuff and we had a whole bunch of announcements

at Sessions back in May, uh, and then it only as of September

basically started going vertical.

Meta is building increasingly capable models, including Muse Wonder,

and their models are available on OpenRouter, Alongside hundreds of others.

Stripe has agreed to acquire OpenRouter, who's the leading model gateway.

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of the complexity of working with different models through a single

API, making it easier to experiment, find competitive prices, and automatically

route around provider outages.

Try it for yourself at openrouter. ai.

Hey, could you order a watermelon to Stripe's office in South San Francisco?

I'm doing a podcast here. This would be funny.

Funny podcast idea.

I'll check that watermelon delivery now.

Great. We'll see how we get on.

Well, okay.

This actually raises an interesting question.

People have been trying to make The HAL 9000.

It's working.

I really like this.

I think this is an awesome touch where it's like clacking away at its laptop.

I really like that in the app.

We'll just leave them there and we'll see what we get.

Yeah, it's tough to prop up.

Yeah, there we go.

People have been trying to make the speak to a computer AI vision

happen for a really long time.

Where Apple launched Siri in 2014, 2015, something like that.

And obviously there's been Voice Mode and various AI products for a few years now.

Why is it so hard? Like, it's still not the case.

If you were driving your car and you wanted to check on the baking or move

a meeting or something like that, you actually probably

wouldn't do it with the voice.

I mean, maybe almost now with the latest things.

Yeah, well, there's a lot.

I mean, so— But you wouldn't develop Siri like you're doing.

Siri right now is actually a good example. As we're talking over each other.

And I mean, I don't know, when you're using different voices—

So is it just technical limitations? That's why we haven't done it?

Well, I think that there's a few things.

And one is you actually want it to go do stuff.

So there's a question of when you speak with it, even if it understands,

how's that going to work?

Then I think that there's a lot of stuff about the way that people

have conversations.

It isn't typically, I will give a prompt, and now I will wait, and you will speak.

And then, so it just goes back and forth.

Um, how many of your sessions when you're

talking to an agent in voice mode, do you basically, oh,

it starts saying something, but then like, I don't know, it's like,

I kind of feel like if I pause for 3 seconds, it's going to think

I'm done and want to speak.

And then if it starts speaking, but then I cut back in, is it going to

think that it's like a whole new question?

Um, I think there's a lot of the stuff about the conversational dynamics

that you want to get right.

But then there's this other issue, which is that For voice overall,

it's quite high bandwidth for you to communicate with voice,

but in terms of consuming information, having someone speak to you is very

inefficient compared to having it be visual.

Yeah.

So I think like that's why, you know, partially when we do these,

these interfaces, I think it's, it's kind of working on its task.

I fired it off now.

So I think the ideal interface is probably not just strictly voice-voice.

Although I think voice output from you is pretty useful.

Yeah, I think, I think there very clearly already is an explosion of voice input.

Like, I think you even see this in coding where like—

Yeah, people are speaking to the— Yeah, yeah, yeah.

Most of the researchers just kind of like whisper into their keyboard because it is

like, as Mark's mentioning, it's like the highest bandwidth way to get your like

thoughts out, or usually the lowest friction way to get your thoughts out.

Yeah, yeah, yeah.

I think the most extreme version of this, by the way, is I think how

college students now use AI is maybe the most interesting,

where they just— I think a very common modality of how to use

AI is that they just will completely word vomit for 10, 15 minutes,

being full stream of consciousness, chain of thought of all the things.

And then have AI clean it up and infer. Yeah.

And have AI effectively do all of this organization of it and pulling things

together and structuring everything.

It's quite interesting.

It's a very interesting, I think, modality of tech usage.

Yeah.

But yeah, I think voice input has been big.

I do think agents is like, that's a big step change, because I think

the mental model is just very different.

Like, if it's just like an informational thing that you're talking to, that like,

you will clearly get into situations where it just feels useless.

Whereas now that it can do things for you, it's much closer to the experience

of talking to a human. Totally.

Or to a person.

And so I think there's all these interesting uncanny valleys in interacting

with AIs that until they've been able to take actions have felt kind of strange.

And now that we have agents, I think one of the cool things about Muse

is this confluence of a number of things.

I think The fact that Muse is embodied and it like is this little guy

that like you feel like is working on your behalf is also only possible

and a coherent like representation and embodiment of the product because

it has persistent memory and it has the ability to do things for you

and the ability to take actions and has like a little bit of a personality.

Like, is the confluence of all these things coming together that make

this like embodiment concept— Yes.

Even work in the first place.

Then it turns out, okay, actually this is the real glasses, right?

It's like, okay, a lot of people want it even with just like no

display or a small display. Yes.

The camera and audio is enough of a useful product.

Yeah, yeah.

And I— and then we started shipping the display glasses that aren't the kind

of wide field of view full augmented reality, which, I mean,

I thought would be awesome too, but even the small display is pretty powerful.

So I think you'll start getting some interesting interactions there that are

kind of fully multimodal, right?

So it's voice in and out and It'll be able to see what you see, and it'll be

able to kind of paint the pixels.

And one of the— I mean, the other exciting thing that

we've done here is Muse Gadgets, which is really,

I think, unleashing the ecosystem to try cool device formats and form factors

that could be really exciting.

Like, since releasing that, we've gotten actually even a number

of phone manufacturers, you know, talk to us about excited You

know, we're like, we're just going to integrate Muse Gadgets

into our phone and see how well it works. And like, we're really excited about that.

Like, I think, um, I think part of what is cool about

the current moment of technology is that like, it's very clear

agents are really exciting.

It's really clear that like, this should unlock lots of things.

Will you guys come back to the household devices?

Like, this is obviously a personal device, but, um, we actually had

during COVID the, um, Portress— the Portal.

Portal. There we go.

Yeah, I mean, like, he's finding the Portal inside.

One of the gadgets that we just shipped was the little HomeLink.

It's like the size of a little, like, nickel or something,

but with a USB, right? Yeah.

Um, uh, so you can basically connect that, and then your Muse can connect

to everything in your home.

This is actually one of the most fun things too is just having it connect to

your Sonos and connect to all your lights.

I mean, I have it basically like when it's time for me to start

winding down in the evening, I just have it like dim all the lights

across the house and it's nice.

How are we going to fund the AI buildout?

Everyone's worried about, you know, Google went free cash flow negative.

You know, you were talking about 1 gigawatt and then 5 gigawatts

and everything like this.

Meta's now free cash flow negative yet, but there's always more CapEx to fund.

And so what's the answer? Where is Susan Lee?

Exactly.

No, I mean, look, I think that there are going

to be a lot of ways to create value from this.

So what we're trying to do with Muse specifically is make it so that it can be

widely used, because I think that there are going to be

some kind of valuable network effects at scale as people start being able

to interact with each other's, and that's going to be great.

So I think there are all these reasons why you want it to get to large scale.

Also, you know, we were talking about partners.

Partners are going to want to integrate more the more people are using it.

So it will get more valuable if we can get it to scale.

The best way to do that is to basically have a model where it is affordable

and free for people to use.

But there are going to be a lot of ways that I think we can scale revenue.

Subscriptions is going to be a big one.

You know, basically, just because the base version of it is

free and we want the free version to be excellent doesn't mean that there are

things that just naturally aren't going to be economical to include in that.

So, you know, if you want the kind of— you know, we talked about this earlier.

If you want to start using Muse Wonder in Muse tomorrow, then, you know,

the way that you'll do that is you'll pay for a subscription.

So I think that that will be a big business.

There are a bunch of other things around commerce that we're quite excited about

too, where we basically think— we believe that Muse is actually going to make people

money and save people money by using it, and that over time there are lots

of different ways to effectively take a small cut of what you are saving or

making for people, and that will end up being a very large revenue stream.

And then there's all the other things, right?

So, you think Muse will generally just cost negative money for people?

because it helps them out.

Well, there are already all these sites that I think are interesting where people

are like, go look through my emails, find a bunch of subscriptions I don't

actually use, cancel them, whatever.

Um, yeah, so yeah, I mean, that, um, I don't know if that's the thing that we

would monetize, but, but like, but you get, you get, you kind of get the

point and where it helps you make money.

I mean, one of the ideas that we kind of talk about, I mean, this is,

this is something that I think you've been kind of obsessed with, is making it like

a business in a box for small businesses.

So someone can just kind of— it can help them administer all these

parts of what they're doing.

And you can imagine that being very valuable and being very profitable,

both for the business owner and for Meta.

But I think one of the things that's interesting

is, like, OK, if we're building the models to build the personal agents

and the business agent, there is this large surplus of value

because now the model, it's very general, right?

So it doesn't just do those things.

We'll also have the API that'll be profitable.

A big part of why we want to start the Meta Enterprise Platform

and have CJ Desai join us to build this is we have this view, which is like,

all right, yeah, sure.

So traditionally Meta hasn't been an enterprise company,

but we believe that a huge part of the challenge is building

the leading models in the first place. And they're very general.

And once we do that, if we make a significant investment in building

an enterprise group that can provide that in the way that enterprises need

to consume it, then I mean, I think a lot of enterprises are

going to want to use Muse Wonder.

It's going to be an excellent model and affordable.

And like, it's so, um, so I think that that is going to end

up being a very big business too.

And all of these things put together, coupled with the fact that we were already

starting off as a very profitable business that can,

that has good credit and can kind of underwrite these large investments,

I think puts us in a pretty good position as a lab to be able to build out

the compute that we need to build leading products, build up the network around

this, and position the company to really lead in the area for decades to come.

Okay.

So, you think ultimately there'll just be a lot of revenue from these products,

and so they'll be self-funding in fairly short order?

Yeah.

I mean, that's a much shorter answer than mine.

It's like— But you're saying people are underestimating—

The question was, Are we going to be able to make money from this?

The answer better be yes.

I think the history— Yes.

The answer is yes.

But the history is very front-loaded expense, right?

Well, the history of AI as an industry, I think, to some degree is like,

you know, people have always sort of been hand-wringing about, you know, how are

these businesses going to make money?

And I think, you know, every year people are sort of generally

surprised at the upside.

That superintelligence is Is a useful, incredibly valuable—

Yeah, exactly. This is what—

this is kind of Meta's whole playbook, right?

Is we build something, we get it to a billion people,

then we scale the revenue while growing our revenue primarily

in the meantime by continuing to scale the things that are already

kind of at scale and growing. Yeah.

I think this is also one of the dynamics, like, I think,

you know, I think Dario's talked about this as well,

but like, just in general, because it requires so much advanced

planning to build these models.

Like, if you look at the R&D expense at any given time,

that is like over— an overlapping R&D expense of multiple model

generations at once. Yes.

And, you know, these, they require a long time to work on.

And then ultimately, you know, you see like immense payoff

when, when you ultimately deliver them.

And this is kind of what we saw with the Muse Spark family of models.

Like, you know, again, we started working on these a long time ago.

And are seeing like incredible consumer adoption through the Muse product.

The other thing is that we use the models to accelerate our core business, right?

So I mean, you have this core business that's hundreds of billions of dollars

and still growing extremely quickly.

And now, like, if you can grow it a few percent faster by using these models

to accelerate these things, then that ends up funding a huge amount

of this too as you're scaling it out.

The Meta core business is a, is a like 8th wonder of the world.

It's extremely good business. It really is.

is quite incredible and is growing at sort of astonishing rates.

Yeah, you guys really cooked with that one.

Yeah, I think you're also getting at something, Alex, which is like there's

an interesting accounting artifact where if you go build a data center,

you have CapEx and you get to depreciate that over a useful life of many years.

And people debate what that useful life is, but the point is you do get

to depreciate it, whereas a lot of the AI costs are also just

in researcher salaries, and those salaries are being spent on,

you know, model training that also has a useful life of many years,

but you do not get to capitalize generally.

And that's also a financial advantage for us building out the data centers as

opposed to a bunch of the other companies that are paying clouds,

whereas they're paying the annual rate, but then at some point

the GPUs will be fully depreciated, but we're probably still going to be using

them for a while because they I mean, the value of even like an H100 at this

point is higher than it was last year, right?

Just because if compute continues being constrained.

So yeah, I think it's— yeah, I think—

Any advice for us with OpenRadar?

We have agreed to acquire them.

It has not closed yet.

Yeah, I think part of the exciting— part of what we're starting to see

in terms of the models, especially the frontier models that are

being developed, like Muse Wonder versus, let's say, a Fable or an Astra,

is that we actually are starting to focus in quite different directions

from the other models.

And I think there's a lot of intentionality that goes into the model

development process that we've discussed as part of this, you know,

in this conversation, that is genuinely different

from how other models are focusing.

And so, you know, when these models kind of get

collapsed to the same set of benchmarks, that elides all of the—

This is a good point.

The interesting, um, sort of like, uh, texture that differs

between the various models.

And so I think one thing that is an exciting opportunity for OpenRadar,

generally speaking, is like, you know, um, helping to Helping developers

understand what these sort of subtle differences are so that they can make

the most informed decisions about what models ultimately they select.

And then also you can imagine the flip side of that, which is,

you have all these developers using these models.

And so those developers are great people to ask about what are the interesting

textual differences between these models that are more than just tweet length.

Like, this model's good, this model's bad, more than just that level of insight.

And I think this is like getting to the wine sommelier

version or form of understanding the models and their sort of relative

strengths and weaknesses, I think would be quite exciting.

Okay, so based on your experience, the OpenRadar view that there will be

a diversity of models in the future and they're optimized for different tasks and—

I agree with that. Exactly.

That's just your guys' experience based on—

Yeah, we are focusing On different things.

We care a lot more about personal and professional agents, my observation,

than the other major labs, which is good.

There should be differentiation in the economy.

This is the point of capitalism to some extent.

Yeah. So you're going to say something?

No, I just really agree with that point.

I mean, I think that part of what I love about you guys working on OpenRouter

and being able to scale it is just the more infrastructure that can get

developed, that supports a diversity of players

in the ecosystem, I think is good overall.

I also, for the same reason, am quite a fan of some of the recent NVIDIA

moves to support the open source ecosystem.

I don't know, but this just kind of goes with our whole theory and philosophy

about, like, what does a good future look like?

Mm-hmm.

We think that a good future is one where you achieve a,

like, you basically get prosperity by empowering people.

That's kind of the personal superintelligence thing that we're doing,

but there's the whole business version. Yeah.

But for a lot of the important safety topics,

like, our view is that the way to get there is having a diversity

and having checks and balances by kind of having a lot of different things out

there and accessible to people, not like just having a couple of good

models that are only in a couple companies' hands.

I think that that would be— from my perspective, I think that is

probably a riskier configuration for the future than lots of people

having access to different things.

Do you guys have a theory as to why that is?

Where I feel like maybe people predicted more of a winner-takes-all dynamic in AI,

or one person clearly— like, a story that people told back in 2023 is

that one AI lab will start to develop an advantage,

and that will allow them to get recursive self-improvement going,

And then they'll just pull further and further away from everyone else.

Whereas it has remained extremely competitive where

there's kind of multiple people at the frontier.

You guys with Wonder today are coming along with your frontier-class model.

Open source models have done an impressive job of keeping up, all told.

And so we had this story a few years ago that someone was just going

to run away with the prize.

Why hasn't that happened?

I think there's a lot of underlying reasons and mechanics that drive this.

One certainly is just execution.

I think for any given frontier lab, there are periods of strong

execution and weak execution.

And I think that is true across all the labs.

And so that sort of starts to average itself out over time.

I think another—Very diplomatic.

Sorry. Yeah.

Is it that some of the Players in the lead screwed up?

Well, I think if all players,

when they were in the lead, executed perfectly, they would

probably be able to stay in the lead.

So I think that's one factor.

I think the power and strength of distillation is

certainly another factor that's certainly benefiting the open source

ecosystem quite a bit. And I think this is just like a—

There's an anti-hegemonic force in the phenomenon of distilling a model.

Yeah.

And this is just a quality of the technology.

It's not like anyone planned for distillation to work super duper well,

but it just turns out that you can get a lot of capabilities naturally

through some form of distillation.

And this is, broadly speaking, distillation is quite

a good force for the entire ecosystem because it means that you can pack very

strong intelligence capabilities into smaller and smaller models,

which enables us to serve them at much wider scale.

And that's certainly one of the very exciting stories is just

like really small models today are like, you know, much more powerful than the

biggest models a year ago or 2 years ago.

And then I think the last is that there's just sort of this— I think there's like,

I actually think a lot of people in the ecosystem want this future

where it is not just this like, you know, definitely.

One final company in all of capitalism kind of kind of end outcome.

Like, I think this is one of the reasons—

Not just individuals, but companies putting dollars against things.