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Tell me the history of Muse. The first
thing to start with is probably like

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what our vision was when we started Meta

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Super Intelligence Labs. And when we
started it, Mark published this memo

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that myself and Natt Friedman and a
bunch of others like worked together

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with him closely on called Personal

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Super Intelligence. And the whole idea

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was like how do we make

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AI how do we build powerful AI that
actually makes all of our lives better?

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like how do we build something that
actually like lifts the human experience

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broadly speaking. You know, in that memo,
we we spoke to a bunch of the

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ideas and we basically alluded to I

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think a lot of what muses ultimately

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become, but we were gesturing at it like

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very very early like this was in you

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know June 2025 um before really agents

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had happened in any meaningful way you
know in the summer of 2025 like AI was

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just you know in a totally different spot.
But that's where what we wanted to

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build towards. That was the mission
of MSL and that was really the where we

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wanted to take everything. Then we
kind of just got to work building models

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because you know the endto-end process
of developing any of these frontier

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models is many many many months. Um like
you have to go through you have to build

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your entire stack you have to produce
the data sets you have to you know

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pre-train the model then you have to do
pro training the model you have to build

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a whole stack for that. like is an it's
a very lengthy end to- end process. Um

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and kind of this whole um production

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line at the start of this year um you

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know Opus 4.5 obviously like really

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caused many people including including
us to sort of like see what the future

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of agents could be or was and really

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brought the sort of like potential of

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agents to the foreground and openclaw

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was happening at the start of the year.

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Natt Freriedman, who you know I work

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closely with, was I think the first

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person within MSL to uh work with

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OpenClaw and try OpenClaw. He had an

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experience I would I would describe as

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somewhere between terrifying and

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euphoric. Like I think he he really like

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I think went all in. He trusted entirely.
you know, he's told some of

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these stories that uh he had like
a a Stripe Sessions interview where he

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talked about some of this where,
you know, he told his open claw that he

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wanted to drink more water and then, you
know, his openclaw would like watch him

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in security footage to make sure that he
was drinking water and tell him good job

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and he had like many many crazy stories
like this and it also makes you realize

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like, oh, there's something quite quite
magical about what's happening here. And

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I remember one of the things that
that Nat told me and this is one of the

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things I remember the most is he said I
think there really is something here and

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I don't know like early adopter Nat
Freedman is like a pretty good indicator

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cuz he was one of the he was one of the
first customers of Stripe back in the

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day and then was really ear early on a
lot of open source developer stuff and

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he also you know helped create GitHub
copilot and many other things. So that

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was like a clear signal. It's like,
okay, there's clearly something pretty

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magical about personal agents generally
speaking. But you know, when you

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actually kind of trust the personal
agent and and really bring it into your

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life, it is the ability to do so many
things. And after hearing all of these

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crazy stories, I start using it very
deeply. You know, I think for many weeks

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I think both of us were sort of like
every spare moment we would just spend

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talking to our open claws and like
you know, you know, really immersing

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ourselves in like what
personal agents could be.

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>> What was your initial reaction?
Euphoria or terror?

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>> Nat at the time he had this um he had

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built this prompt to do this like

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basically like full psychoanalysis of

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yourself. There were like multiple waves
of like deep psychological probing. He

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basically wrote this prompt for you
to to use an open claw. And I think that

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was like one of the very first things
I did with with my own openclaw. It was

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maybe the equivalent of like 3 years
of therapy all, you know, compressed into

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this one moment. >> Wait,
I'm curious why when you get your

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first shot at open call,
this is one of the first things you do.

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>> You know, I downloaded it. I installed
it. And then Nat was like, "Oh, you

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should do this." I was like I was like
next to him. He was like, "Okay, I'll

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just do the thing you're telling
me to do." And then >> okay um

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>> and then you know
this crazy experience.

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>> Wait say more about
the crazy experience though.

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>> Oh well it was just like very intense.
I mean it was like again it was like 3

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years of therapy because it was just
like successive rounds of like deep uh

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psychological probing cuz it had other
information about me. It had you know

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access to my email and access to a bunch
of my photos and like it could like go

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and look up other information and sort
of pull it all together. I was very

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vulnerable through that process. This

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was in February. uh both of us I think

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had come to this like joint conclusion

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that this is this is clearly like you

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know there's clearly something really

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special happening here and we both wrote

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memos at that time I wrote a memo about
and both of these we you know kind of

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like sent up through the meta board um
cuz you know for whatever reason it's

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board season I wrote a memo about how I

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felt that in many ways this was like the

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final consumer product and that the

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super agent was something that was like

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as far as consumer products went like a

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clear end point in many ways. And then
he wrote one called he wrote actually

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two uh which had very memorable blames

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the clause the law and trust is a must

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which ultimately defined um a lot of the
a lot of the core of how we built these

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products. So we came to this conclusion.

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This was in February. There
was something very important here. Um and

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then we built you know our teams we

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built a prototype within

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like a a week maybe two weeks. Um and

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demoed it to the board like it was this
like very fast cycle and many of the

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decisions that made its way into the

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final Muse product were present in the

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first prototype. The Jollybot was there
know the early version of the Muse Charm

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was in that first that very first meeting.
Not all of the ideas and not

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all the tabs were there but a lot
of them were present. Then from there, you

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know, obviously that was February, we

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launched the product 7 months later was

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like the really really hard process of

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taking this, you know, prototype where

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it it like sometimes worked and was
magical but most of the time didn't work

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and would constantly break and like
going from there to something that would

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be highly reliable like you know kind of

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like a beautiful crafted consumer

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product that we felt confident as a

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contribution to the consumer world. And
that that was like this very lengthy and

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quite painful process. And >> before
we get there, I want to go back

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to the idea that you wrote the prototype.
You guys created the

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prototype in like a one to two weeks.

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You show to the board. What's Mark's

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role in all this?

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>> He was like experiencing the sort of

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like euphoria/ terror of the product at
the same time as all of us. I think he's

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talked about this in various interviews,
but like I think he he used it for a

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bunch of things around the home and he

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was using it with his kids and he used

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it in his um uh in his MMA training.

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Like he had it watch videos of his of

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himself doing MMA training and like
it would give him tips on like how to

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improve. there were like some staff
meetings and by the way like you know

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this kind of became a thing for for much

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of the the meta team um was like having

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these having a staff meeting and saying

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like what were the what were the crazy
experiences that you had with um with

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with your agent again this was back
in February. It's very interesting to kind

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of um think back to this whole cycle

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because obviously the openclaw phenomena

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it peaked and then disappeared.
Yeah, there were, you know, it didn't it

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didn't persist and and I think a lot of
people kind of had this experience which

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was it was just like you'd have these
magic moments but for the most part

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didn't like work that well and there
were all these problems and then you

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know it just kind of like died down and

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so part of I think the building
the product a lot of the focus of the

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product was just like how do you make

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this into an actual reliable consumer

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experience that you feel comfortable

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putting in front of over time billions
of people and you can feel confident

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that like most of those people
have an amazing experience.

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>> Why were you able to do that and
the developers open call were not?

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>> Well, first of all, I think we respect
Peter Seinberger and everyone who's

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contributed to OpenClaw immensely. Like
I think Peter is definitely a visionary.

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I think whatever happened in his
beautiful brain that created OpenClaw is

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magical. And I think maybe the

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unglamorous part of what made made Muse

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so magical was just really grinding out

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and sanding out all of the details to

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make the combined model plus product

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experience really amazing and we did

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many many iterations on the model

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itself. We aimed the overall model

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roadmap towards building towards
personal super intelligence broadly

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speaking but certainly personal agents as
like >> say more about that and how that

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differentiates between
your other competitors.

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>> Yeah. Well, this is this is kind
of >> that's actually really interesting.

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>> Yeah. It's actually
useful to go back to

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the moment because if you go back to the

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start of the year, February, Opus
and Claude were way ahead in coding, way

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ahead of anyone else and they were
exploding. It was the beginning of the

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sort of like you know enthropic takeover
in the whole industry and it was a very

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scary moment I think generally speaking
which is like oh wow this is you know

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they're so ahead on coding and and then
obviously they had mythos and you know

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they were they had like this clear lead.

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I think the the conventional wisdom in

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the industry was that coding agents were
the only form factor that mattered or

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maybe the only technology that mattered
or only the only capability that

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mattered for advanced AI. you know
there's this incredible force in the

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industry to just like go and crowd that

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use case and compete with enthropic on

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coding agents because of our experiences

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and our conviction that I think formed
around this idea of personal agents and

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the sort of taking a step back I think
the reason why we did personal super

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intelligence in the first place was that
it was also what we believed meta was

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uniquely positioned to do and we can
talk more about that but like I think

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because of that >> well
let's talk about that now

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>> okay we'll just do
do the weave. I think

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one of the first things I told Mark even

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before the whole scale and meta deal

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happened and and everything, one of the

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first things I told him was I think Meta

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is actually a really special company if

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we have AGI or ASI or whatever you want

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to call it. Because part of the promise

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of AI is that for all of us, we have
a mix of things that we want to do and

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things that we have to do. And the

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promise of AGI is that we're going to

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spend almost none of the time doing
things that we have to do because the

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AIS will just start doing a lot of that
for us and we can spend all of our time

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on things that we want to do. And for

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me, Meta's products represent the things

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that we all want to do. Like my use of

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Instagram or WhatsApp or Facebook like
these are products that represent what

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I, you know, I follow my interests.
I follow my hobbies. I follow all people

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that I like my friends and the people I

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care about like the product span of meta

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encapsulates the sort of like what we

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want to spend our times doing sort of

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realm. I think on a long arc meta is an

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incredible winner through all this.

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That's like one of the first things that

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I believed about meta and super
intelligence. And then ultimately a lot

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of those ideas made its way into this
concept of personal super intelligence.

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You know, you can kind of look at it
a few ways. One is what's the heritage of

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the company? The heritage of meta is

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connecting people with their friends and
family. Um, helping them discover their

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interests. Like these are things that
are very human. And then there's also

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the sort of like you can take
the business lens which is Meta has

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distribution to three and a half billion

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people using the apps every day. And the

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natural area where Meadow was going to be

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successful is consumer AI.
>> It's not work. It's not coding. It's

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personal.

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>> Yeah. Exactly. So personal agents and

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personal AI and and consumer AI, broadly

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speaking, were always the zones where
we always felt that Meta was going to do

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some be able to do something very

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special. So that brings us to uh okay,

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early 2026. Now you have the prototype,

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you have the buy in, everybody knows
Mark's on board and now you're doing the

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seven months of the difficult
work of refining this product.

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>> Yes. There was kind
of the spectre which

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was like oh personal agents like they

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came and went and we always felt that it
was because you know the the experience

235
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wasn't perfect. It wasn't polished yet
you know all that kind of stuff. But

236
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like we knew that that was something
that we had to like think about as you

237
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know as we were developing this product.

238
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We focused our model development road

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map. The top focus uh or one of the top

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focuses was towards building on personal

241
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agents and we had like these long

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spreadsheets of hundreds or even

243
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thousands of different not thousands but

244
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but more than a hundred like specific
behaviors and capabilities that we knew

245
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the model needed to have to be able
to build the ideal personal agent. Like it

246
00:13:31,600 --> 00:13:35,997
was very very meticulous. And then over
time we built evals for all of those. We

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figured out how to train the model to be
really really good at every single one

248
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of those things. And we have these
reviews where we like look at that

249
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spreadsheet and we figure out the things
that we're bad at and make sure that

250
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we're improving on them. Like we did
this like very meticulous process to

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slowly just keep improving and sanding
out all the edges of the model so that

252
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it could power the product.
>> Is that really the culture of meta too?

253
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This like constant like
sanding of the products?

254
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>> Yeah. A lot of the culture of meta
is like once you know what you're

255
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measuring, there's very talented people
and you can optimize for those things,

256
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you can send out all the details,
you can make those things amazing. I think

257
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in the sort of like preAI consumer
um product era, like a lot of this was

258
00:14:14,400 --> 00:14:18,477
around how do you optimize your
conversion? How do you optimize the

259
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referral rates? How do you optimize like
every part of the onboarding flow? Like

260
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how do you make all that stuff just
perfect? And then I think the AI era I

261
00:14:26,160 --> 00:14:28,637
think uh a lot of that especially from

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Muse has been in the form of sanding out
every detail of the model to be perfect

263
00:14:33,200 --> 00:14:35,837
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>> Did you see this profile that's written
on Zuck in Colossus written by Jeremy

301
00:16:56,320 --> 00:17:00,797
Stern? >> I saw it. If I'm being honest,
it was like too long. Didn't read. But

302
00:17:00,880 --> 00:17:04,877
>> it's 15,000 words. I just had Jeremy
on the podcast. He's he's my favorite.

303
00:17:04,960 --> 00:17:08,557
>> I saw I saw I saw that. He's he's just
I think he's writing the best profiles on

304
00:17:08,640 --> 00:17:11,437
on entrepreneurs and investors in the
world right now. I think he's literally

305
00:17:11,520 --> 00:17:12,557
the best in the world at what he does.

306
00:17:12,640 --> 00:17:16,637
But in that Zach says he was talking
about the advantage that he has relative

307
00:17:16,720 --> 00:17:20,317
to I think Elon and like Sam Alman
and he's just like listen my skill set is

308
00:17:20,400 --> 00:17:23,517
very unsexy but I'm really good at
building teams and then just improving a

309
00:17:23,600 --> 00:17:28,397
product slowly over a long period
of time and relative to Elon and and Sam

310
00:17:28,480 --> 00:17:31,357
Alman and others. like I'm not going
to lose control of my company and I don't

311
00:17:31,440 --> 00:17:34,397
need to raise any more money and so I
can just do this for an excessively long

312
00:17:34,480 --> 00:17:36,877
period of time. That section of the

313
00:17:36,960 --> 00:17:41,517
profile is popping in my mind when
you're talking about this 7-month uh

314
00:17:41,600 --> 00:17:44,557
stretch of trying
to refine and build muse.

315
00:17:44,640 --> 00:17:49,437
>> I think that's exactly right. Like
I think that it took immense patience. I

316
00:17:49,520 --> 00:17:53,037
mean 7 months like you know maybe it
doesn't sound like that long but AI is

317
00:17:53,120 --> 00:17:55,277
like fierce. seven months in AI time.

318
00:17:55,360 --> 00:17:58,877
>> Seven months in AI time, which maybe
like a decade and like nonAI time. And I

319
00:17:58,960 --> 00:18:02,717
think it it for all of us. Um but but I
really give Mark a lot of credit for

320
00:18:02,800 --> 00:18:05,837
this. Like it took a lot of patience and

321
00:18:05,920 --> 00:18:08,637
restraint and a lot of trust in the

322
00:18:08,720 --> 00:18:10,477
process. And like you'll you can go back

323
00:18:10,560 --> 00:18:13,437
and look at the you know what Mark said

324
00:18:13,520 --> 00:18:16,157
in earnings calls for this entire period

325
00:18:16,240 --> 00:18:18,557
from February through through um

326
00:18:18,640 --> 00:18:23,997
September and he will allude to the fact
that we were building personal agents

327
00:18:24,080 --> 00:18:27,437
and we weren't really trying to hide it
like we were we were building personal

328
00:18:27,520 --> 00:18:31,277
agents and we were talking about it but I
think you know there's a lot of doubt

329
00:18:31,360 --> 00:18:35,837
in the company for that entire period
like people obviously the one of the

330
00:18:35,920 --> 00:18:39,837
dominant Wall Street narratives is like
oh my god Gosh, Meta is just burning all

331
00:18:39,920 --> 00:18:43,037
this money. Like, are they even
going to pull anything off in AI?

332
00:18:43,120 --> 00:18:47,197
>> It's more than that. It's like Zuck
cannot win AI. It's said over and over

333
00:18:47,280 --> 00:18:50,157
again for a long period of time.
And again, going back to Jeremy Stern's

334
00:18:50,240 --> 00:18:53,357
profile on him. He then off the record

335
00:18:53,440 --> 00:18:57,437
starts talking to his competitors,
including one former researcher that is

336
00:18:57,520 --> 00:19:01,837
now at a competitor that used to work
for with Zuck and is now at another lab.

337
00:19:01,920 --> 00:19:04,477
And he goes, "I don't want to have
to compete with them cuz Mark's like the

338
00:19:04,560 --> 00:19:08,077
[__] I think the line is Mark is the
[__] Terminator." And it was like he

339
00:19:08,160 --> 00:19:13,117
just does will not stop. And he's like
uh then he talks about um one of his

340
00:19:13,200 --> 00:19:15,917
superpowers that he's never happy with
the state of the product. So he always

341
00:19:16,000 --> 00:19:19,757
wants to constant prove but he's not
an [__] Like he's not you know dict

342
00:19:19,840 --> 00:19:23,757
he's he is definitely a dictator
but like he's not you know rude and like

343
00:19:23,840 --> 00:19:26,717
kind of will destroy the chemistry of a
team. He actually talks about in the in

344
00:19:26,800 --> 00:19:30,477
the piece that team cohesion
is excessively important to him.

345
00:19:30,560 --> 00:19:35,037
>> I think it's all right. I think one
of the magical things about Muse was that

346
00:19:35,120 --> 00:19:38,877
we really like took quite a bit of time to

347
00:19:38,960 --> 00:19:43,357
sweat all the details before ultimately
coming out with a product because I

348
00:19:43,440 --> 00:19:45,357
think that that personal agents was one

349
00:19:45,440 --> 00:19:50,557
of these product areas that kind of rested
on a knife's edge, which is if it

350
00:19:50,640 --> 00:19:53,837
can reliably deliver these magical
experiences, it has the potential to be

351
00:19:53,920 --> 00:19:56,957
one of the greatest consumer products
ever. But you're dealing with models,

352
00:19:57,040 --> 00:19:58,557
you're dealing with something
nondeterministic, you're dealing with

353
00:19:58,640 --> 00:20:03,037
agents, they can break, they can be uh
unreliable. Like if people have these

354
00:20:03,120 --> 00:20:06,557
unreliable experiences, they think it's
trash. It was one of these things where

355
00:20:06,640 --> 00:20:08,957
like getting to the product to a point

356
00:20:09,040 --> 00:20:11,917
of like quality and value such that it

357
00:20:12,000 --> 00:20:16,157
resonated with so many of the people who
downloaded and used it. I want to talk

358
00:20:16,240 --> 00:20:19,917
more about this process. I'm curious
how much resources, how big the team, how

359
00:20:20,000 --> 00:20:23,357
many people working on it. Do you guys
have any data on like okay I'm using

360
00:20:23,440 --> 00:20:26,717
Muse nine great experiences two like

361
00:20:26,800 --> 00:20:30,477
inconsistent or you know shitty
experiences and then I turn out like do

362
00:20:30,560 --> 00:20:33,517
you have any information on this?
You can see this from like a lot of the

363
00:20:33,600 --> 00:20:37,517
other agent products at the start of the
year like almost everyone turned out of

364
00:20:37,600 --> 00:20:38,637
those and it's because they were

365
00:20:38,720 --> 00:20:41,997
unreliable. We did a lot of user testing

366
00:20:42,080 --> 00:20:46,317
of Muse. Like we did many many rounds of

367
00:20:46,400 --> 00:20:49,277
new people trying to like download it

368
00:20:49,360 --> 00:20:51,757
and and use it from scratch and just

369
00:20:51,840 --> 00:20:55,037
seeing what that experience was like. A

370
00:20:55,120 --> 00:20:57,597
huge part of the premise of Muse and I

371
00:20:57,680 --> 00:21:00,717
think what's really landed is that it is

372
00:21:00,800 --> 00:21:03,837
like a different thing from what most

373
00:21:03,920 --> 00:21:05,677
people have experienced with consumer

374
00:21:05,760 --> 00:21:08,477
AI. Like it is not a chatbot. It is an

375
00:21:08,560 --> 00:21:10,957
agent and it can do agent things for

376
00:21:11,040 --> 00:21:15,757
you. And like I think to a developer
this is kind of like old news because

377
00:21:15,840 --> 00:21:18,397
they've had to like you know download
agents and have been using them for a

378
00:21:18,480 --> 00:21:20,557
while. But for most people like just

379
00:21:20,640 --> 00:21:22,717
even that graduation process like you

380
00:21:22,800 --> 00:21:25,517
know it needs to be explained and needs

381
00:21:25,600 --> 00:21:28,077
to be onboarded on on a very thoughtful

382
00:21:28,160 --> 00:21:32,877
way. And then for sure we definitely
have seen like when because we've done

383
00:21:32,960 --> 00:21:36,717
tests with like different versions of
the model and various model versions and

384
00:21:36,800 --> 00:21:40,477
continuously do those AB tests. You
know the product experience is very very

385
00:21:40,560 --> 00:21:42,717
sensitive to model quality. It's very

386
00:21:42,800 --> 00:21:46,317
sensitive to how reliably the models can

387
00:21:46,400 --> 00:21:49,757
undergo these tasks, how reliably they
communicate with the user about them,

388
00:21:49,840 --> 00:21:52,877
how reliably they like, you know,
check back with the user if there's things

389
00:21:52,960 --> 00:21:54,637
that they require guidance on.

390
00:21:54,720 --> 00:21:56,557
>> But how'd you know it was ready to be

391
00:21:56,640 --> 00:22:00,797
released? Part of it was was pretty

392
00:22:00,880 --> 00:22:05,437
numerical which is like we had set you
know you go back to that spreadsheet of

393
00:22:05,520 --> 00:22:09,437
100 plus things. >> We had
set for each of those rows what

394
00:22:09,520 --> 00:22:13,277
threshold is launch blocking and above
what threshold is good. We had many

395
00:22:13,360 --> 00:22:17,357
checkpoints that like were like green
on 80 of the rows but red on 20 of the

396
00:22:17,440 --> 00:22:18,877
rows. We knew that wasn't good enough.

397
00:22:18,960 --> 00:22:23,357
And we had our first model checkpoint
which was green across the board which

398
00:22:23,440 --> 00:22:25,517
was a specially trained version of

399
00:22:25,600 --> 00:22:28,317
Musepark 1.3. So there was like the very

400
00:22:28,400 --> 00:22:33,517
numerical part of it and then there
was just the experiential part which is we

401
00:22:33,600 --> 00:22:36,797
tried the model. We could tell that it
was a lot better than than anything we

402
00:22:36,880 --> 00:22:39,677
had iterated with before. So going
back to that seven-month period you were

403
00:22:39,760 --> 00:22:41,517
talking about, how many people
were working on the product

404
00:22:41,600 --> 00:22:44,237
>> between the model work, model work and

405
00:22:44,320 --> 00:22:49,677
the product work? Like under 200 people
total, which I guess sounds like a lot,

406
00:22:49,760 --> 00:22:52,637
but in big company land is >>
it's actually a lot smaller.

407
00:22:52,720 --> 00:22:55,917
>> It's not very many. That was kind
of cool looking back on this and thinking

408
00:22:56,000 --> 00:23:00,717
about it is like we knew this was going
to be the most important product that

409
00:23:00,800 --> 00:23:02,717
the whole executive team, everyone knew

410
00:23:02,800 --> 00:23:06,157
that this was probably going to be one

411
00:23:06,240 --> 00:23:08,077
of the most important products that Meta

412
00:23:08,160 --> 00:23:11,757
would ship this year, but we had the

413
00:23:11,840 --> 00:23:15,437
restraint organizationally I think
to make sure that like it was a really

414
00:23:15,520 --> 00:23:17,597
small focused team working on it and

415
00:23:17,680 --> 00:23:22,477
that it didn't balloon to this like oh
you have like you know huge swasts of

416
00:23:22,560 --> 00:23:25,437
the company working on because we knew
it was a single work of art. Like it

417
00:23:25,520 --> 00:23:29,037
required strong point of view and strong

418
00:23:29,120 --> 00:23:33,277
focus to like pull that through into
one beautiful experience. Say more about

419
00:23:33,360 --> 00:23:36,477
this. Why did you just call it like
a single piece of art and you need a

420
00:23:36,560 --> 00:23:40,637
single perspective or point
of view? I think one

421
00:23:40,720 --> 00:23:42,957
um

422
00:23:43,040 --> 00:23:45,757
criticism of products that come from

423
00:23:45,840 --> 00:23:47,917
larger organizations and I think this

424
00:23:48,000 --> 00:23:52,237
like is even now starting to plague

425
00:23:52,320 --> 00:23:54,317
a lot of the AI labs is that they become

426
00:23:54,400 --> 00:23:57,037
kind of this Frankenstein

427
00:23:57,120 --> 00:24:00,397
almost like Croninberg amalgamation of a

428
00:24:00,480 --> 00:24:02,157
bunch of different people's points of

429
00:24:02,240 --> 00:24:05,997
view and visions and and beliefs and

430
00:24:06,080 --> 00:24:08,557
it's sort of like it's sort of PM hell

431
00:24:08,640 --> 00:24:13,037
in some sense where like every PM has
like a thing that they're trying to get

432
00:24:13,120 --> 00:24:15,757
into the product and jam into
the product and then all that kind of like

433
00:24:15,840 --> 00:24:18,077
blends together into this like like

434
00:24:18,160 --> 00:24:21,917
smoothie almost. People feel it as users

435
00:24:22,000 --> 00:24:25,757
and consumers like you feel you can
feel when a product is just like it feels

436
00:24:25,840 --> 00:24:28,877
like lots of different people worked on it
and there were like clearly different

437
00:24:28,960 --> 00:24:32,157
people who were gold differently so
they just kind of like jammed it all

438
00:24:32,240 --> 00:24:34,397
together. Muse had to be the exact

439
00:24:34,480 --> 00:24:39,677
opposite, which is like there's one
clear point of view. There is one

440
00:24:39,760 --> 00:24:43,917
cohesive experience that we're trying to
deliver um to the world. I love that you

441
00:24:44,000 --> 00:24:46,797
said that and it just happens to be
pure coincidence. Like we're I don't know

442
00:24:46,880 --> 00:24:48,877
when this is going to come out,
but we're recording this on the 15th

443
00:24:48,960 --> 00:24:52,317
anniversary of Steve Jobs death.
And when you think of somebody like a pro

444
00:24:52,400 --> 00:24:56,077
consumer products that had a single
point of view, you obviously think of

445
00:24:56,160 --> 00:24:59,517
Steve first. I've read every single book
on Steve and the history of Apple and

446
00:24:59,600 --> 00:25:03,117
I've done like I don't know 15 or 20
episodes on them uh for my other podcast

447
00:25:03,200 --> 00:25:07,437
founders, but one of my favorite lines
to describe Steve's impact was it says

448
00:25:07,520 --> 00:25:09,517
that um Apple is just Steve Jobs with

449
00:25:09,600 --> 00:25:14,477
10,000 lives. The products are to his
taste, his perspective, his point of

450
00:25:14,560 --> 00:25:18,477
view. What he wanted to see in the world
is exactly what the the the end user

451
00:25:18,560 --> 00:25:21,117
got. In Muse's case, like Nat Friedman

452
00:25:21,200 --> 00:25:25,517
deserves a lot of credit. like his taste
and sensibilities ultimately dictated

453
00:25:25,600 --> 00:25:27,437
and shaped what that product became.

454
00:25:27,520 --> 00:25:30,077
>> Do you talk about how many people are
using Muse? Are you guys talking about

455
00:25:30,160 --> 00:25:31,357
this publicly or no?

456
00:25:31,440 --> 00:25:36,237
>> We have said millions and then I have

457
00:25:36,320 --> 00:25:40,637
retweeted other people who have shown

458
00:25:40,720 --> 00:25:43,357
graphs where we would imply that Muse is

459
00:25:43,440 --> 00:25:48,077
the fastest growing consumer app of all
time. Consumer AI app of all time.

460
00:25:48,160 --> 00:25:49,437
>> So you won't say a number though.

461
00:25:49,520 --> 00:25:53,357
>> We've said millions. Yeah. Well,
millions could be hundreds of millions.

462
00:25:53,440 --> 00:25:56,877
That's also millions. So, you're just
not adding the the the beginning to

463
00:25:56,960 --> 00:26:00,157
that. I want to like go into like you've
been kind of going like uh and I mean

464
00:26:00,240 --> 00:26:05,677
this in a loving way like unhinged on X.
So, you talk about like your is

465
00:26:05,760 --> 00:26:08,317
this like marketing strategy? Like what
is going on? Like I feel like you're

466
00:26:08,400 --> 00:26:13,357
there's a distinct difference in your
public tweeting since Muse launch. What

467
00:26:13,440 --> 00:26:15,677
are you doing? >> There's
a few pieces of what's happening

468
00:26:15,760 --> 00:26:18,877
here. This has got to be more fun than
scale AI when you were building scale.

469
00:26:18,960 --> 00:26:22,237
It has to be. We'll talk about that later,
but it has to be the past few

470
00:26:22,320 --> 00:26:27,357
weeks have been some of my most fun uh
building like yeah certainly my career

471
00:26:27,440 --> 00:26:31,037
and I think there's a few things
happening. So, one is it felt important

472
00:26:31,120 --> 00:26:33,917
that we figured out a way to make Muse

473
00:26:34,000 --> 00:26:38,477
break through. It's a pretty like
torrential going back to this like this

474
00:26:38,560 --> 00:26:40,397
torrential stream of updates. Like for

475
00:26:40,480 --> 00:26:43,117
most people, there's just so they're

476
00:26:43,200 --> 00:26:45,997
just like constantly being like like

477
00:26:46,080 --> 00:26:50,397
kind of bombarded with new AI products
and startups and releases and things and

478
00:26:50,480 --> 00:26:53,357
models and here's another model
and here's another product and here's

479
00:26:53,440 --> 00:26:57,597
another feature and it's just like
this torrential stream. We had actually

480
00:26:57,680 --> 00:27:00,557
meeting large meetings where we talked
about this as a team. It was like we

481
00:27:00,640 --> 00:27:04,637
need to break through and obviously like
meta we have lots of distribution like

482
00:27:04,720 --> 00:27:07,837
we can make sure everyone knows about
it but that's not enough to like make it

483
00:27:07,920 --> 00:27:11,357
break through in an interesting cultural

484
00:27:11,440 --> 00:27:14,077
or fascinating way for the world to kind

485
00:27:14,160 --> 00:27:17,437
of like you know pay attention to. I

486
00:27:17,520 --> 00:27:21,117
separately in my life have happened to

487
00:27:21,200 --> 00:27:25,277
have met and um become friends with like

488
00:27:25,360 --> 00:27:29,917
memers and and uh I have these uh I

489
00:27:30,000 --> 00:27:33,437
affectionately call them my skitso
friends but but people who like

490
00:27:33,520 --> 00:27:37,197
literally they spend all their day like
thinking about memes in the internet and

491
00:27:37,280 --> 00:27:41,037
and understanding that I have people
that are obsessed with Muse and they're

492
00:27:41,120 --> 00:27:44,637
also obsessed with your X feed
and they're convinced that you have like a

493
00:27:44,720 --> 00:27:49,277
hundred muses coming up
with memes. to to be used.

494
00:27:49,360 --> 00:27:52,637
Um, turns out you just have a collection
of schizophrenic friends that are

495
00:27:52,720 --> 00:27:55,597
terminally online. >> The X feed,
just for what it's worth, is

496
00:27:55,680 --> 00:28:00,157
like literally every one of those tweets
I write. There were like a few a quite a

497
00:28:00,240 --> 00:28:03,517
period where I was like, it was like
really hard for me to pay attention in

498
00:28:03,600 --> 00:28:06,317
meetings because I would just like think
of a meme and I'd be like, "Oh god, I

499
00:28:06,400 --> 00:28:10,237
have to get this out." And I would
like it was like moments of inspiration.

500
00:28:10,320 --> 00:28:13,277
There was like um I was uh I was with

501
00:28:13,360 --> 00:28:17,517
one of my like I was trying to have
like dinner with one of my co-workers and

502
00:28:17,600 --> 00:28:20,877
then during the dinner I was like wait
I figured it out and then I like posted a

503
00:28:20,960 --> 00:28:24,237
different me. It was like it was almost
I almost felt like I was like an

504
00:28:24,320 --> 00:28:27,997
engineer solving bugs again cuz it
was like you know you could tell when

505
00:28:28,080 --> 00:28:30,957
something was going to click.
But yeah so one things we wanted to break

506
00:28:31,040 --> 00:28:32,957
through. Another thing that like I just

507
00:28:33,040 --> 00:28:34,877
thought was important is like how do we

508
00:28:34,960 --> 00:28:38,477
make meta cool again? cuz like for

509
00:28:38,560 --> 00:28:41,197
whatever reason despite having like

510
00:28:41,280 --> 00:28:42,877
these incredible pieces of cultural

511
00:28:42,960 --> 00:28:45,997
software um like Instagram and and uh

512
00:28:46,080 --> 00:28:49,117
Facebook historically meta was not cool

513
00:28:49,200 --> 00:28:53,037
for a while and so wanted to make meta

514
00:28:53,120 --> 00:28:54,877
cool and interesting and kind of like

515
00:28:54,960 --> 00:28:57,757
funny and then the unhingedness was

516
00:28:57,840 --> 00:29:00,637
something that happened kind of like

517
00:29:00,720 --> 00:29:04,957
naturally over time when Muse first came
out I was just like mostly focused on

518
00:29:05,040 --> 00:29:08,797
promoting all the interesting use cases
that I saw I was like retweeting like

519
00:29:08,880 --> 00:29:11,837
tens of interesting use cases that I saw

520
00:29:11,920 --> 00:29:16,317
of people using Muse. And then at some
point it was like because I was just

521
00:29:16,400 --> 00:29:20,077
like tweeting so much. There were like
a few tweets where I was just like didn't

522
00:29:20,160 --> 00:29:23,197
think that much about it and just said
[__] it post. And then some of those

523
00:29:23,280 --> 00:29:26,877
tweets like did really well. Have you
seen this like time spent thinking about

524
00:29:26,960 --> 00:29:29,197
it and then banger you seen that? Great.

525
00:29:29,280 --> 00:29:30,637
>> I haven't se but I agree with that.

526
00:29:30,720 --> 00:29:34,477
That's like I just some of these I
didn't spend that much time on. I like

527
00:29:34,560 --> 00:29:36,797
tweeted them. I didn't even check
my phone for a few hours and I was like,

528
00:29:36,880 --> 00:29:38,397
"Oh, wow. That one went really well."

529
00:29:38,480 --> 00:29:41,517
And then I kind of like remembered
something about the internet which I it

530
00:29:41,600 --> 00:29:45,037
wasn't super intentional, but I
remembered like the internet rewards

531
00:29:45,120 --> 00:29:48,397
risk and it rewards like surprise and

532
00:29:48,480 --> 00:29:52,077
like things that like people don't expect.
And so then I just kind of like

533
00:29:52,160 --> 00:29:54,557
went off. A lot of my co-workers uh I

534
00:29:54,640 --> 00:29:59,757
think were somewhat surprised at uh
something that um Nat said for for the

535
00:29:59,840 --> 00:30:02,797
whole week when I was posting all the
memes. He was like, "You know, I thought

536
00:30:02,880 --> 00:30:06,317
we would learn." He said this like
in a bunch of meetings. He was like, "I

537
00:30:06,400 --> 00:30:10,557
thought we were going to learn a lot
about um you know, Muse users and like

538
00:30:10,640 --> 00:30:14,077
how people were using Muse, but actually
like we're just learning a lot about

539
00:30:14,160 --> 00:30:17,037
Alex's mind."

540
00:30:17,120 --> 00:30:19,037
It was a lot of fun. I felt like I was

541
00:30:19,120 --> 00:30:21,277
drawing on all of my internet knowledge

542
00:30:21,360 --> 00:30:26,397
for years and years and years to like
pull into those moments. And I think it

543
00:30:26,480 --> 00:30:28,557
actually, you know, I don't know,
we don't have the tracking and it's like

544
00:30:28,640 --> 00:30:32,317
hard to tell, but I actually think it
actually genuinely made an impact on

545
00:30:32,400 --> 00:30:34,157
grow growth, which is kind of hilarious.

546
00:30:34,240 --> 00:30:38,557
>> I definitely think it did. I I can't
stop hearing about it. So, and all the

547
00:30:38,640 --> 00:30:41,757
people I'm hearing about it from also
downloaded the app and are now using it.

548
00:30:41,840 --> 00:30:42,957
Go back to what you were saying though.

549
00:30:43,040 --> 00:30:45,517
It's like, hey, we have distribution,
but that doesn't make mean that we can

550
00:30:45,600 --> 00:30:48,237
make something that people are going
to come back to and actually sticky. So,

551
00:30:48,320 --> 00:30:49,837
like, how did you do this with Muse?

552
00:30:49,920 --> 00:30:53,197
>> I think it was like, how do you make
this into how do we how do we make this

553
00:30:53,280 --> 00:30:55,597
breakthrough as a product? And I think
there were like a few parts of that, but

554
00:30:55,680 --> 00:30:57,837
I think one is how do you get people to

555
00:30:57,920 --> 00:30:59,597
a match the experience? Like how do you

556
00:30:59,680 --> 00:31:03,517
get someone to to a wow moment with the

557
00:31:03,600 --> 00:31:07,357
product as early as possible? And this
product I think naturally lends itself

558
00:31:07,440 --> 00:31:11,437
to that because I think you know there's
it's like it's AI. It can do a lot. It

559
00:31:11,520 --> 00:31:16,717
can there's like if if someone has never
used an AI agent, there's a lot that it

560
00:31:16,800 --> 00:31:20,477
can do that will surprise you. So that I
think was a big part of it. I think

561
00:31:20,560 --> 00:31:23,357
another part of it was like how do you

562
00:31:23,440 --> 00:31:26,077
harness all of those stories of people

563
00:31:26,160 --> 00:31:28,717
having these wow moments and like use

564
00:31:28,800 --> 00:31:33,117
those to help make the product
sing and make the product fly.

565
00:31:33,200 --> 00:31:34,477
>> How are you using the stories though?

566
00:31:34,560 --> 00:31:36,557
Cuz you you guys aren't
running ads for me, are you?

567
00:31:36,640 --> 00:31:39,037
>> We ran a few ads.
I mean honestly like I

568
00:31:39,120 --> 00:31:43,677
think at first it was just the fact that I
would retweet so many of them. Like I

569
00:31:43,760 --> 00:31:45,677
was just >> anytime I
saw someone do something

570
00:31:45,760 --> 00:31:49,037
interesting about news I was like quote
tweet. I couldn't understand why both

571
00:31:49,120 --> 00:31:52,637
Enthropic and OpenAI's um ads are so bad

572
00:31:52,720 --> 00:31:56,397
because remember Anthropic was doing
this huge like outdoor campaign and it's

573
00:31:56,480 --> 00:32:00,797
all about them and same thing with um
Chet. It's like they'd have like the the

574
00:32:00,880 --> 00:32:03,757
icon which I don't even think people
recognize that as a logo and it just say

575
00:32:03,840 --> 00:32:06,637
CEX and just be two people sitting at
a desk. I'm like what the [__] does this

576
00:32:06,720 --> 00:32:10,237
mean? Where it's like you have all these
use cases. All you have to do your ad is

577
00:32:10,320 --> 00:32:12,477
to show the benefit of your product.

578
00:32:12,560 --> 00:32:15,277
That's it. >> You have
millions of them. Run those

579
00:32:15,360 --> 00:32:18,957
ads. It's it's so like elementary that
it's interesting that people make this

580
00:32:19,040 --> 00:32:21,357
mistake over and over again. >> Yeah.
I think one thing that we came

581
00:32:21,440 --> 00:32:24,637
back to a lot in in both building news

582
00:32:24,720 --> 00:32:29,037
as well as just like building stuff
in general at me was just like why would

583
00:32:29,120 --> 00:32:31,997
someone care? They don't care about
you or your company. They care about

584
00:32:32,080 --> 00:32:36,077
everybody's self-interested. This is
like I'm there's this very old book.

585
00:32:36,160 --> 00:32:39,677
It's called Scientific Advertising.
It was written in the early 1900s. is now

586
00:32:39,760 --> 00:32:42,877
it was so valuable that the guy that
made the most money as an advertising

587
00:32:42,960 --> 00:32:47,277
agency founder in the like 1920s and 30s
he it was written by this guy named

588
00:32:47,360 --> 00:32:50,397
Hopkins who was this best copyriter
he's like these are all the ideas how I'm

589
00:32:50,480 --> 00:32:53,917
making this money I'm putting this
manuscript in a safe for 20 years right

590
00:32:54,000 --> 00:32:56,477
but essentially it's not about
advertising it's about human nature

591
00:32:56,560 --> 00:32:59,757
which doesn't change and his whole point
was just like your ads are about you

592
00:32:59,840 --> 00:33:03,117
your ads about your product your ads
are like hey I have this thing buy people

593
00:33:03,200 --> 00:33:05,437
don't care about that they're
self-interested and if you just all you

594
00:33:05,520 --> 00:33:10,157
have to do is just do the work of what
this does if for you and you just lead

595
00:33:10,240 --> 00:33:13,997
with that and there's people that took
that ideas those ideas were written in

596
00:33:14,080 --> 00:33:17,517
the early 1900s and went on to build
massive companies advertising agency

597
00:33:17,600 --> 00:33:20,317
foundations in the ' 50s '
60s7s and even to this day

598
00:33:20,400 --> 00:33:23,357
>> yeah 100%. I think there are a few

599
00:33:23,440 --> 00:33:25,757
insights. Let's say first is most people

600
00:33:25,840 --> 00:33:31,277
in the world, they don't give a [__]
about like OpenAI or the research or you

601
00:33:31,360 --> 00:33:34,317
know these other companies or the
research or even us or our research or

602
00:33:34,400 --> 00:33:36,077
whatever. They care about like what it

603
00:33:36,160 --> 00:33:38,877
can do for them. We had this gift of the

604
00:33:38,960 --> 00:33:42,317
mascot which was the little jolly guy. I

605
00:33:42,400 --> 00:33:46,637
remember the tweet where I where I
realized I was like, "Oh, wow. This is

606
00:33:46,720 --> 00:33:48,397
mileage." because I was basically I was

607
00:33:48,480 --> 00:33:52,317
um I was driving into work uh and while

608
00:33:52,400 --> 00:33:56,157
I was on the way I like generated a a

609
00:33:56,240 --> 00:33:58,237
version of the guy holding a briefcase

610
00:33:58,320 --> 00:34:03,357
and I tweeted MFW I go to work on Monday.
I remember thinking like this is

611
00:34:03,440 --> 00:34:06,717
like one of my best tweets in a while
and then I kind of realized like you can

612
00:34:06,800 --> 00:34:11,277
put him into a lot of situations. One
thing I like kind of realized as we were

613
00:34:11,360 --> 00:34:16,157
going through that process was like how
do you create surprising moments that

614
00:34:16,240 --> 00:34:19,517
are very different from any other
marketing that you've seen of like a

615
00:34:19,600 --> 00:34:21,677
product. I don't like really remember

616
00:34:21,760 --> 00:34:23,677
the last time someone like you know

617
00:34:23,760 --> 00:34:28,797
leaned into racy memes to like market
a product. Um I don't actually know if

618
00:34:28,880 --> 00:34:31,997
it's been done before but I kind of like
realize like oh wow this is like a thing

619
00:34:32,080 --> 00:34:34,797
that we can do with this little guy.
And that's something that like is

620
00:34:34,880 --> 00:34:38,237
interesting. That's something that like
drives intrigue to like the average

621
00:34:38,320 --> 00:34:41,917
person. >> Okay. But that might get them
raise awareness, might even get them to

622
00:34:42,000 --> 00:34:45,917
download the app. But then how do you
get them to stay engaged once the app is

623
00:34:46,000 --> 00:34:48,157
they the app is on their phone? There's

624
00:34:48,240 --> 00:34:52,237
a cycle that people who who really love

625
00:34:52,320 --> 00:34:55,037
the app and and we even like we back in

626
00:34:55,120 --> 00:34:58,397
February had which is you try to give it

627
00:34:58,480 --> 00:35:02,637
a little thing and it does it. You're
like, "Oh, wow. It did that." And then

628
00:35:02,720 --> 00:35:05,837
you try to give it a little bit
something like a little bit more and

629
00:35:05,920 --> 00:35:09,197
then it does it and you're like, "Oh,
wow. It can do that. It could do that

630
00:35:09,280 --> 00:35:11,917
first thing. It could do the second
thing." And then you give it like a

631
00:35:12,000 --> 00:35:14,877
little bit bigger of a of a problem. And

632
00:35:14,960 --> 00:35:17,197
it's kind of this like trust fall almost

633
00:35:17,280 --> 00:35:20,237
that you have with an AI agent, which is

634
00:35:20,320 --> 00:35:24,237
at first you like don't really trust it,
but you're like kind of intrigued. So

635
00:35:24,320 --> 00:35:26,957
you're going to give it a little
bit nibble and then you give it like

636
00:35:27,040 --> 00:35:29,117
something a little bigger and something
a little bigger and something a little

637
00:35:29,200 --> 00:35:32,637
bigger. Certainly like when I talk
to like college students or whatever like

638
00:35:32,720 --> 00:35:36,397
some of the ways that people use AI it's
like a full trust fault. They just like

639
00:35:36,480 --> 00:35:39,997
are recording voice notes and brain
dumping to the AI and they send it and

640
00:35:40,080 --> 00:35:43,677
then they just trust the AI to like
organize all the thoughts and give them

641
00:35:43,760 --> 00:35:48,077
like clear plans and think clear things
to do etc. I think for the successful

642
00:35:48,160 --> 00:35:50,157
users of Muse it really is like getting

643
00:35:50,240 --> 00:35:53,197
them onto this trust fall process of

644
00:35:53,280 --> 00:35:57,197
using it for slightly bigger and bigger
and bigger things and then having it

645
00:35:57,280 --> 00:35:59,677
work every one of those times.

646
00:35:59,760 --> 00:36:02,637
I found one of my all-time favorite
quotes when I was reading the book 0ero

647
00:36:02,720 --> 00:36:06,557
to1. The quote says, "The single most
powerful pattern I have noticed is that

648
00:36:06,640 --> 00:36:11,277
successful people find value
in unexpected places." And they do this by

649
00:36:11,360 --> 00:36:15,117
thinking about business from first
principles instead of formulas. That is

650
00:36:15,200 --> 00:36:19,117
exactly what Apploven has done with
their advertising platform. Apploven

651
00:36:19,200 --> 00:36:22,637
connects you with over a billion
potential new customers in mobile games.

652
00:36:22,720 --> 00:36:27,357
Apploven allows you to capture undivided
attention. Apploving ads are full screen

653
00:36:27,440 --> 00:36:31,757
videos that are watched for an average
of 35 seconds. That is retention that

654
00:36:31,840 --> 00:36:36,237
blows other ad platforms out of the water.
And you can launch on Apploven in

655
00:36:36,320 --> 00:36:40,557
minutes. You set the goal and Apploven
achieves it. No complex setup, no

656
00:36:40,640 --> 00:36:44,957
expertise needed. And Apploven scales
quickly. They can put your ads in front

657
00:36:45,040 --> 00:36:47,277
of over a billion potential customers.

658
00:36:47,360 --> 00:36:51,197
Other businesses have seen immediate
results scale to hundreds of thousands

659
00:36:51,280 --> 00:36:55,437
of dollars of spend per day and increase
their revenue by millions. So, you want

660
00:36:55,520 --> 00:36:59,277
to get started quickly before all
of your competitors are on AppLoven. And

661
00:36:59,360 --> 00:37:01,997
you can do that by going to apploven.com.

662
00:37:02,080 --> 00:37:04,877
That's apploven.com.

663
00:37:04,960 --> 00:37:09,837
I wonder how much of this is actually
driven by word of mouth. So, obviously

664
00:37:09,920 --> 00:37:12,477
talking to you, I was going to have
to download the app no matter what. But

665
00:37:12,560 --> 00:37:15,597
what really piqued my interest
is a friend of mine was sending me

666
00:37:15,680 --> 00:37:18,717
screenshots of how he was using Muse.

667
00:37:18,800 --> 00:37:21,517
So, like when you download the app, it's
it doesn't really tell you like too much

668
00:37:21,600 --> 00:37:24,557
of like what you can do. It was kind
of like open-ended like a you know like a

669
00:37:24,640 --> 00:37:27,437
open text box but a friend of mine who's

670
00:37:27,520 --> 00:37:32,237
running like a hundred billion dollar
company he left his ID on a flexjet and

671
00:37:32,320 --> 00:37:35,517
then he went through and he's just like
well I want to he tell me I don't want

672
00:37:35,600 --> 00:37:39,037
to be involved in this process at all
you have to figure out how to get the ID

673
00:37:39,120 --> 00:37:41,677
that like go pick up the ID this is where
it's at and then this is where I'm

674
00:37:41,760 --> 00:37:44,477
at and then figure out all the steps
along the way and then like arranged the

675
00:37:44,560 --> 00:37:48,637
courier went there got to his security
in his office somehow and got upstairs

676
00:37:48,720 --> 00:37:52,797
to his office like and it was like holy
[__] this is the best marketing. It's

677
00:37:52,880 --> 00:37:56,397
just three or four screenshots of Muse
going back and forth and him saying, "I

678
00:37:56,480 --> 00:37:59,117
don't want to be involved. You have
to do everything." And it figuring out on

679
00:37:59,200 --> 00:38:04,317
the way. Yeah. Know, this is actually
one of the things about um uh I don't

680
00:38:04,400 --> 00:38:08,877
know how it was like somewhat intentional,
but definitely like modern

681
00:38:08,960 --> 00:38:12,477
product marketing is like very
screenshot driven. Like I think to have

682
00:38:12,560 --> 00:38:14,877
a modern consumer product like it has to

683
00:38:14,960 --> 00:38:16,317
work on the screenshot. like the

684
00:38:16,400 --> 00:38:20,397
screenshot has to has to fly so to speak

685
00:38:20,480 --> 00:38:24,397
either through like group chats or word
of mouth or like online. And I think

686
00:38:24,480 --> 00:38:27,597
that's been a big part of it. This wasn't
intentional or engineered, but I

687
00:38:27,680 --> 00:38:29,837
think the fact that we had the little

688
00:38:29,920 --> 00:38:33,037
guy in the screenshot is like was a big

689
00:38:33,120 --> 00:38:36,397
deal because I think having Jolly or

690
00:38:36,480 --> 00:38:40,317
whoever your muse is like in the
screenshot immediately communicates this

691
00:38:40,400 --> 00:38:43,597
is a Muse screenshot. It's just like the
best marketing you could possibly have.

692
00:38:43,680 --> 00:38:46,637
So, are you on your Muse like all day
long? like what is your own personal

693
00:38:46,720 --> 00:38:47,597
usage?

694
00:38:47,680 --> 00:38:49,837
>> I mostly like set up a lot of workflows

695
00:38:49,920 --> 00:38:54,237
using it and then I um I guess my

696
00:38:54,320 --> 00:38:59,357
meetings a lot of the time but um
so in practice like I think in my job

697
00:38:59,440 --> 00:39:02,717
quoteunquote I'm supposed to like pay
attention and and be pretty present and

698
00:39:02,800 --> 00:39:06,477
interact with people. I set up a bunch
of workflows and like kind of treated as

699
00:39:06,560 --> 00:39:10,237
like a second brain. >> Yeah.
You said that in the post that you

700
00:39:10,320 --> 00:39:13,117
wrote and it was like really short and I
think I saw last time it was like 5 and

701
00:39:13,200 --> 00:39:16,637
a half million views so far but you
said uh I love how you describe Muse like

702
00:39:16,720 --> 00:39:18,157
well if everyone had a second mind

703
00:39:18,240 --> 00:39:21,597
beyond their own. I think this is

704
00:39:21,680 --> 00:39:26,397
philosophically speaking one of the most
interesting questions about like what

705
00:39:26,480 --> 00:39:28,157
does it mean to be human when you have

706
00:39:28,240 --> 00:39:31,357
like powerful AI and what is that

707
00:39:31,440 --> 00:39:35,677
relationship like and and how exactly
does that look and different people have

708
00:39:35,760 --> 00:39:38,877
different takes on what exactly that
looks like. I think the sort of like

709
00:39:38,960 --> 00:39:42,877
fear cases obviously that you know AI
is above us and that we're just sort of

710
00:39:42,960 --> 00:39:46,797
like doing what they do and there's like
other worlds where that relationship is

711
00:39:46,880 --> 00:39:49,437
different. And before you go on,
what is your own personal view though?

712
00:39:49,520 --> 00:39:53,117
>> I really believe in muse. I believe
in the personal agent as like a long-term

713
00:39:53,200 --> 00:39:55,597
form factor of just it is human nature

714
00:39:55,680 --> 00:39:58,157
to have wants and desires and dreams.

715
00:39:58,240 --> 00:40:01,997
And I think like AI and personal agents

716
00:40:02,080 --> 00:40:07,037
like muse will be the mechanism and
the bridge that enables us to accomplish

717
00:40:07,120 --> 00:40:10,557
those things and continue accomplishing
them. And then like humans will dream

718
00:40:10,640 --> 00:40:11,917
bigger and bigger and bigger and bigger.

719
00:40:12,000 --> 00:40:15,437
Kids will dream of like having their own
star systems or whatever. And like Muse

720
00:40:15,520 --> 00:40:16,717
will help them do that. Like there's

721
00:40:16,800 --> 00:40:21,037
this thing that like um Bezos uh wrote

722
00:40:21,120 --> 00:40:25,197
in like well I think it was one of his
shareholder letters which is like the

723
00:40:25,280 --> 00:40:29,597
beautiful thing about uh consumers
is that they're like always beautifully

724
00:40:29,680 --> 00:40:33,037
unhappy or something like that or like
beautifully unsatisfied with the options

725
00:40:33,120 --> 00:40:37,357
they have. That is like one modality
of our relationship with AI. I think that

726
00:40:37,440 --> 00:40:41,757
there's other modalities like I think
that AI will start doing more and more

727
00:40:41,840 --> 00:40:46,077
of the scientific discovery process.
I think AIs will start inserting more and

728
00:40:46,160 --> 00:40:48,477
more into various parts of the economy,

729
00:40:48,560 --> 00:40:51,677
but I do believe in this mod in this

730
00:40:51,760 --> 00:40:54,477
like form factor long term of everyone

731
00:40:54,560 --> 00:40:58,877
having an AI that supports them and what
they want. So you see AI as kind of like

732
00:40:58,960 --> 00:41:02,477
removing all like the cludge of life,
right? The stuff that we don't want to

733
00:41:02,560 --> 00:41:05,357
do. I think in the the piece you said
something like the world is full of like

734
00:41:05,440 --> 00:41:09,437
gatekeepers and obstacles and like
Muse can get around this for you without

735
00:41:09,520 --> 00:41:11,597
spending any more of your time so you
can actually focus on the stuff you want

736
00:41:11,680 --> 00:41:15,437
to do. I think Muse can

737
00:41:15,520 --> 00:41:19,517
give everyone the adulthood they dreamed

738
00:41:19,600 --> 00:41:21,597
of in childhood. Everyone when they're

739
00:41:21,680 --> 00:41:24,237
kids, they have like big ideas and big

740
00:41:24,320 --> 00:41:29,437
dreams and when they like imagine their
life and they sort of like, you know,

741
00:41:29,520 --> 00:41:33,037
play it out. They they imagine this life
where they can like be an astronaut or

742
00:41:33,120 --> 00:41:36,077
they can save the planet or they can
change the world or, you know, whatever

743
00:41:36,160 --> 00:41:38,157
it might be. It's like a big they they

744
00:41:38,240 --> 00:41:41,597
think big and then for, you know, a

745
00:41:41,680 --> 00:41:44,477
variety of reasons by the time people

746
00:41:44,560 --> 00:41:48,877
finish school, they enter the workforce,
by the time they get to their, you know,

747
00:41:48,960 --> 00:41:50,957
have been in a job for a while, it's

748
00:41:51,040 --> 00:41:56,557
that like all that hope and ambition and
and ability to dream has been sucked out

749
00:41:56,640 --> 00:41:59,117
of them. >> No, it's been
beaten out of them. The

750
00:41:59,200 --> 00:42:03,677
life is beaten beaten out of them.
I think as adults, I don't think we like

751
00:42:03,760 --> 00:42:08,077
just like appreciate the degree to which
we're all zombies. We lost our agency

752
00:42:08,160 --> 00:42:10,637
and we lost our I think ambition is

753
00:42:10,720 --> 00:42:12,797
really a good word for it. One of the

754
00:42:12,880 --> 00:42:16,077
promises of AI broadly speaking and I

755
00:42:16,160 --> 00:42:20,637
think through products like Muse is to um
keep it going from when you're a kid.

756
00:42:20,720 --> 00:42:24,317
Like you're a kid, you have big dreams,
use AI, help make those things happen.

757
00:42:24,400 --> 00:42:27,357
Then like dream bigger, make those
things happen, dream bigger, make those

758
00:42:27,440 --> 00:42:31,277
things happen. and kind of like
experience this like escalator of

759
00:42:31,360 --> 00:42:34,077
agency, an increase in agency throughout

760
00:42:34,160 --> 00:42:38,637
your life versus a sort of like you
have lots of agency and then it gets like

761
00:42:38,720 --> 00:42:42,077
crushed. You know what I was thinking of
when you were speaking just now? You you

762
00:42:42,160 --> 00:42:45,037
mentioned earlier this this experience
that you and Nat Freeman went through

763
00:42:45,120 --> 00:42:47,517
where you had this like disturbing uh

764
00:42:47,600 --> 00:42:52,077
psychological audit and like coming in
waves from this AI. It's like yeah, what

765
00:42:52,160 --> 00:42:53,757
if we had that but it's like
the opposite of what you're saying. It's

766
00:42:53,840 --> 00:42:56,717
like actually gave you more
self-confidence like gave you

767
00:42:56,800 --> 00:42:59,197
understanding like the world
is malleable and if you push on it hard

768
00:42:59,280 --> 00:43:01,517
enough you go after it with enough
energy and drive you can actually change

769
00:43:01,600 --> 00:43:04,077
it around you. >> Yeah.
I think this is something that

770
00:43:04,160 --> 00:43:08,397
like certainly the most impressive
entrepreneurs exhibit which is like I

771
00:43:08,480 --> 00:43:12,557
think they like dream big they
accomplish that then they dream bigger

772
00:43:12,640 --> 00:43:16,317
and then maybe they work on that for like
a decade but then like you know if

773
00:43:16,400 --> 00:43:19,277
they accomplish that then they dream
bigger. Like Elon is obviously a great

774
00:43:19,360 --> 00:43:22,797
example of this. Um I think Mark
is a great example of this. I think but I

775
00:43:22,880 --> 00:43:24,557
think a lot of like the iconic

776
00:43:24,640 --> 00:43:26,797
entrepreneurs this is like like kind of

777
00:43:26,880 --> 00:43:30,077
like their lived experience and I think

778
00:43:30,160 --> 00:43:31,837
there's a version of that that should be

779
00:43:31,920 --> 00:43:34,557
true for every person and I think what's

780
00:43:34,640 --> 00:43:38,157
kind of like tragic is I think for most

781
00:43:38,240 --> 00:43:40,637
people you have like bigger dreams big

782
00:43:40,720 --> 00:43:43,917
ideas you enter the corporate workforce

783
00:43:44,000 --> 00:43:46,557
you're you kind of become a zombie maybe

784
00:43:46,640 --> 00:43:50,957
at some point you want to go tackle your
dreams but then it's like really really

785
00:43:51,040 --> 00:43:54,077
hard cuz you all these commitments. You
maybe you have a family, maybe you have

786
00:43:54,160 --> 00:43:56,237
whatever it is. It's just like it's

787
00:43:56,320 --> 00:44:01,117
almost you become trapped in the sort
of like zombiehood. There's one more thing

788
00:44:01,200 --> 00:44:05,997
on miuse before I want to get to scale
AI and like the the partnership that you

789
00:44:06,080 --> 00:44:10,237
do that you did with Meta and making
that decision. But in this essay that

790
00:44:10,320 --> 00:44:12,637
you wrote or this like short postage you
wrote, I love what you said. said, "The

791
00:44:12,720 --> 00:44:15,597
world until now has been shaped
by the small number of fanatics who have

792
00:44:15,680 --> 00:44:19,437
somehow found a way to make their wants
real, but we've never seen humanity with

793
00:44:19,520 --> 00:44:21,437
every single person's agency fully

794
00:44:21,520 --> 00:44:23,997
switched on." I think the promise of

795
00:44:24,080 --> 00:44:27,117
this world that I'm describing where

796
00:44:27,200 --> 00:44:31,597
every single person has this like agency
increase throughout their lifetime and

797
00:44:31,680 --> 00:44:33,997
they have the ability to accomplish

798
00:44:34,080 --> 00:44:36,157
their wants and their dreams. I think

799
00:44:36,240 --> 00:44:39,037
that world looks crazy in a very good

800
00:44:39,120 --> 00:44:44,317
way. Like I think it will be very
interesting. It'll be artistic and cool

801
00:44:44,400 --> 00:44:47,677
and you'll like go into different
pockets of the world and it will be very

802
00:44:47,760 --> 00:44:52,397
like diverse. It'll be kind of insane to
think about what exactly that looks like

803
00:44:52,480 --> 00:44:56,317
where literally billions
of people have, you

804
00:44:56,400 --> 00:44:59,437
know, all of a sudden because of abundant
intelligence like the resources

805
00:44:59,520 --> 00:45:04,717
to make incredible things happen.
I think that's part of the promise. Like I

806
00:45:04,800 --> 00:45:08,557
think in my head it's almost like
the Rick and Morty interdimensional cable

807
00:45:08,640 --> 00:45:11,197
but but just like somehow manifested

808
00:45:11,280 --> 00:45:13,677
into like reality for humans. Like it

809
00:45:13,760 --> 00:45:19,037
could just be really awesome. So I
keep hearing uh this line from that show

810
00:45:19,120 --> 00:45:22,317
where it's like the universe
eats smart people.

811
00:45:22,400 --> 00:45:27,757
>> Um okay so I want to go back. What
I'm personally curious uh about is you're

812
00:45:27,840 --> 00:45:31,357
you founded Scale AI, you're running
Scale AI, and then one day Mark

813
00:45:31,440 --> 00:45:34,557
Zuckerberg reaches out and says, "Hey,"
I assume he says, "Hey, I want to talk.

814
00:45:34,640 --> 00:45:37,917
Can you tell me about this?"
>> Yeah, I think the exact message was

815
00:45:38,000 --> 00:45:39,997
something like, "Hey,
do you have time for a call?"

816
00:45:40,080 --> 00:45:42,637
>> Did you have a relationship with Mark
previously? Did you spend any time with

817
00:45:42,720 --> 00:45:47,277
him? Like what was what's the background
there? I had a friend who uh this guy

818
00:45:47,360 --> 00:45:49,357
Alex Schultz uh who I've known actually

819
00:45:49,440 --> 00:45:52,877
for many many years um from when scale

820
00:45:52,960 --> 00:45:54,957
was maybe a year old. I met him in San

821
00:45:55,040 --> 00:45:56,797
Francisco is currently the chief data

822
00:45:56,880 --> 00:46:00,237
officer at Meta but was longtime Meta

823
00:46:00,320 --> 00:46:04,477
executive lieutenant
and he introduced me to Mark

824
00:46:04,560 --> 00:46:06,317
I want to say in about like I think it

825
00:46:06,400 --> 00:46:11,757
was 2021 or 2022 to talk about AI
because I think at that time Mark was

826
00:46:11,840 --> 00:46:14,397
getting a lot more interested in AI scale.
We were doing a lot of stuff in

827
00:46:14,480 --> 00:46:16,477
AI. My memory is that I think it took

828
00:46:16,560 --> 00:46:22,157
like 1 year to schedule that first
meeting. Like from from the intro to the

829
00:46:22,240 --> 00:46:25,757
meeting being scheduled, I think it
was like a full year. Why? Mark obviously

830
00:46:25,840 --> 00:46:28,797
has like an insane calendar and like
has like a bajillion different things to

831
00:46:28,880 --> 00:46:31,437
deal with. And he's also very good at

832
00:46:31,520 --> 00:46:34,877
prioritization. Like he will spend a lot

833
00:46:34,960 --> 00:46:39,197
of time on the things that are really
really important in some ways like too

834
00:46:39,280 --> 00:46:42,237
much time on the things that are like
really really important. the only way

835
00:46:42,320 --> 00:46:45,677
he's able to do that and like be a good
dad and be a good partner and all this

836
00:46:45,760 --> 00:46:50,237
other stuff is to just like ruthlessly
prioritize. I had spoken to him a few

837
00:46:50,320 --> 00:46:54,317
times kind of after that and had gotten
his advice as a founder to get advice

838
00:46:54,400 --> 00:46:58,317
from you know Mark Zuckerberg obviously
like a really big deal. Um and I

839
00:46:58,400 --> 00:46:59,837
actually remember at that time like he

840
00:46:59,920 --> 00:47:03,197
he wrote these like like I would ask him

841
00:47:03,280 --> 00:47:07,597
a question on WhatsApp and then he would
write these like really long responses

842
00:47:07,680 --> 00:47:11,277
and I was really confused. I was like,
how does he have time to write these

843
00:47:11,360 --> 00:47:12,877
like really long responses? But now I

844
00:47:12,960 --> 00:47:15,917
realize he like is just like really fast

845
00:47:16,000 --> 00:47:20,157
and rigorous at writing to like a lot
of WhatsApp messages. He'll write like

846
00:47:20,240 --> 00:47:23,917
write a very rigorous like long
response very quickly. It's like a very

847
00:47:24,000 --> 00:47:26,797
impressive skill of his. >> Gives you
a sneak peek in what's going on his mind.

848
00:47:26,880 --> 00:47:29,597
>> Yeah. Like yeah, there's racing. Meta

849
00:47:29,680 --> 00:47:32,317
was a important customer of scale. Um,

850
00:47:32,400 --> 00:47:37,357
and I would say like prior to this
instance, maybe we spoke once every like

851
00:47:37,440 --> 00:47:41,837
six months or so. Maybe that was like
the cadence of our interactions. And

852
00:47:41,920 --> 00:47:43,997
then yeah, I think he he had called and

853
00:47:44,080 --> 00:47:46,717
I think it was it was actually like not

854
00:47:46,800 --> 00:47:50,317
obvious at first what the like end point

855
00:47:50,400 --> 00:47:54,957
was going to be because the first
conversation was just like, you know,

856
00:47:55,040 --> 00:47:59,437
this was after Llama 4 and it was kind
of just like asking what do you think we

857
00:47:59,520 --> 00:48:02,397
should be doing? Why is that an
important point that this is this

858
00:48:02,480 --> 00:48:04,237
conversation is happening after Llama 4?

859
00:48:04,320 --> 00:48:09,197
>> Llama 4 was not on the trajectory that
Meta wanted as a company. It definitely

860
00:48:09,280 --> 00:48:12,797
was like I think internally speaking
a disappointment and it was also at a time

861
00:48:12,880 --> 00:48:17,837
where AI was clearly becoming just like
increasingly increasingly important. It

862
00:48:17,920 --> 00:48:20,637
was clear that AI was going to be like
really critical to the future of meta.

863
00:48:20,720 --> 00:48:24,797
So yeah the first few conversations we
spoke on the phone and he was sort of as

864
00:48:24,880 --> 00:48:27,997
saying you know asking for advice
on like what do you think we should be

865
00:48:28,080 --> 00:48:30,397
doing? What do you think we should
be focused on? This is what I've heard

866
00:48:30,480 --> 00:48:34,317
about him privately that he has this
insane it's not a board of directors

867
00:48:34,400 --> 00:48:37,437
like a meta board although there's some
people on the board that he also does

868
00:48:37,520 --> 00:48:41,197
this with but he's got this group of
world-class entrepreneurs around him and

869
00:48:41,280 --> 00:48:43,597
I've spoken to some of them like you
don't understand like he asked for

870
00:48:43,680 --> 00:48:46,877
advice constantly he's like hitting us
up he's like here's what's going on in

871
00:48:46,960 --> 00:48:50,477
my life or the work would you do
and he'll do that over and over and over

872
00:48:50,560 --> 00:48:54,237
again I think this is like quite
an impressive trait yes because it takes

873
00:48:54,320 --> 00:48:58,637
humility obviously like continue
doing that even you know after all the

874
00:48:58,720 --> 00:49:01,597
incredible things that, you know,
he's been able to accomplish. I mean, the

875
00:49:01,680 --> 00:49:04,557
whole, you know, timeline from when he

876
00:49:04,640 --> 00:49:07,917
first reached out to when we announced

877
00:49:08,000 --> 00:49:12,237
the deal was like five, six weeks. Like,
it was like pretty quick. Another thing

878
00:49:12,320 --> 00:49:15,277
that world-class entrepreneurs have
in common. We had Jonathan Ross, the

879
00:49:15,360 --> 00:49:19,197
founder of Grock, on this podcast.
The first time we ever spoke uh publicly

880
00:49:19,280 --> 00:49:23,197
about the $20 billion deal he did with
Nvidia was on the show. And he's like,

881
00:49:23,280 --> 00:49:25,437
"From the time first call from Jensen to

882
00:49:25,520 --> 00:49:27,437
money's in my bank account was 3 weeks."

883
00:49:27,520 --> 00:49:30,237
>> Yeah. That's amazing. Um, I just had

884
00:49:30,320 --> 00:49:34,717
lots of ideas on like what I'd be doing
if I choose and like how I'd be thinking

885
00:49:34,800 --> 00:49:37,677
about Meta's AI strategy. One of the
first things I said was what I said

886
00:49:37,760 --> 00:49:43,117
earlier, which is like, hey, I actually
think because Meta is like heritage wise

887
00:49:43,200 --> 00:49:48,397
focus on the things that people want to do
is actually one of the most specially

888
00:49:48,480 --> 00:49:49,997
placed companies with this incredible

889
00:49:50,080 --> 00:49:53,357
shift AI. I like had lots and lots of

890
00:49:53,440 --> 00:49:57,917
ideas that I sort of sent his way
and then that was kind of this like

891
00:49:58,000 --> 00:50:02,237
conversation happening in parallel
with this like oh yeah maybe we should

892
00:50:02,320 --> 00:50:04,557
potentially consider if
there's a way to work together.

893
00:50:04,640 --> 00:50:07,997
>> So when he says that what do you
think cuz this kid you this was not

894
00:50:08,080 --> 00:50:10,957
predictable to you. >> Not predictable.
No. The whole thing was

895
00:50:11,040 --> 00:50:15,757
like it was like a really crazy um
sequence at the time. I was just like,

896
00:50:15,840 --> 00:50:18,237
"Oh, that's nice,
but like there's no way that

897
00:50:18,320 --> 00:50:19,437
>> Well, you know what
that means when they

898
00:50:19,520 --> 00:50:21,997
say that, right? They want to buy you.

899
00:50:22,080 --> 00:50:24,637
It's always the same thing, but they say
it's like, "Oh, we got to find a way to

900
00:50:24,720 --> 00:50:27,597
work together." >> Yeah.
I don't think I'd had that that

901
00:50:27,680 --> 00:50:30,957
kind of coaching at the at the time. One

902
00:50:31,040 --> 00:50:34,797
thing that I explicitly felt at the time
was like, "Yeah, like it probably

903
00:50:34,880 --> 00:50:38,317
doesn't make that much sense even."
Like I think I personally was like, "Yeah,

904
00:50:38,400 --> 00:50:43,357
like you know, maybe he's like, you know,
teasing it or or like throwing it

905
00:50:43,440 --> 00:50:45,597
out there, but like does he
even like make that much sense?"

906
00:50:45,680 --> 00:50:47,037
>> Why wouldn't you think it makes sense?

907
00:50:47,120 --> 00:50:50,797
>> I mean, obviously like a deal ended
up happening, so you know, what do I know?

908
00:50:50,880 --> 00:50:52,797
But um >> No, no,
but back then, not we know what

909
00:50:52,880 --> 00:50:55,917
happened, but like I'm very curious
your thinking as you're going experiencing

910
00:50:56,000 --> 00:50:59,117
this like the fact that you said,
"Oh, yeah, you said this was like that

911
00:50:59,200 --> 00:51:01,197
doesn't make sense."
Like why wouldn't it make sense?

912
00:51:01,280 --> 00:51:05,677
>> Taking a step back. Obviously the the
like iconic meta acquisitions have been

913
00:51:05,760 --> 00:51:07,837
Instagram and WhatsApp and these were

914
00:51:07,920 --> 00:51:11,037
like very

915
00:51:11,120 --> 00:51:14,877
clear you know product logic driven

916
00:51:14,960 --> 00:51:19,037
decisions. Scale is like you know a deep

917
00:51:19,120 --> 00:51:21,917
enterprise government sales like you

918
00:51:22,000 --> 00:51:23,517
know a entirely different kind of

919
00:51:23,600 --> 00:51:27,437
business from from Meta. And so the the

920
00:51:27,520 --> 00:51:29,757
logic that I had thought at the time

921
00:51:29,840 --> 00:51:35,037
would have made sense is if meta wanted
to get into those things that then I

922
00:51:35,120 --> 00:51:38,237
think you know like you know maybe
that's that's the industrial logic that

923
00:51:38,320 --> 00:51:39,677
would have made sense but this wasn't

924
00:51:39,760 --> 00:51:42,317
that cuz I think Mark clearly was like

925
00:51:42,400 --> 00:51:47,357
predominantly interested in getting
the llama program and over his overall AI

926
00:51:47,440 --> 00:51:48,717
program on >> he wanted talent.

927
00:51:48,800 --> 00:51:52,077
>> Yeah he wanted talent. It was kind of
shrouded and unclear the whole time, you

928
00:51:52,160 --> 00:51:55,597
know, a lot of the way.
>> When did it become clear? After the

929
00:51:55,680 --> 00:51:58,637
money hit the account when, you know,

930
00:51:58,720 --> 00:52:00,477
when we started talking numbers, I was

931
00:52:00,560 --> 00:52:06,237
like, "Oh, wow. Okay. All right."
Um, that's when things became like more

932
00:52:06,320 --> 00:52:09,437
clear. >> At first, you're like, "Oh, this
this will make sense." Then you guys keep

933
00:52:09,520 --> 00:52:13,517
talking. And how fast did he convince
you like this is a good path for you?

934
00:52:13,600 --> 00:52:14,877
>> There was a part of it was just like,

935
00:52:14,960 --> 00:52:18,077
"Wow, this is like a really fascinating

936
00:52:18,160 --> 00:52:20,637
deal construct." We ultimately landed on

937
00:52:20,720 --> 00:52:25,037
this deal where you know Meta invested

938
00:52:25,120 --> 00:52:28,797
uh owns 49% of scale scale continues.

939
00:52:28,880 --> 00:52:31,437
Myself and a few people join join Meta.

940
00:52:31,520 --> 00:52:32,957
What I was kind of incredulous about at

941
00:52:33,040 --> 00:52:35,757
the time was like it's such a weird kind

942
00:52:35,840 --> 00:52:39,597
of deal but is one that I think

943
00:52:39,680 --> 00:52:43,437
genuinely kind of was like this very

944
00:52:43,520 --> 00:52:46,317
interesting winwin win so to speak.

945
00:52:46,400 --> 00:52:49,997
There's a win for the shareholders
of scale because all shareholders of scale

946
00:52:50,080 --> 00:52:52,877
got a great deal, benefited a lot. Scale

947
00:52:52,960 --> 00:52:57,117
continues and I truly believe the best
days of scale are ahead of it. My

948
00:52:57,200 --> 00:52:58,557
question to you is like or what I'm

949
00:52:58,640 --> 00:53:00,477
personally interested in is like okay is

950
00:53:00,560 --> 00:53:04,317
this he can explain this to you and you

951
00:53:04,400 --> 00:53:08,157
come around to that perspective in one
phone call. Is this just like wait Mark

952
00:53:08,240 --> 00:53:10,157
I got to think this is so [__] crazy.

953
00:53:10,240 --> 00:53:13,197
I got to think about this for a few days.
Like explain this process as much

954
00:53:13,280 --> 00:53:16,157
as you can. many weeks of being like,

955
00:53:16,240 --> 00:53:18,877
"This is insane. Is this even real? Does

956
00:53:18,960 --> 00:53:21,277
this make any sense? If it does make

957
00:53:21,360 --> 00:53:24,157
sense, like how do I feel about it?" The

958
00:53:24,240 --> 00:53:28,877
predominant emotion was more like
this is kind of insane. And then as I

959
00:53:28,960 --> 00:53:31,357
thought more about like, oh, I actually
have to make a decision of whether or

960
00:53:31,440 --> 00:53:33,837
not I do this. That was it in and of

961
00:53:33,920 --> 00:53:38,797
itself. There were like a lot of like
conversations there with the people at

962
00:53:38,880 --> 00:53:41,677
scale, with our investors. Like there
were a lot of interesting conversations

963
00:53:41,760 --> 00:53:44,557
in in that uh on that side. I mean it's

964
00:53:44,640 --> 00:53:49,277
obviously like really hard to to like
let go of or give up your baby

965
00:53:49,360 --> 00:53:50,557
>> cuz you worked on scale for how long?

966
00:53:50,640 --> 00:53:53,677
How many years? >> Yeah.
9 years from founding until the

967
00:53:53,760 --> 00:53:56,717
>> And you're still young. You're not even
30 yet. So this is like a third of your

968
00:53:56,800 --> 00:53:58,557
life. >> There was something
that Paul Graham

969
00:53:58,640 --> 00:54:02,237
said for a long time which is like you
know if you think of your company as

970
00:54:02,320 --> 00:54:04,637
your life's work like you will operate

971
00:54:04,720 --> 00:54:09,677
differently. I did really genuinely
think of of scales my life's work for

972
00:54:09,760 --> 00:54:13,037
the whole time that I was I was working
on it. That was a tough emotional

973
00:54:13,120 --> 00:54:14,957
process. And then I think what

974
00:54:15,040 --> 00:54:16,797
ultimately got me was like a combination

975
00:54:16,880 --> 00:54:21,277
of wow this is like a win-win winwin

976
00:54:21,360 --> 00:54:24,477
like kind of all um it's it's a good

977
00:54:24,560 --> 00:54:26,797
deal for all parties and also just this

978
00:54:26,880 --> 00:54:29,197
like I saw the potential of what could

979
00:54:29,280 --> 00:54:32,237
happen at Meta. And you know, this is a

980
00:54:32,320 --> 00:54:36,317
point at which like I think meta on AI

981
00:54:36,400 --> 00:54:40,557
certainly looked like
damaged goods in many ways

982
00:54:40,640 --> 00:54:42,637
and like was maybe not the most

983
00:54:42,720 --> 00:54:46,397
appealing place to work on AI, but I

984
00:54:46,480 --> 00:54:51,277
think that kind of like triggered
my entrepreneur side where I was like

985
00:54:51,360 --> 00:54:54,557
>> you can essentially rebuild it.
It's like a refounding of the lab.

986
00:54:54,640 --> 00:54:59,037
>> Yeah. Yeah. Like I think this is this
is one of the things that ended up being

987
00:54:59,120 --> 00:55:03,037
kind of fascinating which is like because

988
00:55:03,120 --> 00:55:05,037
it was you know it was clear that there

989
00:55:05,120 --> 00:55:07,357
was so much work to do. It really like

990
00:55:07,440 --> 00:55:12,477
cleared the way for me to be able
to to kind of build a lot of stuff up from

991
00:55:12,560 --> 00:55:16,077
scratch, set up the right principles,
build the right culture. Like it it

992
00:55:16,160 --> 00:55:19,437
created a the conditions for us
to build something amazing.

993
00:55:19,520 --> 00:55:20,877
>> And is that how you
and Mark discuss it?

994
00:55:20,960 --> 00:55:24,957
It's like hey we have to hit a reset here.
Like obviously like we're not if

995
00:55:25,040 --> 00:55:28,077
we keep on this path like we're
essentially going to throw out what we

996
00:55:28,160 --> 00:55:32,957
have and redo it with a completely
different uh like level of talent. Are

997
00:55:33,040 --> 00:55:36,157
these the conversations that are that
occurring between you and him? It didn't

998
00:55:36,240 --> 00:55:39,357
seem like he wanted like a minor
adjustment. You understand what I'm

999
00:55:39,440 --> 00:55:42,637
saying? He's just like, "Oh, this is not
working at all. It's not going to work.

1000
00:55:42,720 --> 00:55:46,557
Then let's, you know, rip it down to the
foundation and rebuild." Correct. It was

1001
00:55:46,640 --> 00:55:49,757
an evolving conversation where I think
there were like like one of the things

1002
00:55:49,840 --> 00:55:51,917
that I believe really strongly I think

1003
00:55:52,000 --> 00:55:54,397
Elon is like has has demonstrated this

1004
00:55:54,480 --> 00:55:57,437
in in various times in his career where

1005
00:55:57,520 --> 00:56:01,757
you just have like a small incredibly
cracked highly technical team that's

1006
00:56:01,840 --> 00:56:05,597
like very flat and like if you do
that you can accomplish a lot very very

1007
00:56:05,680 --> 00:56:08,317
quickly. So that was something that I

1008
00:56:08,400 --> 00:56:12,877
had a lot of conviction in and like I
believed in AI like that is the right

1009
00:56:12,960 --> 00:56:16,157
approach especially if you had to you
know do what we had to do which is like

1010
00:56:16,240 --> 00:56:18,957
move very very quickly and I think early

1011
00:56:19,040 --> 00:56:24,317
on that was one of the things I talked
to Mark a lot about and I think he was

1012
00:56:24,400 --> 00:56:25,917
excited about as well that ended up

1013
00:56:26,000 --> 00:56:28,237
becoming one of the tent poles of our

1014
00:56:28,320 --> 00:56:31,437
overall strategies like small very flat

1015
00:56:31,520 --> 00:56:35,517
highly technical team very high talent
density how quickly can you move if you

1016
00:56:35,600 --> 00:56:37,997
have those ingredients From
an outside perspective, it just

1017
00:56:38,080 --> 00:56:41,917
seems like Mark like empties the clip is
the way I think about this, right? Where

1018
00:56:42,000 --> 00:56:44,077
um have you ever read this book
called The Mind of Napoleon?

1019
00:56:44,160 --> 00:56:45,677
>> No. >> Okay. So,
mind and Napoleon published in

1020
00:56:45,760 --> 00:56:49,757
1957. Um I found it because I saw
this interview. I think Tyler Cowan

1021
00:56:49,840 --> 00:56:53,037
interviewed Sam Alman. And uh I think
this is 2018 and he asked him like

1022
00:56:53,120 --> 00:56:55,277
what's the most important book that you
read this year? And he said the mind of

1023
00:56:55,360 --> 00:56:58,717
Napoleon. So it's very hard to find.
I buy the book. It's 300 pages of just

1024
00:56:58,800 --> 00:57:01,997
Napoleon his own words organized by topic.
And in that he talks about over

1025
00:57:02,080 --> 00:57:03,757
and over again that hesitation is fatal.

1026
00:57:03,840 --> 00:57:07,357
But he says before you engage in a
course of action, there's a lot of

1027
00:57:07,440 --> 00:57:11,037
deliberation. He'll like study from
every angle, make sure he's making the

1028
00:57:11,120 --> 00:57:15,437
right decision. I feel there's like
a echo with the way Mark operates. Does a

1029
00:57:15,520 --> 00:57:18,397
lot of deliberation, make sure he's
on the right path. But once he makes that

1030
00:57:18,480 --> 00:57:21,837
decision, he just goes all in. He
won't like he just empties the clip. And

1031
00:57:21,920 --> 00:57:25,757
that's the way I felt about like what
was was occurring right at this point

1032
00:57:25,840 --> 00:57:28,237
where he comes and gets you and then
you guys start rebuilding this entire

1033
00:57:28,320 --> 00:57:30,877
organization. I think he really has

1034
00:57:30,960 --> 00:57:33,517
internalized that failure doesn't

1035
00:57:33,600 --> 00:57:35,757
matter. It matters like when you win,

1036
00:57:35,840 --> 00:57:40,557
how big you win. And I think this
overall concept I think enables him to

1037
00:57:40,640 --> 00:57:44,717
operate I think truly without fear in a
lot of circumstances because I think he

1038
00:57:44,800 --> 00:57:46,557
is just very comfortable
taking lots of risk.

1039
00:57:46,640 --> 00:57:48,477
>> He's got that great
line where it's like

1040
00:57:48,560 --> 00:57:51,597
in a world that's changing all the time,
the biggest risk is not taking any.

1041
00:57:51,680 --> 00:57:53,597
>> Yeah, exactly.
I think he's internalized

1042
00:57:53,680 --> 00:57:56,797
this quite deeply. Like there's
certainly like versions of the world

1043
00:57:56,880 --> 00:58:01,757
where Meta looks a lot more like Google
in some sense like it's just like you

1044
00:58:01,840 --> 00:58:06,237
know more slowm moving and more
bureaucratic and just like looks very

1045
00:58:06,320 --> 00:58:09,837
different as a company but I think he
because he's internalized some of these

1046
00:58:09,920 --> 00:58:11,757
lessons so deeply keeps the thing

1047
00:58:11,840 --> 00:58:14,397
dynamic and moving and live. talk me

1048
00:58:14,480 --> 00:58:18,877
through the difference between how like
how you were as like you're you're the

1049
00:58:18,960 --> 00:58:20,797
founder, you're running scale, now

1050
00:58:20,880 --> 00:58:24,157
you're refounding this lab, working with

1051
00:58:24,240 --> 00:58:28,317
Mark, like the different ways that you
approach your work in those two uh

1052
00:58:28,400 --> 00:58:33,117
environments at scale.
One of the things like I really

1053
00:58:33,200 --> 00:58:36,237
internalized and I don't even remember
where this advice came from, but it was

1054
00:58:36,320 --> 00:58:40,157
kind of one of these ideas that like if
you want to be a good founder, you have

1055
00:58:40,240 --> 00:58:43,677
to be able to do every job. had a very
strong point of view about how like

1056
00:58:43,760 --> 00:58:47,197
every little thing at the company
should happen and should work. And that

1057
00:58:47,280 --> 00:58:49,117
resulted in a company that was like very

1058
00:58:49,200 --> 00:58:53,277
much so a product of my effort and like

1059
00:58:53,360 --> 00:58:56,637
my point of view and it like expressed
itself in all sorts of ways throughout

1060
00:58:56,720 --> 00:59:01,197
the company but also made me
the bottleneck in very big ways. There's

1061
00:59:01,280 --> 00:59:04,957
like pros and cons of that for a company.
I think definitely I think

1062
00:59:05,040 --> 00:59:07,837
that's like you know if you were to start
a company and it's your first time

1063
00:59:07,920 --> 00:59:09,757
starting a company you should like
definitely take that approach because

1064
00:59:09,840 --> 00:59:12,637
it's like much more likely to be
successful than if you don't take that.

1065
00:59:12,720 --> 00:59:15,197
>> Scale your first company.
>> Scale is my first company.

1066
00:59:15,280 --> 00:59:17,517
>> Yeah. But at Meta like it was a very

1067
00:59:17,600 --> 00:59:19,917
different assignment because we had to

1068
00:59:20,000 --> 00:59:23,357
accomplish so much in so little time.

1069
00:59:23,440 --> 00:59:27,437
Like I didn't have the luxury of being
able to quote unquote do every job

1070
00:59:27,520 --> 00:59:30,237
within the lab. I

1071
00:59:30,320 --> 00:59:32,877
uh certainly am not an AI researcher.

1072
00:59:32,960 --> 00:59:35,757
I'm also not a like product designer.

1073
00:59:35,840 --> 00:59:37,197
I'm not you know I'm not all these

1074
00:59:37,280 --> 00:59:40,477
things that are like totally pivotal to

1075
00:59:40,560 --> 00:59:43,037
um the organization being successful. So

1076
00:59:43,120 --> 00:59:48,317
at Meta very much so I adopted this like

1077
00:59:48,400 --> 00:59:52,077
philosophy around just how do we create

1078
00:59:52,160 --> 00:59:54,477
an environment where we're going to hire

1079
00:59:54,560 --> 00:59:57,277
incredible people like it was like job

1080
00:59:57,360 --> 01:00:01,197
number one hire brilliant and incredible

1081
01:00:01,280 --> 01:00:04,637
people like who are at the top
of their field across every discipline.

1082
01:00:04,720 --> 01:00:07,197
>> Why do you think you're able to do
that at Meta? Is it just because you have

1083
01:00:07,280 --> 01:00:09,037
more resources? Like why can you do

1084
01:00:09,120 --> 01:00:13,597
that? I think one of the things that was

1085
01:00:13,680 --> 01:00:15,757
charis very charismatic and attractive

1086
01:00:15,840 --> 01:00:20,637
about the founding moment of MSL was

1087
01:00:20,720 --> 01:00:23,837
there was an opportunity to build a lab

1088
01:00:23,920 --> 01:00:29,357
from scratch with a really really small
team and we had a strong point of view

1089
01:00:29,440 --> 01:00:33,437
of where we're going personal super
intelligence that I think resonated with

1090
01:00:33,520 --> 01:00:37,037
people like I think everyone who works
on AI wants to help people like wants it

1091
01:00:37,120 --> 01:00:40,397
to ultimately like mean something
to their mom or to their grandma or to

1092
01:00:40,480 --> 01:00:44,157
their grandpa wants it to like be
something that's like meaningful to

1093
01:00:44,240 --> 01:00:48,237
every single person in the world because
of the fact that we're doing this with

1094
01:00:48,320 --> 01:00:51,997
the resources of meta. There was an
opportunity to we weren't going to be

1095
01:00:52,080 --> 01:00:54,957
bottlenecked by comput. We weren't
going to be bottlenecked by like

1096
01:00:55,040 --> 01:00:57,837
infrastructure. We weren't going to be
bottlenecked by by distribution. like we

1097
01:00:57,920 --> 01:00:59,917
weren't going to be bottlenecked
by the things that like startups often are

1098
01:01:00,000 --> 01:01:03,677
bottlenecked, but it was an opportunity
to like really build something that you

1099
01:01:03,760 --> 01:01:08,477
could like put your stamp on and have
like a lot of influence over and really

1100
01:01:08,560 --> 01:01:11,117
shape. I think that was like
quite exciting for a lot of people.

1101
01:01:11,200 --> 01:01:14,397
>> And so that's why he chooses to spend
an insane amount of money, what looks like

1102
01:01:14,480 --> 01:01:18,717
an insane amount of money, getting
a handful of top talent because then he

1103
01:01:18,800 --> 01:01:23,357
knows that that those top talent will
recruit in turn other top talent. But

1104
01:01:23,440 --> 01:01:25,277
you need that as a starting point.

1105
01:01:25,360 --> 01:01:27,517
Correct. >> Yeah. Yeah.
And I think one thing that

1106
01:01:27,600 --> 01:01:29,117
like is maybe somewhat underappreciated

1107
01:01:29,200 --> 01:01:33,277
about that specific part is that like I

1108
01:01:33,360 --> 01:01:36,397
mean the stock prices of OpenAI and

1109
01:01:36,480 --> 01:01:40,157
Anthropic like ran up a lot. So it just

1110
01:01:40,240 --> 01:01:42,317
happened to be the case that like you

1111
01:01:42,400 --> 01:01:44,557
know if you looked at how much these

1112
01:01:44,640 --> 01:01:47,597
people were making if they stayed at

1113
01:01:47,680 --> 01:01:52,237
Open Eye or Enthropic like it also
was like it was a lot of money. So I think

1114
01:01:52,320 --> 01:01:55,437
for a lot of the talent it ended up
being like kind of net neutral in many

1115
01:01:55,520 --> 01:01:59,437
ways. um compwise, but obviously I think
it's kind of like the same thing like

1116
01:01:59,520 --> 01:02:02,797
oftentimes we don't we don't a lot
of us don't think about how much like the

1117
01:02:02,880 --> 01:02:05,437
early employees or the early researchers

1118
01:02:05,520 --> 01:02:07,997
at these like labs that have had immense

1119
01:02:08,080 --> 01:02:10,637
stock uh stock value appreciation you

1120
01:02:10,720 --> 01:02:13,037
know what their comp looks like we

1121
01:02:13,120 --> 01:02:15,117
started this like fully on the belief of

1122
01:02:15,200 --> 01:02:17,917
having and hiring like most brilliant

1123
01:02:18,000 --> 01:02:20,557
people that's one Slar personal super

1124
01:02:20,640 --> 01:02:23,037
intelligence um those two how do we

1125
01:02:23,120 --> 01:02:25,437
create the environment where people do

1126
01:02:25,520 --> 01:02:30,077
their best work at Meta and in MSL. I'm

1127
01:02:30,160 --> 01:02:34,317
doing much more of like quote unquote
traditional management, so to speak. Cuz

1128
01:02:34,400 --> 01:02:36,797
these are the things I'm like thinking
a lot about is like how do I create the

1129
01:02:36,880 --> 01:02:39,037
environment where all these brilliant

1130
01:02:39,120 --> 01:02:42,237
people who are exceptional and very very

1131
01:02:42,320 --> 01:02:46,077
special can all operate and achieve the

1132
01:02:46,160 --> 01:02:48,157
greatest expression of their talent. I'm

1133
01:02:48,240 --> 01:02:49,837
thinking a lot more like a almost like a

1134
01:02:49,920 --> 01:02:53,437
coach in some in some ways versus at

1135
01:02:53,520 --> 01:02:57,117
scale it was like very different like I
was the aur I was like the person whose

1136
01:02:57,200 --> 01:02:59,837
like point of view had to be
expressed in everything that we did.

1137
01:02:59,920 --> 01:03:01,277
>> Say more about
that you're you feel like

1138
01:03:01,360 --> 01:03:05,757
a coach. I think that like my job is to

1139
01:03:05,840 --> 01:03:09,677
a like help set the like north star and

1140
01:03:09,760 --> 01:03:13,917
the like directionality of where we
want to go, which again personal super

1141
01:03:14,000 --> 01:03:16,157
intelligence, winning on consumer AI,

1142
01:03:16,240 --> 01:03:18,957
like building AI that means something to

1143
01:03:19,040 --> 01:03:22,237
every person and then spotting in the

1144
01:03:22,320 --> 01:03:26,317
team special talent and then giving

1145
01:03:26,400 --> 01:03:30,797
people the environment and resources
to be able to express that maximally. Like

1146
01:03:30,880 --> 01:03:35,117
I think one thing that big companies
often mess up is that there's like

1147
01:03:35,200 --> 01:03:39,117
exceptional people at most big companies
but they just like are not empowered to

1148
01:03:39,200 --> 01:03:42,637
do their best work and that's why they
often leave to go to smaller companies

1149
01:03:42,720 --> 01:03:44,557
or to startups or whatever it might be.

1150
01:03:44,640 --> 01:03:47,517
You mentioned Jeff Bezos's shareholder
letters earlier. That's something he

1151
01:03:47,600 --> 01:03:50,237
would repeat as well and all the books
I read about him but also shareholder

1152
01:03:50,320 --> 01:03:52,717
letters like if you have great people
that can't build they're going to leave.

1153
01:03:52,800 --> 01:03:55,517
>> Yeah, exactly. And AI is such like a

1154
01:03:55,600 --> 01:04:00,637
it's like such a special thing. Like I
think you know this term research now it

1155
01:04:00,720 --> 01:04:02,157
it feels like it almost doesn't mean

1156
01:04:02,240 --> 01:04:05,677
anything but like by definition humanity

1157
01:04:05,760 --> 01:04:09,277
doesn't know the limits of what these

1158
01:04:09,360 --> 01:04:12,797
like models are capable of and what are
the things you can do with them. Like

1159
01:04:12,880 --> 01:04:15,597
they really are these like unknown

1160
01:04:15,680 --> 01:04:19,997
artifacts that we don't understand super
world. So like it really is science and

1161
01:04:20,080 --> 01:04:24,237
research. Like we are exploring
the limits of what you can do with this

1162
01:04:24,320 --> 01:04:25,437
technology and what are the things that

1163
01:04:25,520 --> 01:04:27,997
can be built and it is a process that

1164
01:04:28,080 --> 01:04:33,277
requires lots of trial and error.
It requires like brilliant people to have

1165
01:04:33,360 --> 01:04:37,037
incredible insight. Like it's one
of these things that you can't think about

1166
01:04:37,120 --> 01:04:39,757
this process like a very opishly intense

1167
01:04:39,840 --> 01:04:42,957
process. Like people need space and time

1168
01:04:43,040 --> 01:04:47,677
and resources to be able to like you
know do their best work. And I think

1169
01:04:47,760 --> 01:04:49,837
about in many dimensions, right? Like I

1170
01:04:49,920 --> 01:04:53,197
think Nat Freiedman is absolutely

1171
01:04:53,280 --> 01:04:58,077
brilliant and as I mentioned like so
much of Muse if there were a single

1172
01:04:58,160 --> 01:05:02,317
person whose like single point of view
came through on Muse it is now it's

1173
01:05:02,400 --> 01:05:05,917
important for him to be in a position
where where he can like fully express

1174
01:05:06,000 --> 01:05:10,157
all of that. And then we have some
brilliant researchers. They have like

1175
01:05:10,240 --> 01:05:12,877
very very exciting ideas. How do we
create an environment where they are

1176
01:05:12,960 --> 01:05:16,477
able to explore those? And like most
of them won't work out. Some of them will

1177
01:05:16,560 --> 01:05:18,157
work out incredibly well. The cases

1178
01:05:18,240 --> 01:05:20,637
where that works out justify all the

1179
01:05:20,720 --> 01:05:23,757
investment. So there's two kinds of

1180
01:05:23,840 --> 01:05:27,997
problems so to speak and that analogy
is like diamond mining or building

1181
01:05:28,080 --> 01:05:32,557
skyscrapers and it depends on what the
distribution of the payoff of the things

1182
01:05:32,640 --> 01:05:37,117
that you're working on are. So there's
like some areas where the distribution

1183
01:05:37,200 --> 01:05:40,797
is super power. Like most of the things
you do will be kind of useless, but then

1184
01:05:40,880 --> 01:05:44,157
some of the things you do can be like
literally a million times more valuable

1185
01:05:44,240 --> 01:05:47,677
than everything else. VC I think
is a great like diamond mining kind of

1186
01:05:47,760 --> 01:05:49,837
industry to give a sense. It's just all

1187
01:05:49,920 --> 01:05:54,877
about like being able to identify the
special ideas and like see those through

1188
01:05:54,960 --> 01:05:58,717
>> and that's easy to do. Just look
at just go to your 19th birthday party.

1189
01:05:58,800 --> 01:06:01,437
>> Yeah. I mean that's like unbelievable.

1190
01:06:01,520 --> 01:06:02,877
Um >> do people listening
that don't know what

1191
01:06:02,960 --> 01:06:06,157
the hell I just referenced? Can you talk
about real quick what who was at your

1192
01:06:06,240 --> 01:06:08,797
19th birthday party and why that was
would have been a good bet if they just

1193
01:06:08,880 --> 01:06:10,957
invested everybody around the table?

1194
01:06:11,040 --> 01:06:14,557
>> This is like a really crazy thing
to think about. my 19th birthday party. I

1195
01:06:14,640 --> 01:06:18,637
was a freshman at MIT. It was in January.
I was doing a win internship, a

1196
01:06:18,720 --> 01:06:21,517
winter internship, win internship at a

1197
01:06:21,600 --> 01:06:26,477
trading firm called HRT. There were like
10 of us, 12 of us, um, something like

1198
01:06:26,560 --> 01:06:30,877
that. Almost all of whom were students
at either Harvard or MIT. Most of us

1199
01:06:30,960 --> 01:06:35,197
already knew each other from various
math and science or computer science

1200
01:06:35,280 --> 01:06:36,877
competitions. Some of the other people

1201
01:06:36,960 --> 01:06:40,557
were Jeffrey Yan who went on to start

1202
01:06:40,640 --> 01:06:44,077
Hyperlquid very successful. Um Scott Woo

1203
01:06:44,160 --> 01:06:46,397
went on to start Cognition very

1204
01:06:46,480 --> 01:06:48,957
successful. Another person there was uh

1205
01:06:49,040 --> 01:06:51,837
uh Jesse Zang went to start Decagon also

1206
01:06:51,920 --> 01:06:54,077
quite successful. Um another person was

1207
01:06:54,160 --> 01:06:57,197
Vicky Yei who is I think a brilliant

1208
01:06:57,280 --> 01:06:59,517
researcher works at Anthropic. Everyone

1209
01:06:59,600 --> 01:07:01,357
was like incredibly talented. I actually

1210
01:07:01,440 --> 01:07:04,557
talked to the uh the founder of HRT

1211
01:07:04,640 --> 01:07:09,837
recently and HRT itself by the way
now is like crushing it. Unbelievable.

1212
01:07:09,920 --> 01:07:10,957
>> What does HRT do?

1213
01:07:11,040 --> 01:07:13,037
>> HRT it's a proprietary trading firm. So

1214
01:07:13,120 --> 01:07:17,037
they they trade their own money in Q2 of

1215
01:07:17,120 --> 01:07:18,717
2020

1216
01:07:18,800 --> 01:07:23,037
um 6 had 11 billion of revenue and like

1217
01:07:23,120 --> 01:07:25,437
I think 8 billion of profit is the the

1218
01:07:25,520 --> 01:07:30,077
record number. This is a I think it's
under a thousand people work at HRT. I

1219
01:07:30,160 --> 01:07:34,157
mean, it's like unbelievable. But I I
recently uh met up with the founder of

1220
01:07:34,240 --> 01:07:36,557
HRT and they were talking about like,
"Oh yeah, we're starting to do some

1221
01:07:36,640 --> 01:07:38,237
private investments." I was like, "Just

1222
01:07:38,320 --> 01:07:42,077
invest into the interns. All the interns

1223
01:07:42,160 --> 01:07:45,277
get seed checks."

1224
01:07:45,360 --> 01:07:50,557
That's great. >> I grew up
doing uh math and uh science

1225
01:07:50,640 --> 01:07:52,237
and computer science competitions and

1226
01:07:52,320 --> 01:07:55,437
Olympiads and got to know like all sorts

1227
01:07:55,520 --> 01:07:59,757
of people from all around America doing
this kind of stuff. It's like quite

1228
01:07:59,840 --> 01:08:04,477
surreal to see the people that I did
competitions with become so successful.

1229
01:08:04,560 --> 01:08:07,677
>> It's going to be fascinating what all
of you do the next few decades. Before I

1230
01:08:07,760 --> 01:08:10,637
interrupted you, did you have more
to say about diamond mining and skyscraper

1231
01:08:10,720 --> 01:08:14,637
stuff? Basically, it's like does it
have a power law payoff where some things

1232
01:08:14,720 --> 01:08:17,117
just like pay for like literally like a

1233
01:08:17,200 --> 01:08:21,677
million times the investment or is it
something more like building skyscrapers

1234
01:08:21,760 --> 01:08:26,717
which I would I would describe data
annotation or or or data businesses as

1235
01:08:26,800 --> 01:08:30,157
more like this which is like Amazon
Prime deliveries is another example

1236
01:08:30,240 --> 01:08:33,837
where the payoff is pretty linear
and you have to just get develop a process

1237
01:08:33,920 --> 01:08:37,037
where you're like really really
amazing at doing it. If you're building

1238
01:08:37,120 --> 01:08:40,237
skyscrapers, then it's like a very
operational business and it's all about

1239
01:08:40,320 --> 01:08:43,677
like squeezing every ounce of efficiency

1240
01:08:43,760 --> 01:08:47,437
out of that process and you just look at
it and you figure out like I can make it

1241
01:08:47,520 --> 01:08:51,517
like 2% more efficient this way and 1%
more efficient that way and half a

1242
01:08:51,600 --> 01:08:54,957
percent more efficient that way and you
just try to squeeze every ounce of

1243
01:08:55,040 --> 01:08:58,157
optimization out of it. And then there's

1244
01:08:58,240 --> 01:08:59,757
diamond mining where you're kind of

1245
01:08:59,840 --> 01:09:03,517
like, "Hey everyone, just like you know,

1246
01:09:03,600 --> 01:09:08,157
go forth, try all your crazy ideas
and like you're just nurturing the ideas

1247
01:09:08,240 --> 01:09:10,637
that seem promising." >> That's what
you feel you're doing at Meta right now.

1248
01:09:10,720 --> 01:09:12,157
>> That's what I feel I'm doing at Meta.

1249
01:09:12,240 --> 01:09:15,837
And and and the building server stuff
generally speaking is what I felt I was

1250
01:09:15,920 --> 01:09:20,957
doing at at scale. And that's like
this big paradigm um paradigm difference.

1251
01:09:21,040 --> 01:09:24,237
Going back to the coaching and the way
the difference of how you're essentially

1252
01:09:24,320 --> 01:09:26,797
managing now at meta compared to way
scale. Why do you have so many direct

1253
01:09:26,880 --> 01:09:29,437
reports? You have like what 100 direct
reports? How many direct reports?

1254
01:09:29,520 --> 01:09:33,357
>> Yeah, I think more than 200.
Yeah. H >> how and why?

1255
01:09:33,440 --> 01:09:35,357
>> Going back to it like one of the one of

1256
01:09:35,440 --> 01:09:39,277
the founding thesis of MSL and and and

1257
01:09:39,360 --> 01:09:40,957
what we want to do and like rebuilding

1258
01:09:41,040 --> 01:09:44,397
this this lab was how do you have like

1259
01:09:44,480 --> 01:09:49,597
really really high talent density? How
do you make it like the best place for a

1260
01:09:49,680 --> 01:09:53,997
lot of these brilliant people to do
their best work and how do you build an

1261
01:09:54,080 --> 01:09:57,997
organization that's like technically
focused where the technical people are

1262
01:09:58,080 --> 01:10:01,837
doing their best work, you know, have
have the opportunity to really do their

1263
01:10:01,920 --> 01:10:05,357
life's work. And one of the design
principles around this was making it

1264
01:10:05,440 --> 01:10:08,957
very flat. you know, effectively all
the researchers that we hired into this

1265
01:10:09,040 --> 01:10:13,597
group called TBD, they report directly
to me. And it's more of a statement of

1266
01:10:13,680 --> 01:10:17,837
the fact that like this is like
anti-bureaucracy. Like TBD is like

1267
01:10:17,920 --> 01:10:20,557
literally anti-bureaucracy. The point is

1268
01:10:20,640 --> 01:10:23,437
we're hiring brilliant people and you

1269
01:10:23,520 --> 01:10:28,237
all have like demonstrated clearly
that you can do brilliant, brilliant work.

1270
01:10:28,320 --> 01:10:31,757
You have like done it before
in your careers. So I don't need to be

1271
01:10:31,840 --> 01:10:34,237
micromanaging you to do brilliant work.

1272
01:10:34,320 --> 01:10:38,397
you're going to be able to do that on
your own. We need technical leadership.

1273
01:10:38,480 --> 01:10:41,277
So, some of you are going to be
responsible for like setting clear

1274
01:10:41,360 --> 01:10:44,797
technical directions for your groups and
we have this kind of like pod structure

1275
01:10:44,880 --> 01:10:48,797
where we have various pods and there's
various technical leads of the pods who

1276
01:10:48,880 --> 01:10:52,397
who tie break on the technical decisions.
So, there's like a technical

1277
01:10:52,480 --> 01:10:56,477
leadership structure in place,
but from a quote unquote people management

1278
01:10:56,560 --> 01:10:59,757
perspective or from a bureaucracy
perspective, we're just

1279
01:10:59,840 --> 01:11:02,397
anti-bureaucracy. And then the other

1280
01:11:02,480 --> 01:11:04,637
part of this is like we do make a lot of

1281
01:11:04,720 --> 01:11:07,037
group decisions. Like we want to debate

1282
01:11:07,120 --> 01:11:11,757
because it is like a very talent dense
group of like brilliant people. We want

1283
01:11:11,840 --> 01:11:15,677
to have those conversations where we're
all discussing what do we think the

1284
01:11:15,760 --> 01:11:19,117
right path is. Everyone in the lab has
done brilliant things. Like we want to

1285
01:11:19,200 --> 01:11:22,317
hear all these opinions. We want to arrive
at things that we all believe are

1286
01:11:22,400 --> 01:11:26,317
the best path forward. So it's certainly
like an unconventional way to run the

1287
01:11:26,400 --> 01:11:28,317
team. And like I would be lying if I

1288
01:11:28,400 --> 01:11:30,797
said I was like the best manager to more

1289
01:11:30,880 --> 01:11:33,597
than 200 um people. But this part of the

1290
01:11:33,680 --> 01:11:36,317
point is that like they don't need

1291
01:11:36,400 --> 01:11:38,957
managers, they need a great environment.

1292
01:11:39,040 --> 01:11:43,277
They are all brilliant. They're all
extremely capable. They just need the

1293
01:11:43,360 --> 01:11:45,597
room to cook. >> Yeah.
And so maybe not a coach. You're

1294
01:11:45,680 --> 01:11:48,397
kind of like the steward of a great
environment so they can do great work.

1295
01:11:48,480 --> 01:11:51,277
>> Yeah. Exactly. >> Alex,
this was awesome, man. Thanks for

1296
01:11:51,360 --> 01:11:53,917
making taking the time. >> Yeah.

1297
01:11:54,000 --> 01:11:56,957
>> I hope you enjoyed this episode. Please
remember to subscribe wherever you're

1298
01:11:57,040 --> 01:12:00,157
listening and leave a review. And make
sure you listen to my other podcast,

1299
01:12:00,240 --> 01:12:04,557
Founders. For almost a decade, I've
obsessively read over 400 biographies of

1300
01:12:04,640 --> 01:12:07,917
history's greatest entrepreneurs,
searching for ideas that you can use in

1301
01:12:08,000 --> 01:12:09,917
your work. Most of the guests you hear

1302
01:12:10,000 --> 01:12:15,160
on this show first found
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