Meta CFO Susan Li 最新采访

0:00

The joke people have about Instagram

0:01

is the ads are better than the content.

0:02

As someone who bought, like, 25 umbrellas

0:05

that change color in the rain off of a ad-

0:06

Do they work? They do.

0:08

They are a source of real delight.

0:10

Headcount's really easy to account for

0:11

because you have org chart.

0:12

GPUs don't have that property.

0:13

In fact, you often wanna build out your infrastructure...

0:16

You have no shenanigans. ...for it to be very fungible.

0:20

Susan Li joined Facebook in 2008.

0:22

She became CFO in 2022.

0:25

It's a really interesting time for this discussion

0:26

because Meta has a core business

0:27

that's firing on all cylinders

0:29

and Susan's had a front row seat

0:31

for the growth of the company.

0:33

Cheers. Cheers.

0:36

You went to high school at 11, college at 15,

0:40

Morgan Stanley at 19,

0:42

and you're now the youngest CFO of Fortune 100 company.

0:46

So just, what's... Was this you?

0:49

Was this your parents?

0:51

What's going on there?

0:52

Well, you know, some might say,

0:53

because I started kindergarten when I was four

0:55

and I graduated from college when I was 19,

0:58

that having 15 years of formal education is... you know,

1:03

I'm woefully undereducated as it were,

1:05

so I'm really just having to make up

1:07

for, you know, that rough start.

1:08

But if I remember right, you were also

1:09

done with formal education at the age of 19, 20?

1:17

That's because I dropped out.

1:18

I wasn't early in progressing through the milestones,

1:20

I just quit, whereas you actually got your bachelor's.

1:22

Well, it seems like we shared the same disdain

1:24

for sort of getting out of the schooling system

1:25

as soon as possible.

1:26

Hang on, this is your interview, not mine.

1:30

I was in a school system that identified

1:32

when kids were bored in school,

1:33

and then just gave you opportunities to keep moving ahead

1:36

and my parents always took them.

1:39

When I showed up at Morgan Stanley for my first day,

1:41

I was on the trading floor

1:42

in the big Broadway headquarters at 1585.

1:46

And the equivalent of an HRBP basically got the attention

1:51

of everyone on the trading floor.

1:52

They were like- Right 'cause this is

1:53

investment banking, which is known

1:54

for being an inclusive and nurturing culture.

1:57

Very, very much so.

1:58

And so she wanted everyone on the floor to stop

2:00

and look at me and know that no one was to serve me

2:03

any alcohol at any company gathering.

2:05

So it was exactly the way you think about

2:07

sort of beginning your career on Wall Street,

2:09

you know, by being mortified.

2:13

But it improved from there.

2:14

Yes.

2:16

You worked under Michael Grimes at Morgan Stanley.

2:20

He, for people who don't know,

2:22

he's been leading tech investment banking at Morgan Stanley

2:25

for 20 years and he's just a phenom,

2:28

like, I don't know how to describe him.

2:29

He's just one of the most energetic people I've ever met.

2:33

What did you learn from working with Michael?

2:35

Grimes is extraordinarily sort of, like you said,

2:40

very high-energy, applies that to a whole host of things.

2:43

You go talk to Michael about tech companies, about banking,

2:47

about parenting,

2:48

about why there should be more undergraduate sales programs

2:53

and colleges in the country.

2:54

He's got a point of view on everything

2:57

and he's endlessly curious.

2:59

He is going to outwork you and outlearn you.

3:02

It's actually a pretty spectacular thing

3:06

as a young person starting in your career

3:08

to see what excellence at this looks like.

3:13

You've been at Meta for a very long time.

3:17

April 2008.

3:18

So, you joined in 2008.

3:21

And one thing I've observed before is the

3:22

senior leadership at Meta are all very tenured

3:27

and often have done multiple things around the company

3:29

or grown up around the company.

3:31

What traits do the successful leaders at Meta

3:33

have in common?

3:36

Oh, that's a good question.

3:39

Infinite patience.

3:40

No, it's more than that.

3:42

So, I joined as an IC4 and in finance,

3:47

so actually pretty far away from the center of the-

3:50

A few rungs down from the CFO.

3:51

Yes, and also far away from the core of engineers

3:54

building News Feed, right? Yes.

3:55

I mean when you kind of talk to some of the folks now,

3:59

they've always been at the sort of very heart

4:01

of what the product was, you know, building or doing.

4:05

But what I think is unique about Meta

4:09

is we have a pretty strong culture

4:11

of internal succession planning

4:13

and trying to identify people who are talented

4:17

quite early in their careers, actually,

4:20

and think about a many-year runway

4:22

in which you're going to grow and develop them.

4:25

How did Meta succession-plan you?

4:27

I started off my career

4:29

doing mostly revenue forecasting,

4:31

which was kind of like the mathiest part of finance.

4:36

At some point,

4:37

I had done that probably for about five-ish years,

4:40

and I was trying to figure out what to do next.

4:42

And the two kind of paths in front of me were,

4:47

I was talking to some folks in News Feed

4:49

about whether I should just go

4:50

do something totally different and go be a PM in News Feed.

4:54

Or the other option was to broaden my scope in finance,

4:58

to take on sort of more traditional finance responsibilities

5:01

that I really had not had that much exposure to.

5:04

I remember sitting down with David Ebersman,

5:06

who was our CFO at the time. He looked at me and said,

5:10

"Look, I know you're considering these options,

5:12

and I can tell you I think doing

5:14

that News Feed PM job would be really fun

5:16

and I think it'd be a great learning experience for you.

5:19

I totally get it.

5:20

But I also want you to know that I think you could be,

5:23

you know, a CFO of this company someday."

5:25

And to have someone who I admired as much as David Ebersman

5:28

say that about me was an extraordinarily

5:32

confidence building thing,

5:34

and I will remember that conversation forever,

5:35

very viscerally.

5:38

So, I've had managers who I think have really invested in me

5:43

by pushing me to take on things that I wouldn't have said,

5:50

hey, can I please go do this thing next?

5:51

It wouldn't have made obvious intuitive sense to me,

5:54

but I think they thought it would be a good opportunity

5:56

and that I was ready for it.

5:57

And you know, and I think they were right.

6:01

That's really cool.

6:03

In the 17 years you've been at Meta,

6:07

how has Mark changed as a leader?

6:09

There are ways in which you clearly see someone

6:12

evolve over 17 years.

6:13

You know, Mark has done all-hands for all of those 17 years,

6:16

and he clearly has become now

6:18

a truly excellent public speaker.

6:21

Mark is really good at giving feedback,

6:22

like really world class at it.

6:27

Maybe you should try to get yourself into a position

6:29

where you can get some feedback...

6:30

I'm sure Mark already has feedback for me.

6:33

...so you can experience it, but it's very timely,

6:38

it's very direct, it's very respectful.

6:42

But the sort of direct and respectful... It's never mean,

6:45

it's never like belaboring some point,

6:46

but you cannot be mistaken

6:48

after you have received the feedback.

6:49

He's really good at it.

6:53

He kind of walks that line between being direct but kind

6:57

in an extremely good way.

6:59

One of the things people will often ask me

7:01

is, "What kind of skills do you need

7:05

to stay at a company for 17 years?" Or whatever it is.

7:07

And when I think about it, I go back to...

7:13

When I was IC4 and I joined in 2008,

7:14

I'm building these first revenue models.

7:17

I'd gone from banking, which is super organized,

7:19

super structured, they don't even need to know your name,

7:20

they just train you to immediately figure out

7:23

how to find the backup to everything,

7:25

so that two years later someone else can do this

7:27

and so on and so forth,

7:29

to, there was no infrastructure, right?

7:31

So I'm hunting down the exact engineer

7:33

who has built some ad server

7:35

so that he can tell me what the parameters mean.

7:38

And of course, the next time he changes them,

7:39

he's not gonna tell me, and I have to go find him again,

7:41

and he's like, "Oh, she's coming. Don't look her way."

7:45

A few months in, I got a meeting invite

7:49

for power users of SQL.

7:51

And I thought, "My gosh." I'd been getting,

7:52

a good amount of feedback

7:54

about how things could be better,

7:56

and here was finally this moment of recognition

7:58

that... I didn't even know how to write queries in SQL

8:01

when I started.

8:02

And I show up to this meeting

8:03

and there are five other people

8:04

and the meeting organizer tells us that we have been called

8:07

because we are the five users of SQL

8:10

who consume too much power.

8:13

And we have just been churning

8:15

with our massive joint, tables through the-

8:19

I love that you were all called

8:20

to the principal's office.

8:22

Basically, yes.

8:23

But I often think back to this

8:25

because this was was a data analyst

8:28

who didn't know any of us that well,

8:30

but had just generated his reports

8:32

of who's using the most infrastructure

8:34

and looked at the top people on the list

8:36

and you know, thought, "Okay, this person in finance,

8:38

it doesn't make sense why she's the third highest person

8:41

on the list," and called us in

8:43

and then taught us to write better queries.

8:45

And like no one I think specifically told him to do that.

8:48

And I think it's a little awkward

8:50

when you call people in to do this,

8:52

but he did it because it would make us

8:53

all better at our jobs.

8:54

And I think, for 17 years, I have been the beneficiary

8:58

of a lot of feedback that has made me better along the way.

9:01

So when people ask me this question, I always say,

9:03

"Just be a person who's good at receiving feedback."

9:07

You've mentioned your experience in forecasting.

9:11

And what I think is the central challenge

9:13

of a CFO in a large tech company is

9:17

it's so hard to put numbers around the core thing we do.

9:19

And what I mean by that is like if you're Boeing

9:21

and you're producing the 787,

9:23

you can have a very clear model that we're gonna spend

9:24

this much, you know, manufacturing the 787,

9:27

and then each one,

9:28

we're gonna make this much gross profit on.

9:29

And then at the like component-by-component level, you know,

9:32

we're gonna change from hydraulic brakes to electric brakes,

9:34

and it'll add this much cost, but it'll save this much fuel.

9:36

It's all extremely quantified as a domain.

9:38

Now you're dragging me into the

9:39

resource allocation questions.

9:40

Yes. Okay. Here we go.

9:45

We really think about it as,

9:47

there's stuff that we can rigorously measure.

9:49

So that's a lot of the, you know, core family of apps

9:53

work in terms of the impact on engagement,

9:55

the impact on monetization.

9:56

There's a lot of that stuff

9:57

that is really finely tuned.

9:59

And that really does seem extremely finely tuned.

10:01

I was looking at the numbers

10:02

and you doubled ARPU between 2015 and 2020,

10:06

and then you doubled it again between 2020 and 2025.

10:09

But like Meta wasn't bad at monetization in 2020,

10:14

and it's doubled over that five-year period.

10:16

No, and you know what?

10:18

I just, you know, did earnings three weeks ago now.

10:19

I was doing all my investor callbacks,

10:24

and one of our largest investors on the call,

10:28

one of the portfolio managers, said, "feeling pretty good."

10:31

He goes, "The ads are so good."

10:35

And you know what?

10:36

Five years ago, I would've told you

10:38

that the ads were really good,

10:39

and that there was not really room

10:41

for the ads to get better. But here we are,

10:43

five years later, and the ads are even better.

10:49

The joke people have about Instagram

10:50

is the ads are better than the content.

10:52

Well, I have to tell you,

10:54

as someone who bought, like, 25 umbrellas

10:57

that changed color in the rain off of a ad,

10:59

that was not something I knew that I...

11:01

Not that I needed, but that my children

11:03

and all their friends needed...

11:04

Do they work?

11:05

They do. They are a source of real delight.

11:07

So when the ads can be that good,

11:08

that is a extraordinary thing.

11:09

But getting back to your question.

11:11

So, there's this very measurable part of the company

11:14

and we generally try to trade those things

11:17

off against each other, you know,

11:18

when we are thinking of, when we're evaluating things

11:20

within that bucket and we generally try to fund the things

11:22

that are positive ROI.

11:25

And I'm usually the person who's, you know,

11:27

trying to just make sure we understand, like,

11:29

yes, for every individual experiment,

11:31

the expected return is something,

11:32

but that's where we are on the curve today,

11:34

but what about 50 experiments later?

11:36

Does the curve still have the same slope?

11:38

And then there's a set of things, right,

11:40

which we constrain more in terms of, you know,

11:45

there's some envelope of investment

11:48

that we're willing to make

11:50

that's not in this really ROI-driven bucket.

11:53

It is very difficult to pencil out

11:54

what the annual revenue forecast

11:56

for Reality Labs is gonna look like over the next 20 years.

12:01

And so for bets like that, we sort of invert the problem.

12:06

But when we talk about the return on the investment,

12:11

the question, you know,

12:12

that we pose as a finance organization to Mark,

12:17

and make sure that Mark

12:18

and the board understand, is

12:19

what does this have to be worth to pencil out at the end?

12:23

And does that pass sort of the sanity check,

12:27

the intuition about what building,

12:30

about what the size of these markets can be

12:31

based on maybe some comparisons

12:33

to, you know, markets that exist today,

12:34

but of course, you know, in another 10, 20 years,

12:37

you expect that the world will look different

12:39

and maybe those markets should be bigger or smaller

12:41

for, you know, whatever reason.

12:43

And that's kind of the guide which is like,

12:47

hey, for this thing to succeed

12:49

at the rate at which we're investing,

12:50

it needs to be worth this at the end.

12:52

And does that make sense?

12:54

So in a way, investors may underestimate your ambition

13:00

in some of these new areas,

13:01

where it's like, this is not a hobby,

13:02

this is us investing in markets

13:05

that are worth a huge amount of money,

13:07

if we create a new platform here.

13:09

But the thing people may miss is that

13:12

the upside case you're considering is really serious.

13:15

Yes, and we're only building

13:17

because we think that that sort of, it not only exists,

13:20

but it's compelling

13:21

and it's compelling for financial reasons,

13:24

but also strategic reasons

13:25

why we want that version of the world to exist.

13:29

This is a place where I've gotta be honest with you.

13:32

Like I was one of the last people at the company

13:34

to hand my Blackberry over for an iPhone.

13:36

So you're maybe not the-

13:39

I am not a tech visionary.

13:43

You know, there are many things I'm good at,

13:45

but envisioning the future of the world

13:47

and what I want it to be like is not one of them.

13:49

I'm a very happy beneficiary of the technology

13:53

built by the world around me.

13:55

But Mark very much has a vision

13:56

for what he wants that world to be.

14:00

And for him, I think the sort of strategic imperative

14:04

is that we have to be building

14:05

these next states of the world, you know,

14:08

for us to again, be a good business,

14:10

but also just be a compelling company

14:13

that builds technology and puts it out in the world

14:15

and, you know, builds incredible experiences for people.

14:18

I remind people in the finance organization all the time,

14:19

we are very good

14:24

at skeptically evaluating each bet, right?

14:28

But the point is not that we have to look at every bet

14:31

and be like, "This bet is going to work."

14:33

The point is there is a portfolio of bets, right?

14:35

And some of them are going to pay off

14:38

massively beyond, in fact what the case on paper

14:42

looks like when you make the bet,

14:43

and many of them are going to not work out,

14:46

but the ones that pay off are gonna more than

14:48

sort of justify the overall investment strategy

14:51

or the overall sort of roadmap that you're building toward.

14:54

And if we just allowed ourselves to nix everything

14:56

that sort of, you know, the paper case

14:58

didn't seem high-confidence,

14:59

then we would never make a lot of the important bets

15:01

that have been really important

15:03

over the history of the company.

15:05

When did you take over?

15:06

November 1, 2022.

15:08

Okay. Yeah, so I think the- Good timing.

15:11

...the day you took over,

15:13

the market cap troughed as $230-ish billion dollars?

15:17

A real sign of market confidence in me, as you can tell.

15:21

You probably remember what the number was,

15:22

but I think it was around $230 billion dollars.

15:24

And so that means the day that you took over as CFO,

15:28

one could have bought Facebook, sorry, Meta,

15:29

excuse me-

15:31

as an investor for three times 2025 net income.

15:35

And that's like coal plant territory.

15:38

A very easy way to make money is to buy

15:41

good and growing businesses for three times net income.

15:46

Well, I hope you did.

15:47

I did not, and this is why-

15:50

I'm not in the investing business.

15:53

There was something that people deeply misunderstood

15:57

at that point about Meta.

16:00

What did they misunderstand so much?

16:02

Well, there's a bit here, by the way... someday

16:03

I'm gonna ask you, you know, how you feel

16:06

about having public market investors

16:08

someday and when will that day be?

16:10

This is my interview.

16:13

But more to the point.

16:14

You know, that sort of October 2022 moment happened

16:17

at a, like there were multiple things going on,

16:20

if you kind of rewind the clock.

16:22

There were two big revenue headwinds.

16:24

One was that the platform changes with AT&T

16:28

had kind of rolled through from 2021,

16:31

which was when it plunged. This was Apple changing

16:32

their policies around what tracking was permissible

16:36

inside of apps. Yes, exactly.

16:38

So that was one thing.

16:39

And then the second thing

16:40

was just this sort of COVID fueled e-commerce avalanche

16:45

was pulling back, and both of those things very-

16:47

People were buying fewer color-changing umbrellas.

16:50

Sadly for the children of the world, yes.

16:52

And so both of those things had the effect

16:54

of unfortunately having for us at the same time.

16:56

So we really went from this

16:57

e-commerce fueled heyday in 2021

17:01

to now like negative year-over-year growth

17:03

for the first time, which is obviously very alarming.

17:05

Right.

17:06

And so, you know, those stars kind of aligned

17:09

in that stock-price-low kind of way in October, 2022.

17:16

And I think what you've seen since then is a few things.

17:19

One is that yes, there are these two exogenous factors

17:22

that happened that were bad for revenue at the time.

17:24

But the fundamental sort of underlying like business,

17:29

which is can we show the best possible ads

17:32

to the right people at the right time across, you know,

17:34

the surface of consumer experiences that we are building,

17:38

That continued to be very strong.

17:40

And then the second thing is I think we demonstrated

17:42

as a company that we are in fact able to turn the ship

17:44

on costs in a, you know, very meaningful and very quick way.

17:49

Speaking of that, you have to explain

17:51

the free cash flow hat.

17:51

Thank you for the hat, by the way.

17:53

Oh, yes.

17:54

You're welcome. Everyone really should have one.

17:57

I think they are underworn out in the world.

18:00

The joke of the story is that Mark at one point,

18:04

gave me an EBITDA hat,

18:07

which was a very kind gift from him to help me-

18:10

It really sent a message,

18:11

I hope you went and prominently wore it

18:14

in many of the budgetary review meetings that you were in.

18:16

I did.

18:17

This is the EBITDA hat that Mark gave me.

18:18

Yes.

18:20

And I had it in my background,

18:22

like, you know, my Zoom background for a long time.

18:24

But I realized pretty quickly

18:27

that we actually as a company, should be wearing

18:28

free cashflow hats instead,

18:32

because of course, the D of EBITDA

18:35

is a number of growing importance

18:40

through our financials,

18:41

and I didn't want Mark to misinterpret and feel like,

18:44

you know, EBITDA was gonna be

18:45

the end-all-be-all financial metric for us.

18:48

So there's only one EBITDA hat.

18:50

There are many free cashflow hats,

18:52

I give them out like candy,

18:54

and try to make sure that people really understand

18:56

that this is the hat that matters.

18:58

Charlie Munger had the joke

19:00

that anytime you hear "EBITDA,"

19:01

you should substitute it with "bullshit earnings."

19:03

And so you, similarly for a CapEx-intensive business,

19:05

you wanna make sure people are not forgetting

19:06

about the CapEx. Yes, exactly.

19:08

Where does CapEx go for not just Meta,

19:10

but the tech industry broadly?

19:12

Because all of Microsoft, Google and Meta

19:17

have gotten more CapEx intensive over the past few years

19:20

compared to their prior steady states.

19:23

Like just do we continue spending this fraction of revenues

19:26

on CapEx over a 5 or 10-year period?

19:29

Do we somehow get some kind of amazing compute gains?

19:32

Are we ultimately bottlenecked on power,

19:34

and so you just can't keep growing CapEx at this rate

19:37

because you can't plug the data centers into anything?

19:39

Where does CapEx go in an industry-wide level?

19:42

That is the question that I assume

19:44

that all of my counterparts at these companies and I

19:48

are all thinking about.

19:49

For us, there are the drivers

19:52

of the way we're investing in CapEx today.

19:54

Of course, we have, first of all,

19:56

just a massively-scaled consumer business and core,

20:01

you know, AI infrastructure that powers all the ranking

20:04

and recommendations work and so on and so forth.

20:05

So that's always been a reasonably big number for us,

20:08

but also one because it was getting more mature

20:09

that we were driving to be more efficient over time.

20:13

And then now you have,

20:14

among many of our peers and ourselves, this big investment

20:18

to train what we all aspire to be, frontier models.

20:22

And if you use those models to build

20:25

great and scaled consumer experiences,

20:27

then how much inference, you know,

20:29

compute you're gonna need on top of that?

20:31

If compute required

20:33

continues to scale up in this way forever,

20:34

then you're gonna run into some true problems of physics.

20:37

But hopefully, there will be different kinds

20:40

of research innovations along the way

20:42

that will unlock things

20:43

like being able to distribute the training so you don't need

20:45

sort of one extremely large cluster somewhere

20:47

and that will help with a lot of the energy

20:49

and other challenges.

20:51

So there's some question

20:52

about what that looks like over time.

20:54

And then there's this question about, you know,

20:55

great, you can build all this capacity,

20:58

and what do you do with them if it turns out

20:59

you don't need as much compute

21:01

for either training or inference as you thought?

21:03

I think a lot of us

21:05

have different backup use cases.

21:06

So, up to some point, we would use a lot of compute

21:09

very happily still, you know, in the core business

21:12

and what we expect the core business to be

21:14

three years from today.

21:16

But frankly, we'd use more compute in the core business.

21:18

Now, that doesn't scale forever, right?

21:20

So the real question is what happens in like two years

21:22

if you've built so much compute that you cannot envision

21:25

a reasonable ROI on the backup use case

21:29

if what you're building doesn't come to fruition.

21:32

And that's something I think we're all gonna learn

21:35

in the next few years.

21:36

When you say the primary versus backup use case is

21:39

new products like Llama and stuff,

21:42

and the backup use case is ads optimization?

21:45

Yes, exactly.

21:46

You mentioned just doing earnings.

21:47

Is there a specific anecdote

21:49

that you can or want to discuss?

21:54

In the October 2022 period, so we had an earnings call

21:56

at the end of October,

21:58

and as usual, I'm doing investor callbacks.

22:00

And the investors were not shy about their feedback.

22:07

And in fact, one of the calls- Investor callbacks,

22:10

I don't know what this is.

22:11

This is where you call the...

22:13

This is like one one-on-ones, basically.

22:14

Yes. It's pretty standard after earnings calls,

22:17

where you touch base

22:18

with some number of your largest investors.

22:20

Sadly, it is not one-on-one.

22:21

It's, you know, one of you

22:23

and many, many people from their teams.

22:26

And most of the time,

22:27

they just ask you to clarify things.

22:29

Obviously, everything is, you know, Reg FD compliant,

22:33

but it mostly takes the form of questions.

22:35

And you know, in October, 2022 for the first time,

22:38

there were sometimes no questions.

22:40

I mean, there was a call where basically

22:42

one of the portfolio managers said,

22:43

"We actually don't have any questions for you today.

22:45

We just want you to hear, you know, feedback from us."

22:48

Wow. More of a comment than a question.

22:50

Yes, it was actually very memorable.

22:53

And it was blunt feedback, I presume.

22:55

Yes.

22:55

And one of the things that really stuck with me

22:57

from one of those conversations is someone said,

23:02

"Look, I get that you're building the next,

23:05

you know, the future of computing

23:07

and the next mobile platform and all that.

23:08

And that is great and I am glad someone wants to do it

23:11

and I am rooting for you,

23:14

but why should I invest in your stock today?

23:16

Why don't I just wait for your phone equivalent,

23:21

your scaled consumer product to come out,

23:24

you know, and invest in you then,

23:25

and you tell me that that's going to be years away?"

23:29

And the way that question was framed actually

23:34

really stuck with me.

23:35

And, you know, is the way that frankly,

23:38

now Mark and I think about this, which is like great,

23:40

we've got a lot of these bets and you know,

23:43

the bets are technologically exciting,

23:45

people can get excited about them

23:46

and the vision of the world.

23:48

But as investors, they're like,

23:50

"Cool, why don't I just wait for your bets to be ready,

23:54

you know, be ready to succeed before I come?"

23:56

We need people to invest with us along the way.

24:00

When we think about the financial outlook

24:02

of the company, you know, a large part of it

24:05

is not just okay, cool, you're building the next,

24:07

you know, massive platform out here in some decades,

24:11

it's, why would you hold our shares until then?

24:13

What do we need to keep delivering

24:15

in terms of consolidated results?

24:16

I found it really interesting

24:18

how when the AI revolution started really ramping up,

24:24

people realized, "Oh, we need a ton of GPUs

24:27

to train leading-edge foundation models."

24:30

You guys had done a huge GPU scale-up

24:35

because you're doing a lot of AI in the core feed.

24:39

And so I think there's some interesting optionality

24:41

in being a scaled infra and AI player

24:45

where we are very good at putting GPUs

24:50

towards their highest and best use,

24:52

and you have seen that we're very good

24:56

at allocating compute, and that is why you should invest?

24:59

That's quite different from the pitch maybe 10 years ago

25:01

where it's "We're good at scaling social products."

25:04

Yes.

25:05

I think there's definitely an interesting point there.

25:07

You know, as part of not wanting to miss the boat, you know,

25:09

we built out, you know, enough capacity

25:12

for Reels but also for future things,

25:14

and we found that we were in fact

25:16

able to put that capacity

25:18

towards very good use, exactly as you said.

25:21

So I do think an interesting question in the future will be

25:24

allocating compute as a resource,

25:28

It's a muscle we've built later as a company,

25:30

because we, you know, had gotten very good

25:33

at allocating headcount as a resource,

25:35

and head count's really easy to account for

25:37

because you have org charts, so you know exactly,

25:39

this person reports to this person, to this person,

25:39

this person is incontrovertibly working

25:43

on Facebook Marketplace, for example.

25:45

GPUs don't have that property.

25:47

In fact, you often want to build out your infrastructure...

25:49

You have no shenanigans. ...for it to be very fungible.

25:50

Because you need to divert capacity to where...

25:51

Suddenly something has happened

25:55

in India and you want a lot of compute to be available

25:58

to be used there. So it's not all like

26:00

this GPU is labeled for Facebook Marketplace,

26:02

and this is labeled for...

26:04

So it's actually quite a bit more difficult

26:06

to account for where the capacity is being used

26:10

at any given point in time.

26:11

And that means it's harder to manage,

26:13

and it's harder to create the incentives

26:15

around like are you using GPUs efficiently?

26:18

You allow people to trade between people and GPUs, right?

26:22

In the budgeting process, we have allowed people to trade.

26:28

And not too surprisingly, even though

26:31

you'll find that groups are often asking for compute,

26:35

when that particular trade is on offer,

26:39

people almost never trade for compute

26:41

for exactly the reason I described,

26:43

which is that if they get allocated

26:45

100 new head count, there is no chance

26:48

that 26 of those head count

26:49

will accidentally be working for something else.

26:52

Yes, I see.

26:53

So again, it's harder to account for.

26:55

But you could joke that AI has shown up everywhere

26:59

except in the large company hiring plans.

27:01

When I talk to startups sometimes,

27:03

they are actually delivering,

27:05

they're having a huge amount of impact

27:08

with a very small number of people

27:11

and they plan to grow headcount slower

27:13

than maybe the generation of startups that came before them.

27:14

How do you think AI productivity actually shows up

27:19

as more established companies, like a Stripe or like a Meta

27:22

that just have a larger installed base?

27:25

Yes.

27:26

When we think about AI for productivity at Meta,

27:29

I think there are two dimensions.

27:30

One is how do you make

27:32

the most operational parts of people's jobs

27:34

less so and more interesting?

27:36

And I say that as a person who is like

27:38

a very expensive machine learning model

27:40

for approving expenses.

27:43

I'm not certain that when I approve expenses,

27:44

I'm really adding a lot of deep human intelligence

27:47

to this process, I'm scanning

27:48

for a fairly checklistlist-able set of things.

27:51

And yet I get multiple expenses every day.

27:54

So how do you take that part of people's jobs...

27:55

Do you ever get really funny ones?

27:57

Those are concerning,

27:58

yes, some of them have taken me down

28:00

some really interesting rat holes.

28:02

So, how do you basically

28:06

make those parts of people's jobs

28:07

automated so they can do more interesting things?

28:09

And the second thing is, you know, there are actually things

28:12

we don't do enough of today because right now,

28:14

they're pretty low-ROI to do.

28:16

The canonical example is,

28:20

everyone knows someone who has gotten locked out

28:22

of their Facebook or Instagram account.

28:23

It is a pain to get back in.

28:25

We know it is a pain to get back in.

28:27

But it's super laborious,

28:28

the process of like verifying that you're a real person,

28:31

you have real friends in the platform.

28:32

It's a hard problem, yeah. It's a hard problem.

28:35

If we could actually make that more efficient

28:39

and more productive and enable

28:41

a currently a human reviewer

28:44

or customer service agent to go from, you know, reviewing-

28:49

I'm making up these numbers, but five a day to 50 a day,

28:52

unlocking 50 accounts a day,

28:54

you can actually make this a pretty high-ROI thing to do

28:57

that you would invest in on an ROI basis alone.

29:00

So I think there is a bit where...

29:02

I think everyone is worried

29:03

about the world where like the machines

29:05

have come for all of our jobs,

29:06

definitely my expense approval job, and maybe more.

29:10

But I think there's actually a window before that

29:14

where I think it's really about making humans

29:17

substantially more productive than they are today.

29:18

And makes new kinds of things possible

29:20

that weren't economic or just not possible before.

29:23

Yeah.

29:24

I've kept you for way too long. Thank you.

29:26

Thank you so much for having me.

29:27

I really look forward to seeing that free cashflow hat

29:29

everywhere in the wild.

29:31

It is the perfect photo accessory.

29:33

There we go.

29:34

It's a good look. And it's green.

29:35

And it's green, exactly.

29:36

Thank you, it's very culturally on brand.

29:37

Yes.

29:38

Alrighty. Thank you.