18 Jun 2025 · 29m · Susan Li, John Collison · 25 quoted facts
Meta's CFO Susan Li argues that running a huge tech company means mixing two kinds of spending decisions. Some are measured tightly, like ad improvements judged on return on investment. Others are long-term bets like Reality Labs, judged by what they would have to be worth to pay off. She says the company must keep funding the bets while earning investors' patience through strong core results.
The main tension is between disciplined skepticism and big ambition. In late 2022 the stock collapsed and investors asked why they should own it before the bets paid off. Li says Meta answered by cutting costs while its ad business stayed strong. The same tension now shows up in AI spending: GPUs are far harder to track and budget than headcount.
For projects like Reality Labs, Li says revenue cannot be forecast over 20 years, so finance asks what the market would have to be worth for the investment to pay off. Leaders then check that figure against comparable markets.
Apple's tracking changes (ATT) and the fading of the COVID e-commerce boom arrived together. Meta went from heyday growth in 2021 to its first year-over-year revenue decline.
In an October 2022 callback, one investor said they were glad Meta was building the next platform but asked why they should hold the stock until it arrived. Li says this still shapes how she and Zuckerberg think about delivering results along the way.
Org charts show who works on what, but GPUs are deliberately pooled so capacity can shift to wherever demand spikes. That makes usage hard to account for and efficiency hard to reward. Teams offered a trade of people for compute almost never take it.
Li says AI first automates checklist work like expense approval. It also makes previously uneconomic tasks worthwhile, such as helping reviewers unlock locked-out accounts at 50 a day instead of five.
Susan Li says finance flips the question and asks what the project would have to be worth for the spending to pay off. They then check that against the size of comparable markets. She says individual bets may fail, but the winners should justify the whole portfolio.
Li says two revenue problems hit at once: Apple's ATT privacy changes reduced ad tracking, and the pandemic e-commerce surge faded. Meta saw its first negative year-over-year revenue growth, while the market doubted its long-term bets.
Mark Zuckerberg gave Li an EBITDA hat (EBITDA is a profit measure that ignores some costs). She worried the company would treat it as the key metric, since heavy infrastructure spending matters. So she hands out many free cash flow hats instead.
GPUs, the chips that run AI, are intentionally shared and shifted between uses, so no chip is labelled for one product. Headcount is easy to track on org charts. Li says that makes GPU efficiency harder to measure and reward.
Li describes two near-term effects rather than mass job loss: automating routine tasks like approving expenses, and making formerly low-return work worthwhile. Her example is helping staff unlock many more locked-out accounts per day.
Stated by the speakers. Each quote matches the transcript, which shows what was said, not that it is true.
Susan Li says she started kindergarten at age four and graduated from college at 19, giving her 15 years of formal education.
“I started kindergarten when I was four and I graduated from college when I was 19, that having 15 years of formal education is, you know, I'm woefully undereducated”
Susan Li's first day at Morgan Stanley was on the trading floor at the company's headquarters at 1585 Broadway.
“When I showed up at Morgan Stanley for my first day, I was on the trading floor in the big Broadway headquarters at 1585.”
John Collison says Michael Grimes has led technology investment banking at Morgan Stanley for 20 years.
“He, for people who don't know, he's been leading tech investment banking at Morgan Stanley for 20 years.”
Susan Li joined Meta in April 2008 as an IC4, a junior individual-contributor level, in finance.
“I joined as an IC4 in finance. So really actually pretty far away from the center of the”
Susan Li began her Meta career mostly doing revenue forecasting, which she describes as the mathiest part of finance.
“So I started off my career doing mostly revenue forecasting, which was kind of like the mathiest part of finance.”
David Ebersman, then Meta's CFO, spoke with Susan Li as she weighed moving into a News Feed product management role or broadening her finance duties.
“I remember sitting down with David Ebersman, who was our CFO at the time, and he looked at me and said, look, I know you're considering these options.”
Susan Li says Mark Zuckerberg has held all-hands meetings throughout her 17 years at Meta and has become an excellent public speaker.
“Mark has done all hands for all of those 17 years and he clearly has become now a truly excellent public speaker”
Early in her Meta career, Susan Li was invited to a meeting of the five heaviest SQL users, who were consuming too much infrastructure power.
“the meeting organizer tells us that we have been called because we are the five users of SQL who consume too much power.”
John Collison says Meta's average revenue per user doubled between 2015 and 2020 and doubled again between 2020 and 2025.
“you doubled ARPU between 2015 and 2020. And then you doubled it again between 2020 and 2025.”
Susan Li says Reality Labs' annual revenue over the next 20 years is very hard to forecast, so Meta evaluates such bets by what they must be worth to pay off.
“It is very difficult to pencil out what the annual revenue forecast for Reality Labs is going to look like over the next 20 years.”
John Collison says Meta's market capitalization bottomed at roughly $230 billion on the day Susan Li became CFO, which she dated as November 1, 2022.
“I think the day you took over, the market cap troughed as $230-ish billion.”
John Collison describes Apple's ATT as a policy change limiting what tracking was permitted inside apps.
“It was Apple changing their policies around what tracking was permissible inside of apps.”
Susan Li says Meta went from an e-commerce-fueled peak in 2021 to negative year-over-year revenue growth for the first time.
“we really went from this e-commerce-fueled heyday in 2021 to now negative year-over-year growth for the first time, which is obviously very alarming.”
Susan Li says Mark Zuckerberg once gave her an EBITDA hat as a gift.
“the story is that Mark at one point gave me an EBITDA hat which was a very kind gift from him”
Susan Li decided Meta should emphasize free cash flow rather than EBITDA, and now gives out free cash flow hats.
“I realized pretty quickly that we actually as a company shouldn't be wearing free cash flow hats instead”
John Collison recalls Charlie Munger joking that EBITDA should be read as 'bullshit earnings.'
“Charlie Munger had the joke that anytime you hear EBITDA, you should substitute with bullshit earnings.”
Susan Li says Meta and its peers are making large capital investments to train frontier AI models.
“And then now you have, among many of our peers and ourselves, this big investment to train what we all aspire to be frontier models.”
Susan Li hopes research advances will allow AI training to be distributed, easing the need for one giant cluster and reducing energy constraints.
“hopefully, there will be different kinds of research innovations along the way that will unlock things like being able to distribute the training so you don't need sort of one extremely large cluster”
John Collison characterizes Meta's primary compute use as new AI products like Llama and the backup use as ads optimization, and Susan Li agreed.
“And sorry, when you say the primary versus backup use cases, the primary use case is new products like Llama and stuff. And the backup use case is ads optimization.”
Susan Li says that after Meta's October 2022 earnings call, some investor callbacks had no questions and only feedback.
“And in October 2022, for the first time, there were sometimes no questions.”
Susan Li says headcount is easy to account for at Meta because org charts show who works on what.
“And headcount's really easy to account for because you have org charts.”
Susan Li says Meta keeps GPU capacity fungible so it can be redirected to where demand spikes, such as in India.
“you need to divert capacity to where suddenly something has happened in India and you want a lot of compute to be available to be used there.”
Susan Li says that when Meta budgeting allowed trading headcount for compute, people almost never chose compute.
“people almost never trade for compute for exactly the reason I described, which is that if they get allocated 100 new headcount”
Susan Li jokes that she acts as an expensive machine learning model by approving routine employee expenses.
“I say that as a person who is like a very expensive machine learning model for approving expenses”
Susan Li says account lockouts are a problem where AI could make human reviewers far more productive and justify more investment.
“everyone knows someone who has gotten locked out of their Facebook or Instagram account.”
Li explains her unusually fast schooling and her mortifying first day at Morgan Stanley. This sets up her path from banking to Meta.
“I was in a school system that identified when kids were bored in school”
Li says Meta spots talent early and grows people over many years. This explains why its leaders tend to be long-tenured.
“we have a pretty strong culture of internal succession planning”
Li recalls choosing between a product manager job and broader finance work. Then-CFO David Ebersman told her she could one day be CFO.
“I think you could be, you know, a CFO of this company someday.”
Li credits Zuckerberg's direct feedback and an early SQL-usage episode for her long tenure. Her advice is to get good at receiving feedback.
“just be a person who's good at receiving feedback”
Li separates measurable ad and engagement work from uncertain long-term bets. For the latter, finance asks what outcome would justify the spending.
“what does this have to be worth to pencil out at the end?”
Li explains how Apple's tracking changes and fading e-commerce hit revenue in 2022. She says Meta then proved it could cut costs quickly while the ads business stayed strong.
“able to turn the ship on costs in a very, very meaningful and very quick way”
A joke about EBITDA and free cash flow leads into where industry capital spending is headed. Li worries about the return on compute built for AI.
“what happens in like two years if you've built so much compute”
An October 2022 callback with blunt feedback shaped how Li and Zuckerberg think about investor patience.
“why should I invest in your stock today?”
Li contrasts the easily tracked headcount budget with GPUs, which are pooled and shifted. That makes compute much harder to manage.
“headcount's really easy to account for because you have org charts.”
Li describes AI automating routine work and making low-ROI tasks, like account recovery, worth doing. She sees a window where AI mainly makes humans more productive.
“making humans substantially more productive than they are today”
The joke people have about Instagram that the ads are better than the content.
As someone who bought like 25 umbrellas that changed color in the rain off of an ad. They work? They do. They are a source of real delight. Headcount's really easy to account for because you have org charts. GPUs don't have that property. In fact, you often want to build out your infrastructure.
You can have shenanigans.
For it to be very fungible.
Susan Li joined Facebook in 2008. She became CFO in 2022. It's a really interesting time for this discussion because Meta has a core business that's firing on all cylinders. and Susan's had a front row seat for the growth of the company. Cheers! You went to high school at 11, college at 15, Morgan Stanley at 19, and you're now the youngest CFO of a Fortune 100 company. So just, what's going on? Like, was this you? Was this your parents?
What's going on there?
Well, you know, some might say, because I started kindergarten when I was four and I graduated from college when I was 19, that having 15 years of formal education is, you know, I'm woefully undereducated, as it were. So I'm really just having to make up for, you know, that rough start. But if I remember right, you are also done with formal education at the age of 19, 20.
But that's kind of dropped out. Like I wasn't early in progressing through the milestones. I just quenched, whereas you actually got your bachelor's.
Well, it seems like we shared the same disdain for sort of getting out of the schooling system as soon as possible. Ah, this is your interview, not mine. I was in a school system that identified when kids were bored in school and then just gave you opportunities to keep moving ahead. And my parents always, always took them. When I showed up at Morgan Stanley for my first day, I was on the trading floor in the big Broadway headquarters at 1585.
And the equivalent of an HRBP basically got the intention of everyone on the trading floor.
Because this is investment banking, which is known for being an inclusive and nurturing culture.
Very, very much so. And so she wanted everyone on the floor to stop and look at me and know that no one was to serve me any alcohol at any company gathering. So it was exactly the way you think about sort of beginning your career on Wall Street, you know, by being mortified.
And it's just so, but it improved from there. You worked under Michael Grimes at Morgan Stanley. He, for people who don't know, he's been leading tech investment banking at Morgan Stanley for 20 years. and he's just a phenom. Like, I don't know how to describe him. He's just one of the most energetic people I've ever met. What did you learn from working with Michael?
Grimes is extraordinarily sort of, like you said, very high energy, applies that to a whole host of things. You go talk to Michael about tech companies, about banking, about parenting, about why there should be more undergraduate sales programs and colleges in the country. He's got a point of view on everything, and he's endlessly curious. He is going to outwork you and outlearn you. And it's actually a pretty spectacular thing as a young person starting in your career to see what really excellence at this looks like.
You've been at Meta for a very long time. You joined in 2008.
April 2008.
So you joined in 2008. And one thing I've observed before is just the senior leadership in Meta are all very tenured and often have kind of done multiple things around the company or grown up around the company. What traits do the successful leaders of Meta have in common?
Oh, that's a good question. Infinite patience. No, it's more than that. So I joined as an IC4 in finance. So really actually pretty far away from the center of the...
A few rungs down from the CFO. Yes.
And also far away from like the core of engineers building newsfeed, right? I mean, when you kind of talk to some of the folks now, they've always been at the sort of very heart of what the product was, you know, building or doing. But what I think is unique about Meta is we have a pretty strong culture of internal succession planning and trying to identify people who are talented. quite early in their careers, actually, and think about a many-year runway in which you're going to grow and develop them.
How did Meta Succession plan you? So I started off my career doing mostly revenue forecasting, which was kind of like the mathiest part of finance. And at some point, I had done that probably for about five-ish years, and I was trying to figure out what to do next. And the two kind of paths in front of me were I was talking to some folks actually in News Feed about whether I should just go do something totally different and go be a PM in News Feed.
Or the other option was to broaden my scope in finance to take on sort of more traditional finance responsibilities that I really had had not that much exposure to. And I remember sitting down with David Ebersman, who was our CFO at the time, and he looked at me and said, look, I know you're considering these options. And I can tell you, I think doing that newsfeed PM job would be really fun. And I think it'd be a great learning experience for you.
I totally get it. But I also want you to know that I think you could be, you know, a CFO of this company someday. And to have someone who I admired as much as David Eversman say that about me was an extraordinarily confidence, you know, building thing. And I will remember that conversation forever, very viscerally. So I've had managers who I think have really invested in me by pushing me to take on things that I wouldn't have said, oh, I wouldn't have said, hey, can I please go do this thing next?
It wouldn't have made obvious intuitive sense to me. But I think they thought it would be a good opportunity and that I was ready for it. And I think they were right.
That's really cool. in the 17 years you've been as meta how has mark changed as a leader you know there are ways in
which you clearly see someone evolve over 17 years you know mark has done all hands for all of those 17 years and he clearly has become now a truly excellent public speaker mark is really good at giving feedback um like really world-class at it uh and maybe you should try to get yourself in a position where you can get some feedback.
I'm sure Mark already has feedback from me. Yeah.
So you can experience it. But he's, it's very timely. It's very direct. It's very respectful. But the sort of direct and respectful, it's never mean. It's never like belaboring some point, but you cannot be mistaken after you have received the feedback. Yes, yes. He's really good at it. He kind of walks that line between being direct but kind in an extremely good way. You know, one of the things people will often ask me is like, you know, what is, you know, what kind of skills do you need to stay at a company for 17 years or whatever it is?
And when I think about it, I go back to, I, when I was in IC4 and I joined in 2008, I'm building these first revenue models. And the, you know, I'd gone from banking, which is super organized, super structured. They don't even need to know your name. Like they just train you to immediately figure out how to find the backup to everything so that two years later, someone else can do this and so on and so forth.
Two, there was no infrastructure, right? So I'm like hunting down the exact engineer who has built some ad server so that he can tell me what the parameters mean. And of course, the next time he changes them, he's not going to tell me and I have to go find him again. He's like, oh, she's coming, you know, if I don't look her way. But a few months in, I got a meeting invite for power users of SQL.
And I thought, my gosh, like I've been getting, you know, a good amount of feedback about how things could be better. And so here was finally this moment of recognition that, like, I didn't even know how to write queries in SQL when I started. And I show up to this meeting, and there are five other people. And the meeting organizer tells us that we have been called because we are the five users of SQL who consume too much power.
And we have just been churning with our massive joins tables, you know, through the infrastructure. Basically, yes. But I often think back to this because this was a data analyst who didn't know any of us that well, but it just generated his reports of, like, who's using the most infrastructure and looked at the top people on the list and, you know, thought, okay, this person in finance, it doesn't make sense why she's the third highest person on the list.
And called us in and then taught us to write better queries. And, like, no one, I think, specifically told him to do that. and I think it's a little awkward when you call people in to do this, but he did it because it would make us like all better at our jobs. And I think for 17 years, I have been the beneficiary of a lot of feedback that has made me better along the way. So when people ask me this question, I always say just be a person who's good at receiving feedback.
Yeah. You've mentioned your experience in forecasting. And what I think is the central challenge of a CFOing a large tech company is it's so hard to put numbers around the core thing we do. And what I mean by that is like, if you're Boeing and you're producing the 787, you're going to have a very clear model that we're going to spend this much, you know, manufacturing the 787, and then each one we're going to make this much gross profit on.
And then at the like component by component level, you know, we're going to change from hydraulic brakes to electric brakes and it'll allow this much cost, but it'll save this much fuel. It's all extremely quantified as a domain.
Oh, you're driving into the resource allocation questions. Yes. Okay, here we go. We really think about it as there's stuff that we can rigorously measure, right? So that's a lot of the core family of apps work in terms of the impact on engagement, the impact on monetization. There's a lot of that stuff that is really finely tuned.
And that really does seem extremely finely tuned. Like I was looking at the numbers and you doubled ARPU between 2015 and 2020. And then you doubled it again between 2020 and 2025. but like meta wasn't bad at monetization in 2020 and it's doubled over that five-year period.
No, and you know what? I just, you know, did earnings two, three weeks ago now and was, you know, doing all my investor callbacks and one of our large, largest investors on the call, one of the portfolio managers said, feeling pretty good. He goes, you know, the ads are so good. And you know what? Five years ago, I would have told you that the ads were really good and that there was not really room for the ads to get better.
But here we are, you know, five years later and, you know, the ads are, you know, are even better. And I mean, I...
The joke people have about Instagram is the ads are better than the content.
Well, I have to tell you, as someone who bought like 25 umbrellas that changed color in the rain off of an ad, that was not something I knew that I, not that I needed, but that my children and all their friends needed.
Do they work?
They do. They are a source of real delight. So, you know, when the ads can be that good, that is a extraordinary thing. But getting back to your question. So there's this very sort of measurable part of the company. And we generally try to trade those things off against each other, you know, when we are thinking of when we're evaluating things within that bucket. And we generally try to fund the things that are positive ROI. And I'm usually the person who's, you know, trying to just make sure we understand, like, yes, for every individual experiment, the expected return is something, but that's where we are on the curve today, but what about 50 experiments later?
Does the curve still have the same slope? And then there's a set of things, right, which we constrain more in terms of, you know, the, you know, there's some envelope of investment that we're willing to make that's not in this really ROI-driven bucket. It is very difficult to pencil out what the annual revenue forecast for Reality Labs is going to look like over the next 20 years. And so for bets like that, we sort of invert the problem.
But when we talk about the return on the investment, the question that we pose as a finance organization to Mark is, and make sure that Mark and the board understand is, what does this have to be worth to pencil out at the end? Right. And does that pass sort of the sanity check, the intuition about what building about what the size of these markets can be based on maybe some comparisons to markets that exist today? But of course, you know, in another 10, 20 years, you expect that the world will look different and maybe those markets should be bigger or smaller for whatever reason.
And that's kind of the guide, which is like, hey, for this thing to succeed at the rate at which we're investing, it needs to be worth this at the end. And, you know, does that make sense?
So in a way, investors may underestimate your ambition in some of these new areas where it's like, this is not a hobby. This is us investing in markets that are worth a huge amount of money if we create a new platform here. But the thing people may miss is that the upside case you're considering is really serious.
Yes. And we're only building because we think that that sort of not only exists, but it's compelling. And it's compelling for financial reasons, but also strategic reasons why we want that version of the world to exist. And this is a place where I've got to be honest with you. Like, I was one of the last people to come and need to hand my BlackBerry over for an iPhone.
So you're maybe not the...
I am not a tech visionary. There are many things I'm good at, but sort of envisioning the future of the world and what I want it to be like is not one of them. I'm a very happy beneficiary of the technology built by the world around me. But Mark very much has a vision for what he wants that world to be, right? And so I think, and for him, I think the sort of strategic imperative is that we have to be building these sort of next states of the world, you know, for us to, again, be a good business, but also just be a compelling company that builds technology and puts it out in the world and, you know, builds
incredible experiences for people. I remind people in the finance organization all the time is like, you know, we are very good at skeptically evaluating each bet, right? But the point is not that we have to look at every bet and be like, this bet is going to work. The point is there is a portfolio of bets, right? And some of them are going to pay off massively beyond, in fact, what sort of the case on paper looks like when you make the bet.
And many of them are going to not work out, but the ones that pay off are going to more than sort of justify the overall investment strategy or the overall sort of roadmap that you're building toward. And if we just allowed ourselves to nix everything that sort of, you know, the paper case didn't seem high confidence, then we would never make a lot of the important bets that are, I think, that have been really important over the history of the company.
When did you take over?
November 1st, 2022.
Okay, yeah. So I think the day you took over, the market cap troughed as $230-ish billion.
A real sign of market confidence in me, as you can tell.
You probably remember what the number was, but I think it was around $230 billion. And so that means the day that you took over as CFO, one could have bought Facebook, I'm sorry, Meta, excuse me, as an investor for three times 2025 net income. And that's like cold plant territory. I mean, it's a very easy way to make money is to buy good and growing businesses for three times.
Well, I hope you did.
I did not. And this is why I'm not in the investing business. There was something that people deeply misunderstood at that point about Meta. What did they misunderstand so much?
Well, there's a bit here, by the way, something I'm going to ask you, you know, how you feel about having public, you know, public market investors someday. And when will that day be? It's my interest. But more to the point, you know, that sort of October 22 moment happened at a, like, there were multiple things going on. If you kind of rewind the clock, there were sort of two big revenue headwinds. One was that the sort of platform changes with ATT had kind of rolled through from 2021.
It was Apple changing their policies around what tracking was permissible inside of apps. Yes, exactly.
So that was one thing. And then the second thing was just this sort of COVID-fueled e-commerce avalanche was pulling back. And both of those things very—
We were buying fewer color-changing umbrellas.
Sadly, for the children of the world, yes. And so both of those things had the effect of, unfortunately, having for us at the same time. So we really went from this e-commerce-fueled heyday in 2021 to now negative year-over-year growth for the first time, which is obviously very alarming. And so those stars kind of aligned in that stock price low kind of way in October 2022. And I think what you've seen since then is a few things.
One is that, yes, there are these two exogenous factors that happened that were bad for revenue at the time, but the fundamental sort of underlying, like, business, which is, can we show the best possible ads to the right people at the right time across, you know, the surface of consumer experiences that we are building, that continued to be very strong, right? And then the second thing is I think we demonstrated as a company that we are, in fact, able to turn the ship on costs in a very, very meaningful and very quick way.
Speaking of that, you have to explain the free cash flow of the hat. And thank you for the hat, by the way.
Oh, yes. Well, you're welcome. Everyone really should have one. I think they are underworn out in the world. the joke is the story is that Mark at one point gave me an EBITDA hat which was a very kind gift from him to help me
it really sends a message like I hope you went and you know prominently wore it or in many of the budgetary review meetings that you were I did this is the EBITDA hat that Mark gave me
yes and I had it in my background you know my Zoom background for a long time but I realized pretty quickly that we actually as a company shouldn't be wearing free cash flow hats instead because, of course, the D of EBITDA is a number of growing importance, you know, through our financials. And so I didn't want Mark to misinterpret and feel like, you know, EBITDA was going to be the end-all be-all financial metric for us.
So I've now, there's only one EBITDA hat. There are many free cash flow hats. I give them out like candy and try to make sure that people really understand that this is
the hat that matters. You know, Charlie Munger had the joke that anytime you hear EBITDA, you should substitute with bullshit earnings. And so you similarly, for a CapEx intensive business, you want to make sure people are not forgetting about the CapEx. Where does CapEx go for not just Meta, but the tech industry broadly? Because all of Microsoft, Google, and Meta have gotten more CapEx intensive over the past few years compared to their prior steady states. Like, do we continue spending this fraction of revenues on CapEx over a five or 10-year period?
Do we somehow get some kind of amazing compute gains? Are we ultimately like we're bottlenecked on power, and so you just can't keep growing CapEx at this rate because you can't plug the data centers into anything? But where does CapEx go at an industry-wide level?
That is the question that I assume that all of my counterparts at these companies and I are all are all thinking about. For us, there are, you know, the drivers of kind of the way we're investing in CapEx today. Of course, we have, first of all, just a massively scaled consumer business and core, you know, AI infrastructure that powers all the ranking and recommendations work and so on and so forth. So that's always been a reasonably big number for us, but also one because it was getting more mature that we were sort of driving to be more efficient over time.
And then now you have, among many of our peers and ourselves, this big investment to train what we all aspire to be frontier models. And then if you use those models to build great and scaled consumer experiences, then how much inference, you know, computer are going to need on top of that. If just compute required continues to scale up in this way forever, then you're going to run into some true problems of physics. But hopefully, there will be different kinds of research innovations along the way that will unlock things like being able to distribute the training so you don't need sort of one extremely large cluster somewhere, and that will help with a lot of the energy and other challenges.
So there's some question about just what that looks like over time. And then there's this question about, you know, great, you can build all this capacity. And what do you do with them if it turns out you don't need as much compute for either training or inference as you thought? And I think a lot of us have different backup use cases, right? So up to some point, we would use a lot of compute very happily still, you know, in the core business and what we expect the core business to be three years from today.
But frankly, we'd use more compute in the core business. Now, that doesn't scale forever, right? So like the real question is what happens in like two years if you've built so much compute that you cannot envision a reasonable ROI on the backup use case if what you're building doesn't come to fruition. And that's something I think we're all going to learn in the next few years.
And sorry, when you say the primary versus backup use cases, the primary use case is new products like Llama and stuff. And the backup use case is ads optimization. Yes, exactly. You mentioned just doing earnings. Is there a specific anecdote that you can or want to discuss?
In the October 22 period, so we had an earnings call at the end of October. And as usual, I'm doing investor callbacks. And, you know, it was a pretty, you know, the investors were not shy about their feedback. And in fact, one of the calls,
you know, investor callbacks, I don't know what this is. This is where you call the,
this is like one-on-ones basically. Yes. It's pretty standard after earnings calls where you touch base with like some number of your largest investors. Sadly, it is not one-on-one. It's, you know, one of you and many, many people from their teams. And most of the time they just ask you to clarify things. Obviously, everything is Reg FD compliant, but it mostly takes the form of questions. And in October 2022, for the first time, there were sometimes no questions.
I mean, there was a call where basically one of the portfolio managers said, we actually don't have any questions for you today. We just want you to hear feedback from us.
Wow. More of a comment than a question.
Yes. It was actually very memorable.
And it was blunt feedback, I presume.
Yes. And one of the things that really stuck with me from one of those conversations is someone said, look, I get that you're building the next, you know, the future of computing and the next mobile platform and all that. And that is great. And I am glad someone wants to do it and I am rooting for you. But why should I invest in your stock today? Like, why don't I just wait for your, you know, your phone equivalent, you know, your scaled consumer product to come out, you know, and invest in you then.
And you tell me that that's going to be like years away. And the way that question was framed actually really stuck with me. And, you know, is the way that, frankly, now Mark and I think about this, which is like, great, we've got a lot of these bets. And, you know, the bets are technologically exciting. People can get excited about them and the vision of the world. But as investors, they're like, cool, why don't I just wait for your bets to, like, be ready, you know, be ready to succeed before I come.
We need people to invest with us along the way. And when we think about the financial outlook of the company, you know, a large part of it is not just, okay, cool, you're building the next, you know, massive platform out here in some decades. It's why would you hold our shares until then? And what do we need to keep delivering in terms of consolidated results?
I found it really interesting how when the AI revolution started really ramping up, people realized, oh, we need a ton of GPUs to train leading edge foundation models. You guys had done a huge GPU scale-up because you're just doing a lot of AI in the core feed. And so I think there's some interesting optionality in being a scaled infra and AI player, where we are very good at putting GPUs towards their highest and best use.
And you have seen that we're very good at allocating compute, and that is why you should invest. that's quite different from the pitch maybe 10 years ago where we're good at scaling social products.
Yes, I think there's definitely an interesting point there. You know, as part of not wanting to miss the boat, you know, we built out, you know, enough capacity for reels, but also for like future things. And we found that we were in fact able to put that capacity towards, you know, very good use, exactly as you said. So I do think an interesting question in the future will be, I think, allocating compute as a resource. That's something we, it's a muscle we've built later as a company, right?
Because we, you know, had gotten very good at allocating headcount as a resource. And headcount's really easy to account for because you have org charts. So you know exactly this person reports to this person, to this person, to this person is incontrovertibly working on Facebook Marketplace, for example. GPUs don't have that property. In fact, you often want to build out your infrastructure. You can have shenanigans. For it to be very fungible because you want it to, you need to divert capacity to where suddenly something has happened in India and you want a lot of compute to be available to be used there.
So it's not all like this GPU is labeled for Facebook marketplace and this is labeled for, and so it's actually quite a bit more difficult to account for, you know, where the capacity is being used at any given point in time. And that means it's harder to manage and it's harder to create the incentives around like, are you using GPUs efficiently? You allow people to trade between people and GPUs, right? In the budgeting process, we have allowed people to trade.
And not too surprisingly, even though you'll find that groups are often asking for compute, when that particular trade is on offer, people almost never trade for compute for exactly the reason I described, which is that if they get allocated 100 new headcount, there is no chance that 26 of those headcount will accidentally be working for something else.
Yes, I see. So again, it's harder to account for. But you could joke that AI has shown up everywhere except in the large company hiring plans. And when I talk to startups sometimes, they are actually delivering, they're having a huge amount of impact with a very small number of people and they plan to grow headcounts slower than maybe the generation of startups that came before them. How do you think AI productivity actually shows up? as more established companies like a Stripe or like a Meta that just have a larger installed base?
Yes. You know, when we think about AI for productivity at Meta, I think there are two dimensions. So one is, how do you make the most operational parts of people's jobs less so and more interesting? And I say that as a person who is like a very expensive machine learning model for approving expenses, right? I'm not certain that when I approve expenses, I'm really adding a lot of deep human intelligence to this process. I'm scanning for a fairly checklistable set of things, and yet I get multiple expenses every day.
And so how do you take that part of people's jobs? Do you ever get really funny ones? Those are concerning, yes. Some of them have taken me down some really interesting rat holes. But so how do you basically make those parts of people's jobs automated so they can do more interesting things? And the second thing is, you know, there are actually things we don't do enough of today because right now they're pretty low ROI to do, right?
And so, like, the kind of canonical example is everyone knows someone who has gotten locked out of their Facebook or Instagram account. It is a pain to get back in. We know it is a pain to get back in. But it's super laborious, the process of, like, verifying that you're a real person, you have real friends on the platform. It's a hard problem. Um, if we could actually make that more, more efficient and more productive and enable a, uh, currently sort of a human reviewer or customer service agent to go from, you know, reviewing, I'm making up these numbers, but five a day to 50 a day, you know, unlocking 50 accounts a day, you can actually make this a
pretty hard, high ROI thing to do that you would invest in on an ROI basis alone. So I think there is a bit where, you know, I think everyone is sort of worried about the world where like the machines, you know, have come for all of our jobs. Definitely my expense approval job and maybe more. But I think there's actually a window before that where I think it's really about making humans substantially more productive than they are today.
And makes new kinds of things possible that weren't economic or just kind of possible before. I've kept you for way too long. Thank you.
Thank you so much for having me. I really look forward to seeing that free capsule hat everywhere in the wild. It is the perfect photo accessory. There we go.
Yes. It's a good look. And it's green. And it's green. Exactly. Thank you. It's very culturally on brand. Alrighty.
Thank you.