Cheeky Pint - Meta CFO Susan Li on headcount vs. GPU allocation, “free cash flow” hats, and almost becoming a PM

Episode Date: June 18, 2025

Susan Li of Meta—the youngest chief financial officer of a Fortune 100 company—joins John Collison to talk about capital allocation, managing investors, and how Mark Zuckerberg has changed over the 17 years of working together.Full episode transcripthttps://cheekypint.transistor.fm/2/transcriptTimestamps(00:00) Intro(01:20) Early education and career(02:15) Lessons from Michael Grimes at Morgan Stanley(03:12) Leadership traits and succession planning at Meta(06:05) Mark Zuckerberg’s leadership and culture of feedback(09:06) Financial forecasting and capital allocation(14:18) ROI on Meta’s portfolio of bets(15:05) Investor sentiment in 2022(17:49) The story behind the “free cash flow” hats(18:58) CapEx trends in the AI era(21:48) A memorable earnings call(24:16) Challenges of allocating compute vs headcount budgets (26:55) AI’s impact on productivity and operations

The joke people have that Instagram if 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 turks.

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 Lee 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 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 Fortune 100 company. So just what's good? 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, 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 quish, whereas you actually got your rush. Well, it seems like we shared the same disdain for sort of getting out of the schooling system as soon as possible. You were not fine. 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 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. Yes. 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 me.

He's just one of the most energetic people I've ever matched. 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. So you joined in 2008. And one thing I've observed before is just the senior leadership in meadow.

are all very tenured and often have 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 join as in IC4 and 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 news feed, 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 pads 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 and newsfeed. 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 news feed 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 do. 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 said 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, you know, and I think they were right. That's really cool. In the 17 years, 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. Like really world-class at it, and maybe you should try to get yourself into a position where you can get some feedback from him. I'm sure Mark already is feedback for 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 at some point. but you cannot be mistaken after you have received the feedback. 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 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 structure. 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 him, he's not going to tell me.

And I have to go find him again. It'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 that 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 CFO and 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 bullying and you're producing the 787, you can 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 allowed this much cost, but it's all extremely quantified as a domain.

Oh, you're dragging 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 where... And that really does seem extremely finely tuned.

Like I was looking at the numbers and you doubled Arpoo between 2015 and 2020, and then you doubled it again between 2020. and twin 25. 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 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,

the joke people have that Instagram that 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 an extraordinary thing. But can you match 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, 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's 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 sort of invert the problem. But when we talk about the return on the investment, the question,

you know, that we pose as a finance organization to Mark is and make sure that Mark and, you know, 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, you know, 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, you know, 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 it 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 that come in a 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 wanted 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. 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 at the market cap troughed as $230-billion. A real sign of market confidence in me, as you can tell. You probably remember the number of us, but I think it was around $230 billion. And so that means the day that you took over is CFO. one could have bought Facebook, or sorry, Medics, excuse me, as an investor for three times 2025 net income. And that's like coal plant territory. I mean, it's a very easy way to make money is to buy good and growing businesses for three times net income.

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. at that point about META, what did they misunderstand so much? Well, there's a bit here, by the way, some of 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 change.

changes with ATT had kind of rolled through from 2021, which was when launched. 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 have the effect of, unfortunately, having for us at the same time, So we really like, you know, went from this e-commerce-fueled heyday in 2021 to now like negative year-over-year growth for the first time, which is obviously very alarming. And so, you know, 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 with 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, you know, 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, of the story, is that Mark at one point gave me an EBITDA hat, which was a very kind gift from him to help me sort of... I really sent a message. Like, I hope you went and prominently wore it or in many

of the budgetary review meetings that you were in. I did. Yeah, yeah. I did. And I... This is the EBDA 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, should 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 any time you hear Eibaba, 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 CAP-X. Yes, exactly.

Where does CAP-X go for, not just meta, but the tech industry broadly, because all of Microsoft, Google, and Meta have gotten more CAP-X-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? Does it, do we somehow get some kind of amazing compute gains? Are we ultimately like we're bottlenecks 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 right level?

That is the question that I assume that all of my counterparts at these companies and I 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 you're 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 extreme large cluster somewhere, and that will help up 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 poor 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 your building doesn't come to fruition. And that's something I think we're all going to learn in the next few years. And so when you say the primary versus backup use cases, the primary use case is new products like Lama 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 periods, 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, you know, clarify things. Obviously, everything is, you know, reg FD compliant. But it mostly takes the form of questions. And, you know, in October 2022, for the first time, there were sometimes no questions.

I mean, there was a call where basically one of the portfolio manager said, we actually don't have any questions for you today. We just want you to hear, you know, feedback from you. us. Wow. More of a comment than a question. Yes. It was actually very memorable.

And one of the things... And it was blunt feedback, I presume. Yes. Yeah. 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 in 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, 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 you're, you're just doing a lot of AI in the core feed. we are very good at putting GPUs towards our highest and best use. And you have seen that we're very good at allocating compute. And that is why you should invest. And 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, that 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, 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 headcount, counts slower than maybe the generation of startups that came before them. Yeah. 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. So how do you take that part of people's jobs? 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. If we could actually make that more efficient and more productive and enable, able a 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 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 a bit where, you know, I think 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. Yeah. 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 capital 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. Yes. All right. Thank you. Thank you.