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

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

• John Collison: 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… Was this you? Was this your parents? What's going on there? Susan Li: Well, 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… I'm woefully under-educated, as it were, so I'm really just having to make up for that rough start. But if I remember right, you were also done with formal education at the age of 19, 20? John Collison: But that's because I dropped out. I wasn't early in progressing through the milestones, I just quit. Whereas you actually got your bachelor's. Susan Li: Well, it seems like we shared the same disdain for getting out of the schooling system as soon as possible. John Collison: Hang on, this is your interview, not mine. Susan Li: 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 attention of everyone on the trading floor, and they were like— John Collison: Right, because it's investment banking, which is known for being an inclusive and nurturing culture. Susan Li: 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 beginning your career on Wall Street, by being mortified. John Collison: But it improved from there. Susan Li: Yes. John Collison: You worked under Michael Grimes at Morgan Stanley. 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. 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? Susan Li: Grimes is extraordinarily, 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 in 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. It’s actually a pretty spectacular thing as a young person starting in your career to see what excellence at this looks like. John Collison: You've been at Meta for a very long time. You joined in… Susan Li: April 2008. John Collison: So, you joined in 2008. And one thing I've observed before is the senior leadership at Meta 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 at Meta have in common? Susan Li: Oh, that's a good question. Infinite patience. No, it's more than that. So, I joined as an IC4 in finance, so actually pretty far away from the center of— John Collison: A few rungs down from the CFO. Susan Li: Yes. And also, far away from the core of engineers building News Feed. When you talk to some of the folks now, they've always been at the very heart of what the product was 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. John Collison: How did Meta succession-plan you? Susan Li: I started off my career doing mostly revenue forecasting, which was kind of like the math-iest part of finance. 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 paths in front of me were… I was talking to some folks 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 more traditional finance responsibilities that I really had not had that much exposure to. I remember sitting down with David Ebersman, who was our CFO at the time. 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 know that I think you could be a CFO of this company someday." And to have someone who I admired as much as David Ebersman say that about me was an extraordinarily confidence-building thing, andI 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, "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. John Collison: That's really cool. In the 17 years you've been at Meta, how has Mark changed as a leader? Susan Li: There are ways in which you clearly see someone evolve over 17 years. 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. Maybe you should try to get yourself into a position where you can get some feedback… John Collison: I'm sure Mark already has feedback for me. Susan Li: …so you can experience it. But 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 belaboring some point, but you could not be mistaken after you have received the feedback. He's really good at it. He walks that line between being direct but kind in an extremely good way. One of the things people will often ask me is, “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… When I was IC4 and I joined in 2008, I'm building these first revenue models. I'd gone from banking, which is super organized, super structured, they don't even need to know your name, 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, so forth, to, there was no infrastructure, right? So I'm 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, and he’s like, "Oh, she's coming. Don't look her way." A few months in, I got a meeting invite for power users of SQL. And I thought, “My gosh.” I'd been getting a good amount of feedback about how things could be better, and here was finally this moment of recognition that… 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 through the— John Collison: I love that you were all called to the principal's office. Susan Li: 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 had just generated his reports of who's using the most infrastructure, and looked at the top people on the list and 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 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." John Collison: You've mentioned your experience in forecasting. And what I think is the central challenge of a CFO in 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 can have a very clear model that we're going to spend this much manufacturing the 787, and then each one, we're going to make this much gross profit on. And then at the component-by-component level, we're going to change from hydraulic brakes to electric brakes, and it'll add this much cost, but it'll save this much fuel. It's all extremely quantified as a domain. Susan Li: Now you're dragging me into the resource allocation questions. Yes. Okay. Here we go. We really think about it as, there's stuff that we can rigorously measure. 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. John Collison: And that really does seem extremely finely tuned. 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. Susan Li: No, and you know what? I just did earnings three weeks ago now. I was 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, "The ads are so good." And you know what? Five years ago, I would've 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, five years later, and the ads are even better. John Collison: The joke people have about Instagram is the ads are better than the content. Susan Li: Well, I have to tell you, as someone who bought, like, 25 umbrellas that change 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… John Collison: Do they work? Susan Li: They do. They are a source of real delight. So when the ads can be that good, that is an extraordinary thing. But getting back to your question. So, there's this very measurable part of the company, and we generally try to trade those things off against each other 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 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 which we constrain more in terms of, 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, and make sure that Mark and the board understand, is, what does this have to be worth to pencil out at the end? And does that pass the sanity check, the intuition about what the size of these markets can be, based on maybe some comparisons to markets that exist today, but of course 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 does that make sense? John Collison: 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. Susan Li: Yes. And we're only building because we think that that 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. This is a place where I've got to be honest with you. I was one of the last people at the company to hand my BlackBerry over for an iPhone. John Collison: So you're maybe not the— Susan Li: I am not a tech visionary. There are many things I'm good at, but 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. And for him, I think the strategic imperative is that we have to be building these next states of the world 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 builds incredible experiences for people. I remind people in the finance organization all the time, we are very good at skeptically evaluating each bet. 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, and some of them are going to pay off massively beyond, in fact, what 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 justify the overall investment strategy or the overall roadmap that you're building toward. And if we just allowed ourselves to nix everything that the paper case didn't seem high-confidence, then we would never make a lot of the important bets that have been really important over the history of the company. John Collison: When did you take over? Susan Li: November 1, 2022. John Collison: Okay. Yeah, so I think the… Susan Li: Good timing. John Collison: …the day you took over, the market cap troughed at $230-ish billion dollars? Susan Li: A real sign of market confidence in me, as you can tell. John Collison: You probably remember what the number was, but I think it was around $230 billion dollars. And so that means the day that you took over as CFO, one could have bought Facebook—sorry, Meta, excuse me—as an investor for three times 2025 net income. And that's like coal plant territory. A very easy way to make money is to buy good and growing businesses for three times net income. Susan Li: Well, I hope you did. John Collison: 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? Susan Li: Well, there's a bit here, by the way… Someday I'm going to ask you how you feel about having public market investors someday, and when will that day be? John Collison: This is my interview. Susan Li: But more to the point. That October 2022 moment happened at a…There were multiple things going on, if you rewind the clock. There were two big revenue headwinds. One was that the platform changes with AT&T had rolled through from 2021, which was when it plunged. John Collison: This was Apple changing their policies around what tracking was permissible inside of apps. Susan Li: 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— John Collison: People were buying fewer color-changing umbrellas. Susan Li: Sadly, for the children of the world, yes. And so both of those things had the effect of unfortunately happening for us at the same time. So we really 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 those stars 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 underlying business, which is, can we show the best possible ads to the right people at the right time across the surface of consumer experiences that we are building, that continued to be very strong. 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 meaningful and very quick way. John Collison: Speaking of that, you have to explain the free cash flow hat. Thank you for the hat, by the way. Susan Li: Oh, yes. You're welcome. Everyone really should have one. I think they are underworn out in the world. The joke or the story is that Mark, at one point, gave me an EBITDA hat, which was a very kind gift from him to help me— John Collison: It really sends a message. I hope you went and prominently wore it in many of the budgetary review meetings that you were in. Susan Li: I did. John Collison: “This is the EBITDA hat that Mark gave me.” Susan Li: Yes. And I had it in 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 through our financials, and I didn't want Mark to misinterpret and feel like EBITDA was going to be the end-all-be-all financial metric for us. 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. John Collison: Charlie Munger had the joke that anytime you hear “EBITDA,” you should substitute it with “bullshit earnings.” And so you, similarly, for a CapEx-intensive business, you want to make sure people are not forgetting about the CapEx. Susan Li: Yes, exactly. John Collison: 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. Do we continue spending this fraction of revenues on CapEx over a 5- or 10-year period? Do we somehow get some kind of amazing compute gains? Are we ultimately 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? Where does CapEx go at an industry-wide level? Susan Li: 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 the drivers of the way we're investing in CapEx today. Of course, we have, first of all, just a massively scaled consumer business and core 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 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 if you use those models to build great and scaled consumer experiences, then how much inference compute are you going to need on top of that? If 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 one extremely large cluster somewhere, and that will help with a lot of the energy and other challenges. So there's some question about 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? I think a lot of us have different backup use cases. So, up to some point, we would use a lot of compute very happily still 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 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. John Collison: When you say the primary versus backup use case, the primary use case is new products like Llama and stuff, and the backup use case is ads optimization? Susan Li: Yes, exactly. John Collison: You mentioned just doing earnings. Is there a specific anecdote that you can or want to discuss? Susan Li: In the October 2022 period… So, we had an earnings call at the end of October, and as usual, I'm doing investor callbacks. And the investors were not shy about their feedback. And in fact, one of the calls— John Collison: Investor callbacks, I don't know what this is. This is where you call the… This is like one-on-ones, basically. Susan Li: Yes. It's pretty standard after earnings calls, where you touch base with some number of your largest investors. Sadly, it is not one-on-one, it's 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." John Collison: Wow. More of a comment than a question. Susan Li: Yes. It was actually very memorable. John Collison: And it was blunt feedback, I presume. Susan Li: 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 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? Why don't I just wait for your phone equivalent, your scaled consumer product to come out and invest in you then, and you tell me that that's going to be years away?" And the way that question was framed actually really stuck with me, and 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 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 be ready to succeed before I come?" We need people to invest with us along the way. When we think about the financial outlook of the company, a large part of it is not just, okay, cool, you're building the next massive platform out here in some decades, it’s, why would you hold our shares until then? What do we need to keep delivering in terms of consolidated results? John Collison: 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 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 it's “We're good at scaling social products.” Susan Li: Yes, I think there's definitely an interesting point there. As part of not wanting to miss the boat, we built out enough capacity for Reels, but also for future things. And we found that we were in fact able to put that capacity towards very good use, exactly as you said. So I do think an interesting question in the future will be allocating compute as a resource. It's a muscle we built later as a company, because we 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, 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… John Collison: You have no shenanigans. Susan Li: …for it to be very fungible. Because 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… So it's actually quite a bit more difficult to account for 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? John Collison: You allow people to trade between people and GPUs, right? Susan Li: 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. John Collison: 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. 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 at more established companies, like a Stripe or like a Meta, that just have a larger install base? Susan Li: When we think about AI for productivity at Meta, I think there are two dimensions. 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. 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 checklist-able set of things. And yet I get multiple expenses every day. So how do you take that part of people's jobs… John Collison: Do you ever get really funny ones? Susan Li: Those are concerning, yes Some of them have taken me down some really interesting rat holes. 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, there are actually things we don't do enough of today because right now they're pretty low-ROI to do. The 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 verifying that you're a real person, you have real friends in the platform, all those things. John Collison: It's a hard problem, yeah. Susan Li: It's a hard problem. If we could actually make that more efficient and more productive and enable a currently human reviewer or customer service agent to go from reviewing–I'm making up these numbers—but 5 a day to 50 a day, 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… I think everyone is worried about the world where the machines 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. John Collison: And makes new kinds of things possible that weren't economic or just not possible before. Susan Li: Yeah. John Collison: I've kept you for way too long. Thank you. Susan Li: Thank you so much for having me. I really look forward to seeing that free cash flow hat everywhere in the wild. It is the perfect photo accessory. John Collison: There we go. It's a good look. Susan Li: And it's green. John Collison: And it's green. Exactly. Thank you. It's very culturally on brand. Susan Li: Yes. John Collison: All righty. Susan Li: Thank you.