# Meta CFO Susan Li on headcount vs. GPU allocation, “free cash flow” hats, and almost becoming a PM - **Show:** Cheeky Pint - **Published:** 18 Jun 2025 - **Source:** https://share.transistor.fm/s/8a8f48cb - **Transcript:** https://podcasts.opennash.com/shows/cheeky-pint/meta-cfo-susan-li-on-headcount-vs-gpu-allocation-free-cash-flow-hats-and-almost-4e68bf/ - **Provider:** OpenNash Podcasts (machine-generated) ## In brief 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. ## Key insights 1. **Meta funds long-shot bets by asking what they must be worth** 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. 2. **Two outside shocks hit Meta's revenue in 2022** 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. 3. **Investors asked why to buy before the bets pay off** 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. 4. **GPUs are much harder to budget than headcount** 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. 5. **AI's near-term value is cheaper formerly low-ROI work** 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. ## Questions this episode answers ### How does Meta decide whether to fund a risky project like Reality Labs when it can't forecast revenue? 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. ### Why did Meta's stock fall so low in late 2022? 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. ### What is the story behind the 'free cash flow' hats? 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. ### Why is it harder to manage GPU spending than hiring? 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. ### How does Meta expect AI to change its own workforce? 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. ## Facts - **Susan Li** [0:00:52]: "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** [0:01:22]: "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** [0:02:12]: "He, for people who don't know, he's been leading tech investment banking at Morgan Stanley for 20 years." - **Susan Li** [0:03:36]: "I joined as an IC4 in finance. So really actually pretty far away from the center of the" - **Susan Li** [0:04:24]: "So I started off my career doing mostly revenue forecasting, which was kind of like the mathiest part of finance." - **Susan Li** [0:04:54]: "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** [0:06:10]: "Mark has done all hands for all of those 17 years and he clearly has become now a truly excellent public speaker" - **Susan Li** [0:07:50]: "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** [0:09:59]: "you doubled ARPU between 2015 and 2020. And then you doubled it again between 2020 and 2025." - **Susan Li** [0:11:36]: "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** [0:15:08]: "I think the day you took over, the market cap troughed as $230-ish billion." - **John Collison** [0:16:31]: "It was Apple changing their policies around what tracking was permissible inside of apps." - **Susan Li** [0:16:50]: "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** [0:17:53]: "the story is that Mark at one point gave me an EBITDA hat which was a very kind gift from him" - **Susan Li** [0:18:20]: "I realized pretty quickly that we actually as a company shouldn't be wearing free cash flow hats instead" - **John Collison** [0:18:57]: "Charlie Munger had the joke that anytime you hear EBITDA, you should substitute with bullshit earnings." - **Susan Li** [0:20:13]: "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** [0:20:13]: "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** [0:21:36]: "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** [0:22:13]: "And in October 2022, for the first time, there were sometimes no questions." - **Susan Li** [0:25:30]: "And headcount's really easy to account for because you have org charts." - **Susan Li** [0:25:30]: "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** [0:26:28]: "people almost never trade for compute for exactly the reason I described, which is that if they get allocated 100 new headcount" - **Susan Li** [0:27:25]: "I say that as a person who is like a very expensive machine learning model for approving expenses" - **Susan Li** [0:28:17]: "everyone knows someone who has gotten locked out of their Facebook or Instagram account." ## Transcript **[0:00:00] John Collison:** The joke people have about Instagram that the ads are better than the content. **[0:00:02] Susan Li:** 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. **[0:00:16] John Collison:** You can have shenanigans. **[0:00:17] Susan Li:** For it to be very fungible. **[0:00:20] John Collison:** 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? **[0:00:51] John Collison:** What's going on there? **[0:00:52] Susan Li:** 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. **[0:01:17] John Collison:** 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. **[0:01:22] Susan Li:** 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. **[0:01:47] Susan Li:** And the equivalent of an HRBP basically got the intention of everyone on the trading floor. **[0:01:53] John Collison:** Because this is investment banking, which is known for being an inclusive and nurturing culture. **[0:01:57] 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 sort of beginning your career on Wall Street, you know, by being mortified. **[0:02:12] John Collison:** 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? **[0:02:36] Susan Li:** 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. **[0:03:13] John Collison:** You've been at Meta for a very long time. You joined in 2008. **[0:03:17] Susan Li:** April 2008. **[0:03:18] John Collison:** 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? **[0:03:36] 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 really actually pretty far away from the center of the... **[0:03:50] John Collison:** A few rungs down from the CFO. Yes. **[0:03:52] Susan Li:** 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. **[0:04:24] Susan Li:** 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. **[0:04:54] Susan Li:** 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. **[0:05:18] Susan Li:** 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? **[0:05:51] Susan Li:** 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. **[0:06:01] John Collison:** 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 **[0:06:10] Susan Li:** 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. **[0:06:30] John Collison:** I'm sure Mark already has feedback from me. Yeah. **[0:06:33] Susan Li:** 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? **[0:07:07] Susan Li:** 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. **[0:07:29] Susan Li:** 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. **[0:07:50] Susan Li:** 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. **[0:08:13] Susan Li:** 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. **[0:08:42] Susan Li:** 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. **[0:09:06] John Collison:** 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. **[0:09:29] John Collison:** 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. **[0:09:38] Susan Li:** 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. **[0:09:59] John Collison:** 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. **[0:10:16] Susan Li:** 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. **[0:10:42] Susan Li:** But here we are, you know, five years later and, you know, the ads are, you know, are even better. And I mean, I... **[0:10:49] John Collison:** The joke people have about Instagram is the ads are better than the content. **[0:10:53] Susan Li:** 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. **[0:11:04] John Collison:** Do they work? **[0:11:04] Susan Li:** 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? **[0:11:36] Susan Li:** 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. **[0:12:06] Susan Li:** 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. **[0:12:44] Susan Li:** 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? **[0:12:54] 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. **[0:13:15] Susan Li:** 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. **[0:13:37] John Collison:** So you're maybe not the... **[0:13:38] Susan Li:** 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 **[0:14:17] Susan Li:** 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. **[0:14:43] Susan Li:** 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. **[0:15:05] John Collison:** When did you take over? **[0:15:06] Susan Li:** November 1st, 2022. **[0:15:08] John Collison:** Okay, yeah. So I think the day you took over, the market cap troughed as $230-ish billion. **[0:15:18] Susan Li:** A real sign of market confidence in me, as you can tell. **[0:15:21] John Collison:** 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. **[0:15:46] Susan Li:** Well, I hope you did. **[0:15:47] 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? **[0:16:02] Susan Li:** 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. **[0:16:31] John Collison:** It was Apple changing their policies around what tracking was permissible inside of apps. Yes, exactly. **[0:16:38] Susan Li:** 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— **[0:16:47] John Collison:** We were buying fewer color-changing umbrellas. **[0:16:50] Susan Li:** 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. **[0:17:19] Susan Li:** 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. **[0:17:49] John Collison:** Speaking of that, you have to explain the free cash flow of the hat. And thank you for the hat, by the way. **[0:17:53] Susan Li:** 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 **[0:18:10] John Collison:** 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 **[0:18:20] Susan Li:** 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. **[0:18:48] Susan Li:** 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 **[0:18:57] John Collison:** 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? **[0:19:28] John Collison:** 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? **[0:19:42] Susan Li:** 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. **[0:20:13] Susan Li:** 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. **[0:20:51] Susan Li:** 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. **[0:21:16] Susan Li:** 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. **[0:21:36] John Collison:** 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? **[0:21:53] Susan Li:** 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, **[0:22:09] John Collison:** you know, investor callbacks, I don't know what this is. This is where you call the, **[0:22:13] Susan Li:** 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. **[0:22:40] Susan Li:** 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. **[0:22:48] John Collison:** Wow. More of a comment than a question. **[0:22:50] Susan Li:** Yes. It was actually very memorable. **[0:22:53] John Collison:** And it was blunt feedback, I presume. **[0:22:54] 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 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. **[0:23:25] Susan Li:** 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. **[0:23:56] Susan Li:** 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? **[0:24:16] 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 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. **[0:24:52] John Collison:** 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. **[0:25:04] Susan Li:** 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? **[0:25:30] Susan Li:** 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. **[0:25:59] Susan Li:** 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. **[0:26:28] Susan Li:** 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. **[0:26:52] 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. 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? **[0:27:25] Susan Li:** 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. **[0:27:54] Susan Li:** 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? **[0:28:17] Susan Li:** 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 **[0:28:55] Susan Li:** 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. **[0:29:18] John Collison:** 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. **[0:29:26] Susan Li:** 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. **[0:29:33] John Collison:** Yes. It's a good look. And it's green. And it's green. Exactly. Thank you. It's very culturally on brand. Alrighty. **[0:29:39] Susan Li:** Thank you.