Okay. I know everyone's still getting settled, but in the interest of time, we're going to keep progressing. It's my pleasure to host Susan Li, the CFO of Meta Platforms. Susan, thanks so much for being part of the conference. Thank you so much for having me. Okay. I do have to read the safe harbor first, so please, everyone, bear with me. Some of the statements made today by Meta may be considered forward-looking. These statements involve a number of risks and uncertainties that may cause actual results to differ materially. Any forward-looking statements made today by Meta are based on assumptions as of today. Meta undertakes no obligation to update them. Please refer to Meta 's most recent Form 10-Q filed with the SEC for a discussion of the various factors that may affect actual results. Okay. The very safest of harbors. Yes. We are fully safe harbored, up now. Susan, the company's been on a significant journey over the last three years since you took on the role of CFO. Why don't you talk a little bit about, at a high level, the balancing of where the company wants to go in terms of investing for growth, achieving scale across multiple opportunities, but also driving efficiencies across the business at the same time? Yes. Great question. I took on, I've been at the company for 17 years, which feels like a real lifetime, but took on this job in November 2022, which some of you may remember as being maybe like a local minima in the financial trajectory of this company. It's been a kind of a difficult, but I think also exciting in many ways, sort of climb from there. Looking forward, we are excited, frankly, to have a portfolio of opportunities and investments that kind of span the range from your sort of near-term, very measurable, we've got very robust instrumentation for how a lot of our core ranking and recommendations work pays off, both in terms of the benefit to core engagement, the way we think about user engagement, and of course, the benefit to monetization. There has been, for a long time, an ongoing pipeline of projects there. About a year ago, in the 2025 budgeting process, we're now beginning to kick off the 2026 budgeting process. Budgeting process, we had a pretty big portfolio of apps from those family of apps and monetization teams. We funded a lot of those. I think one of the things I had actually said about the time, when you're looking at this portfolio of asks, each ask makes a lot of sense. You're like, "Okay. Great. You need 25 engineers. They're going to build this thing, and this is what we think the return will be." The thing that's a little bit unknown is, but like where are you on the marginal curve of returns right now? Sure, maybe we know each 25-engineer unit of work is going to generate some amount of return, but what happens if you add like 1,000 engineers? How quickly does the curve drop off? I'm happy to say, I think we funded a lot of those investments, and a lot of them have been paying off for us. We've been seeing that in our results. There's still a lot of great work to do there that's kind of an ongoing pipeline. We also have, in the medium and further term, a lot of really exciting work that is happening also. That includes the big bets that we're making in the AI, not the core AI, but the generative AI landscape, the work that we are doing to build frontier models. I think there's been a little bit of reporting around some of the work that has coalesced over the summer, and then what we hope to use those models to do and to build and to kind of take advantage of the distribution platform and data flywheel that we have when we're able to put those experiences in front of a lot of people. In the even longer term, sort of building the, if that's sort of the foundational AI model, building what we hope will be the foundations of the next computing platform, how do you bring those AI experiences out with you into the world? Obviously, glasses are, in their current incarnation, a very premature but exciting sort of form factor in terms of what you can take with you into the world and how that evolves over time. We've got things on all of those time horizons and with all of those levels of certainty and measurability. There's no sort of magical formula that links them all together in terms of how you think about how you fund today's roadmap, how you allocate your resources across that portfolio of projects. We have been mindful. I believe Mark has publicly committed to delivering operating profit growth. I realize that is not standalone a benchmark that is extremely exciting and that in practice, we in fact have to make sure that over the long run, we are an attractive investment relative to any of the many other public equity investments that are available to all of you. There will be obviously lumps in the years. It would be a truly amazing thing if you could just deliver nice linear compounding returns in a predictable way forever. We're committed to making sure that we deliver attractive financial returns over time and we think about managing the portfolio of investments in that way. I will add one more thing because you mentioned AI-driven productivity gains also. This is a place where we are really, I think, we recognize that the tools and technologies are evolving very, very quickly. We are really trying to push our own internal teams to become first adopters of a lot of the tools, to figure out how to make their teams substantially more productive in whatever the area of work is. I think actually it's unclear to me how that impact will net out. You can imagine that there are teams like if each of your engineers can produce twice as much product impact because the AI tools have made them twice as productive as they previously were, then we should probably hire a lot more of those engineers. There are other areas around the company where I think this will be more of an efficiency gain than something that multiplies the volume of output that you're able to produce. It will differ by group. We are leaning very hard into it. I suspect many of our performance reviews are increasingly written by AI tools and, for the better, probably. Both in terms of not only saving people time, but probably more, frankly, comprehensively reading all the reviews that are written, looking at the different system metrics, diffs, committed, all of those things, and probably producing kind of a better holistic view. You've got a lot of opportunity even in the really mundane stuff to make processes better and more efficient. Of course, as a software and product development company, the number one thing we care about is just how do we make the process of building consumer products and experiences as efficient and productive as possible. Got it. Okay. There was a lot in there, and I think we're going to touch on some of those topics as we go through the conversation as well. Maybe turning to the core advertising business, you've pretty consistently outgrown the digital advertising industry in over the last 12, 18 months, despite your scale. When you think about the algorithm of growth going forward, what are some of the building blocks that could sustain advertising growth, and how do you think about them as either being impression-driven versus pricing-driven? Okay. I'm going to do my best to—you can call me out on time. My high school English teacher told me that brevity is the soul of wit, said Polonius, and I have never been able to internalize that. You can cut me off when you need to. Kind of in the core business, right? If you think about the building blocks, you've got on the supply side, you've got basically users of the platform, you've got the amount of engagement they spend on the platform, then our ability to monetize that ad load is sort of the traditional way. You have pricing on the demand side. On the supply side, we still see that obviously there are many markets now, especially in more developed markets where user growth is hitting sort of saturation levels. Nonetheless, we're growing users globally across Facebook and Instagram and WhatsApp, and there are still markets where we are not close to saturation yet, where there's a lot of growth to go. We have found that there just continue to be opportunities for us to improve basically the performance of our core ranking and recommendations engine that powers what you see when you use our family of apps. We've been really just happy with, frankly, the pipeline of those investments and how they pay off. We talked about this a little on the Q2 earnings call, but I think ranking optimizations helped drive another, I think, 5% lift in time spent and 6%—it was 5% on Facebook, 6% on Instagram. There's a lot of work that we're doing to continue to really try to make your experience more personalized to you, to make it more adaptive to how you are engaging with whether it's Facebook or Instagram, whatever part of the product that is your wheelhouse, to make it most relevant to you as you're using it, to adapt to your behavior on the personalization side. We're also doing a lot to try to make sure that we are surfacing really the most timely and freshest content. That's especially important for newer and younger creators who are creating content—something that is maybe a meme about something that just happened in the world. That's going to be really interesting and funny for 24 hours, but it might not be for three weeks. If you want to help that creator be able to break through, you've got to do it immediately. You can't sort of hope it percolates through the system and gets to you like four weeks after the event happened. Really trying to make sure that we are helping sort of make sure content recommendations are very timely. That, again, is particularly good for creators also. There is just, I would say, a lot more work to be done on the core engagement side. With ad load, it is also a story of personalization and of increasingly trying to infer when you are using our product, when you are in a session, are you interested in buying something? Are you in a commercial state of mind? If I just bought, you know, like binders and rulers, this is back to school, is top of mind, now is a good time for me to buy like notebooks and protractors and calculators. That is a great time to show me more ads. There will be times when I'm clearly scrolling through friends and family content and probably not thinking about shopping. That is a good time to show me fewer ads. That enables us, without any meaningful sort of engagement impact, to really optimize the impressions that we show you and increase the value of those impressions. There is a lot of work that's being done, I think, to really make ad load, you know, it's gone away from kind of like 12.5%, the one in every eight stories of an ad, to something that feels really, really sort of tailored to when you are most likely to want to have a commercial experience. That is all on the supply side. On the demand side, reported CPM is really an output of the work that we are actually doing to drive prices down, right? All of these sort of efforts that we have to improve the performance of our ads, really what we're trying to do is make any individual ad convert more frequently and to drive higher value conversions, right? Those are the two things that they're really trying to do. If we're able to do that, then even as we are bringing the cost per business objective, the cost per acquisition, whatever it is that the advertiser is looking for, even as we bring that cost down, you should see reported CPMs go up because we are making each impression convert more frequently and be more valuable. That is a place where it is a little bit confusing when you think about what does rising prices mean for us when we report rising CPMs. That's often not reflective of what the fundamental sort of cost per acquisition is for advertisers. It's something we're able to measure on our side and try to normalize across different conversion types, obviously, like mobile app installs and, you know, e-commerce purchase are very different value conversions. When we normalize for those, we feel really good about our ability to drive, again, the cost per business objective down and ROI up for advertisers. Maybe just one more on the core advertising business before we keep moving along. Probably the number one question I get from investors is just the role that AI plays today in driving outcomes in the advertising business and how that's going to evolve in the years ahead. You've launched a number of products that have AI at their core in the advertising solutions. Talk to us a little bit about what you're building to and how should we be thinking about that as a scaling effort in the years ahead. Yeah. Okay. This has the, you know, potential to be an epitome on its own, but we're going to restrain the Beowulf here and really try to keep it pithy. There's a lot of work on the back end today. We have a very complicated ranking and recommendations back end that is separated very broadly into, you know, we think of the ads retrieval stage where there are tens of millions of possible ads for any individual person. We basically have machine learning models that take that and retrieve several thousand to send them into the ranking stage. In the ranking stage, we figure out what is the right order and time and sequence in which to show you those thousands of ads, and then actually deliver the ad to you. There are some other things that we factor into, you know, advertiser bid, the estimated impact on sentiment, things like that. The models, you know, there are very complicated machine learning models that power both of those. We've talked about them, I think, a bit on earnings calls, and Andromeda is the name of the model that powers the ads retrieval. GEM is the name of the model that powers ads ranking. In both of those cases, there's been a lot of work done to basically refine the models, scale up their complexity, enabling us to retrieve more ads and rank more ads at a similar degree of efficiency as we have in the past. That helps make sure that the ad is more relevant to the individual user. In each case, these are things where, you know, in the case of Andromeda, I think we've rolled it out mainly across the Facebook surfaces in Q2, mobile feed and Reels. Now the forward-looking work is to roll it out across Instagram. We're scaling up the complexity of GEM. There's what we call the Meta Lattice architecture that we use to broadly scale our models from individual surfaces and objectives, which is how they all get developed, to trying to make this basically a more like a global model that is ranking and doing ranking and recommendations work across all surfaces and objectives at the same time instead of these focused individual models. There is a lot of work that's happening on the ads back end to continue powering the growing complexity of those models. I think over time, we're also going to find that we will use more LLM architecture to think about how to power ranking and recommendations work. That's relatively newer for us, but that is another, much earlier stage project that I think has the potential for a lot of upside in the recommendation landscape. On the front end, from the advertiser experience, advertisers right now come in. Advantage + is the name of the front-end tool that we make available for advertisers to create their ads. That is a tool where there is a lot of AI-powered automation to basically try and streamline the ads creation and campaign process as much as possible. You know who your audience is. If you think about the history of targeting very specific demographics, now we basically try to serve your ad to the most likely and relevant audience base, and you don't have to tell us that much about who they are. We're going to be able to make those inferences for you. Setting budgets, how you allocate your budget across different campaigns and the campaign set, things like that. We're trying to automate all of those things. Finally, I think the next frontier is using GenAI creative tools to make the creative. Right now, in Advantage+, image expansion is our most commonly used GenAI tool. It's basically, if you upload a creative, you've probably made one or two. We are going to figure out how to size this to all the different possible ad formats that we have so that you don't have to figure out how to upload 12 different ads. You upload one, and we expand the pixels as needed to fill space and so on and so forth. Text translation is another popular one. I think the next frontier for us is video generation, taking a still image and using it to generate a video, in particular because still ads don't feel native in Reels. If you're in Reels and you hit an image ad, it's actually a little bit of a jarring experience. A video ad feels very seamless, feels like this is just part of the Reels experience. Video generation, I think, is kind of the next frontier. That's the place where a lot of active work is happening right now, frankly. In the longer run, the two sort of things I'm most interested in, excited about are, one is sort of ads becoming more interactive with you. I think that's not something we see at all today. I think that'll be super interesting and part of a general trend of content becoming more interactive with you. The second thing is just the idea that ads can be super, you know, super tailored to each person without the advertiser knowing that, you know, you and I could get the same, like a hotel in Hawaii could target you and I with the same ad. It's just trying to reach you and I and get each of us to go to this hotel. We know that you should get an ad that's oriented around big wave surfing, and I should get an ad that's oriented around hiking. It just creates those for us because, you know, it knows that those are our interests, and the hotel doesn't have to. We do all that on behalf of the advertiser. That's the goal. All this is to say the arc of the advertising universe is long, and I think we're still pretty early in what's possible. Okay. Continuing the theme of AI, you know, Mark has laid out his vision for how the company's focus around superintelligence might evolve in the years ahead. Talk a little bit about how this company is uniquely positioned to execute on that vision, and then some of the challenges about delivering compute capacity and personnel to deliver on that vision. Yes, this is sort of one of the most, I mean, frankly, this is just an extraordinarily exciting time to be working on this problem. I feel really very lucky to get to be a part of this. I think clearly the rate of evolution of the way tech, the frontier models are evolving, is very fast. The capabilities that we are going to see applicable to our everyday lives, productivity, and other use cases are going to be tremendous. Obviously, we're very excited about scientific and economic advances that I'm very hopeful for. For Meta specifically, a really interesting angle is how do we make this experience very, how do we make these technologies very applicable to you personally and your goals and your creative output and the way you express yourself and the way you share with the world? We think of it as a very personalized experience. That is rooted in the fact that today we deliver to billions of people around the world an extremely personalized experience. Each of you using Facebook or Instagram has a totally different set of content that is being shared with you than any of the people around you, right? For us, the idea that the AI experiences we build should be an extension of that means it should be a very personalized experience to you. How do we make the content that you see more interesting, more interactive, more engaging? How do we give you the tools to create whatever it is that you want to create and put out into the world? How do we enable you to more productively engage with the people you care about engaging with or undertake projects that you're excited about? For us, the landscape here is we really think about the AI efforts through the lens of building deeply personalized experiences. Now, in terms of what it takes, we've got, I think it takes talent. It takes compute, data, distribution. I think increasingly it takes a lot of capital. Those are all things that I think we have. It is a very exciting and competitive landscape, frankly, in terms of all of the folks who are engaged in doing this work right now. Specifically, I'll say, you know, on the infrastructure side, we are finding that the sort of amount of compute that we think it will take to do pre-training and distillation and post-training and enable us to build frontier models, our sort of ability to see what we need there is that we need more compute, and then we want to be prepared for what the inference sort of use cases will be. We think we are sort of at the forefront of this. We've got some big, exciting infrastructure projects going on. Our first gigawatt plus cluster is going to come online next year. We have called that Prometheus because we've got a lot of history nerds at the company. We have a 5 GW project that is coming online that will have the ability at least to scale to 5 GW if we need that. On the talent side, I'm sure probably a few of you have seen a few headlines here or there about our team-building efforts over the course of the last few months. We're very excited about what we call TBD Labs. It's a placeholder name that's sort of stuck and feels actually very appropriate, in the sense that I think a lot of what the team is going to build is yet to be precisely shaped or determined. It's a pretty small, few dozen people, very talent-dense set of folks. They are kind of working on the next generation of foundation models, and we hope that those will be at the frontier over the course of the next year or two. We're pretty excited about all of this coming together. Okay. One more big-picture one, and then we'll go into sort of rapid-fire mode. How central is Meta AI to your AI efforts, both within the family of apps, existing applications today, and how it informs what Reality Labs might produce as computing experiences longer term? Meta AI is definitely sort of, it's definitely an important sort of part of what we're building. It's pretty interesting. Meta AI is not today powered by a frontier model, and yet, it is very widely used. We find that there are a lot of really interesting use cases on it, and we find that the experience improves a lot every time we improve the underlying model. We're very optimistic about the kind of trajectory ahead of us there, and especially, again, as we build frontier models. I think the notable thing about it, you were asking about Reality Labs, is the sort of, what is the form factor by which you are going to carry AI technology with you into the world? I think for us, the glasses form factor seems very intuitive. It is the best way for AI to replicate the experience of what you are seeing and hearing and doing for it to be able to interact with you, whether it's talking to you, for example, as you are moving about in the world. This is a place where, obviously, I think there will be other interesting modalities that get developed over time, but glasses feels like a very intuitive one. There are like 1 billion glasses wearers in the world today, not counting sunglasses wearers. There's already, it's a very normalized experience, and it seems obvious that at least for, for example, those people, it should make a lot of sense that they might choose to switch from regular to smart glasses. I may have to bring, I wear contacts because I'm like blind as a bat, but I may have to bring glasses back. I would say the AI experience on glasses right now is pretty nascent, but there are a couple of things we are, and as it is today, which is to say a pretty limited AI experience on glasses, RBMs are doing super well. We are having trouble keeping them in stock. Frankly, we're trying to ramp up supply for the second half of the year. Growth in RBM sales accelerating Q2, and we're happy about those. We have new models to announce. We'll be sharing some of them at Connect, the Oakley Meta Houstons, for example. Those have ultra high-def video, better battery life. It's a more sports and performance-oriented glasses. If you really need to capture what the experience of hurdling downhill on skis looks like, those will be the glasses for you. We are pretty excited about that, about the portfolio of glasses as they exist today, and they're just going to get better because the AI experiences will get better. The live translation that we just rolled out for English, Spanish, French, and Italian, I think, that's in my mind what the future was supposed to look like. You wear glasses and you talk to someone in one of those languages and you hear what they're saying, and in my case, it would be English, and they, if they have glasses, can have the inverse experience, or if they have a phone, then they can read what you're saying translated into their language on the Meta AI app. I think this is a place where the sort of forward-looking developments are going to be really exciting. Okay. Quick two-parter. You gave a framework around investments on the last earnings call for the next 12 to 18 months. Mark made some comments at the White House about spending $600 billion in the U.S. by 2028. How should investors think about that broad framework of what you want to invest? The second part would be, how should investors think about earning a return on that investment cadence in the years ahead? Yes. The way, you know, that we talk about these things reflects accurately that one of us is a CFO and one of us is a, you know, builder and tech visionary, who runs one of the largest companies in the world. There has been a lot of excitement about Mark or questions too about Mark's comments. Just to clarify, Mark's comments are referring to sort of the total envelope of our planned U.S. investment from this year, including 2025 through 2028. That includes, obviously, all the data center infrastructure that we are building in the United States, but it also just includes all the investments that go towards supporting our U.S. business operations, all the people we hire in the U.S., where our biggest offices are. That's what that is referring to. Obviously, we don't have a perfect crystal ball, but that's kind of the best line of sight we have today, in terms of what we think we're going to be spending in the U.S. Okay. Just the second part of the question, when you think broadly around some elements of the return profile, just curious because you referenced earlier operating profit growth, how should we be thinking about return profile if there's a prism that you want to share? Yeah. I mean, you know, we don't have anything I think meaningfully more specific to share today. I think the most important parts of the framework here for us, again, are that we have this sort of balance of nearer term, higher certainty, very measurable projects. We have these sort of medium and longer-term sort of portfolio of things that are less sort of high fidelity in our ability to build a revenue forecast and timeline today. At the same time, you know, we think of those in a little bit more of a VC style, like what is the set of opportunities we could unlock? If you have to unlock all of them to make the investment work out, then that's probably not a great investment. If it's a place where a probability-weighted set of returns that seems achievable is going to justify the investment you're making, then that seems like a sort of reasonable path to embark upon. We really weigh all of those things in kind of a cohesive, like, can we do this? Do the near-term sort of high ROI investments today that we have give us the right to continue investing in these longer-term, more uncertain projects? How can we navigate that over the upcoming years and do that in a way that continues to deliver solid financial returns? That's really the framework, not like a margin target in part because if we evaluated new projects relative to our existing business, a lot of projects would frankly look not as attractive. We're really focused on growing operating profit dollars more than any other metric and doing it in a way also making sure that we return capital to shareholders in a thoughtful way, offset the equity dilution that comes from compensating our employees and continuing and growing our dividend program over time. Maybe I'll squeeze just one more in on CapEx because there have been press reports around the company possibly partnering with external parties to look at elements of funding some of the capital needs of the company in the years ahead. What's the framework we should be thinking about, about how much you need to build this yourself rather than look across an array of partners to possibly deploy capital and build capacity against the longer-term vision Mark's trying to build to? Yeah. For most of our history, we basically built O&O data centers. Now, as the ambition of our infrastructure capacity unfolds ahead of us, it kind of dwarfs what we've built before. We need to be more expansive in the way that we are thinking about this. We haven't announced any particular transactions yet, but we are looking at partnership opportunities and financing structures that will enable us to achieve some of the flexibility that we are looking for in the longer-run timeline of these data centers. The fact that there's a lot of unknowns over the 20-year life cycle of a data center and how it might be used over that 20 years, while also being an attractive project, obviously, for investors. We are looking at some structures there. We are also looking at more traditional, frankly, like cloud leases. I think the next speaker may be able to opine more on that. That's a good business to be in right now. There is a whole range, I would say, of financing options from our own balance sheet on one end to fully leased on the other end, and we're kind of looking at everything. Susan, thanks so much for making yourself available. I really appreciate the opportunity to have the conversation. Please join me in thanking Susan and Meta for being part of the conference. Thank you.