Meta Platforms, Inc.

Q1 2026 Earnings Call — April 29, 2026

our first question comes from Brian Nowak with Morgan Stanley. Please go ahead. Thanks for taking my question. Mark, I wanted to ask you just about the level of investment you're making and sort of the signposts you're watching to ensure you're going to generate ROIC and all these investments behind Muse and the other products. So if you could just sort of let us know some of the key factors you're watching over the next 12 to 24 months, whether it's MetAI, Muse Advances, Core Algorithm. What are you sort of watching for most just to make sure that you're on the right path to generating healthy ROIC on all this CapEx and infrastructure spend? That's a very technical question for, you know, basically where... The things that we're watching are to make sure that we're on track building leading models and leading products. The formula for our company has always been build experiences that can get to billions of people and focus on monetizing them once you get to scale.

We're seeing a little bit of that here, where basically we invest in advance to build leading models, then we convert that into leading products, and then we think that these are going to be some of the most important products that get built over the next decade. So I think just like anything else that we've done over time, the basic milestones that I look at are around First, technically, are we delivering the quality to enable a great product? Then second, when you have the product, how is it scaling? And then third, you look at the monetization and then you drive up the efficiency of it towards increasing profitability. I don't think we have a very precise definition plan for exactly how each product is going to scale month over month or anything like that. But I think we have a sense of the shape of where these things need to be. And I think if you look at the usage of these and the quality of the products and the quality of the models that are out there and the use that other frontier models are getting and the trajectory of that, I'm quite comfortable that A, the lab that we're building is on track to be a leading lab in the world.

I think MuseSpark was a very high-quality model. It powers Meta AI, which I think is now a world-class assistant. We have an ability to be able to grow that and have a large amount of engagement. And over the coming quarters, we're just going to be tracking how do our next set of training runs go. How do our products scale? How excited are we about the products in the pipeline? Right now, we're very excited. And then we'll also ramp up monetization over that period of time as well. So I think that those are the set of things that I look at. I think for the kind of specific financial questions, I think Susan can jump in if there's anything more to add.

Your next question comes from the line of Mark Smulek with Bernstein. Please go ahead. Yes, thanks for taking the questions. Mark, you know, I guess now that we've got MuseSpark kind of out there launched, how are you thinking about the team's focus here kind of divided onto further model training runs and kind of further specialization and that personal intelligence goal, you know, versus product launches and kind of shipping more product out the door? And Susan, I guess kind of as a follow-up to Brian's question, I know it's too early to discuss 2027 CapEx, but, you know, we've had peers mention tonight a potential significant step up Any way to think about dimensionalizing kind of how we think about some of the returns or traction this year and how it might affect 2027 spend? Thanks. I mean, I think the roadmap from the team has been pretty consistent.

So we have the research team, which is focused on scaling increasingly intelligent models with capabilities for the specific things that we're focused on, which are business and personal agents. um so we're you know we just released our first model when i talked about in my comments how we're climbing this scaling ladder towards greater capabilities and and scale for the models that work continues we have our next set of more advanced models in training uh now and that is um uh that work will i think just continue. I mean, that's a loop. I don't think we're going to be done with that anytime soon. We're going to have teams that are just consistently focused on training more intelligent and more capable models in the ways that we want. Then we have our product team, and that team is now really unlocked to be able to build things on top of our models because we now have very strong models. So before this, we had been prototyping a bunch of things using other different models, whether it was our previous older models or kind of using the APIs from other companies. And now we're unlocked to be able to go build things and get them to scale on top of our own models.

So I think you will see that over some period of time. I tried in my opening remarks to give a... A bit of a sense of where we're going, but I think that More of the details of that will become clear over the coming months. And I think that these are just both loops that we'll iterate on. We'll keep on iterating on the intelligence. We'll keep on working on building new products and scaling the products. And then as we get to product market fit, we're also going to increasingly focus on building the businesses around them and decreasing the costs. And this is kind of how we've done everything over the last 20 years of running the company. And that is basically the plan. Mark, on your second question, we aren't providing a specific outlook for 2027 CapEx, and we are frankly undergoing a very dynamic planning process ourselves as we're working through what our capacity needs will be over the coming years. Our experience so far has been that we have continued to underestimate our compute needs, even as we have been ramping capacity significantly, as the advances in AI have continued and our team's continue to identify compelling new projects and initiatives.

And now, too, there are very compelling internal use cases. So our expectation is that compute will become even more central to the business going forward. And it will be critical to determining the quality of the models we develop, the types of products we can introduce, how productive we can be as an organization. So we're going to continue building out our infrastructure with flexibility in mind. And if we end up not needing as much as we anticipate, we can choose to bring it online more slowly or reduce our spending in future years as we grow into the capacity that we're building now.

Your next question comes from the line of Eric Sheridan with Goldman Sachs. Please go ahead. Thanks so much for taking the question. Maybe if I can build out on one of the topics that was discussed in the prepared remarks, but just the opportunity set that sits in front of the company with respect to putting agentic compute in front of both consumers and enterprises. You've long been associated with sort of the consumer landscape, and I am curious about how you're thinking about extensions of the media engagement parts of your business model and the commerce parts of the business model to become more agentic over time. But what do you see also as the opportunity set that sits in front of you across SMEs and enterprises where historically you maybe haven't had as much product velocity? Thanks so much. Thanks, Eric. So I would say, you know, in the near term, obviously, the sort of biggest focuses are some of the areas that you mentioned around deepening sort of engagement, obviously, with our existing community and user base, making ad experiences meaningfully more personalized, more engaging, more valuable. helping SMBs find and engage with customers across our platform.

Those are some of the, I think, most intuitive and adjacent opportunities to the business that we have today. And then, of course, as we are able to build out more agentic capabilities, you know, enabling customers agents to help people be more productive, but also agents for businesses and enabling, frankly, those agents to interact with each other and build what we hope will be a thriving commerce ecosystem on our platform. So, you know, i would say some of these are are a little bit further out you know especially in that latter category of things again the focus is on building personal super intelligence you know building a consumer agent that can work for you and help you get things done um that right now is a consumer experience that we're focused on but we think there will be clear monetization opportunities over time you can imagine commission structures or a premium offering And on the business side, we're seeing a large opportunity, of course, around agents and scaling our business AI initiatives. I think I mentioned earlier in my remarks that there are over 10 million weekly conversations between people and business AIs on our messaging platforms.

That's up from 1 million at the start of the year, and we're going to continue expanding globally in Q2. And business AIs today are currently free for most businesses on our messaging apps platform. but as we make more progress, you know, we expect that we will also work towards establishing a longer-term monetization model, and we'll also consider other services that we can offer to businesses in the future, but we don't have anything more to share today.

Your next question comes from the line of Yusuf Squally with Truist Securities. Please go ahead. Great. Thank you very much for taking the question. It's going to be one for Mark and one for Susan. Mark, Ray-Ban Oakley AI glasses continue to perform really well for you guys, but SLR Luxottic owns and manages a lot more brands. What are the gating factors to see the launch of additional glasses under these other brands this year and what would be a successful year for you as you look back at 2026, maybe in terms of units sold? And then, Susan, on that 10% rift, how much of that is due to efficiencies for maybe AI implementation versus just the need to stay fit? And as you look at your employee needs over time, how do you see that growing maybe relative to your overall top line growth? Thank you very much. I can go ahead and take both of those. I might answer your second question first. And I'm just trying to make sure I got all of the parts of the question. So in terms of what these sort of, you know, kind of the, the, the optimal size of the company, I think over time, We don't really know what the optimal size of the company will be in the future.

I think there's a lot of change right now with AI capabilities advancing rapidly. We're very focused on leveraging AI tools to substantially increase our productivity, and we're seeing that reflected in the accelerating output from our engineers. And we're generally approaching this with a bias toward wanting to use these tools to build even more products and services than we would have before. At the same time, we're making very significant investments in infrastructure, and we are very focused on continuing to operate efficiently. So I think we will be continuously evaluating how we're structured just to make sure we're best set up to deliver against our priorities over the coming years. So that is, I think, your second question. The first question was about the AI glasses. We're continuing to see strong growth in AI. Obviously, the AI glasses sales over the course of Q1. Demand for the expanded portfolio lineup has generally been quite strong, and we're seeing sales shift now from the prior generation of Ray-Ban metas to the latest generation, which I think speaks to the value of the improved features like extended battery life and features like higher resolution video capture.

So we're pretty excited about the progress we've made with glasses. We see strong interest now in the meta-ray band displays with the meta-neural band. So that's an encouraging sign that there's consumer appetite for display glasses, which is kind of the next generation of how this product evolves. And yeah, so I think this is an area that we will continue to be excited about and are investing in.

Your next question comes from the line of Justin Post with Bank of America. Please go ahead. Great. Thanks for taking my question. Mark, it took about 10 months to get you Spark out. I think it's a pretty good pace. Just help us understand what kind of unlock that is for some of the new products you're developing and how's the product cadence going to be over the next nine months on either consumer or business enterprise products built on top of that model? I mean, the field is moving pretty quickly. So, I mean, I'm very happy that we're, I think the lab that has gone the fastest from standing up the lab to having a very kind of widely accepted as strong model. Um, so I think that that's good. I take that as a very significant validation of the effort that the team is working well together, that the infrastructure is working, that, uh, that the effort is on track. I think that that's basically the main thing that we've learned over the last quarter, uh, that, that I would take away is like where, um, And we started, what is this pretty big bet? And it's on track for our plan.

In terms of what exactly the cadence is going to be, it's tough for me to say both because I don't really want to share competitively sensitive information. And because I think some of the stuff we are more focused on quality than hitting a specific date. I mean, on the research side, this is research, right? We are trying novel things. You don't exactly know when they're going to land. And on the product side, I think we care a lot about just having, I mean, let me put it this way. There's a lot of agents out there, right? That people are building for different things. And I, there aren't that many that I would want to give to my mother. And I think getting to that quality bar is something that I care about more than hitting a specific week for launching or something like that. But with that said, we're in a zone here where the teams don't check in with me once a quarter. We make meaningful progress day over day. I think that's part of the fun of developing in this world is that people can make very rapid progress. Small groups of people and teams can make very rapid progress. So I think we're going to see a lot of innovation.

You know, the timing of this call is, it's good in some ways because, you know, the Muse Spark release, I think, was positive. The MetAI first release, I think, is positive. I think that that shows that we're on track. I'm trying to kind of paint a picture of the very high level direction that we're going in, but I think that the picture is going to come into focus a lot more over the subsequent quarters.

Your next question comes from the line of Ross Sandler with Barclays. Please go ahead. Yeah, Mark, just sort of related to that last answer, but there's a lot of new consumer applications kind of cropping up, everything from like an open claw to something a little bit more consumer-friendly that you would build for your mom, like you said, with like Pope or Dreamer, which you recently acquired. So how are these new ideas, I guess... changing your view around the direction that, you know, core meta AI or dreamer or kind of your overall agentic strategy needs to go. And then the second part of it would be, do you think the lab will stay in this consumer lane or do you think you need, or you want to go down the route that others are going down with code writing and like the recursive self-improvement loop and, and, in that direction, kind of in parallel. Just thoughts on that. Thank you. Yeah, so look, on the OpenClaw and other agents, I think that they give you a very exciting glimpse of what types of things should be possible. Now, they're pretty rough systems today.

And to set up OpenClaw, you need to install a computer locally and then get into a terminal and configure a bunch of things that, again, there's... Maybe there's hundreds of thousands of people or small numbers of millions of people who could do that. But what we're talking about is delivering personal superintelligence for billions of people around the world. So how do you make a version of that experience that is a lot more polished and dialed and easy and that has all the infrastructure basically done for people already, and that just works. And that's kind of what we're focused on on the consumer side. And I'm really excited about that. I think if you had something like that, that worked quite a bit better than those systems and was easy enough that people could just get, then I think you go from having something that hundreds of thousands or millions of people are going to use to something that is going to be addressable to billions of people. And that has been our... primary focus from day one of the lab is being able to deliver something like that as a product. And I think it's just going to be very exciting.

By the way, the same thing is true for businesses, right? I mean, there's the personal version of this, but there's also, you know, a lot of people's goals are they want to create things, right? They want to create websites. They want to create products. They want to grow their products. These are all things that good agents are going to be able to help people do. which I think is partially why this is so exciting. And, you know, in my opening comments, I talked about how today we can handle a few goals for people. They're big goals, right? We can help people stay connected with people they care about, learn about the world. These are big things that people care about, but they're not the only things that people care about. And one of the things that I would love for our products to be able to do is just understand people's goals specifically, and then be able to just go work on them for them and check back in and whenever you have questions that you need answered. So whether those are personal goals or you're trying to create a business or do work, I think that this is stuff that I think literally every person in the world is going to want some version of it.

And also I think it is something that scales where the more you want to get out of it, I think people are going to also be willing to pay a lot of money to have premium or high compute versions of it. So I think that this is like, it's a very exciting area. But I think what you all should be waiting to see is like whether we can build the version that really like just works and how effective we are at converting people who are using our products into being hundreds of millions and then billions of people using this stuff. And then over time, how can we effectively convert that into something that's increasingly profitable by monetizing it and getting the cost down? So that's the roadmap of what we need to do. You asked about whether we're primarily focused on consumers or also recursive self-improvement. I think that we've talked about two main goals for the team. One is this kind of agents version. vision of what we're doing. The other is that self-improvement is really important because you can't build a leading AI product if you don't have leading models. And you're not going to have leading models in the future if your models can't improve themselves.

You're getting to a point where today the models are still able to learn from people and then i think at some point the models will have to improve themselves and that's how how the growth is going to an improvement in the models is going to happen and if you don't if we don't have an ability to do that then um we or anyone else i think that the companies that don't do that are not going to be leading labs then they're not going to produce leading product so i know that's like that is a table stakes thing that that we are focused on Now, does that make us a developer tools company? Not necessarily. I mean, I'm not against having an API or coding tools or anything like that, but it's not our primary focus. But I actually think people conflate coding with self-improvement more than they should. Coding is one ingredient for the model self-improving. It's not the only thing. And we are focused on all of the parts that are going to be necessary for self-improvement in service of the personal superintelligence vision that we have for people and businesses.

Your next question comes from the line of Ron Josie with Citigroup. Please go ahead. Great. Thanks for taking the question, Mark. Maybe a quick follow-up to a prior question around personal agents and business agents. With Spark News now live and more models in development, do you look at the personal agent opportunity, which we talked about earlier on in the call, more of a short-term, medium-term, long-term goal? I'm sure it's a never-ending goal, but when we see a product, is the question short or medium-term? And then, Susan, I think the ranking recommendation model improvements are are very impressive to see given the size and scale of both Instagram and Facebook. Could you help us understand just how doubling the length of user interaction sequences can drive greater usage? There's a thesis out there that maybe some of the ranking recommendation improvements are along the two. So it seems as if there's a lot more room to go. So any help there would be helpful. Thank you. I think that the agent's work, there's going to be short-term versions of it, but then I think that there's going to be massive upside for delivering more intelligence and more capabilities in the models.

And you're kind of seeing this across the industry. Each month, each generation of models, they just have more capabilities and can do more things and people absorb it and are able to get more superpowers. And it's awesome. It's like the most exciting time in the industry. So I think of the agents as the product vehicle for delivering that capability to people. And we certainly, I think this year is going to be a a key period for establishing that as the vehicle for how people are going to use this. But then the model improvement, I think, is going to be something that's going to go on for a very long time. So there's a lot to do here in both the short, medium, and long term. And then on your second question, which I think is about the ranking and recommendations improvements that we talked about in our, that I talked about in my earlier remarks. You know, I think there, you know, first of all, there is still a lot of room to continue improving recommendations over the rest of the year. And we expect we'll be able to do that to drive additional engagement on both Facebook and Instagram. You know, a couple of the things.

First, we're going to continue to improve our data infrastructure. That's going to allow our models to train on more data. And we're adding more detail to how we describe the content that users have engaged with in the past and scaling up the complexity of our model architecture to take advantage of those larger data sets, like using even longer histories of content interactions. And that should all be in service of improving the overall quality of recommendations. Okay. We also are focused on making the recommendations even more personalized and more relevant to any given user's interests. There's work we're doing to redesign our content retrieval system to show more content that matches the full range of a user's interests and to tailor the diversity of the topics we recommend to the broadness of someone's interests. So someone with particularly concentrated interests might see relatively more of a that content, while people with a broader set of interests might see kind of a greater range in the topics that we show them.

And then finally, we're continuing to make improvements to our sort of LLM-based tune your algorithm features that allow users to provide more granular natural language feedback on what they want to see more of or less of in their feed. So The sequence length, which is the thing that you called out, is one of really many improvements we made in Q1, and there is a big roadmap of further improvements going forward.

Your next question comes from the line of Doug Enmuth with JP Morgan. Please go ahead. Thanks so much for taking the questions. Mark, how do you think about the step up as you go from leveraging smaller models in the ad business to use Spark and future large language models going forward? What are some of the key unlocks across engagement and monetization? And then on Manus, can you just talk at all about the strategic importance and the role in developing agentic products for Meta and then just current status around the tech and the deal? Thanks. I'll take that question. On Manus, we're still working through the details, so we don't have an update right now. On your first question, which is about sort of the going from leveraging smaller ads businesses, smaller models in the ads business to kind of the ads sort of models growing. There's already some work underway, and I think I alluded to some of this in my earlier remarks, even kind of in the current landscape of the ads roadmap, where we're basically trying to advance the architecture here to allow us to leverage the abilities of larger models.

Historically, we haven't used larger model architectures like GEM for inference. because their size and complexity would make them too cost prohibitive. And the way we drive performance from those models is by using them to transfer knowledge to smaller, more lightweight models that are used at runtime. The inference models are bound by strict latency requirements, since they need to find the right ad within milliseconds. And that has, again, historically prevented us from meaningfully sizing up models. scaling up their size and complexity. But in the second half of last year, we introduced a new adaptive ranking model, which enables us to leverage LLM scale model complexity of a trillion parameters. And we made advances in the model architecture and co-designed the system with the underlying silicon so it maintains the sub-second speed that is required to serve ads at scale. We also developed an approach that intelligently routes requests more compute-intensive inference models if it determines that there is a higher probability of conversion, and that lets us drive both better performance and increase inference ROI.

There's a lot of work being done there before we even sort of incorporate more of the LLM work into our underlying ads ranking models. We have time for one more question. Ken Gorowski with Wells Fargo. Your line is open. Thank you very much. Two, if I may. First, you talked on the MuseSpark launch, you talked about two categories or two verticals. You talked about health and wellness and shopping. Can I dive a little bit, ask you to dive a little deeper into the latter on the shopping and commerce side? And maybe if you could, were there any learnings um and uh you know 2021-22 uh phase where uh you push deeper into commerce on instagram and on facebook any learnings from that period that you might apply uh is are these are an opportunity for a next-gen marketplace type business in in commerce and then the second please um maybe susan if you talk a little bit about based on your model improvements and the content recommendations How much visibility do you think you have to kind of the growth trajectory on the core business? You continue to grow at basically double the pace of the industry, despite being a very large share of the industry.

Could you just talk a little bit about your visibility into that continued performance? Thank you. Yeah, so I might give you a somewhat loftier answer to the question you're asking about shopping. I think it's sort of an interesting example of the way in which the work that we're doing is different than what I think others are doing out there. You know, these products, they... AI agents get better when you fully optimize the stack. That's why we believe that we need to be a company that builds frontier models in addition to building the agents. And then in order to do that, you of course need to build your infrastructure in order to be able to do that well. So we're undertaking this large investment to be able to do that top to bottom. And I think a lot of the way to think about the investment that we're making is a bet that the individual things that people care about and that people are going to be more important in the future.

And that's sort of like, I think it should be a pretty obvious thing to say, but I think so much of the rhetoric around AI in the industry is around like a company trying to build some kind of centralized thing that like does all the productive work in society in some way or something like that. And that just is very different from how we see the world. Like our vision for the future is, is one where society makes progress by individuals pursuing their own aspirations. And some people care about big, grand things like curing diseases, and a lot of people care about personal things like finding the right shirt for my daughter. And I just think that we're going to build things that help deliver this vision for personal agents for people. And I think that part of the lane and what is interesting and differentiated about what we're doing is that that's just so different from how I hear everyone else talking about the work that we're doing.

So even though I think some of these ideas, they seem like they should be so obvious, I actually think that our approach of trying to empower individuals and building consumer things is just in the details extremely different from what others are doing. And shopping might be one kind of specific example that I think is going to have interesting commercial implications. And I think people, consumers are goin

Q4 2025 Earnings Call — January 28, 2026

Analyst Brian Nowak (Morgan Stanley): Thanks for taking my questions. One for Mark, one for Susan. Mark, one is a long-term question. As you think about ramping all this investment and the personal intelligence opportunity, the metacompute opportunity, can you walk us through a little bit how you think about the largest revenue or ROIC long-term opportunities you're trying to unlock with those over the next, call it three, five, 10 years for all the investment? And then Susan, a little more near term, more like 26, I think the guide is the fastest growth you've had in almost five years. I know you have a lot of improvements on recommendations and monetization efficiency, but can you just sort of help us a little bit understand two or three of the biggest drivers of this inflection you're seeing on revenue in 26?

Executive Mark (CEO): I guess I can start with the first one. Although I have to say up front that I think my answers to a lot of your questions on this particular call may be somewhat unfulfilling, because we're in this interesting period where we've been rebuilding our AI effort. And we're six months into that, and I'm happy with how it's going. But we are going to be rolling out our initial set of models and products and businesses around that over the coming months and I will have a lot more to share on all of those fronts at that point. So I'm happy to offer kind of a high level view of some of the stuff but I apologize in advance that not much of this is going to be particularly detailed but it will be exciting as we roll it out.

I think the theme on the business, I mean this is I don't think I'm going to break any new ground here, but there are several major business opportunities that we're focused on. I think that one is just going to be improving the core products and accelerating the current business. I talked about that in terms of the connecting of the recommendation systems and the LLMs, which I think will both improve the quality of the organic experience and of advertising. We're going to see the generation of media improve the quality of content which coupled with the improvements in the recommendation systems we expect to generally accelerate the quality and effectiveness of the core business both for people who use it organically and for businesses.

There's going to be several, many, I think, new business opportunities that come up. I mean, we have been working on Meta AI for a while. I think you're starting to see some of the way that products like that get monetized across the industry. When we get that to a scale and depth that we want, we think that there are going to be opportunities both in terms of subscriptions and advertising and all of the different things that you see on that. I mean, yeah, I think there's a number of things on shopping and commerce that I'm quite excited about that I alluded to in the comments up front. And as the models launch and we demonstrate some of the capabilities, both in the first set of models and over the year, I think the models are going to get a lot better too.

We'll be able to have different products paired with those that I think will facilitate different businesses for businesses who use us and our platforms as well as direct consumer businesses. I guess it's probably also worth flagging because I don't think either of us mentioned the Manus acquisition in the upfront comments. I think that that is a good example of you have a significant number of businesses that already pay a subscription to basically use their tool to accelerate their business results. And integrating that kind of thing into our ads and business manager, so that way we can just offer more integrated solutions for the many, many millions of businesses that use and rely on our platforms is gonna be really powerful, both for accelerating their results using the existing products that we have, and I think adding new lines as well.

In 2026, we expect to deliver operating income above 2025 operating income. So to give some context on that, we are going into 2026 with strong revenue growth at the start. Of course, we are just a few weeks in, set against a healthy macro backdrop. So obviously hard to extrapolate the current trends to the full year. And, you know, there are many, many moving variables in the current landscape. We're really taking advantage of the current business strength to reinvest a lot of the revenue into what we see as very attractive investment opportunities in AI infrastructure and talent.

Analyst Eric Sheridan (Goldman Sachs): Thanks so much for taking the question. Maybe two, if I could. In prior periods, you've talked about being capacity constrained internally and not having enough compute to sort of achieve the goals you have on a platform and a product standpoint. I want to know if we get any update on currently how you think about your own internal needs for compute against that roadmap. And the second part of the question would be, as we continue to see the ads business sort of scale, especially in terms of dollar growth year on year. Have we yet seen the full first order effects of scaling the business against applying more compute to it? Or how should investors think about the direction of relationship between applying more compute and rate of change in terms of outcomes on the monetization side?

Executive Susan (CFO): On your first question, we do continue to be capacity constrained. Our teams have done a great job ramping up our infrastructure through the course of 2025, but demands for compute resources across the company have increased even faster than our supply. So we expect over the course of 2026 to have significantly more capacity this year as we add cloud, but we'll likely still be constrained through much of 2026 until additional capacity from our own facilities comes online later in the year. With that said, I think we have done a good job internally mitigating the impact of compute constraints on our business. I expect that will continue to be the case in 2026. We're continuing to focus on increasing our infrastructure efficiency in several ways, including by optimizing workloads, improving infrastructure utilization, diversifying our chip supply, and just investing in efficiency improvements as part of our core technology development efforts in areas like content and ads ranking.

So that was your first question. The second question about how the ads business scales. I don't have an extremely precise answer to this question. What I'd say is one of the ways that we are working to drive ads performance improvements is by improving our larger scale models along with our lighter weight ones that we use for ads inference at runtime. You know, we don't typically use our larger model architectures like GEM for inference because their size and complexity would make it too cost prohibitive. So the way that we drive performance from those models is by using them to transfer knowledge to smaller lightweight models used at runtime. But I would say that we think that there is room for our larger models to benefit from having more compute. And I think as we scale up the compute available to those models and the foundational models in different areas that power the different stages of ads ranking and recommendation, we expect that we will see gains coming from that.

Analyst Mark Shmulek (Bernstein): Yes, thanks for taking the questions, too, if I may. Mark, kind of with your comments that you kind of expect to see some meaningful changes in how work and things are done this year, I guess would you be surprised if kind of by the end of the year we've yet to see meaningful progress and adoption on some of the newer products and initiatives that you're launching? Or, you know, should we just be a bit more patient on the timeline here? And then, Susan, kind of with the guidance provided on why it's still expected to grow kind of faster this year than last year, let's say in a few months we realize we need more investment and resources that continue to go after the AI opportunity, but perhaps macro might be a bit weaker. How hard of a line is there in terms of the tie of investment levels to core performance?

Executive Mark (CEO): I think the first question was asking about kind of when do I expect the product impact to be? I mean, we're going to roll out new products over the course of the year. I think the important thing is we're not just launching one thing and we're building a lot of things. I think AI is going to enable a lot of new experiences. I outlined thematically a bunch of these in the upfront comments around personal AI, around LLMs, combining with the recommendation systems. I think that's a somewhat longer term research project that I think will yield dividends over a long period of time. But we're already definitely seeing optimizations of the recommendation systems as we're including more of the AI research improvements and advances into that, the content is gonna improve.

There are gonna be new formats. There are gonna be improvements on the glasses. There are all these different things, as well as several things that we think are new that we're gonna try that are not just extensions of the current things that we're doing. So yeah, I mean, I would expect that we'll roll these out over the course of the year and that sometimes it takes a few iterations for things to really hit and reach the kind of product market fit that you need. But I think we have enough time hopefully to, you know, we're starting off early enough in the year that I would expect that we'll see some successes by the end of the year on this as well as on the work side.

What we were talking about is I think it's very hard for anyone exactly to predict what the shape of how organizations working is going to feel, but I just think the fact that agents are really starting to work now is quite profound and I think is going to allow, we're already starting to see the people who adopt them are just being significantly more productive. And there's a big delta between the people who do it and do it well and the people who don't. I think that's going to just be a very profound dynamic for, I think, across the whole sector and probably the whole economy going forward in terms of the productivity and efficiency with which we can run these companies.

In 2026, we expect to deliver operating income above 2025 operating income. So this is comparing absolute dollars, not year over year growth. So to give some context on that, we are going into 2026 with strong revenue growth at the start. Of course, we are just a few weeks in, set against a healthy macro backdrop. So obviously hard to extrapolate the current trends to the full year. And, you know, there are many, many moving variables in the current landscape. We're really taking advantage of the current business strength to reinvest a lot of the revenue into what we see as very attractive investment opportunities in AI infrastructure and talent.

Analyst Doug Anmuth (JP Morgan): Thanks so much for taking the questions. One for Mark and one for Susan. Mark, could you just provide more detail on the progress of the MSL team several months in and more on your view on the path to a frontier model this year? And then, Susan, I know you expect to grow operating income in 26. Do you also expect to have positive free cash flow? Just how should we think about the current and any future JVs for data center and compute build out?

Executive Mark (CEO): I'm not sure I have anything else to add on the current progress on this. That's why I said upfront that I think this is somewhat of an unfulfilling time to be answering some of these questions. We're about six months into building MSL. I'm very pleased with the quality of the team. I think we have the most talent dense research effort in the industry and some of the early indicators look positive. But look, I think that this is a long-term effort. We're not here to do this to ship like one model or one product. We're doing a lot of models over time and a lot of different products. And I want to make sure that the work can speak for itself and also that we all internalize that this is a journey that we're on.

We're making very significant investments in infrastructure capacity this year to support our AI efforts. And we believe we're in a strong position to support them with the cash generation of our business this year. And, you know, at the same time, we'll continue to explore different paths as we build out our infrastructure capacity that help us provide, you know, that help provide us the long-term flexibility and option value that we look for as we support our future capacity needs against the backdrop of a very wide range of possible capacity demand over the years to come.

Analyst Justin Post (Bank of America): Great. A couple, maybe one for Mark and one for Susan. It just seems like you're going to have a tremendous amount of capacity. How do you think about expanding your opportunities beyond ads? Things like subscriptions or licensing cloud models, just with all the interesting things you're building. I don't expect any product announcements, but can you do things beyond ads? And then for Susan, it's really interesting to see the acceleration, even XFX in advertising. I'm just wondering if you're seeing a general acceleration in e-commerce activity, where do you think the dollars are coming from? And is the entire internet ecosystem accelerating? I'm just wondering your thoughts on that.

Executive Mark (CEO): So yes, we are focused on things beyond ads. I think the numbers make it so that for the next couple of years, ads are going to be by far the most important driver of growth in our business. So that's why as we're working on this, we have a balance of new things that we're trying to do while also investing very heavily in making sure that all of the work that we're doing in AI improves both the quality and business performance of the core apps and businesses that we run there.

Executive Susan (CFO): Justin, on your second question, we saw healthy year over year growth across all verticals in Q4 with the exception of politics as we lapped the U.S. presidential election last year. The online commerce vertical was the largest contributor to year-over-year growth. That was followed by professional services and technology. So in online commerce, year-over-year growth was strong. It was actually relatively consistent with Q3 levels, and that was broad-based across advertiser regions and sizes. In general, we saw that the demand leading up to the holiday shopping period sustained through cyber five and into the end of the year, you know, it was very healthy for us.

Analyst Ross Sandler (Barclays): Mark, you mentioned bringing Horizon World into mobile. We haven't heard much from the Horizon World Squad on these calls. So interesting that that's making it in. It seems like the combo of AI and what you guys have built with Horizon might open up the door to a bunch of new potential areas in gaming or new forms of communication. So could you just elaborate on what the plan is there?

Executive Mark (CEO): Yeah. So let me talk about the basic theme here. One core idea that I've talked about on some of these calls over the years is that people always want to express themselves and experience the world in whatever the richest format is that they can. So I talked about this up front today. When we started, a lot of this was text, right? That was the kind of the best we could do. Then we all got phones, they had cameras, like a lot of this medium became visual, but with photos. We went through a period where the mobile networks were kind of weak and every time you wanted to watch a video would buffer. And once that got worked out, now the majority of the content is video.

I think that we're going to get more formats that are more interactive and immersive. And you're going to get them in your feeds. So you can imagine this. I mean, there's obviously a lot of details to fill in on this. But you can imagine being able to easily, through a prompt, create a world or create a game and be able to share that with people who they care about. And you see it in your feed and you can jump right into it and you can engage in it. And there are 3D versions of that and there are 2D versions of that. And Horizon, I think, fits very well with the kind of immersive 3D version of that.

Analyst Ron Josie (Citi): Great, thanks for taking the question. I wanted to drill down maybe, Susan, on your comments around ranking recommendation model changes. You know, clearly lots of tailwinds here, given the results from GEMS, Andromeda, Lattice, consolidation of models, et cetera. So can you help us understand a little bit more just about the roadmap and where we stand within ranking recommendation model changes?

Executive Susan (CFO): Yeah, thanks for the question, Ron. You know, we have, I'm just, I was sorting out if your question was more specific to ads or if it was more specific to kind of the engagement side, but I'll try to talk a little bit about both. So on the sort of core engagement piece, you know, we launched several ranking improvements in Q4 on Facebook and Instagram that drove incremental engagement. And there isn't really one single launch that is driving most of the gains. It's multiple optimizations to our recommendation systems that are helping us make more accurate predictions about what will be interesting to each person.

We see a lot of headroom to improve recommendations in 2026, which we expect will drive additional engagement growth on both apps. First, we plan on to continue scaling up our models and increase the amount of data we use, including a longer history of content interactions to further improve the overall quality of recommendations. We're also going to start validating the use of ad signals and organic content recommendations as we continue to work towards having a more shared platform for organic and ads recommendations over time.

Analyst Ken Gawrowski (Wells Fargo): Thank you very much. Two, if I may, please. First, for Mark, how critical is it for META to have a leading general purpose model? Or is there a sufficient capability in a model that really excels at specific use cases, maybe similar to what you see at Anthropic in coding today? We'd love to, if you could, opine on that. And then second, maybe, I just want to push again, maybe on this last question, Susan, on the visibility you have. You talked about the improvements you're making in 26 on the models, the fine tuning of the core, both in engagement and ad relevance. Could you talk about, are you seeing any signs of diminishing returns to those investments? And do you think, do you have visibility beyond 26 into further opportunities there?

Executive Mark (CEO): I think the question was around how important is it for us to have a general model. You know, the way that I think about Meta is we're a deep technology company. Some people think about us as we build these apps and experiences, but the thing that allows us to build all these things is that we build and control the underlying technology that allows us to integrate and design the experiences that we want and not just be constrained to what others in the ecosystem are building or allow us to build.

I think it's very important to be able to have the capability to build the experiences that you want if you want to be one of the major companies in the world that helps to shape the future of these products. But yeah, I mean, I think it's quite important, otherwise we wouldn't be so focused on this where we're clearly extremely focused on this.

Executive Susan (CFO): On your second question, interestingly, a year ago on this call, I think I talked about the set of investments we were making in 2025 as part of our 2025 budgeting process across our ads performance and organic engagement initiatives. And those investments have generally paid off. And we feel really good about the process we ran in terms of using projected ROI to stack rank investments, make sure that we had a robust measurement system, funded things that were positive ROI, and then tracking how they performed over the course of the year.

And we've just finished running our 2026 budgeting process, and we have funded a similar set of investments, which we expect will enable us to continue delivering strong revenue growth in 2026. Having said that, I expect both full year reported and constant currency revenue growth to be below the levels in Q1 for a few reasons. First, we would expect that currency tailwinds will dissipate later in the year based on current rates. Second, we'll be lapping stronger periods of growth later in the year that benefited from our 2025 ad performance investments and the strong macro landscape.

Analyst Mark Mahaney (Evercore): Okay, two questions, please. Meta AI, any update on what you're seeing there in terms of engagement and usage? And do you think you're just starting to be able to apply improvements to that specific functionality? And then just real quickly on share repurchases, Susan, I don't think you bought any stock back in the quarter. It's been a while, maybe a year since you haven't bought anything back. You talked about capital allocation a little bit into the year. It didn't sound like...