Quarter 1
Q2 2026 Earnings Call — July 29, 2026
Analyst Brian Nowak (Morgan Stanley): Thanks for taking my questions. I have two, one for Mark, one for Susan. Mark, I appreciate all the color on the big pipeline for new products across consumer and business agents, API tools, and compute rental. There's a lot of opportunities here. But my question is, as you look at these opportunities and the state of the current offerings, the compute capacity, which of them do you expect to be able to scale first sort of in 26 and 27 to sort of showcase quantifiable material ROIC for investors? And then Susan, there's been some public comments about capacity in 27 and doubling capacity, and I appreciate your color about CapEx. Any early comments on 27 CapEx, even the philosophy around sources of upside or sources of downward pressure? Just sort of think through different ways to finance this multi-year build and have 27 CapEx.
Executive Mark: I can take the first one. So in terms of the different opportunities and how we think about the compute, overall, a substantial amount of the compute goes towards training models to be a leading lab. And I think that's an important investment. But then the rest of it goes towards a set of different products and revenue opportunities, which spans from optimizing and improving our core business to building new consumer products that we're releasing soon to the API, to the business agents work, to the developer tools work on the roadmap that I alluded to, and then also the opportunity to sell compute directly. I mentioned that we have quite a number of offers at a meaningful premium over what we paid for the compute. The question in thinking about this is we believe that there will continue to be a significantly higher margin on selling intelligence rather than selling compute directly. But we think that there's a big opportunity, obviously, to sell compute as well. So we're thinking through, I think you asked what one area would likely scale the most, but I mean, I'm actually quite optimistic that we're going to see meaningful growth in all of these areas. And I think we will have more to share soon on a number of them.
Ryan, on your second question, we aren't providing a specific outlook for 2027 CapEx
at this time.
Infrastructure planning remains highly dynamic. And even this year, there are a range of outcomes embedded in our outlook. I alluded in my main remarks to our focus on being on gearing our current infrastructure plans towards maximizing capacity in 2026 and 2027 and giving us the flexibility to continue to grow in 28 and beyond, but also giving us the ability to make server decisions when we come to being able to evaluate our actual needs in 28 and beyond. So we're still working through what our capacity needs are going to be over the coming years. Generally, we believe near-term capacity is more valuable than long-term capacity, and it remains a very dynamic planning process.
Analyst Eric Sheridan (Goldman Sachs): Thanks for taking the question, maybe two if I can. When you frame up the enterprise opportunity, how much of that opportunity do you think is available to you today based on what you've built out in terms of go-to-market strategy as extensions of the advertising and the marketing business you have already versus go-to-market strategies that might have to be built to capitalize on the opportunity? And then maybe, Susan, if I could just squeeze a second one in. When you think about sources of capital for the business for the next couple of years, you referenced the deal that was announced yesterday as an example of looking at ways to finance forward obligations. We continue to get questions from investors about how to think about the mix of debt and equity and sources of capital. Philosophically, how are you guys thinking about wanting to be ambitious on the spend but then marrying that with the need for capital? Thanks so much.
Executive Mark: Sure, I could talk about the first part. I think for enterprise, there's going to be a combination of just like the ad system, effectively, we will get paid when we deliver results for those businesses. So we view this as an extension of the sales and the partnerships that we have with many millions of advertisers and hundreds of millions of small businesses that use our platforms. So that one, I think, should be quite a natural opportunity for us. And we're focused on delivering it in a way where we're not trying to kind of maximize the sale in the near term. We're trying to maximize the results for people and build out a robust auction. And that's what we've seen has served the business well over time.
There are other enterprise customers who I think we're increasingly going to serve too. We're building, coding and developing internal productivity tools partially because we need to build them ourselves. And we need to make sure that we have tools that are tuned for ourselves. And now that we have those, we feel like there's a large opportunity to serve, whether that's small businesses or larger businesses. That is a somewhat different muscle than we have historically had. We will share more soon on how we're planning to build that out. But I think that there is just a very large opportunity on this. And the way that I think about this is there's obviously been a bunch of news about the compute side, but I think that the enterprise opportunity is kind of the sum of all of these different things. It's not just the selling compute, also the API services, the productivity services, the kind of business agents for other parts of the business beyond marketing are all parts of the overall offering. And I think that there's just a very, very large opportunity there. So we're quite focused on that. That's going to be somewhat of a new muscle that we build as a company. But I think it's a very important one that we build so that way we can make sure that we can maximize the opportunity ahead of us.
Executive Susan: Eric, on your second question, in terms of sources of capital, this is something that we have been looking at thoughtfully as we think about financial planning for the future. Obviously, our strong operating cash flow certainly has put us in a position of strength as it pertains to funding our infrastructure build-out, but we've also been evolving our capital structure in recent years to include a greater mix of debt as we work to bring down our cost of capital. We have generally found it prudent to continue adding cost-efficient, long-duration sources of capital as we make investments in initiatives that themselves have long time horizons, especially AI infrastructure projects. We've also broadened our aperture there to include partnerships like the one we announced with BlackRock yesterday. And we'll continue to be thoughtful about evaluating the appropriate different sources of capital over time as we evaluate future projects.
Analyst Mark Shmulek (Bernstein): Yes, thanks for taking the question. Mark, everyone's got a story of, you know, kind of someone coming back from Silicon Valley, deep writing code, building agents with AI, and then they head home, you know, and they kind of tell their parents they're using AI wrong. It's just kind of like a glorified search tool. You know, consumer behavior is always pretty difficult to predict, but kind of reading your op-ed on AI for everyone, how do you think about whether consumer adoption can close this AI utility gap? Are we on the cusp of something kind of breaking through or do we just need to be a bit more patient? Thanks.
Executive Mark: Well, I think that some things have already broken through. And I think one of the interesting things about AI compared to other technologies is that every year or so, and the cycle may accelerate, but every year or so you get new capabilities that create new possible product lines. So there's obviously the AI assistant market for consumers where that's what we're doing with Meta AI. And then there's the other products that competitors have in that space. And then in the last year, I think the coding agent market has really grown very quickly. And that's the first real agentic market. And I think that there's a number of reasons why coding is first. You have technical customers who are willing to do the work to make it work.
One of the bets here that we're making is that we think that consumer personal agents is going to end up being an extremely important and massive market. I think that it's extremely unlikely if you look out five years from now, for example, that a lot of the kind of proto agents is, you know, it takes a bunch of fiddling, you have to get into a terminal to get it set up, you get a use case going, it kind of seems magical, but then maybe it breaks down over time. And I think that the companies that can deliver the personal agents that just work, I think that this is going to be almost inevitable that someone does. And I think it really plays to Meta's strengths as a company. We build consumer products that reach billions of people. We're great at once we get something working, scaling it to a large number of people. And we're good at building the infrastructure to be able to support these intensive applications. So I feel very good about this. I kind of understand that we need to deliver it for our community. And that's what we're very focused on.
Analyst Doug Enmuth (JP Morgan): Great. Thanks for taking the questions. One for Susan, one for Mark. Susan, you talked about how LLMs are increasingly capable of delivering ranking and recommendation gains. Can you just talk more about the roadmap here and how far along you are in just leveraging better models and more compute? And then Mark, just in terms of the number of offers to monetize your compute externally, you also at the same time are purchasing capacity from a number of third parties. So just hope you can help us understand some of the differences here. Is it just timing and stopgap issues or is it training versus inference and leveraging the chips that are best suited for each? Thanks.
Executive Susan: Thanks, Doug. I'll take that first question about where we are on the recommendations roadmap. First of all, we certainly see further headroom to continue improving recommendations over the rest of the year and into 2027. We expect that will help us drive additional gains on both engagement on Facebook and Instagram. A couple of things I would highlight. First, we'll continue to make recommendations even more personalized and relevant to user interests, and that's in part by advancing our recommendation models and architectures to further capture user interests more precisely and respond faster to what people care about in a given moment. Our AI investments are going to play a significant role in delivering on this vision, including the expansion of LLM-based content understanding to develop a deeper understanding of posts and creators that people value to capture user interests more precisely and respond more quickly to what they care about in the moment and using AI to surface high quality, fresh and trending content and reduce the share of low quality content.
Second, we're continuing to improve our data infrastructure to allow our models to train on more data and leverage that data more effectively. We're adding more detail to how we describe content that users have engaged with in the past and enriching past user interaction sequences with more granular content. That allows our models to more precisely learn which engagements are more or less valuable to users. We're continuing to scale up both the length of user interaction sequences we use during training as well as the complexity of our model architectures across Facebook and Instagram to take advantage of the larger data sets. And then we've already made significant strides leveraging LLMs for content understanding. We're going to further incorporate them into both our recommendations and content policy enforcement stacks, given their capability to more deeply understand content. And I would say, you know, broadly, I think we're very optimistic about that body of work. We've also invested in using LLM-based agentic approaches to transforming our recommendation system, and we grew the number of launches from our ranking agents this half. That also helps make our engineers more productive, and that's another path that we're very excited about as well.
Executive Mark: I can answer the second part of that. I think your question was about how we think about offers that we're getting to sell the compute, but then we're also buying the compute. The high-level observation is that there's just nowhere near enough compute for all the demand. That is why we see that basically we are getting a large number of offers for the compute that we have, but also we have a lot of internal uses that we think are going to be quite valuable. Now, in terms of running the business, obviously a common trade-off that we need to make is around how much do you monetize something today versus develop future assets for the future. I think that it's always a portfolio. You don't want to only do long-term things and not prove the markets out that exist in the near term, but I also think it would be foolish to basically just sell all of the compute and take a short-term profit. But when you have the opportunity to build intelligence on top of it, which will be a kind of a multiple and that compounds the value of the compute on top of that.
So I think the answer is and many more, and including just the core business, which is not even necessarily new products that we haven't talked about, but just in terms of using that to be able to further add intelligence and improve the ranking and recommendations and ads in the core services. I think all of that is true. And it creates this dynamic where there is a lead time where we're investing in building out these data centers now. They come online at some point in the future. You obviously are not getting value out of them until they're online. But we basically see a very large demand for all of this and want to go maximize the opportunity to build out all of these different businesses.
Analyst Justin Post (Bank of America): Great. Thanks. Mark, you hired the senior leadership of your AI labs about a year ago. Could you just give us your thoughts on how the lab's performing? And do you think the street will really see an uptick in product velocity as far as models or chips or other things coming forward? And then what kind of sustainable competitive advantages do you think the lab is building? Thank you.
Executive Mark: Yeah, so I mean, I'm quite happy with the trajectory that we're on. We've released a few models that I think are quite impressive on our early scaling ladder. And as I've said, we're in the process of scaling much larger and more advanced models, and we're excited about those too. I think that there's the intelligence aspect of this, and then there's the data aspect of it. I think it is true that in any product category that you want to go into, there is going to be some kind of flywheel where you learn from the behaviors and how people in that community are using the product. The reason for working on a number of these different things up front is that, first of all, the technology is general. So when you build the intelligence out, it can apply to a number of different uses. But then you want to invest in building up the flywheel on them, because I think that that's how you build the sustainable advantage over time.
But I think that we've definitely shown at Meta that when we have a product in a format that works, and so on. I think that those are durable advantages as well as the data flywheels that we're building on them. And then obviously on the research side, you want to build good culture. I mean, this is just kind of the simple stuff, but I think that you just want to make sure that you're building the team in a way where you're managing things well and kind of consistently with low drama compounding over time. And that's sort of the goal that you aspire to. And I think if you can do that well, then that is hopefully, you know, may not be kind of a clear mathematical explanation of something. But I think that that's kind of how business works. So, yeah, that's what we do.
Analyst Ross Sandler (Barclays): Yeah. Hey, Mark, sticking with the comments on the AI lab, so MUSE Spark 1.1 is pretty close to the Pareto frontier, but it's at the lower end of the intelligence spectrum or lower cost end, I should say. And it sounds like from your last answer, you think it's important to compete at both the lower cost end and the more expensive performant end. Could you just talk about that a little bit? And then the leadership of your lab has also talked about going back into open source kind of where you were a couple of years ago. So how does that fit into the strategy and all this discussion around monetization of AI products and models? Thank you very much.
Executive Mark: Thank you for having me. The models that we've released so far are based on, they're a certain scale in climbing up the scaling ladder and we're continuing to scale larger models. So I think for MuseSpark 1 and MuseSpark 1.1, I think that they are very impressive models for the scale of the model, the kind of stage of development of the lab and I feel quite good about them. Now, we want to have models that are more advanced as well. And that's why we're scaling larger models. And that's why we're building out a bunch of the research infrastructure around that. But at the same time, we also want to have good, more efficient models that will be a lot of what we serve to consumers at scale. If you're serving billions of people, you want the ability to have more advanced models for things that are very hard problems.
On open source, I think we have always felt like open source was an important part of the ecosystem and it's good for the world and it creates its own feedback loops that are positive for us around getting the community invested in our infrastructure stack and our work and contributing improvements. But we've always basically said that we were going to do a mix of open and closed. And that continues to be true. Now, in ramping up meta superintelligence labs, in some ways, actually counterintuitively, it takes some more work to do open source models because you're not necessarily, if you're doing something as a closed system that you're only building for your own use cases, it can be a little more jagged. Whereas if you release it as open and it's going to be used for a lot of things, you want to make it more well-rounded. And I just wanted to make sure that the MSL team was uninhibited in building out the most intelligent models that we could. And we expect that we will get back to releasing some open source models at some point soon. But like we've always said, we're not dogmatic about this. We think open source is important. We want to contribute to that ecosystem. We plan to do a combination of open and closed models.
Analyst Ken Gowrowski (Wells Fargo): Thank you, too, if I may, please. I just want to maybe, Mark, just touch on the last point again around open weight models. There's been a lot of discussion you've weighed in on the topic. Why or why not does that change META's view of developing close proprietary frontier models? Is there an opportunity if open weight models proliferate that you don't have to develop your own frontier models? So that's question one. And the second one, a clarification, if I may, for Susan. You noted that in your prepared remarks that you plan to maximize 26 and 27 capacity. Is that a demand or a supply comment? Meaning, are you suggesting that Meta plans for internal use of all the capacity built through 27? Or are you saying that you will evaluate 28 and beyond builds based on demand for metaproducts and services?
Executive Mark: I can take the open source question. Let's see. Do we, or is it basically the question is, do we think that because there are some open weight models that we can just rely on those? I mean, right now, the open source models are not as strong as the frontier models. So no, is the basic answer. And then there's also just always the perpetual, both policy debate and question around, around other companies' actions and whether that's actually a thing that we can, that we, that a company like Meta can rely on. If you look at Meta, take a step back on this, a lot of people view the surface layer of we build some social media apps and we have an ad business. We are really a full-stack technology company. We built our own data centers, our own infrastructure, our own chips, our own low-level software. A lot of the reason why Facebook worked was because it just worked. It literally worked when other social networks did not work fast and efficiently. I think we just have the ability to build things that can be more personalized, more optimized, more efficient. Some qualitative experiences are just not even possible for others to build because we go all the way down the stack.
It just seems to me pretty clear that having kind of sovereignty over building your own models is going to be an important part of that stack going forward, which is why it is important for Meta, but is also important, is also why other people care about open source and why open source matters overall, because like other companies, even if they don't have the ability to build these models, do not want to have to just rely on a small number of closed labs. Like that there is a very important place in the world for there to be open source models. And to be clear, that doesn't take away from the API opportunity or any of the things that I'm talking about, because someone still needs to run the models and run inference on them and be able to kind of run those models efficiently and have the compute to do that is going to continue to be a source of business advantage.
So I don't really think that these things are necessarily at odds, but when I look at what a lot of discerning customers and companies are gonna want around the world, they're going to want to know that they have control of their destiny and that they can trust the models that they're using and that their data is safe and that they're not sending it to competitors and all that. So I think open source is going to be important, but I think for us also building the models is going to be a critical part of it. It also, for what it's worth, I know that the models all get evaluated on a common set of evals and they get chalked up to some models within a couple of points of another model or whatever. But they all really do have different combinations of skills, kind of like people, right? It's like people have spikes in certain areas and have different personalities and are better and worse at different things. And if you're trying to build personal superintelligence for people or you're trying to build a business agent for small businesses, that needs to have potentially some different skills than what the other labs are tuning.
Just like being able to do the full stack work on our Instagram recommendations or on our ad system is how we've gotten the results there over time. I believe that building the kind of full stack model is going to be a lot of the advantage over time and how we build personal superintelligence agents, business agents, all these different use cases for all of the different customers that we want to serve. In addition to the distribution that we have and the ability to reach all these people and scale products that work, I think this is a lot of the durable advantage is that you build something that is kind of specific to that use case and excellent at those use cases. And I mean, look, I get that this is a big investment and it's a big bet. We see the technology working. We're happy with the trajectory of the lab. I'm excited about the products that are coming and we believe that this is going to be a big thing. So I mean, I get that this is sort of a big bet across the industry. My personal bet is that the people who invest in this are going to be rewarded and feel very good over time.
Executive Susan: Can I just quickly answer your second question? When we refer to focusing on 2026 and 2027 capacity, there are really two factors. One is we are today, and you expect to be in in the sort of foreseeable future demand constraint, not really includes our core business to where there are, you know, we still have numerous ROI positive places that we would put that we would put compute toward if we had it. And the second, of course, is just the uncertainty over long-term constraints on building capacity. And we talked about some of the need to build out more of the supply chain earlier in my comments. So I think beyond 27, when we look into 28 and further than that, the world is going to evolve a lot. Our own internal demand will evolve. We will have turned over a lot of cards by then. And so when we think about planning today for 28, we're really focusing on flexibility. That's just giving us kind of the ability to have land and power, but to really make the actual decisions about buying chips and other big ticket items further in the future. So for now, I think we know we have a lot of good use cases for capacity in 26 and 27, and that's really what we're building toward.
Executive Mark: Great. Thank you for joining us today and we look forward to speaking with you again soon. This concludes today's conference call. Thank you for your participation and you may now disconnect.
Quarter 2
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