Meta (META) Q1 2026 Earnings Call Transcript

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DATE

Wednesday, April 29, 2026 at 5:30 p.m. ET

CALL PARTICIPANTS

• Chief Executive Officer — Mark Elliot Zuckerberg
• Chief Financial Officer — Susan Li

TAKEAWAYS

• Daily Active People (DAP) -- 3.5 billion in March, with a slight sequential decline attributed to Internet disruptions in Iran and WhatsApp access restrictions in Russia.
• Total Revenue -- $56.3 billion, up 33%, or 29% on a constant currency basis.
• Family of Apps Ad Revenue -- $55.0 billion, up 33%, with constant currency growth at 29%.
• Ad Impressions -- Increased 19%, driven by engagement, user growth, and ad load optimizations.
• Average Price per Ad -- Rose 12%, enabled by improved ad performance, favorable macro conditions, and currency tailwinds, partially offset by impression growth in lower-monetizing regions.
• Family of Apps Other Revenue -- $885 million, up 74%, primarily driven by WhatsApp paid messaging and subscriptions.
• Reality Labs Revenue -- $402 million, down 2% due to lower Quest headset sales, partially offset by strong AI glasses growth.
• Total Expenses -- $33.4 billion, up 35%, mainly from increased infrastructure costs and technical hiring, especially in AI.
• Operating Income -- $22.9 billion, representing a 41% operating margin.
• Net Income -- $26.8 billion, or $10.44 per share; would have been $18.7 billion and $7.31 per share excluding the $8.03 billion tax benefit.
• Tax Rate -- Negative 23% due to the $8.03 billion tax benefit, excluding which the tax rate would have been 14%.
• Capital Expenditures -- $19.8 billion, largely allocated to servers, data centers, and network infrastructure.
• Free Cash Flow -- $12.4 billion for the quarter.
• Cash and Marketable Securities -- $81.2 billion at quarter-end; Debt -- $58.7 billion.
• Headcount -- Over 77,900, down 1% from Q4, reflecting optimizations offset by hiring in monetization and infrastructure.
• Q2 Revenue Guidance -- Expected between $58 billion and $61 billion, factoring an approximate 2% foreign currency tailwind.
• 2026 Full-Year Expense Outlook -- $162-$169 billion, unchanged from the previous outlook.
• 2026 Capital Expenditure Guidance -- $125-$145 billion, raised from the previous $120-$135 billion range, mainly due to elevated component pricing and increased data center costs.
• Q1 Infrastructure Commitments -- Multiyear contractual commitments rose by $107 billion.
• Meta AI and Muse Spark Model -- Following the model’s launch, Meta AI sessions per user recorded double-digit percentage increases, and the standalone Meta AI app has consistently ranked near the top of app stores.
• Ad Conversion Rates -- Enhancements in Lattice and GEM architectures delivered a 6%+ conversion rate gain for landing page view ads; expansion of adaptive ranking to off-site conversions drove a 1.6% conversion rate increase on Facebook and Instagram.
• Meta AI Business Assistant -- Now fully deployed to eligible advertisers, resolving account issues at a 20% higher rate since Q4 testing.
• GenAI Creative Tools -- Over 8 million advertisers are using at least one GenAI creative tool; advertisers using video generation observed more than a 3% boost in conversion rates in tests.
• Value Optimization Suite -- Revenue run rate now over $20 billion, more than doubling in the past year.
• Partnership Ads -- Revenue run rate more than doubled to $10 billion.
• AI Glasses Daily Usage -- Daily users tripled year over year; new Ray-Ban MetaOptics launched for all-day wear.
• Business AIs Conversations -- Over 10 million weekly conversations, up from 1 million at the year's start, with recent expansion to SMBs across Latin America, Indonesia, and APAC.
• Contracted Cloud and Infrastructure Expansion -- New multiyear cloud and infrastructure deals support future training and inference requirements through 2027.
• Employee Reductions -- Internal plans announced to reduce the workforce in May to offset infrastructure investments and promote agility.

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RISKS

• Susan Li stated, "we continue to monitor active legal and regulatory matters, including headwinds in the EU and the US that could significantly impact our business and financial results."
• Susan Li also highlighted ongoing youth protection scrutiny and additional trials scheduled for this year in the US. These may ultimately result in a material loss.
• Total family daily active people fell slightly sequentially due to Internet disruptions and access blocks.
• Capital expenditures are increasing due to higher component costs, particularly memory pricing.

SUMMARY

Management detailed a material increase in AI-powered engagement across platforms, citing record video consumption and exponential growth in Meta AI sessions as a result of the Muse Spark deployment. Expansion of ad monetization capabilities included the broad rollout of LLM-scale models and new creative tools, driving substantial gains in advertiser performance and creative adoption. Substantial increases in capital commitments and infrastructure spending were clarified, with explicit guidance on elevated CapEx and contractual obligations supporting anticipated AI and agentic product launches. Company leadership announced targeted workforce reductions designed to offset infrastructure investment, while monitoring legal and regulatory pressures that could affect near- and longer-term results.

• The leadership distinguished its personal and business agent strategy from broader industry approaches to AI commercial rollout, describing a vision focused on enabling individual and entrepreneurial productivity. A phased monetization roadmap is still developing.
• Product development speed for advanced AI models was framed as rapid, with teams already training successor models beyond Muse Spark and an explicit strategy to balance model improvement and scalable product launches.
• Meta Platforms (NASDAQ:META) is leveraging foundational AI advances to accelerate both internal productivity and consumer-facing product cycles, promoting output from small teams and streamlining resource allocation for experimentation.
• Quarter-over-quarter engagement metrics on Facebook and Instagram set four-year highs for video watch time via ongoing model improvements. Ranking enhancements drove measurable increases in ad conversion rates and content recency.
• Business AIs enabled a tenfold increase in weekly conversations in early 2026. Management indicated further global rollout and a pathway toward future monetization, though current use is largely free for businesses.
• Management's Q2 revenue guidance incorporates foreign currency tailwinds. The company’s multiyear strategic focus remains on high-flexibility infrastructure expansion, with management acknowledging past underestimation of compute needs.

INDUSTRY GLOSSARY

• Muse Spark: The first advanced AI model launched by Meta Superintelligence Labs, powering Meta AI features and applications.
• Meta Superintelligence Labs (MSL): Internal Meta research division focused on building frontier AI models and agentic systems.
• Lattice: Meta's ads system modeling and learning framework, used to improve conversion rates.
• GEM model architecture: A proprietary Meta ads model designed to optimize ad relevance and performance.
• Adaptive ranking model: An LLM-scale ads recommender that selectively utilizes high-compute models for ad placements with higher probability of conversion.
• GenAI creative tools: Meta's generative AI suite for building creative ad content, including video generation features.
• Value optimization suite: A Meta platform suite for advertisers enabling revenue maximization through higher-value conversion targeting.
• Reels: Short-form video content featured on Facebook and Instagram, targeted by Meta’s ranking and recommendation AI.

Full Conference Call Transcript

Mark Elliot Zuckerberg: Alright. Hey, everyone. Thanks for joining today. We had a strong quarter for our community, our business, and our progress towards AI. More than 3.5 billion people use at least one of our apps every day. We saw a small decrease in total family dailies due to Internet outages in Iran and blocks in Russia, but otherwise, trends across our apps are strong. Daily and monthly actives on Instagram and Facebook continue to grow, with video driving all-time-high engagement across both apps. WhatsApp continues to see strong momentum too, including in the US, and Threads continues on its trajectory to be the leading app in its category.

Our biggest milestone so far this year has been the release of our Muse family of models—our first model, Muse Spark, along with a significantly upgraded new version of Meta AI. This was the first release from Meta Superintelligence Labs, and it shows that our work is on track to build a leading lab. Over the past ten months, we have built the strongest research team in the industry and established the scientific and technical foundations to scale very advanced models. Spark is just one step on that scaling ladder, and we are already training even more advanced models.

But Spark has already made Meta AI a world-class assistant that leads in several areas related to our vision of personal superintelligence, including visual understanding, health, shopping, social content, local, creating games, and more. We are hearing very positive feedback on it so far. We have seen large increases in Meta AI use since releasing the updates, and the Meta AI app has consistently been near the top of the app stores as well. Now that we have a strong model, we can develop more novel products as well. Since I first wrote about our vision for personal superintelligence last year, we have been focused on delivering personal and business agents to billions of people around the world.

Our goal is not just to deliver Meta AI as an assistant, but to deliver agents that can understand your goals and then work day and night to help you achieve them. My view of AI is very different from many others in the industry. I hear a lot of people out there talk about how AI is going to replace people. Instead, I think that AI is going to amplify people's ability to do what they want, whether that is to improve your health, your learning, your relationships, your ability to achieve your personal career goals, and more.

My view is that human progress has always been driven by people pursuing their individual aspirations, and I believe that this will continue to be true in the future. People will be more important in the future, not less. Meta Platforms, Inc. believes in empowering individuals. Those are the kinds of products that we are going to build, and I believe that they are going to be some of the most important and valuable products of all time. We are building a personal agent focused on helping people achieve the diverse goals in their lives.

We are also building a business agent focused on helping entrepreneurs and businesses across the world use our tools and others to grow their efforts, reach new customers, and serve existing customers better. These agents will work together to form an ecosystem. And whether you use our personal or business agents to achieve your goals, I believe that the future will see a massive increase in entrepreneurship from people creating new things that they have always wanted to exist but previously did not have the tools to bring into the world. We are already testing an early version of business AIs and weekly conversations have grown 10 times since the start of this year.

We are also working on using Spark and our upcoming models to improve our recommendation systems and core business in Facebook, Instagram, and ads. Right now, our apps primarily help people accomplish three important goals: connecting with people, learning about the world, and entertainment. But we have always wanted our apps to understand more of people's goals so we can help their lives in all the ways that they want. These new AI models will let us understand this in more detail.

So instead of just looking at statistical patterns of what types of people engage with what content, for the first time in Meta Platforms, Inc.'s history, we are going to be able to develop a first-principles understanding of what you care about and what each piece of content in our system is about so that we can show you more useful things for what you are trying to accomplish. We will also be able to create personalized content specifically for people to help you achieve your goals as well. Since our recommendation systems are operating at such large scale, we will phase in this new research and technology over time.

But the trend over the last few years seems clear that we are seeing an increasing amount that we can improve engagement for people and value for advertisers. This encourages us to continue investing heavily in what we expect will provide increasing value over the coming years as well. On that note, we are increasing our infrastructure CapEx forecast for this year. Most of that is due to higher component costs, particularly memory pricing. But every sign that we are seeing in our own work and across the industry gives us confidence in this investment.

That said, we are very focused on increasing the efficiency of our investments, and as part of that, we are rolling out more than one gigawatt of our own custom silicon that we are developing with Broadcom as well as a significant amount of AMD chips to complement the new NVIDIA systems that we are rolling out as well. One of the primary goals of our Meta compute initiative is to lead the industry in efficiency of building compute, and we expect that will be a strategic advantage over time. Talking about building physical goods at scale, our AI glasses continue to perform well with the number of people using them daily tripling year over year.

This continues to be one of the fastest growing categories of consumer electronics ever. We released Ray-Ban MetaOptics this quarter designed for all-day wear rather than primarily as sunglasses. And building on our release of Oakley last year, we have some exciting new partnerships and styles that I think are going to have the potential to reach even more people coming later this year. All of our glasses are designed to easily update to use our newest AI models and features. I am also really excited to see the glasses evolve from being able to answer questions to being able to be a personal agent that is with you all day long, helping you remember things and achieve your goals.

Beyond glasses, I am excited for more of our metaverse efforts to be powered by the AI models we are training as well. We remain the biggest investors in the VR space across the industry, but we are focused on making our VR business sustainable as we invest more in other areas like AI and glasses. Before wrapping, I want to talk for a moment about how AI is transforming our work. We are seeing more and more examples where one or two people are building something in a week that would have previously taken dozens of people months.

And I want to make sure that Meta Platforms, Inc. is the best place in the world for these types of people to come and make an impact. We are building the next evolution of our company around these people. And there is a lot that we can do to enable this: building the best infrastructure for creating and delivering products at scale, streamlining our teams so they are not bigger than they need to be, recognizing and rewarding the people who are having outsized impacts, and setting ourselves up to try many more ideas and take on many new projects in the future.

Of course, we will continue pushing to increase our efficiency as well, but overall, I think the future is about building many more higher quality products than we have ever built before. Alright. That is what I wanted to cover today. We are living through a historic technological transformation. We are among the few companies positioned to shape the future, and we are on track to do that. I am looking forward to delivering personal superintelligence to billions of people, and as always, I am grateful for the hard work of our teams and to all of you for being on this journey with us.

Susan Li: Thanks, Mark, and good afternoon, everyone. All comparisons are on a year-over-year basis unless otherwise noted. We estimate 3.5 billion people used at least one of our family of apps on a daily basis in March, which declined slightly from December due to Internet disruptions in Iran and a restriction on access to WhatsApp in Russia. Absent these impacts, growth in family daily active people would have been positive quarter over quarter. Q1 total family of apps revenue was $55.9 billion, up 33% year over year. Q1 family of apps ad revenue was $55.0 billion, up 33%, or 29% on a constant currency basis. In Q1, the total number of ad impressions served across our services increased 19%.

Impression growth was healthy across all regions driven primarily by growth in engagement and users, as well as ad load optimizations. The global average price per ad increased 12% year over year in Q1 with broad-based growth as we benefited from ad performance improvements, better macro conditions versus Q1 of last year, and currency tailwinds in international regions. This was partially offset by strong impression growth including from lower-monetizing regions. Family of apps other revenue was $885 million, up 74%, driven primarily by WhatsApp paid messaging and subscriptions revenue.

Within our Reality Labs segment, Q1 revenue was $402 million, down 2% year over year due to lower Quest headset sales, which were partially offset by continued strong growth in AI glasses revenue. Moving now to our consolidated results. Q1 total revenue was $56.3 billion, up 33%, or 29% on a constant currency basis. Q1 total expenses were $33.4 billion, up 35% compared to last year. Year-over-year growth was driven mainly by infrastructure costs and employee compensation. The growth in infrastructure costs was due to higher depreciation, data center operating costs, and third-party cloud spend. The growth in employee compensation was driven by technical hires we have added over the past year, particularly AI talent.

We ended Q1 with over 77.9 thousand employees, down 1% from Q4 as the impact of headcount optimization efforts in certain functions was partially offset by hiring in priority areas of monetization and infrastructure. First quarter operating income was $22.9 billion, representing a 41% operating margin. Q1 interest and other income was negative $1.1 billion driven by unrealized losses on our equity investments. Our tax rate for the quarter was negative 23%, which was favorably impacted by a tax benefit of $8.03 billion.

This benefit partially relieves the $15.93 billion non-cash tax charge we recorded in 2025, which reflects updated guidance from the US Treasury issued in February 2026 regarding the tax treatment of previously capitalized R&D expenditures in the United States. Absent the tax benefit, our Q1 tax rate would have been 14%. Net income was $26.8 billion or $10.44 per share. Absent the tax benefit, our net income and EPS would have been $18.7 billion and $7.31 respectively. Capital expenditures, including principal payments on finance leases, were $19.8 billion, driven by investments in servers, data centers, and network infrastructure. Free cash flow was $12.4 billion.

We ended the quarter with $81.2 billion in cash and marketable securities and $58.7 billion in debt. Turning now to the business performance. There are two primary factors that drive our revenue performance: our ability to deliver engaging experiences for our community, and our effectiveness at monetizing that engagement over time. On the first, we are continuing to see significant gains from our content recommendation initiatives. On Instagram, the ranking improvements that we made in Q1 drove a 10% lift in Reels time spent. On Facebook, total video time increased more than 8% globally in Q1, the largest quarter-over-quarter gain in four years.

Within the US and Canada, ranking improvements we made drove a 9% increase in video watch time on Facebook in Q1. These gains are benefiting from advances we are making across the full stack. Starting with data, we doubled the length of user interaction sequences we use for training on Instagram in Q1 and increased the richness of how each user interaction is described, enabling our systems to develop a deeper understanding of user interests. Within our models, we have significantly increased the speed with which our ranking models index new posts, which is enabling us to recommend them sooner after they are published.

We are also applying more advanced content understanding techniques, which is enabling us to quickly identify posts that may be interesting to someone even if they have not engaged with a lot of similar content. These and other improvements have enabled us to increase the diversity and recency of recommended content, with same-day posts now representing more than 30% of recommended Reels on both Instagram and Facebook, more than double the levels one year ago. We are also using AI to unlock more inventory by auto-translating and dubbing videos into a viewer's local language, enabling us to recommend a more diverse set of content.

Over half a billion users on each of Facebook and Instagram are now watching AI-translated videos weekly. Looking forward, we are making several investments we expect will deliver more valuable recommendations. This year, we will continue scaling up our models in several dimensions, including their size and complexity, while incorporating LLMs to deepen content understanding across our platform. This will enable us to better match people to a wider variety of content aligned to their interests. At the same time, we are executing on our longer-term efforts to develop the next generation of our recommendation systems. This includes building foundation models that power organic content and ads recommendations as well as developing LLM-based recommender systems.

Our focus this year is validating the model architectures and techniques in these domains before we scale them out in future years. Aside from our recommendation work, we are focused on deploying the models from Meta Superintelligence Labs to enable a new set of product experiences. We are seeing encouraging results within Meta AI since we began powering responses with the first model from MSL, MuSpark. In tests we ran leading up to the launch, we saw meaningful engagement gains that accelerated week over week with each new iteration of the model. We are seeing similar gains within Meta AI following the broad rollout of our new model with double-digit percent increases in Meta AI sessions per user.

MuSpark is now powering Meta AI in direct chat threads across our family of apps, as well as the standalone Meta AI app and website, giving billions of people globally access to our latest model. Overall, we are very encouraged by the momentum within our research and product roadmap and look forward to sharing more detail on what we are building over the course of the year. Turning to the second driver of our revenue performance, increasing monetization efficiency. The first part of this work is optimizing the level of ads within organic engagement. Here, we continue to enhance our systems to show ads at the optimal time and location.

In Q1, we also expanded availability of ads on our newer surfaces, including bringing ads on Threads to people in more markets. On WhatsApp, we are making good progress with the rollout of ads in Status, with hundreds of millions of people now viewing them daily. Moving to the second part of increasing monetization efficiency, improving performance for the businesses who use our services. To do so, we are deploying AI more deeply across each layer of our systems and tools. Within our ad systems, we are delivering performance gains as we deploy more complex and predictive models.

In Q1, enhancements we made to Lattice’s modeling and learning techniques along with advances in our GEM model architecture drove a more than 6% increase in conversion rate for landing page view ads. In addition, we have been investing in more performant inference models for when we are serving ads. In the second half of last year, we began rolling out our new adaptive ranking model, which is an LLM-scale ads recommender model that we use for inference. This model improves our inference ROI by routing requests to more compute-intensive inference models when it determines there is a higher probability of conversion.

In Q1, we expanded coverage of our adaptive ranking model to off-site conversions, which drove a 1.6% increase in conversion rates across the major surfaces on Facebook and Instagram. We are also leveraging AI to make it easier for businesses to manage their customers, develop ad creative, and engage with customers. The Meta AI business assistant has now been fully rolled out to all eligible advertisers on supported Meta buying services, providing personalized recommendations to advertisers, resolving account issues, and surfacing campaign insights to help optimize results. Performance has been strong since we began testing the assistant in Q4, with common account issues being resolved at a 20% higher rate.

This week, we are also introducing Meta Ads AI Connectors in open beta, providing advertisers the ability to connect their Meta ad account directly to an AI agent. We have always supported advertisers both on our platform and through tools like the Marketing API, and now we are extending that to AI so businesses and agencies can analyze and optimize campaigns with the tools they are already using. Usage of our ad creative tools is also scaling, with more than 8 million advertisers using at least one of our GenAI ad creative tools and particularly strong adoption among small and medium-sized businesses.

These tools are benefiting performance as well, with advertisers using our video generation feature seeing more than 3% higher conversion rates in tests. We are also seeing good traction in using AI to facilitate customer engagement. In Q1, we expanded business AIs on WhatsApp to SMBs across Latin America and Indonesia as well as on Messenger in Asia Pacific. We now have more than 10 million conversations each week being facilitated through business AIs, up from 1 million at the start of the year. We will further expand access to more countries this quarter while adding more capabilities to the AIs.

We also continue to invest in the value optimization suite, which helps advertisers maximize their return on ad spend by prioritizing the highest-value conversions rather than optimizing solely for the most conversions at the lowest cost. Adoption by businesses has been strong following performance improvements we have made over the past year, with the annual revenue run rate of our value optimization suite now over $20 billion, more than doubling year over year. Last, I want to touch on our commerce efforts. People discover products on our platforms through ads and organic posts, with brands increasingly turning to creators to promote their products.

This is contributing to rapid growth in our partnership ads product, with its revenue run rate more than doubling year over year in Q1 to $10 billion. To support the product discovery and purchasing happening through creators, we are expanding our solutions beyond ads. Last month, we rolled out our affiliate partnerships offering on Facebook to more test partners so creators can tag products from participating retailers on their posts and earn a commission when someone makes a purchase. We have also started testing similar experiences on Instagram. We see a real opportunity to help people more easily discover and buy products within our services, particularly as we incorporate AI deeply across our platforms.

Next, I would like to discuss our approach to capital allocation. Compute is becoming increasingly important as a driver of the quality of services we can provide, including powering more capable models and delivering innovative new products. It is also becoming more critical to how we work at Meta Platforms, Inc., as we are entering a world where employees are managing agents to help them generate new ideas, run experiments, execute tasks, and build products. We are investing aggressively to meet our infrastructure needs and ensure we maximize our strategic flexibility over the coming years. This includes substantially expanding our own data center footprint and striking deals throughout the supply chain to secure necessary components for future capacity.

We are also signing cloud deals that will come online over the course of this year through 2027, allowing us to scale more quickly. These multiyear cloud deals and our infrastructure purchase agreements drove a $107 billion step up in our contractual commitments this quarter. Our investments will support our training needs for future models and, most importantly, provide us the inference capacity necessary to deliver personal and business agents to billions of people around the world, along with several other AI product experiences we are developing. As we grow our infrastructure spend, we remain committed to operating efficiently, and we recently shared internally that we plan to reduce the size of our employee base in May.

We believe a leaner operating model will allow us to move more quickly while also helping to offset the substantial investments we are making. Moving to our financial outlook. We expect second quarter 2026 total revenue to be in the range of $58 to $61 billion. Our guidance assumes foreign currency is an approximately 2% tailwind to year-over-year total revenue growth based on current exchange rates. Turning to the expense and CapEx outlooks. We expect full year 2026 total expenses to be in the range of $162 to $169 billion, unchanged from our prior outlook. We continue to expect to deliver operating income this year that is above 2025 operating income.

We anticipate 2026 capital expenditures, including principal payments on finance leases, to be in the range of $125 to $145 billion, increased from our prior range of $120 to $135 billion. This reflects our expectations for higher component pricing this year and, to a lesser extent, additional data center costs to support future-year capacity. Absent any changes to our tax landscape, we expect our tax rate for the remaining quarters of 2026 to be between 13%–16%. Finally, we continue to monitor active legal and regulatory matters, including headwinds in the EU and the US that could significantly impact our business and financial results.

For example, we continue to see scrutiny on youth-related issues and have additional trials scheduled for this year in the US, which may ultimately result in a material loss. In closing, Q1 was a solid start to the year, with strong execution across our core ads and engagement initiatives. We are also making exciting progress on our AI research and product efforts and expect to build that momentum over the course of this year. With that, Krista, let us open up the call for questions.

Operator: Thank you. We will now open the lines for a question and answer session. Please limit yourself to one question. Please pick up your handset before asking your question to ensure clarity. If you are streaming today's call, please mute your computer speakers. And your first question comes from Brian Thomas Nowak with Morgan Stanley. Please go ahead.

Brian Thomas Nowak: Thanks for taking my question. Mark, I wanted to ask you just about the level of investment you are making and the signposts you are watching to ensure you are going to generate ROIC on all these investments behind Muse and the other products. So if you could just let us know some of the key factors you are watching over the next 12 to 24 months—whether it is Meta AI, Muse advances, core algorithm—what are you watching most to make sure that you are on the right path to generating healthy ROIC on all this CapEx and infrastructure spend?

Mark Elliot Zuckerberg: That is a very technical question. Basically, the things that we are watching are to make sure that we are on track building leading models and leading product. 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. I think that we are seeing a little bit of that here where 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 just like anything else that we have done over time, the basic milestones that I look at are around: first, technically, are we delivering the quality to enable a great product? Second, when you have the product, how is it scaling? And third, you look at the monetization, and then you drive up the efficiency of it towards increasing profitability. I do not think we have a very precise 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 if you look at the usage of these and the quality of the product, 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 am quite comfortable that the lab that we are building is on track to be a leading lab in the world. I think Muse Spark 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 are just going to be tracking how our next set of training runs go, how our products scale, how excited we are about the products. Right now, we are very excited. And then we will also ramp up monetization over that period of time as well. So those are the set of things that I look at. I think for the more specific financial questions, I think Susan can jump in if there is anything more to add.

Operator: Your next question comes from the line of Mark Elliott Shmulik with Bernstein. Please go ahead.

Mark Elliott Shmulik: Yes. Thanks for taking the questions. Mark, now that we have got Muse Spark out there launched, how are you thinking about the teams’ focus divided between further model training runs and pushing further in that personal superintelligence goal versus product launches and shipping more products out the door? And Susan, as a follow-up to Brian’s question, I know it is too early to discuss 2027 CapEx, but we have had peers mention a potential significant step up. Any way to think about dimensionalizing how we think about some of the returns or traction this year and how it might affect 2027 spend? Thanks.

Mark Elliot Zuckerberg: I think the roadmap from the team has been pretty consistent. We have the research team, which is focused on scaling increasingly intelligent models with capabilities for the specific things that we are focused on, which are business and personal agents. We just released our first model, and I talked about in my comments how we are climbing the scaling ladder towards greater capabilities and scale for the models. That work continues. We have our next set of more advanced models in training now, and that work will just continue. That is a loop. I do not think we are going to be done with that anytime soon.

We are going to have teams that are 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. Before this, we had been prototyping a bunch of things using other different models, whether it was our previous older models or using the APIs from other companies. And now we are unlocked to be able to go build things and get them to scale on top of our own models. I think you will see that over some period of time.

I tried in my opening remarks to give a bit of a sense of where we are going, but I think that more of the details of that will become clear over the coming months. And these are both loops that we will iterate on. We will keep iterating on the intelligence. We will keep working on building new products and scaling the products. And then as we get to product-market fit, we are also going to increasingly focus on building the business around them and decreasing the costs. This is how we have done everything over the last twenty years of running the company, and that is basically the plan.

Susan Li: Mark, on your second question, we are not providing a specific outlook for 2027 CapEx, and we are frankly undergoing a very dynamic planning process ourselves as we are 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 teams 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, and how productive we can be as an organization. So we are 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 are building now.

Operator: Your next question comes from the line of Eric James Sheridan with Goldman Sachs. Please go ahead.

Eric James Sheridan: Thanks so much for taking the question. Maybe if you can build out on one of the topics that was discussed in the prepared remarks: the opportunity set that sits in front of the company with respect to putting compute in front of both consumers and enterprise. You have long been associated with the consumer landscape, and I am curious about how you are 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 across SMBs and enterprises where historically you maybe have not had as much product velocity? Thanks so much.

Susan Li: Thanks, Eric. So I would say in the near term, the biggest focuses are some of the areas that you mentioned—deepening engagement with our existing community and user base, making ad experiences meaningfully more engaging and more valuable, and helping SMBs find and engage with customers across our platform. Those are some of the 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—enabling agents to help people be more productive, but also agents for businesses and enabling those agents to interact with each other—we hope to build a thriving commerce ecosystem on our platform.

Some of these are a little bit further out, especially in that latter category. Again, the focus is on building personal superintelligence—building a consumer agent that can work for you and help you get things done. That right now is a consumer experience that we are 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 are seeing a large opportunity around agents and scaling our business AI initiatives.

I mentioned earlier that there are over 10 million weekly conversations between people and business AIs on our messaging platforms—up from 1 million at the start of the year—and we are going to continue expanding globally in Q2. Business AIs today are currently free for most businesses on our messaging apps, but as we make more progress, we expect that we will also work towards establishing a longer-term monetization model, and we will also consider other services that we can offer to businesses in the future, but we do not have anything more to share today.

Operator: Your next question comes from the line of Youssef Squali with Truist Securities. Please go ahead.

Youssef Squali: Great. Thank you very much for taking the questions. Maybe one for Mark and one for Susan. Mark, Ray-Ban and Oakley AI glasses continue to perform really well for you, but EssilorLuxottica looks like it owns 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% RIF, how much of that is due to efficiencies from 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 relative to your overall top-line growth? Thank you very much.

Susan Li: I can go ahead and take both of those. I might answer your second question first. In terms of the optimal size of the company over time, we do not really know what the optimal size will be in the future. There is a lot of change right now with AI capabilities advancing rapidly. We are very focused on leveraging AI tools to substantially increase our productivity, and we are seeing that reflected in the accelerating output from our engineers. We are 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 are making very significant investments in infrastructure, and we are very focused on continuing to operate efficiently. So we will be continuously evaluating how we are structured to make sure we are best set up to deliver against our priorities over the coming years. On the AI glasses, we are continuing to see strong growth in AI glasses sales over the course of Q1. Demand for the expanded portfolio lineup has generally been quite strong, and we are seeing sales shift from the prior generation of Ray-Ban Metas to the latest generation, which speaks to the value of the improved features like extended battery life and higher-resolution video capture.

We are pretty excited about the progress we have made with glasses. We see strong interest now in the Meta Ray-Ban displays with the Meta Neural band, so that is an encouraging sign that there is consumer appetite for display glasses, which is the next generation of how this product evolves. So this is an area that we continue to be excited about and are investing in.

Operator: Your next question comes from the line of Justin Post with Bank of America. Please go ahead.

Justin Post: Great. Thanks for taking my question. Mark, it took about ten months to get Muse Spark out. I think it is a pretty good pace. Help us understand what kind of unlock that is for some of the new products you are developing. And how is the product cadence going to be over the next nine months on either consumer or business/enterprise products built on top of that model?

Mark Elliot Zuckerberg: The field is moving pretty quickly. I am very happy that we are, I think, the lab that has gone the fastest from standing up the lab to having a very widely accepted strong model. I take that as a significant validation of the effort—that the team is working well together, that the infrastructure is working, that the effort is on track. That is basically the main thing that we have learned over the last quarter: we started this pretty big bet, and it is on track for our plan.

In terms of what exactly the cadence is going to be, it is tough for me to say both because I do not want to share competitively sensitive information and because we are more focused on quality than hitting a specific date. On the research side, this is research—we are trying novel things and do not exactly know when they are going to land. And on the product side, we care a lot about just having something I would give to my mother. There are a lot of agents out there that people are building for different things, and there are not that many that I would want to give to my mother.

Getting to that quality bar is something that I care about more than hitting a specific week for launching. But with that said, we are in a zone where the teams make meaningful progress day over day. Small groups and teams can make very rapid progress. So I think we are going to see a lot of innovation. The timing of this call is good in some ways because the Muse Spark release was positive. The Meta AI first release is positive. That shows that we are on track.

I am trying to paint a picture of the very high-level direction that we are going in, but the picture is going to come into focus a lot more over the subsequent quarters.

Operator: Your next question comes from the line of Ross Sandler with Barclays. Please go ahead.

Ross Sandler: Thanks. Mark, related to that last answer, there are a lot of new consumer applications cropping up—everything from something like OpenClaw to something a little bit more consumer-friendly that you would build for your mom, like Poe or Dreamer, which you recently acquired. How are these new ideas changing your view around the direction that core Meta AI or Dreamer or your overall agentic strategy needs to go? And second, do you think the lab will stay in this consumer lane, or do you want to go down the route that others are going down with code writing and the recursive self-improvement loop? Just thoughts on that. Thank you.

Mark Elliot Zuckerberg: On OpenClaw and other agents, I think they give you a very exciting glimpse of what should be possible. They are pretty rough systems today. To set up OpenClaw, you need to install on a computer locally and then get into a terminal and configure a bunch of things that hundreds of thousands or maybe a small number of millions of people could do. But we are talking about delivering personal superintelligence for billions of people around the world. How do you make a version of that experience that is a lot more polished, dialed, and easy, that has all the infrastructure done for people already, and that just works?

That is what we are focused on the consumer side, and I am really excited about that. If you had something like that worked quite a bit better than those systems and was easy enough that people could just get, then I think you go from 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. That has been our primary focus from day one of the lab: being able to deliver something like that as a product, and I think it is going to be very exciting. The same thing is true for businesses.

There is the personal version of this, but a lot of people's goals are to create things—to create websites, products, grow their products. These are all things that good agents are going to be able to help people do, which is partially why this is so exciting. In my opening comments, I talked about how today we can handle a few goals for people. They are big goals—helping people stay connected with people they care about, learn about the world. These are big things, but not the only things people care about.

One of the things that I would love for our products to be able to do is understand people's goals specifically and then be able to go work on them for them and check back in when needed. Whether those are personal goals or you are trying to create a business or do work, I think this is something literally every person in the world is going to want some version of. It also scales where the more you want to get out of it, people are going to be willing to pay a lot of money to have premium or high-compute versions of it. That is a very exciting area.

What you should be waiting to see is whether we can build the version that really just works, and how effective we are at converting people who are using our products into hundreds of millions and then billions of people using this stuff, and then over time how we can effectively convert that into something that is increasingly profitable by monetizing it and getting the cost down. You asked whether we are primarily focused on consumers or recursive self-improvement. We have talked about two main goals for the team. One is this agents vision of what we are doing.

The other is that self-improvement is really important because you cannot build a leading AI product if you do not have leading models. You are not going to have leading models in the future if your models cannot improve themselves. Today, the models are still able to learn from people, and then at some point, the models will have to improve themselves. That is how improvement in the models is going to happen. If we—or anyone else—do not have an ability to do that, then we are not going to be leading labs, and we are not going to produce leading products. That is table stakes that we are focused on. Does that make us a developer tools company?

Not necessarily. I am not against having an API or coding tools, but it is not our primary focus. People conflate coding with self-improvement more than they should. Coding is one ingredient for the model self-improving; it is not the only thing. 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.

Operator: Your next question comes from the line of Ronald Victor Josey with Citigroup. Please go ahead.

Ronald Victor Josey: Great. Thanks for taking the question. Mark, a quick follow-up around personal agents and business agents: with Muse Spark now live and more models in development, do you look at the personal agent opportunity more as a short-, medium-, or long-term goal? When will we see a product—short or medium term? And then, Susan, the ranking/recommendation model improvements are very impressive given the size and scale of both Instagram and Facebook. Could you help us understand how doubling the length of these interaction sequences can drive greater usage? There is a thesis that maybe some of the ranking recommendation improvements are along the same lines, so it seems as if there is more room to go.

Any help there would be helpful. Thank you.

Mark Elliot Zuckerberg: I think that the agents work will have short-term versions, but there is going to be massive upside from delivering more intelligence and more capabilities in the models. You are seeing this across the industry. Each generation of models has more capabilities, can do more things, and people absorb it and are able to get more superpowers. It is the most exciting time in the industry. I think of the agents as the product vehicle for delivering that capability to people, and I think this year is going to be a key period for establishing that as the vehicle for how people are going to use this.

But then the model work is going to be something that goes on for a very long time. There is a lot to do in the short, medium, and long term.

Susan Li: On your second question about the ranking and recommendations improvements, there is still a lot of room to continue improving recommendations over the rest of the year, and we expect we will be able to drive additional engagement on both Facebook and Instagram. We will continue to improve our data infrastructure to allow our models to train on more data. We are 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 datasets—like using even longer histories of content interactions—and that should improve the overall quality of recommendations.

We are also focused on making the recommendations even more personalized and more relevant to any given user's interests. We are redesigning our content retrieval system to show more content that matches the full range of a user's interests and tailoring the diversity of topics to the broadness of someone's interests. Someone with particularly concentrated interests might see relatively more of that content, while people with a broader set of interests might see a greater range in the topics we show them. Finally, we are continuing to make improvements to our LLM-based user control 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.

The sequence length you called out is one of many improvements we made in Q1, and there is a big roadmap of further improvements going forward.

Operator: Your next question comes from the line of Douglas Till Anmuth with JPMorgan. Please go ahead.

Douglas Till Anmuth: 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 Muse 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 talk at all about the strategic importance and the role in developing agentic products for Meta Platforms, Inc., and the current status around the tech and the deal? Thanks.

Susan Li: I will take that question. On Manus, we are still working through the details, so we do not have an update right now. On your first question about going from leveraging smaller models in the ads business to larger models, there is already work underway. Even in the current landscape of the ads roadmap, we are advancing the architecture to allow us to leverage the abilities of larger models. Historically, we have not used larger model architectures like GEM for inference because their size and complexity would make them too cost prohibitive. 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, and they need to find the right ad within milliseconds, which has historically prevented us from meaningfully 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 trillion-parameter class, and we made advances in the model architecture and the system with the underlying silicon so it maintains the sub-second speed required to serve ads at scale.

We also developed an approach that intelligently routes requests to 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 increased inference ROI. There is a lot of work being done there before we even incorporate more of the LLM work into our underlying ads ranking models.

Operator: We have time for one more question. Kenneth Gawrelski with Wells Fargo. Your line is open.

Kenneth Gawrelski: Thank you very much. Two, if I may. First, on the Muse Spark launch, you talked about two verticals: health and wellness and shopping. Can I ask you to dive a little deeper into shopping and commerce? Were there any learnings in the 2021–2022 phase where you pushed deeper into commerce on Instagram and on Facebook that you might apply? Is there an opportunity for a next-gen marketplace-type business in e-commerce? And second, Susan, based on your model improvements and the content recommendations, how much visibility do you think you have into the growth trajectory on the core business? You continue to grow basically at double the pace of the industry despite being a very large share.

Could you talk about your visibility into continued outperformance?

Mark Elliot Zuckerberg: I might give you a somewhat loftier answer to the shopping question. It is an interesting example of how the work we are doing is different from what others are doing. AI agents get better when you fully optimize the stack. That is why we believe we need to be a company that builds frontier models in addition to building the agents. To do that well, you need to build your infrastructure. So we are undertaking this large investment to do that top to bottom. A lot of the way to think about the investment that we are making is a bet that the individual things that people care about—and that people—are going to be more important in the future.

So much of the rhetoric around AI in the industry is around a company trying to build some kind of centralized thing that does all the productive work in society. That is very different from how we see the world. Our vision for the future is one where society makes progress by individuals pursuing their own aspirations. Some people care about big, grand things like curing diseases. A lot of people care about personal things like finding the right shirt for my daughter. I think that we are going to build things that help deliver this vision for personal agents for people.

Part of what is interesting and differentiated about what we are doing is that this is so different from how I hear everyone else talking about the work. Even though some of these ideas seem like they should be obvious, our approach of trying to empower individuals and building consumer things is, in the details, extremely different from what others are doing. Shopping might be one specific example that will have interesting commercial implications and that consumers will like. But I do not hear any other labs talking about how they are building an AI that is really good at shopping.

The reason for that is not because shopping is the most important thing by itself but because empowering people to do the things that matter in their lives—whether that is local, understanding social context, shopping, personal health, or understanding what is going on around them visually (which is going to be really important on the glasses)—these are all elements of the personal superintelligence vision. When you are thinking about the investment in Meta Platforms, Inc. over time, you should think about it as coming down to these values around what we want AI to do in society.

If what you want it to do is empower individuals and build a world where the AI is in service to individuals' goals, then that is what we are going to build, and I think it is going to be incredibly valuable.

Susan Li: Gosh. I almost wish we could end on that answer, but I will answer the second question. There are two versions. One is the revenue outlook, and we gave the Q2 guide, which embeds a range of macro outcomes as well as the ongoing work to continue improving usage and engagement on our family of apps and our ability to make the ads better and more performant. The second version is more of a higher-level question about the overall trajectory of the roadmap. One of the things I will say, having been working on this for a very long time, is I am always impressed by the team’s ability to continue to advance the state of the art here.

Our planning process is really fine-tuned around this. I have mentioned on a couple calls the budgeting process in which we run a very ROI-based process to make sure that we are funding all of the ads initiatives that we think will drive growth in future years. That process is quite dialed in. Our ability to measure the impact has been robust, and it has been a very important driver of our ads revenue growth, and that continues to be a process that we ran in this past budget. As far as we have line of sight, we feel good about the investment opportunities ahead of us.

Kenneth J. Dorell: Great. Thank you everyone for joining us today. We look forward to speaking with you again soon.

Operator: This concludes today’s conference call. Thank you for joining and you may now disconnect.

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