Meta Platforms, Inc. (NASDAQ:META) Q3 2025 Earnings Call Transcript

Meta Platforms, Inc. (NASDAQ:META) Q3 2025 Earnings Call Transcript October 29, 2025

Meta Platforms, Inc. beats earnings expectations. Reported EPS is $7.25, expectations were $6.72.

Operator: Good afternoon. My name is Christa, and I will be your conference operator today. At this time, I would like to welcome everyone to the Meta Third Quarter Earnings Conference Call. [Operator Instructions] This call will be recorded. Thank you very much. Kenneth Dorell, Meta’s Director of Investor Relations. You may begin.

Kenneth Dorell: Thank you. Good afternoon, and welcome to Meta’s Third Quarter 2025 Earnings Conference Call. Joining me today are Mark Zuckerberg, CEO; and Susan Li, CFO. Our remarks today will include forward-looking statements, which are based on assumptions as of today. Actual results may differ materially as a result of various factors, including those set forth in today’s earnings press release and in our quarterly report on Form 10-Q filed with the SEC. We undertake no obligation to update any forward-looking statement. During this call, we will present both GAAP and certain non-GAAP financial measures. A reconciliation of GAAP to non-GAAP measures is included in today’s earnings press release. The earnings press release and an accompanying investor presentation are available on our website at investor.atmeta.com. And now I’d like to turn the call over to Mark.

Mark Zuckerberg: All right. Thanks, Ken. Thanks, everyone, for joining today. We had another strong quarter with 3.5 billion people using at least one of our apps every day. Instagram had a major milestone with 3 billion monthly actives, and we’re seeing good momentum across our other apps as well, including Threads which recently passed 150 million daily actives and remains on track to become the leader in its category. I am very focused on establishing Meta as the leading frontier AI lab. Building personal superintelligence for everyone and delivering the app experiences and computing devices that will improve the lives of billions of people around the world. Our approach of advancing open source AI means that when Meta innovates, everyone benefits.

Meta Superintelligence Labs is off to a strong start. I think that we’ve already built the lab with the highest talent density in the industry. We’re heads down developing our next generation of models and products and I’m looking forward to sharing more on that front over the coming months. We’re also building what we expect to be an industry-leading amount of compute. Now there’s a range of time lines for when people think that we’re going to get superintelligence. Some people think that we’ll get there in a few years. Others think it will be 5, 7 years or longer. I think that it’s the right strategy to aggressively frontload building capacity so that way we’re prepared for the most optimistic cases. That way, if superintelligence arrives sooner, we will be ideally positioned for a generational paradigm shift in many large opportunities.

If it takes longer, then we’ll use the extra compute to accelerate our core business which continues to be able to profitably use much more compute than we’ve been able to throw at it. And we’re seeing very high demand for additional compute, both internally and externally. And in the worst case, we were just slow building new infrastructure for some period while we grow into what we build. The upside is extremely high for both our existing apps and new products and businesses that are becoming possible to build. Across Facebook, Instagram and Threads, our AI recommendation systems are delivering higher quality and more relevant content, which led to 5% more time spent on Facebook in Q3 and 10% on Threads. Video is a particular bright spot with video time spent on Instagram up more than 30% since last year.

And as video continues to grow across our apps, Reels now has an annual run rate of over $50 billion. Improvements in our recommendation systems will also become even more leveraged as the volume of AI-created content grows. Social media has gone through 2 eras so far. First was when all content was from friends, family and accounts that you followed directly. The second was when we added all of the creator content. Now as AI makes it easier to create and remix content, we’re going to add yet another huge corpus of content on top of those. Recommendation systems that understand all this content more deeply and can show you the right content to help you achieve your goals are going to be increasingly valuable. Our ads business continues to perform very well, largely due to improvements in our AI ranking systems as well.

This quarter, we saw meaningful advances from unifying different models into simpler, more general models, which drive both better performance and efficiency. And now the annual run rate going through our completely end-to-end AI-powered ad tools has passed $60 billion. And one way that I think about our company overall is that there are 3 giant transformers that run Facebook, Instagram and ads recommendations. We have a very strong pipeline of lots of ways to improve these models by incorporating new AI advances and capabilities. And at the same time, we are also working on combining these 3 major AI systems into a single unified AI system that will effectively run our family of apps and business using increasing intelligence to improve the trillions of recommendations that we’ll make for people every day.

I’m also very excited about the new products that we’re going to be able to build. More than 1 billion monthly actives already use Meta AI and we see usage increase as we improve our underlying models. I’m very excited to get a frontier model into Meta AI and I think that the opportunity there is very large. The same goes for our business AI. Every day, people have more than 1 billion active threads with business accounts across our messaging platforms, ranging from product questions to customer support. Our business AIs will enable tens of millions of businesses to scale these conversations and improve their sales at low cost and the better our models get, the better this is going to work for all businesses. This quarter, we also launched Vibes which is the next generation of our AI creation tools and content experiences.

Retention is looking good so far. And its usage keeps growing quickly week over week. I’m looking forward to ramping up the growth of Vibes over the coming months. More broadly, I think that Vibes is an example of a new content type enabled by AI, and I think that there are more opportunities to build many more novel types of content ahead as well. As our new models become ready, I’m looking forward to starting to show everyone some of the new kinds of products that we’re working on. At Connect, we announced our 2025 line of AI glasses, and the response so far has been great. The new Ray-Ban Meta glasses and Oakley Meta Vanguards are both selling well. As people love the improved battery life, camera resolution, new AI capabilities and the great design.

And there’s our new Meta Ray-Ban display glasses, our first glasses with a high-resolution display and the metaneural band to interact with them. They sold out in almost every store within 48 hours with demo slots fully booked through the end of next month. So we’re going to have to invest in increasing manufacturing and selling more of those. This is an area where we are clearly leading and have a huge opportunity ahead. Taking a step back, if we deliver even a fraction of the opportunity ahead for our existing apps and the new experiences that are possible, then I think that the next few years will be the most exciting period in our history. We’ve got a lot to do. But we’re making real progress, delivering strong business results, building the talent density and infrastructure needed for the next era and leading the way on AI devices that will define the next computing platform.

I am proud of how our teams are rising to the challenge, and I’m grateful for their dedication, hard work and creativity. As always, thank you all for being a part of this journey with us. And now here is Susan.

Susan Li: Thanks, Mark, and good afternoon, everyone. Let’s begin with our segment results. All comparisons are on a year-over-year basis, unless otherwise noted. Our community across the family of apps continues to grow, and we estimate more than 3.5 billion people used at least 1 of our family of apps on a daily basis in September. Q3 total family of apps revenue was $50.8 billion, up 26% year-over-year. Q3 family of apps ad revenue was $50.1 billion, up 26% or 25% on a constant currency basis. In Q3, the total number of ad impressions served across our services increased 14%. Impression growth was healthy across all regions driven by engagement and user growth, particularly on video services. The average price per ad increased 10% year-over-year, benefiting from increased advertiser demand, largely driven by improved ad performance.

This was partially offset by impression growth, particularly from lower monetizing regions and services. Family of Apps other revenue was $690 million, up 59%, driven by WhatsApp paid messaging revenue growth as well as meta verified subscriptions. Within our Reality Labs segment, Q3 revenue was $470 million, up 74% year-over-year. The significant year-over-year growth in Q3 was partly due to retail partners stocking up on Quest headsets ahead of the holiday season. We did not have a similar benefit in the third quarter of last year since our Quest 3S headset launched in the fourth quarter of 2024. Aside from this, strong AI glasses revenue also contributed to revenue growth in Q3. Moving now to our consolidated results. Q3 total revenue was $51.2 billion, up 26% or 25% on a constant currency basis.

Q3 total expenses were $30.7 billion, up 32% compared to last year. Year-over-year expense growth accelerated 20 percentage points from Q2 due primarily to 3 factors: First, legal-related expense growth was higher than in Q2 due to charges we recorded in the third quarter as well as us lapping a period of accrual reversals in the third quarter a year ago. Second, employee compensation growth accelerated, driven by technical hires, particularly AI talent. Finally, growth in infrastructure costs accelerated due to increased infrastructure operating costs associated with our expanded data center fleet, depreciation on our incremental CapEx spend and third-party cloud spend. We ended Q3 with over 78,400 employees, up 8% year-over-year, driven by hiring in priority areas of monetization, infrastructure, Reality Labs, Meta Superintelligence Labs as well as regulation and compliance.

Third quarter operating income was $20.5 billion, representing a 40% operating margin. Q3 interest and other income was $1.1 billion driven primarily by unrealized gains on our marketable equity securities. Our tax rate for the quarter was 87%, which was unfavorably impacted by a onetime noncash reduction in deferred tax assets that we no longer anticipate using under new U.S. tax law. Our tax rate would have been 14%, excluding this charge. Although the transition to the new U.S. tax law resulted in an accounting charge in the third quarter, we continue to expect we will recognize significant cash tax savings for the remainder of the current year and future years under the new law, and this quarter’s charge reflects the total expected impact from the transition to the new U.S. tax law.

Net income was $2.7 billion or $1.05 per share. Excluding the onetime tax charge, our net income and EPS would have been $18.6 billion and $7.25 per share, respectively. Capital expenditures, including principal payments on finance leases were $19.4 billion, driven by investments in servers, data centers and network infrastructure. Free cash flow was $10.6 billion. We repurchased $3.2 billion of our Class A common stock and paid $1.3 billion in dividends to shareholders. We ended the quarter with $44.4 billion in cash and marketable securities and $28.8 billion in debt. Turning now to the business outlook. There are 2 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, daily actives continue to grow year-over-year across Facebook, Instagram and WhatsApp. We’re continuing to see improvements to our products and recommendations drive incremental engagement with year-over-year growth in global time spent accelerating on both Facebook and Instagram in Q3. In the U.S., overall time spent on Facebook and Instagram grew double digits year-over-year, driven by continued video strength as well as healthy growth in non-video time on Facebook. The engagement gains continue to be driven by product work and ongoing improvements to our recommendation systems as we optimize our model architectures, implement advanced modeling techniques and integrate more signals about people’s interests. We also continue to focus on increasing the freshness of recommended content.

On Facebook, our systems are now surfacing twice as many Reels published that day than at the start of the year. Looking to 2026, we expect to advance our recommendation systems across several dimensions. On Instagram, one focus is evolving our systems to surface content across a broader set of topics that cater to the diverse interest of each person. This follows a similar approach we’ve implemented on Facebook that has driven good results. We also expect to make significant progress on our longer-term ranking innovations in 2026. We’re seeing promising new results from our research efforts to create foundational ranking models and expect the new model innovations we’re developing as part of this will enable us to significantly scale up the amount of data and compute we use to train our recommendation models in 2026, yielding more relevant recommendations.

Another large focus next year is leveraging LLMs to improve content understanding. We expect this is going to enable our systems to more precisely label the keywords and topics within videos and posts, which will allow our systems to both develop deeper intuition about a person’s interest and retrieve the content that matches them. Finally, we’re making good progress with Meta AI and Threads. The number of people using Meta AI across our family of apps continues to grow, and we’re increasingly leveraging first-party content into Meta AI results with the majority of Meta AI’s responses to Facebook Deep Dive queries in the U.S. now showing related Reels. We’re also seeing a lot of traction with media generation. People have created over 20 billion images using our products.

And since launching Vibes within Meta AI in September, we have seen media generation in the app increased more than tenfold. On Threads, we see strong growth in both daily actives and the depth of engagement as we continue to improve recommendations. The ranking optimizations we made in Q3 alone drove a 10% increase in time spent on Threads. We also continue to ship new features, including launching direct messaging in Q3, so anyone on Threads can now message one another within the app. Now 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. We continue to refine ad supply across each of our major surfaces within Facebook and Instagram to better deliver ads at the time and place they are most relevant to people.

Longer term, we have exciting ad supply opportunities on both Threads and WhatsApp status. Ads are now running globally and feed on Threads and we’re following our typical monetization playbook of optimizing the ads formats and performance before we ramp supply. Within WhatsApp status, we’re continuing to gradually introduce ads and expect to complete the rollout next year. The second part of increasing monetization efficiency is improving marketing performance. Advancing our ad systems remains a critical aspect of this work and we are driving performance gains through ongoing improvements in our larger scale ads ranking models. For example, we continue to broaden the adoption of Lattice, our unified model architecture. In Q3, we rolled out Lattice to app ads, which drove a nearly 3% gain in conversions for that objective.

Since introducing Lattice back in 2023, along with other back-end improvements, we have now cut the number of ads ranking and recommendation models by approximately 100 as we consolidated smaller and more specialized models into larger ones that use the Lattice architecture to generalize learnings across surfaces and objectives. We continue to observe performance improvements as we combine models and expect to drive additional gains as we consolidate another 200 models over the coming years into a smaller number of highly capable models. In addition to advancing our foundational ads models, we’re innovating on our run time models we use downstream of them for ads inference. For example, we began piloting a new run time ads ranking model in Q3 that leverages more compute and data than our prior models to select more relevant ads.

In testing, we’ve seen this new model drive a more than 2% lift in conversions on Instagram. We also significantly improved performance of Andromeda in Q3 by combining models across retrieval and early-stage ranking into a single model, driving a 14% increase in ads quality on Facebook surfaces. Within our ads products, we’re seeing continued momentum with Advantage+. In Q3, we completed the rollout of our streamlined campaign creation flow for Advantage+ lead campaigns. So now advertisers running sales app or lead campaigns have end-to-end automation turned on from the beginning, allowing our systems to look across our platform to optimize performance by automatically choosing criteria like who to show the ads to and where to show them. The annual run rate of revenue running through our end-to-end automated solutions has now reached $60 billion following the implementation of the new streamlined creation flow, as we continue to see more advertisers leverage the performance benefits of our solutions.

Within our Advantage+ creative suite, the number of advertisers using at least 1 of our video generation features was up 20% versus the prior quarter as adoption of image animation and video expansion continues to scale. We’ve also added more generative AI features to make it easier for advertisers to optimize their ad creatives and drive increased performance. In Q3, we introduced AI generated music. So advertisers can have music generated for their ad that aligns with the tone and message of the creative. Finally, business messaging remains a significant opportunity for us. We’re seeing strong growth across our portfolio of solutions, including with click-to-WhatsApp ads, which grew revenue 60% year-over-year in Q3. We’re also making good progress on our business AI efforts, where we’ve been focused on building a turnkey AI that helps businesses generate leads and drive sales.

We’ve been opening access in recent months to more businesses within our initial test markets, the Philippines and Mexico. And I’ve seen strong usage with millions of conversations between people and business AIs taking place since July. This month, we expanded availability within WhatsApp and Messenger to all eligible businesses in Mexico and the Philippines, respectively. In the U.S., we’re also starting to roll out the ability for merchants to add their business AIs to their website so we can support the full sale funnel from ad to purchase. Next, I would like to discuss our approach to capital allocation. Our primary focus is deploying capital to support the company’s highest order priorities including developing leading AI products models and business solutions.

As we make significant investments in infrastructure to support this work, we are focused on preserving maximum long-term flexibility to ensure we can meet our future capacity needs while also being able to respond to how the market develops in the years ahead. We’re doing so in several ways, including staging data center sites so we can spring up capacity quickly in future years as we need it as well as establishing strategic partnerships that give us option value for future compute needs. The strong financial position and cash generation of our business enable us to make these investments while also accessing additional pools of cost-efficient capital. Moving to our financial outlook. We expect fourth quarter 2025 total revenue to be in the range of $56 billion to $59 billion.

Our guidance assumes foreign currency is an approximately 1% tailwind to year-over-year total revenue growth based on current exchange rates. Our outlook reflects an expectation for continued strong ad revenue growth partially offset by lower year-over-year Reality Labs revenue in Q4. The anticipated reduction in Reality Labs revenue is due to us lapping the introduction of Quest 3S in Q4 of last year. As well as retail partners procuring Quest headsets during Q3 of this year to prepare for the holiday season, which were recorded as revenue in the third quarter. Turning to the expense and CapEx outlooks. I’ll first start with 2025 before providing some commentary on our planning for 2026. We expect full year 2025 total expenses to be in the range of $116 billion to $118 billion, updated from our prior outlook of $114 billion to $118 billion and reflecting a growth rate of 22% to 24% year-over-year.

We currently expect 2025 capital expenditures, including principal payments on finance leases to be in the range of $70 billion to $72 billion, increased from our prior outlook of $66 billion to $72 billion. On to tax. Absent any changes to our tax landscape we expect our fourth quarter 2025 tax rate to be 12% to 15%. Turning now to 2026. We are at an exciting point for our company. where we have continued runway to improve our core services today as well as the opportunity to build new AI-powered experiences and services that will transform how people engage with our products in the future. We expect the set of investments we’re making within our ads and organic engagement initiatives next year will enable us to continue to deliver strong revenue growth in 2026.

While our progress on AI models and products will position us to capitalize on new revenue opportunities in the years to come. A central requirement to realizing these opportunities is infrastructure capacity. As we have begun to plan for next year, it’s become clear that our compute needs have continued to expand meaningfully, including versus our own expectations last quarter. We are still working through our capacity plans for next year, but we expect to invest aggressively to meet these needs, both by building our own infrastructure and contracting with third-party cloud providers. We anticipate this will provide further upward pressure on our CapEx and expense plans next year. As a result, our current expectation is that CapEx dollar growth will be notably larger in 2026 than 2025.

We also anticipate total expenses will grow at a significantly faster percentage rate a than 2025, with growth primarily driven by infrastructure costs, including incremental cloud expenses and depreciation. Employee compensation costs will be the second largest contributor to growth. As we recognize a full year of compensation for employees hired throughout 2025, particularly AI talent and add technical talent in priority areas. Finally, we continue to monitor active legal and regulatory matters, including the increasing headwinds in the EU and the U.S. that could significantly impact our business and financial results. For example, in the EU, we continue to engage constructively with the European Commission on our less personalized ads offering.

However, we cannot rule out the commission imposing further changes to that offering that could have a significant negative impact on our European revenue as early as this quarter. In the U.S., a number of youth-related trials are scheduled for 2026 and may ultimately result in a material loss. In closing, this was another good quarter for our business. We have an exciting set of opportunities to continue improving our core business while delivering innovative new experiences and services for the people and businesses using our products in the years to come. With that, Christa, let’s open up the call for questions.

Q&A Session

Operator: [Operator Instructions] And your first question comes from the line of Brian Nowak with Morgan Stanley.

Brian Nowak: I have 2 for Susan. The first one, Susan. So the pipeline for core improvements to come in ’26 with models and ad ranking models and more [ FPS ] of compute seems very exciting and the infrastructure build team sizable behind that. So can you help us a little to understand some of the early quantifiable signals you’re seeing on AB tests from some of these improvements to come that sort of make you most excited and give you confidence you’re going to get ROIC from all this CapEx? That’s the first one. Second one is a little faster. How large is the Reality Labs revenue headwind in the 4Q guidance?

Susan Li: Thanks, Brian, for the question. I think your first question had a couple of parts to it. So I’m going to try to disaggregate those parts, and let me know if this addresses what you’re getting to. I will say that the growth in 2026 CapEx relative to 2025 comes from growth in each of the core areas, MSL, core AI as well as non-AI spend. So all of those areas are growing, but the MSL AI needs are growing the most. In terms of the core AI pipeline, I think, we talked about last year when we were going into the 2025 budget process, we had a road map of resource investments across both head count and compute that we thought would pay off in 2026. And it’s really a very broad range of sort of different ads ranking and performance efforts.

And we’re continuing to see that those have paid off through the course of the year. There is a long list of specific efforts, but 1 of the measures that we look at to monitor this is how are we driving ad performance, how are conversions growing? Conversions is a complex metric for us because advertisers optimize for so many different conversions on different values. But when we control for that and look at value-weighted conversion rates, we’re seeing very strong year-over-year growth and conversion — weighted conversions continue to grow faster than impressions. We also talked about some of the new model architecture over the course of the year and the degree to which the new model architecture is enabling us also to take advantage of having more data and more compute to drive ads performance.

So we expect that, that’s going to be a continued story in 2026. We are, in fact, at the beginning of our 2026 budgeting process now, and we see a similar list of revenue investments that we’re excited to be able to invest in. And so we think that, that’s going to be a big part of our ability to continue to drive strong revenue performance throughout the year. On your second question, which is the Reality Labs revenue headwind. I don’t think we have quantified the exact size of that. We expect that Q4 Reality Labs revenue will be lower than last year for a couple of reasons that I alluded to, the biggest factor is we’re lapping the introduction of Quest 3S in Q4 of last year, and we don’t have a new headset in the market this year. We also recorded all of our holiday-related Quest 3S sales in Q4 ’24, since the headset was launched in October ’24.

This year, we’re recognizing some of those Quest 3S sales in Q3 as retail partners have procured Quest headsets in advance of the holiday season. We’re still expecting significant year-over-year growth in AI Glasses revenue in Q4 as we benefit from strong demand for the recent products that we’ve introduced, but that is more than offset by the headwinds to the Quest headsets.

Operator: Your next question comes from the line of Doug Anmuth with JPMorgan.

Douglas Anmuth: I appreciate the strategy to front-load capacity for superintelligence. Can you just talk about your thought process and kind of triangulating the CapEx dollar growth and the significantly faster expense growth next year with core growth in the business and then the impact on earnings and free cash flow? And do you have targets that we should be thinking about for cash on hand or net cash overall?

Susan Li: Thanks, Doug. We’re, right now, I would say, in the process of — relatively early, actually still in the process of putting together our budget for 2026. And it is on the capacity side, a particularly dynamic process. We’re certainly seeing that we wish we had more capacity today than we do. We would be able to put it towards good use certain not only with the MSL team appreciate having more capacity, but we’d be able to put it towards good and ROI-positive use in the core business as well. So we’re really trying to plan ahead not only to ensure that we have the capacity we need in 2026, but also to give ourselves the sort of flexibility and option value to have the capacity that we think we could need in ’27 and ’28.

So that said, there are lots of moving pieces in the budget. It’s not baked yet. It’s still sort of in the process of coming together. We don’t have specific targets to share — but we do feel like our strategic priority is really making sure that we have the compute that we need to be well positioned to succeed at AI, and that’s sort of the foremost priority as we’re putting together the budget.

Mark Zuckerberg: Yes. I mean, I’ll add a few thoughts on this, too, although I mean, as Susan said, we’re still working through the actual budget, and I think we’ll typically have more to share on that early next year. But to date, we keep on seeing this pattern where we build some amount of infrastructure to what we think is an aggressive assumption. And then we keep on having more demand to be able to use more compute, especially in the core business in ways that we think would be quite profitable, then we end up having compute for. So I think that suggests that being able to make a significantly larger investment here is very likely to be a profitable thing over some period because if the primary use of it is going to be to accelerate the AI research and the new AI work that we’re doing and how that relates to both the core business and new products.

But any compute that we don’t need for that we feel pretty good that we’re going to be able to absorb a very large amount of that to just convert into more intelligence and better recommendations in our family of apps and ads in a profitable way. Now I mean, it’s, of course, possible to overshoot that, right? And if we do, I mean, this is what I mentioned in my comments, then we see that there’s just a lot of demand for other new things that we build internally, externally, like almost every week, people come to us from outside the company asking us to stand up an API service or asking if we have different compute that they could get from us and we haven’t done that yet. But obviously, if you got to a point where you ever built, you could have that as an option.

And then the kind of the very worst case would be that we effectively have just prebuilt for a couple of years, in which case, of course, there would be some loss and depreciation, but we’d grow into that and use it over time. So my view on this is that rather than continuing to be constrained on CapEx and feeling in the core business like we have significant investments that we could make that we’re not able to make that would be profitable. But the right thing to do is to try to accelerate this to make sure that we have the compute that we need, both for the AI research and new things that we’re doing and to try to get to a different state on our compute stance on the core business. So that’s kind of how I’m thinking about that overall. Of course, there’s a lot of operational constraints too on what one can build, right?

So we’re basically trying to work through this all, and I think we’ll have more to share in the coming months and over the course of next year. But I can think there’s just a huge, huge amount of opportunities ahead here.

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

Eric Sheridan: Mark, I wanted to reflect on some of your comments with respect to scaling towards superintelligence and bringing it back to consumer AI, maybe reflect a little bit on the signals you’ve gotten on the way consumers across family of apps interact with Meta AI today? And how you think about scaling and exiting models from the superintelligence effort might change the utility and behavior around Meta AI in the years ahead.

Mark Zuckerberg: Yes. I mean a lot of people use Meta AI today. I mean, as I said in my comments upfront, there’s more than 1 billion people who use it on a monthly basis. And what we see is that as we improve the quality of the model, primarily for post-training Llama 4 at this point. We are — we continue to see improvements in usage. So our view is that when we get the new models that we’re building in MSL in there and get like truly frontier models with novel capabilities that you don’t have in other places, then I think that this is just a massive latent opportunity, right? We know — I mean I would guess that Meta I think has the best track record of any company out there of taking a new product that people love and getting it to billions of people in terms of usage.

So I think that the ability to plug in leading models is going to — I would predict lead to a very large amount of use of these things over the coming years. So I’m very excited about that in terms of new products. It’s not just Meta AI is an assistant. I think that there are going to be all kinds of new products around different content formats, and we’re starting to see that with video and content creation, but I think there’s going to be a lot more like that, that I’m quite excited about. And then there are the business versions of all these 2, like business AI. And then that’s, of course, one part of the story is the new things that will be possible to build. And then the other part is how more intelligent models are just going to improve the core business.

And improve the recommendations that we make across the family of apps and improve the recommendations and advertising. And I think there’s just a — as we’ve shown, there’s sort of this very large amount of headroom and the opportunity there keeps growing as we as we are improving and optimizing the AI there. And I think that, that really shows no sign of being near the end. I think that there’s quite a bit more to do there. And like I said in response to the last question, we are sort of perennially operating the family of apps and ads business in a compute starved state at this point, which is, on the one hand, sort of an odd thing to say, given the compute that we built up. But we really are taking a lot of the resources and using them to advance future things that we’re doing, and we think that there’s a lot more compute that we could put towards these that would just unlock a huge amount of opportunity in the core business as well.

Operator: Your next question comes from the line of Mark Shmulik with Bernstein.

Mark Shmulik: Susan, as you think about the visibility into kind of the runway next year of continued add performance and engagement improvements, how do you think about kind of the scale of those improvements versus kind of the progress we’ve seen over the last 2 years? And then, Mark, as you think about kind of the timing of some of these newer efforts coming out of Superintelligence Labs, is that anchoring to kind of an updated frontier model launch sometime next year like the right way for us to think about it? Or should we be looking at kind of progress from new products you’re excited to see shift like Vibes?

Susan Li: Thanks, Mark. So on the sort of adds improvement side, some of the innovations that we have been launching actually involve sort of improving our larger scale models. So we don’t use our larger model architectures like GEM for inference because their size and complexity would make it too cost prohibitive. The way that we drive performance from those models is by using them to transfer knowledge to smaller lightweight models that are used at run time. And then in addition to the foundation model work, we are working on advancing our inference models by developing new techniques in architectures that then allow us to scale up compute and complexity in an ROI-positive way. So in general, we obviously, have a very large base of advertisers.

There’s a lot of demand liquidity in the system and even small-scale improvements that we are able to make in terms of driving basis point improvements in the performance of ads or single-digit increases in conversions relative to impressions in a given quarter off of a large base mean that we’re really able to continue to grow the absolute dollars of revenue growth in a pretty meaningful way.

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

Kenneth Dorell: Justin, just give us one second. I think there was a second Mark’s question that we just want to get to on MSL.

Mark Zuckerberg: Yes. I mean, I’ll keep it quick. I mean I don’t think we have any specific timing to announce certainly on the models or products, but I expect that you will see both. We expect to build novel models and novel products, and I’m excited to share more when we have it.

Justin Post: So Mark, you mentioned the prior 2 content cycles, and obviously, you’ve been able to generate very attractive margins on them. As we get into the AI cycle, obviously, some concerns on the investment. But can you talk a little bit about how you’re thinking about tools that could be coming out for users? I know there’s some new competition. And then secondly, how do you think about margins in this content cycle? Any reason to think they would be different versus prior cycles.

Mark Zuckerberg: I think it’s too early to really understand what the margins are going to be for the new products that we build. I mean, I think certainly, every — each product has somewhat different characteristics. And I think we’ll kind of understand how that goes over time. I mean, my general goal is to build a business that maximizes value for the people who use our products and maximizes profitability, not margin. So I think we’ll kind of just try to build the best things that we can and try to deliver the most value that we can for most people.

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

Ross Sandler: Great. And Mark, some of the goals for competing AI labs are around achieving AGI or these other milestones that are kind of like out there and a little esoteric. How are you setting up your new team in terms of achieving those types of goals versus products that can generate revenue for Meta kind of right out of the gate. And is the goal that you had articulated to us previously around giving billions of people kind of a personal AI to use. Is that still the direction of travel that you see? Or is there other things like kind of the Vibes or [ Sora ] angle that you think are potentially important. How should we think about like the overall direction.

Mark Zuckerberg: Sure. So the way that I think about this is that the research is going to enable new technological capabilities to exist. And then those capabilities can get built into all kinds of different products. So the ability to reason more intelligently is, for example, very important across a large number of things. It would be useful for an assistant. It will also be useful in business AI. It will also be useful in the AI agent that we’re building to help advertisers figure out what their campaigns are going to be. It will also have implications for eventually how we do ranking and recommendations of people’s feeds and make different decisions there. That’s just one example. I mean certainly, the capability to be able to produce very high-quality good video is going to be useful for giving people new creative tools.

It will help increase the amount of content inventory that can be shown in Instagram and Facebook and therefore, should enable an increase in engagement there. It should help advertisers be able to create creative that will help us monetize better. So you can just go kind of down the list of capabilities that you’d expect. And I think each one will enable a bunch of different things. And I think the art of product development here is looking at the list of technology capabilities and figuring out what new products are going to be useful and prioritizing those. But fundamentally, I would sort of expect this exponential curve in new technology capabilities that are going to become available. And the other thing that I expect is that I think being the best in a given area will drive great returns rather than — this is not like a check-the-box exercise of like, okay, we can generate some kind of content and someone else can.

I think that like the company that is the best at each of these capabilities, I think, will get a large amount of the potential value for doing that. So there are lots of different capabilities to build. I’m not sure that any one company is going to be the best at all of them. I doubt that’s going to be the case. But a lot of what we’re trying to do is not like not kind of do some things that others have done. We’re really trying to build novel capabilities. And I’m keeping this high level because I’m not — I don’t want to necessarily from a competitive or strategic perspective, get into what we’re prioritizing. But that hopefully gives you a sense of how we’re thinking about what we’re doing. We want to be able to kind of build novel things, build them into a lot of our products and then have the compute to scale them to billions of people.

And we think that that’s going to both show up in terms of new products being possible in new businesses and very significant improvements to the current business, too.

Operator: Your next question comes from the line of Mark Mahaney with Evercore ISI.

Mark Stephen Mahaney: Just a question on Meta AI and both product and a monetization path. So when you look at it, what you’ve seen that’s most encouraging to you in terms of the adoption and the use of Meta AI? And then when you think about — I know you generally like to roll out and then deepen engagement and then later think about monetization. Like where do you think you are on that path now? Is it clear to you what the monetization options are for Meta AI.

Mark Zuckerberg: I mean, I think the most promising thing that we’re seeing is, one, that we were able to build something that a large number of people use, and I think that’s valuable. And then secondly, that as we — there is a clear correlation as we improve the models in ways that we think make them better, that people use them more. So that shows that we have a runway to basically be able to improve engagement and turn this into a product that’s leading over time. In terms of where we are on this, and we basically just did this huge effort to boot up Meta Superintelligence Labs and build what I am very proud of is, I think, the highest talent density lab in the industry at this point. There are a lot of really great researchers and infrastructure folks and data folks who are now a part of this effort who are focused on training the next generation of work and doing some really novel work.

And when that is ready, I think that we will be able to plug that into a number of the products that we’re building, and I think that, that will be very exciting. But I think that’s really the next thing that we’re looking at. And then from there, I think that these models will also improve monetization in all of the different ways that we’ve talked about so far in terms of improving engagement, improving advertising, helping advertisers engage. I mean there’s one opportunity that we just usually talk about on these calls, but hasn’t come up as much here is just the ability to make it so that advertisers are increasingly just going to be able to give us a business objective and give us a credit card or bank account and like have the AI system basically figure out everything else that’s necessary.

Including generating video or different types of creative that might resonate with different people that are personalized in different ways, finding who the right customers are. All of these — all of the capabilities that we’re building, I think, go towards improving all of these different things. So I’m quite optimistic about that.

Operator: Your next question comes from the line of Ronald Josey with Citi.

Ronald Josey: And this maybe dovetails perfectly off Mark, what you just talked about. And we heard a lot about end-to-end automation here, I think, reaching a $60 billion ARR. I wanted to hear about — if you can talk to us more just about adoption rates amongst the advertisers? And then maybe bigger picture, as you incorporate ranking recommendation changes like Andromeda, GEMS or Lattice, just talk to us how this automation is driving, call it, a higher ROI for advertisers overall, as we bring it all together.

Susan Li: Yes. So we’ve been sort of laying the continued brick-by-brick build of Advantage+ and extending the set of objectives that it applies to over time. And so in Q3 we completed the global rollout of the streamlined campaign creation flow for Advantage+ lead campaigns. So now advertisers who are running sales apps or lead campaigns have end-to-end automation turned on from the beginning. And like the kind of application of the streamlined campaign creation flow for other objectives, this generally allows advertisers to optimize and automate several aspects of the campaign setup process at once. That includes things like audience selection where to show the ad, how the budget gets paced and distributed across ad sets to drive the most efficient outcomes.

And we see that Advantage+ continues to drive performance gains, advertisers who run lead campaigns using Advantage+ are seeing a 14% lower cost per lead on average than those who are not. And I would say that we think there is still a lot of opportunity generally to grow adoption of Advantage+. A lot of advertisers only use our end-to-end automated solutions for a portion of their campaigns so we can grow share there. And to capture that opportunity, we’re focused on driving continued performance improvements and addressing some of the key use cases that we still need in order to grow adoption. We’re also working to broaden adoption among advertisers who use 1 of our single step automated solutions. For example, advertisers who might only use a piece of it like Advantage+ audiences by helping them understand the benefits of using more than one automated solution at the same time.

So I would say Advantage+ is sort of an ongoing platform by which we both continue to expand the feature set that is available in Advantage+ and then expand the extensibility or the coverage of that feature set to sort of the broader set of advertisers. I think Mark mentioned that the annual revenue run rate now for advertisers who are using these automated options is $60 billion. And again, we see that there is room to continue growing that.

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

Youssef Squali: Great. Mark, on wearables, in particular, do you think you’ll be able to sell enough hardware to recoup your investment? Or is that dependent on maybe creating new avenues for revenue from things like advertising services and commerce through that new computing platform? And if so, what are kind of the gating factors there? And then Susan, how do you see the on-balance sheet versus off-balance sheet financing of your AI initiatives? You’ve recently struck a deal with Blue Owl for the Louisiana data center. Is that part of the CapEx guide for ’26? And if it’s not, how significant will that way of funding for Meta going forward? And basically, would that slow down your CapEx growth past 2026.

Mark Zuckerberg: I can talk about wearables, and then Susan can jump in on the other part. So I know there are a few pieces here. One is that the work on Ray-Ban Meta and the Oakley Meta product is going very well. I think, yes, I mean, at some point, if these continue going as well as it has been, then I think it will be a very profitable investment. I think that there’s some revenue that we get from basically selling the devices and then some that will come from additional services from the AI on top of it. So I think that there’s a big opportunity. Certainly, the investment here is not just to kind of build just the device. It’s also to build these services on top. Right now, a lot of people get the devices for a range of things that don’t even include the AI even though they like the AI.

But I think over time, the AI is going to become the main thing that people are using them for and I think that that’s going to end up having a big business opportunity by itself. But as products like the Ray-Ban Meta and Oakley Metas are growing, we’re also going to keep on investing in things like the more full field of view, product form of the Orion prototype that we showed at Connect last year. So those things are obviously earlier in their curve towards getting to being a sustaining business. And our general view is that we want to build these out to reach many hundreds of millions or billions of people and that’s the point at which we think that this is going to be just an extremely profitable business.

Susan Li: Youssef, to your second question, so the JV that we announced with Blue Owl is sort of an example of finding a solution that enabled us to partner with external capital providers to codevelop data centers in a way that gives us long-term optionality in supporting our future capacity needs just given both the magnitude, but also uncertainty of what the capacity outlook in future years looks like. In terms of how that is recognized as CapEx, our prior CapEx reflected a portion of the data center build cost prior to the joint venture being established. Going forward, the construction cost of the data center will not be recorded in CapEx as the data center is constructed, we will contribute 20% of the remaining construction costs required, which is in line with our ownership stake, and those will be recorded as other investing cash flows.

Operator: Your last question comes from the line of Ken Gawrelski with Wells Fargo.

Kenneth Gawrelski: Just one for me, please. Mark, as you think about with the — hopefully, a leading frontier model next year in hand, could you talk about where you think the value will accrue in this evolving ecosystem? Will it be with the platforms? Or do you think that this will be mostly — the value will accrue to the scaled first-party applications.

Mark Zuckerberg: I guess I’m not exactly sure what you mean by platform versus application in this context. But I mean I think that — I mean, I think there’s just a lot of value to create with AI overall. So I mean, clearly, you’re seeing the people who are making the hardware. NVIDIA is doing an amazing job, right, I think, extremely well-deserved success. The cloud partners and companies are making — are doing very well. I think that will likely continue. I think there’s a huge opportunity there. And — but if you look at it today, the companies that are building apps, I mean, a lot of the apps are still relatively small. And I think that’s obviously going to be a huge opportunity. And I think what we’ve seen overall is basically people take like individual technology advances and build them into products that then build either communities or other kinds of network effects and then end up being very sustaining businesses.

And I think what we haven’t really seen as much in the history of the technology industry is the rate of new capabilities being introduced because around each of these capabilities, you can build many new products that I think each will turn into interesting businesses. So yes. I don’t know. I mean I’m generally pretty optimistic about there being a very large opportunity. But in terms of new things to build, I think being able to build them and then scale them to billions of people is a huge muscle that Meta has developed, and I think we do very well. And I certainly think that, that’s going to deliver a huge amount of value, both in the core business for all the ways that we talked about, how it’s going to improve recommendations and the quality of the services as well as unifying the models together.

And so that way, when these systems are deciding what to show they can just pull from a wider pool. And that we’ve — these are things that we’ve just seen over the 20-plus years of running the company that they just deliver consistent wins. That we’re going to keep on being able to make the systems more general and smarter and make better recommendations for people and have a larger pool of inventory. And that is all going to be great. And then there’s going to be a lot of new things that I think we’re going to be able to take and scale to billions of people over time and build new businesses, whether that’s advertising or commerce supported or people paying for it or different kinds of things. So yes, it’s — I think it’s pretty early, but I think we’re seeing the returns in the core business.

That’s giving us a lot of confidence that we should be investing a lot more, and we want to make sure that we’re not under-investing.

Kenneth 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 your participation, and you may now disconnect.