META Meta Platforms, Inc. · Q2 2025 Earnings Call

Second Quarter 2025 Results Conference Call

July 30th, 2025

Thank you. Good afternoon and welcome to Meta’s second quarter 2025 earnings conference call.

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.

We had another strong quarter with more than 3.4 billion people using at least one of our apps

each day -- and strong engagement across the board. Our business continues to perform very

well, which enables us to invest heavily in our AI efforts.

Over the last few months we have begun to see glimpses of our AI systems improving themselves.

The improvement is slow for now, but undeniable. Developing superintelligence -- which we define

as AI that surpasses human intelligence in every way -- we think is now in sight.

Meta's vision is to bring personal superintelligence to everyone -- so that people can direct it

towards what they value in their own lives. We believe this has the potential to begin an exciting

new era of individual empowerment.

A lot has been written about the economic and scientific advances that superintelligence can

bring. I am extremely optimistic about this. But I think that if history is a guide, then an even more

important role will be how superintelligence empowers people to be more creative, develop

culture and communities, connect with each other, and lead more fulfilling lives.

To build this future, we've established Meta Superintelligence Labs, which includes our

foundations, product, and FAIR teams, as well as a new lab that is focused on developing the next

generation of our models. We're making good progress towards Llama 4.1 and 4.2 -- and in

parallel, we're also working on our next generation of models that will push the frontier in the next

year or so.

We're building an elite, talent-dense team. Alexandr Wang is leading the overall team, Nat

Friedman is leading our AI products and applied research, and Shengjia Zhao is Chief Scientist for

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the new effort. They're all incredibly talented leaders and I'm excited to work closely with them

and the world-class group of AI researchers, and infrastructure and data engineers that we're

assembling.

I've spent a lot of time building this team this quarter, and the reason that so many people are

excited to join is because Meta has all the ingredients required to build leading models and deliver

them to billions of people. The people who are joining us will have access to unparalleled compute

as we build out several multi-GW clusters. Our Prometheus cluster is coming online next year and

we think it'll be the world's first 1GW+ cluster. We're also building out Hyperion, which will be able

to scale up to 5GW over several years. And we have multiple more titan clusters in development as

well.

We're making all these investments because we have conviction that superintelligence is going to

improve every aspect of what we do.

From a business perspective, I mentioned last quarter that there are five basic opportunities that

we're pursuing: improved advertising, more engaging experiences, business messaging, Meta AI,

and AI devices. So I can go into a bit of detail on each.

On advertising, the strong performance this quarter is largely thanks to AI unlocking greater

efficiency and gains across our ads system. This quarter, we expanded our new AI-powered

recommendation model for ads to new surfaces and improved its performance by using more

signals and a longer context. It's driven roughly 5% more ad conversions on Instagram and 3% on

We're also seeing good progress with AI for ad creative -- with a meaningful percent of our ad

revenue now coming from campaigns using one of our Generative AI features. This is going to be

especially valuable for smaller advertisers with limited budgets, while agencies will continue the

important work to help larger brands apply these tools strategically.

The second opportunity is more engaging experiences. AI is significantly improving our ability to

show people content that they’re going to find interesting and useful. Advancements in our

recommendation systems have improved quality so much that it has led to a 5% increase in time

spent on Facebook and 6% on Instagram just this quarter.

There's a lot of potential for content itself to get better too. We're seeing early progress with the

launch of our AI video editing tools across Meta AI and our new Edits app, and there's a lot more to

do here.

The third opportunity is business messaging. I've talked before about how I believe every business

will soon have a business AI just like they have an email address, social media account, and

website. We're starting to see some product market fit in a number of countries where we're

testing these agents, and we're integrating these business AIs into ads on Facebook and

Instagram, as well as directly into e-commerce websites.

The fourth opportunity is Meta AI. Its reach is already quite impressive with more than a billion

monthly actives. Our focus is now deepening the experience and making Meta AI the leading

personal AI. As we continue improving our models we see engagement grow, so our next

generation of models is going to continue to really help here.

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The fifth opportunity is AI devices. We continue to see strong momentum with our Ray-Ban Meta

glasses, with sales accelerating. We're also launching new performance AI glasses with the Oakley

Meta HSTNs. They have longer battery life, higher resolution camera, and are designed for sports.

The percent of people using Meta AI is growing and we're seeing new users' AI retention increase

too, which is a good sign for that continued use. I think that AI glasses are going to be the main

way that we integrate superintelligence into our day-to-day lives, so it's important to have all

these different styles that appeal to different people in different settings.

Finally, we’re seeing people continue to spend more time with our Quest ecosystem and the

community continues to grow steadily. We launched the Meta Quest 3S Xbox Edition last month,

and we're seeing record interest in cloud gaming. And beyond gaming, we continue to see a

broader set of use cases with media and web-browsing contributing a significant portion of

engagement.

We’re going to have more to share on all of this, especially our Reality Labs work, at Connect on

September 17th, so I encourage you to tune into that.

Overall, this has been a busy quarter. Strong business performance and real momentum in

assembling both the talent and the compute needed to build personal superintelligence for

everyone. I am very grateful for our teams who are working hard to deliver this, and thanks to all of

you for being on this journey with us. And now, here's Susan.

Let’s begin with our consolidated results. All comparisons are on a year-over-year basis unless

otherwise noted.

Q2 total revenue was $47.5 billion, up 22% on both a reported and constant currency basis.

Q2 total expenses were $27.1 billion, up 12% compared to last year.

In terms of the specific line items:

Cost of revenue increased 16%, driven mostly by higher infrastructure costs and payments to

partners, partially offset by a benefit from the previously announced extension of server useful

lives.

R&D increased 23%, mostly due to higher employee compensation and infrastructure costs.

Marketing & Sales increased 9%, primarily due to an increase in professional services related to

our ongoing platform integrity efforts as well as marketing costs, partially offset by lower

employee compensation.

G&A decreased 27%, driven mostly by lower legal-related costs.

We ended Q2 with over 75,900 employees, down 1% quarter-over-quarter as the vast majority of

the employees impacted by performance-related reductions earlier this year were no longer

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captured in our headcount. This was partially offset by continued hiring in priority areas of

monetization, infrastructure, Reality Labs, AI, as well as regulation and compliance.

Second quarter operating income was $20.4 billion, representing a 43% operating margin.

Our tax rate for the quarter was 11%, which reflects excess tax benefits from share based

compensation due to the increase in our share price versus prior periods.

Net income was $18.3 billion or $7.14 per share.

Capital expenditures, including principal payments on finance leases, were $17.0 billion, driven by

investments in servers, data centers and network infrastructure.

Free cash flow was $8.5 billion. We repurchased $9.8 billion of our Class A common stock and paid

$1.3 billion in dividends to shareholders. We also made $15.1 billion in non-marketable equity

investments in the second quarter, which includes our minority investment in Scale AI along with

other investment activities. We ended the quarter with $47.1 billion in cash and marketable

securities and $28.8 billion in debt.

I’ll begin with our Family of Apps segment.

Our community across the Family of Apps continues to grow, and we estimate more than 3.4

billion people used at least one of our Family of Apps on a daily basis in June.

Q2 Total Family of Apps revenue was $47.1 billion, up 22% year-over-year.

Q2 Family of Apps ad revenue was $46.6 billion, up 21% or 22% on a constant currency basis.

Within ad revenue, the online commerce vertical was the largest contributor to year-over-year

growth.

On a user geography basis, ad revenue growth was strongest in Europe and Rest of World at 24%

and 23%, respectively. North America and Asia-Pacific grew 21% and 18%.

In Q2, the total number of ad impressions served across our services increased 11%, with growth

mainly driven by Asia-Pacific. Impression growth accelerated across all regions due primarily to

engagement tailwinds on both Facebook and Instagram and, to a lesser extent, ad load

optimizations on Facebook. The average price per ad increased 9%, benefiting from increased

advertiser demand, largely driven by improved ad performance. Pricing growth slowed modestly

from the first quarter due to the accelerated impression growth in Q2.

Family of Apps other revenue was $583 million, up 50%, driven by WhatsApp paid messaging

revenue growth as well as Meta Verified subscriptions.

We continue to direct the majority of our investments toward the development and operation of

our Family of Apps. In Q2, Family of Apps expenses were $22.2 billion, representing 82% of our

overall expenses. Family of Apps expenses were up 14%, mainly due to growth in employee

compensation and infrastructure costs, partially offset by lower legal-related costs.

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Family of Apps operating income was $25.0 billion, representing a 53% operating margin.

Within our Reality Labs segment, Q2 revenue was $370 million, up 5% year-over-year due to

increased sales of AI glasses, partially offset by lower Quest sales.

Reality Labs expenses were $4.9 billion, up 1% year-over-year driven by higher non-headcount

related technology development costs.

Reality Labs operating loss was $4.5 billion.

Turning now to the business outlook. 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, daily actives continue to grow across Facebook, Instagram and WhatsApp as we

make additional improvements to our recommendation systems and product experiences.

We continue to see momentum with video engagement in particular. In Q2, Instagram video time

was up more than 20% year-over-year globally. We’re seeing strong traction on Facebook as well,

particularly in the US where video time spent similarly expanded more than 20% year-over-year.

These gains have been enabled by ongoing optimizations to our ranking systems to better identify

the most relevant content to show.

We expect to deliver additional improvements throughout the year as we further scale up our

models and make recommendations more adaptive to a person’s interests within their session.

Another emphasis of our recommendations work is promoting original content. On Instagram,

over two-thirds of recommended content in the US now comes from original posts. In the second

half, we’ll be focused on further increasing the freshness of original posts so the right audiences

can discover original content from creators soon after it is posted.

We are also making good progress on our longer-term ranking innovations that we expect will

provide the next leg of improvements over the coming years. Our research efforts to develop

cross-surface foundation recommendation models continue to progress. We are also seeing

promising results from using LLMs in Threads recommendation systems. The incorporation of

LLMs are now driving a meaningful share of the ranking-related time spent gains on Threads.

We’re now exploring how to extend the use of LLMs in recommendation systems to our other

apps. We’re leveraging Llama in several other back-end processes as well, including actioning bug

reports so we can identify and resolve recurring issues more quickly and efficiently. This has

resulted in top-line bug reports in the US & Canada in Facebook Feed and Notifications dropping

by roughly 30% over the past 10 months.

The primary way we’re using Llama in our apps today is to power Meta AI, which is now available

in over 200 countries and territories. WhatsApp continues to be the largest driver of queries as

people message Meta AI directly for tasks such as information gathering, homework assistance,

and generating images. Outside of WhatsApp, we’re seeing Meta AI become an increasingly

valuable complement to our content discovery engines. Meta AI usage on Facebook is expanding

as people use it to ask about posts they see in Feed and find content across our platform in

Search. Another way we expect Meta AI will help with content discovery is through the automatic

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translation and dubbing of foreign-language content into the audience’s local language. We'll have

more to share on our efforts there later this year.

Moving to Reality Labs. The growth of Ray-Ban Meta sales accelerated in Q2, with demand still

outstripping supply for the most popular SKUs despite increases to our production earlier this

year. We’re working to ramp supply to better meet consumer demand later this year.

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 optimize ad supply across each surface to better deliver ads at the time and place

they are most relevant to people. In Q2, we also began introducing ads within Feed on Threads

and the Updates tab on WhatsApp, which is a separate space away from people’s chats.

As of May, advertisers globally can now run video and image ads to Threads users in most

countries, including the United States. While ad supply remains low and Threads is not expected to

be a meaningful contributor to overall impression growth in the near-term, we are optimistic

about the longer-term opportunity with Threads as the community and engagement grow and

monetization scales.

On WhatsApp, we are rolling out ads in Status and Channels, along with Channel Subscriptions in

the Updates tab to help businesses reach the more than 1.5 billion daily actives who visit that part

of the app. We expect the introduction of ads in Status will be gradual over the course of this year

and next, with low levels of expected ad supply initially. We also expect WhatsApp ads in Status to

earn a lower average price than Facebook or Instagram ads for the foreseeable future, due in part

to WhatsApp’s skew toward lower monetizing markets and more limited information that can be

used for targeting. Given this, we do not expect ads in Status to be a meaningful contributor to

total impressions or revenue growth for the next few years.

The second part of increasing monetization efficiency is improving marketing performance. There

are three areas of this work that I’ll focus on today: improving our ads systems, advancing our ads

products, including by building tools that assist in ads creation, and evolving our ads platform to

drive results that are optimized for each business’ objectives.

First is our ads systems, where we’re innovating in both the ads retrieval and ranking stages to

serve more relevant ads to people. A lot of this work involves us continuing to advance the

modeling innovations we’ve introduced previously while expanding their adoption across our

platform.

The Andromeda model architecture we began introducing in the second half of 2024 powers the

ads retrieval stage of our ads system, where we select the few thousand most relevant ads from

tens of millions of potential candidates. In Q2, we made enhancements to Andromeda that

enabled it to select more relevant and more personalized ads candidates, while also expanding

coverage to Facebook Reels. These improvements have driven nearly 4% higher conversions on

Our new Generative Ads Recommendation System, or GEM, powers the ranking stage of our ads

system, which is the part of the process after ads retrieval where we determine which ads to show

someone from candidates suggested by our retrieval engine. In Q2, we improved the performance

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of GEM by further scaling our training capacity and adding organic and ads engagement data on

Instagram. We also incorporated new advanced sequence modeling techniques that helped us

double the length of event sequences we use, enabling our systems to consider a longer history of

the content or ads that a person has engaged with in order to provide better ad selections. The

combination of these improvements increased ad conversions by approximately 5% on Instagram

and 3% on Facebook Feed and Reels in Q2.

Finally, we expanded coverage of our Lattice model architecture in Q2. We first began deploying

Lattice in 2023 with our later stage ads ranking efforts, allowing us to run significantly larger

models that generalize learnings across objectives and surfaces in place of numerous, smaller ads

models that have historically been optimized for individual objectives and surfaces. In April, we

began deploying Lattice to earlier stage ads ranking models as well. This is leading not only to

greater capacity and engineering efficiency, but also improved performance, with the recent

Lattice deployments driving a nearly 4% increase in ad conversions across Facebook Feed and

Reels in Q2.

Next, ads products. Here, we’re seeing strong momentum with our Advantage+ suite of AI

powered solutions.

In Q2, we completed the roll out of our streamlined campaign creation flow for Advantage+ sales

and app campaigns, which makes it easier for advertisers to realize the performance benefits from

Advantage+ by having it turned on at the beginning. We’ve seen lifts in advertiser adoption of

Sales and App campaigns since we’ve expanded availability and are working to complete the

rollout for leads campaigns in the coming months.

Within our Advantage+ creative suite, adoption of gen AI ad creative tools continues to broaden.

Nearly 2 million advertisers are now using our video generation features - Image Animation and

Video Expansion, and we’re seeing strong results with our text generation tools as we continue to

add new features. In Q2, we started testing AI-powered translations so that advertisers can

automatically translate the caption of their ads to 10 different languages. While it’s early, we’ve

seen promising performance lifts in our pre-launch tests.

We are also continuing to see strong adoption of Image Expansion among small and medium-sized

advertisers, which speaks to how these tools help businesses who have fewer resources to

develop creative. With larger advertisers, we expect agencies will continue to be valuable partners

in helping apply these new tools to drive performance.

Outside of Advantage+, we’re seeing good momentum in business messaging, particularly in the

US where click-to-message revenue grew more than 40% year-over-year in Q2. The strong US

growth is benefiting from a ramp in adoption of our Website to Message ads, which drive people

to a businesses’ website for more information before choosing to launch a chat with the business

in one of our messaging apps.

Finally, we continue to evolve our ads platform to drive results that are optimized for each

business’ objectives and the way they measure results.

In Q2, we completed the global roll out of our incremental attribution feature, which is the only

product on the market that optimizes for and reports on incremental conversions, which are

conversions that would not have happened without a person seeing the ad.

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We also launched Omnichannel ads globally in Q2, which enable advertisers to optimize for

incremental sales both in-store and online with just one campaign. In tests, Advertisers using

Omnichannel ads have seen a median 15% reduction in total Cost Per Purchase compared to

website-only optimization.

Next, I would like to discuss our approach to capital allocation. Our primary focus remains

investing capital back into the business, with infrastructure and talent being our top priorities.

I’ll start with hiring. Our approach to adding headcount continues to be targeted at the company’s

highest priority areas. We expect talent additions across all of our priority areas will continue to

drive overall headcount growth through this year and 2026, while headcount growth in our other

functions remains constrained. Within AI, we’ve had a particular emphasis on recruiting leading

talent within the industry as we build out Meta Superintelligence Labs to accelerate our AI model

development and product initiatives.

Next, infrastructure. We expect having sufficient compute capacity will be central to realizing

many of the largest opportunities in front of us over the coming years. We continue to see very

compelling returns from our AI capacity investments in our core ads and organic engagement

initiatives, and expect to continue investing significantly there in 2026. We also expect that

developing leading AI infrastructure will be a core advantage in developing the best AI models and

product experiences, so we expect to ramp our investments significantly in 2026 to support that

work.

Moving to our financial outlook. We expect third quarter 2025 total revenue to be in the range of

$47.5-50.5 billion. Our guidance assumes foreign currency is an approximately 1% tailwind to

year-over-year total revenue growth, based on current exchange rates. While we are not providing

an outlook for fourth quarter revenue, we would expect our year-over-year growth rate in the

fourth quarter of 2025 to be slower than the third quarter as we lap a period of stronger growth in

the fourth quarter of 2024.

We expect full year 2025 total expenses to be in the range of $114-118 billion, narrowed from our

prior outlook of $113-118 billion and reflecting a growth rate of 20-24% year-over-year.

While we’re still very early in planning for next year, there are a few factors we expect will provide

meaningful upward pressure on our 2026 total expense growth rate. The largest single driver of

growth will be infrastructure costs, driven by a sharp acceleration in depreciation expense growth

and higher operating costs as we continue to scale up our infrastructure fleet. Aside from

infrastructure, we expect the second largest driver of growth to be employee compensation as we

add technical talent in priority areas and recognize a full year of compensation expenses for

employees hired throughout 2025. We expect these factors will result in a 2026 year-over-year

expense growth rate that is above the 2025 expense growth rate.

Turning now to the capex outlook. We currently expect 2025 capital expenditures, including

principal payments on finance leases, to be in the range of $66-72 billion, narrowed from our prior

outlook of $64-72 billion and up approximately $30 billion year-over-year at the mid-point. While

the infrastructure planning process remains highly dynamic, we currently expect another year of

similarly significant capex dollar growth in 2026 as we continue aggressively pursuing

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opportunities to bring additional capacity online to meet the needs of our AI efforts and business

operations.

On to tax. With the enactment of the new U.S. tax law, we anticipate a reduction in our U.S. federal

cash tax for the remainder of the current year and future years. There are several alternative ways

of implementing the provisions of the Act, which we are currently evaluating. While we estimate

that the 2025 tax rate will be higher than our Q2 tax rate, we cannot quantify the magnitude at

this time.

In addition, we continue to monitor an active regulatory landscape, including the increasing legal

and regulatory headwinds in the EU that could significantly impact our business and our financial

results. For example, we continue to engage with the European Commission on our Less

Personalized Ads offering, or LPA, which we introduced in November 2024 based on feedback

from the European Commission in connection with the DMA. As the Commission provides further

feedback on LPA, we cannot rule out that it may seek to impose further modifications to it that

would result in a materially worse user and advertiser experience. This could have a significant

negative impact on our European revenue, as early as later this quarter. We have appealed the

European Commission’s DMA decision but any modifications to our model may be imposed during

the appeal process.

In closing, this was another strong quarter for our business as our investments in infrastructure

and technical talent continue to improve core ads performance and engagement on our platforms.

We expect the significant investments we’re making now will allow us to continue leveraging

advances in AI to extend those gains and unlock a new set of opportunities in the years to come.

past. And I think, certainly, some of the work that we’re seeing with teams

internally being able to adapt Llama 4 to build autonomous AI agents that can

help improve the Facebook algorithm to increase quality and engagement, or

like.

I mean that’s like a fairly profound thing if you think about it. I mean it’s

happening in low volume right now. So I’m not sure that, that result by itself

was a major contributor to this quarter’s earnings or anything like that.

But I think the trajectory on this stuff is very optimistic. And I think it’s one of

the interesting challenges in running a business like this now is there’s just a

very high chance, it seems, like the world is going to look pretty different in a

few years from now. And on the one hand, there are all these things that we can

do, there are improvements to our core products that exist.

And then I think we have this principle that we believe in across the company,

which we tell people, take Superintelligence seriously. And the basic principle is

this idea that we think that this is going to really shape all of our systems

sooner rather than later, not necessarily on the trajectory of a quarter or two,

but on the trajectory of a few years.

And I think that, that’s just going to change a lot of the assumptions around

how different things work across the company. So anyway, I think it’s basically

just we’re continually observing how this works and what the trajectory or the

pace of AI progress has been.

I think it continues to be on the faster end. And that I think informs a lot of the

decisions from everything from the importance and value of having the

absolute best and most elite talent dense team at the company to making sure

that we have a leading compute fleet so that the people here can do – so that

the researchers here have more compute per person to be able to leave their

research and then roll it out to billions of people across our products, making

sure that we build and drive these products through all the different things that

we do.

Which I think is one of the things that our company is the best in the world at is

basically when we take a technology, we’re good at driving that through all of

our apps and our ad systems and all that stuff, it’s not just going to kind of sit

on the vine.

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I think that there’s no other company, I think that is as good as us at kind of

taking something and kind of getting it in front of billions of people. So yes, I

mean we’re just going to push very aggressively on all of that.

But at some level, yes, this is -- there’s sort of a bet and a trajectory that we’re

seeing and those are the signals that we’re seeing. But we’re just trying to read

it.

Susan Li: Eric, for the second part of your question, we haven’t, in fact, kicked off our

budgeting process for 2026. So thinking about next year, there are clearly

many, many moving pieces in a very dynamic operating environment.

But there are certain aspects that we have some visibility into today including

the rough shape of our 2026 infrastructure plans. And that flows through into

our expense expectations next year. And we also have some visibility into the

compensation expense growth that we’ll recognize from the AI talent that

we’re hiring this year.

And so those two things are part of why we gave a little bit of an early preview

into the expectations for growth for 2026 total expenses as well as for 2026

So on the total expenses side, as I mentioned, we expect infrastructure will be

the single largest contributor to 2026 expense growth. That’s driven primarily

by a sharp acceleration in depreciation expense growth in 2026, largely driven

by recognizing incremental depreciation from assets that we purchased and

placed in service in ‘26 as well as from infrastructure deployed through 2025

that we’ll recognize a full year of depreciation next year.

and ‘26 than it has been in prior years. And then the other component of infra

cost growth next year would come from higher operating expenses including

energy costs, leases, maintenance and operational expenses that are

associated with maintaining that fleet.

And we also expect some increased spend on cloud services in ‘26 to meet our

capacity needs as well as growth in network-related costs.

So a lot going on, on the infrastructure side as it contributes to the 2026 total

expense number. After that, employee compensation is the next largest driver

of expense growth in ’26, again, driven primarily in the investments that we’re

making in technical talent including recognizing a full year of compensation

expense for the AI talent we hire this year.

I realize this answer is getting a little long, so I’ll try to wrap up quickly. On the

CapEx side, the big driver of our increased CapEx in ‘26 will be scaling GenAI

capacity as we build out training capacity that’s going to drive higher spend

across servers, networking, data centers next year.

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We also expect that we’re going to continue investing significantly in core AI in

2026. And again, this is a pretty very dynamic area of planning, but we wanted

to share kind of our early thoughts as things are shaping up.

Operator: Your next question comes from the line of Brian Nowak with Morgan Stanley.

Brian Nowak: Thanks for taking my questions. I have two. The first one, Mark, just to kind of

go back to the intelligence labs and sort of the vision for Superintelligence.

As you sort of sit here now versus 12 months ago, can you just sort of walk us

through any changes of technological constraints or technological gating

factors that you are most focused on overcoming in the next 24 months that

may have been different than they were in the past just to make sure you can

really lead in the idea of Superintelligence over the next ten years?

And then the second one to Susan or Mark, one on the core, you’ve made so

many improvements to the core to drive higher engagement,

recommendations, et cetera.

Can you just walk us through a couple of the factors you’re still most excited

about to come in the next 18 months that you think could drive a further lift to

engagement on the core platform? Thanks.

Mark Zuckerberg: Yes. Sure. I mean in terms of the research agenda and a bunch of the areas that

we’re very focused on.

And there’s obviously different scaling paradigms, and I don’t want to get too

much into the detail of research that we’re doing on this.

But I think that for developing superintelligence at some level, you’re not just

going to be learning from people because you’re trying to build something that

is fundamentally smarter than people.

So it’s going to need to learn how to -- or you’re going to need to develop a way

for it to be able to improve itself.

implications for how we build products, how we run the company, new things

that we can invent, new discoveries that can be made, society more broadly.

I think that, that’s just a very fundamental part of this. In terms of the shape of

the effort overall, I guess I’ve just gotten a little bit more convinced around the

ability for small talent-dense teams to be the optimal configuration for driving

frontier research. And it’s a bit of a different setup than we have on our other

world-class machine learning systems.

So if you look at like what we do in Instagram or Facebook or our ad system, we

can very productively have many hundreds or thousands of people basically

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working on improving those systems, and we have very well-developed

systems for kind of individuals to run tests and be able to test a bunch of

different things. You don’t need every researcher there to have the whole

system in their head.

But I think for this -- for the leading research on superintelligence, you really

want the smallest group that can hold the whole thing in their head, which

drives, I think, some of the physics around the team size and how -- and the

dynamics around how that works. But I’ll hand it over to Susan to talk about

more of the practical stuff.

Susan Li: Brian, on the sort of forward-looking roadmap for the core recommendation

engine. There are a handful of shorter-term things that we’re focused on in the

near term.

One is we’re focused on making recommendations even more adaptive to what

a person is engaging with during their session so that the recommendations we

surface are the most relevant to what they’re interested in at that moment.

And we’re making optimizations to help the best content from smaller creators

break out by matching it to the right audiences sooner after it gets posted. And

we’re also working on improving the ability for our systems to discover more

diversified and niche interests for each person through interest exploration and

learning explicit user preferences.

We’re also planning to scale up our models further and incorporate more

advanced techniques that should improve the overall quality of

recommendations. But we also have a lot of long-term bets in the hopper

around areas like developing foundational models that will support

recommendations across multiple services, incorporating LLMs more deeply

into our recommendation systems.

And a big focus of this work is going to be on optimizing the systems to make

them more efficient, so that we can continue to scale up the capacity that we

use for our recommendation systems without eroding the ROI that we deliver.

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

Douglas Anmuth: Thanks so much for taking the questions. One for Mark and one for Susan.

thinking changed here at all, just as you pursue superintelligence and push for

even greater returns on your significant infrastructure investments? And then,

Susan, your comments on ‘26 CapEx suggest more than $100 billion of spend

next year potentially. Do you continue to expect to finance all this yourself? Or

could there be opportunities to partner here? Thanks.

Mark Zuckerberg: Yes. I mean on open source, I don’t think that our thinking has particularly

changed on this. We’ve always open-sourced some of our models and not open

sourced everything that we’ve done.

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So I would expect that we will continue to produce and share leading open

source models. I also think that there are a couple of trends that are playing

out.

One is that we’re getting models that are so big that they’re just not practical

for a lot of other people to use. So it’s -- we would kind of wrestle with whether

it’s productive or helpful to share that or if that’s really just primarily helping

competitors or something like that.

So I think that there’s that concern. And then obviously as you approach real

superintelligence, I think there is a whole different set of safety concerns that I

think we need to take very seriously that I wrote about in my note this morning.

sourcing work.

I expect us to continue to be a leader there. And I also expect us to continue to

not open source everything that we do, which is a continuation of kind of what

we’ve been kind of working on. And yes, I mean I think Susan will talk a little bit

more about the infrastructure, but it really is a massive investment.

We think it will be good over time. But we do take very seriously that this is a

just massive amount of capital to convert into many gigawatts of compute

which we think is going to help us produce leading research and quality

products and running the business, I do look for opportunities to basically

convert capital into quality of products that we can deliver for people.

But this is certainly a massive bet that we’re kind of -- we’re focused on and we

want to make sure that what we build -- accrues to building the best products

that we can deliver to the billions of people who use our services.

Susan Li: Doug, on your second question about how we expect to finance the growing

CapEx next year. We certainly expect that we will finance some large share of

that ourselves, but we’re also exploring ways to work with financial partners to

codevelop data centers.

We don’t have any finalized transactions to announce, but we generally believe

that there will be models here that will attract significant external financing to

support large-scale data center projects that are developed using our ability to

build world-class infrastructure while providing us with flexibility should our

infrastructure requirements change over time. So we are exploring many

different paths.

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

Justin Post: Great, thank you. I’ll ask another one on the infrastructure. Mark, your spend is

now approaching some of the biggest hyperscalers out there. Do you think of

all this capacity mostly for internal uses? Or do you think there’s a way to share

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or even come up with a business model where leveraging that capacity for

external uses?

And then Susan, when you think about the ROI on this CapEx, I’m sure you

have internal models, I’m sure you can’t share all that, but how are you thinking

about the ROI? And are you optimistic about the long-term returns? Thank you.

Susan Li: Justin, I can go ahead and take a crack at both of those. And obviously Mark,

you should feel free to weigh in. Right now we are focused on ensuring that we

have enough capacity for our internal use cases, which includes both all of the

core AI work that we do to support the recommendation engine work on the

organic content side, to support all the ads ranking and recommendation work.

And then, of course, to make sure that we are building the training capacity

that we think we need in order to build frontier AI models. And to make sure

that we’re preparing ourselves for the types of inference use cases that we

think might -- that we might have ahead of us as we eventually focus not only

on developing frontier models, but also how we can expand into the kinds of

consumer use cases that we think will be hopefully widely useful and engaging

for our users.

So at present, we’re not really thinking about external use cases on the

infrastructure, but I’d say it’s a good question. On your second question, which

is really around the sort of ROI on CapEx, there are a couple of things.

So again, on the core AI side, we continue to see strong ROI. Our ability to

measure that is quite good, and we feel sort of very good about the rigorous

measurement and returns that we see there.

On the GenAI side, we are clearly much, much earlier on the return curve and

we don’t expect that the GenAI work is going to be a meaningful driver of

revenue this year or next year.

But we remain generally very optimistic about the monetization opportunities

that will open up, and Mark spoke to them in his script, the sort of five pillars, so

I won’t repeat them here.

opportunities that are very adjacent and intuitive for where -- in terms of where

our business is today, why they would be big opportunities for us and that

there will be sort of big markets attached to each of them.

So we, again, are also -- I would say, the last thing I would add here is we are

building the infrastructure with fungibility in mind. Obviously there are a lot of

things that you have to build up front in terms of the data center shells, the

networking infrastructure, et cetera.

But we will be ordering servers, which ultimately will be the biggest bulk of

CapEx spend as we need them and when we need them and making sort of the

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best decisions at those times in terms of figuring out where the capacity will go

to use.

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

go ahead.

Mark Shmulik: Yes, thank you for taking my questions. Mark, as you go after the

Superintelligence vision, especially for those of us on the outside, what are kind

of some of the markers or KPIs that you’re tracking on whether you’re on track

and making progress? Is it really against kind of those five pillars you outlined

above? Or should we be thinking more broadly?

And Susan, obviously AI is delivering great ROI today, all those investments

and also building towards kind of longer-term goals. Just curious, has there just

been any change or adjustment to how you think about the relationship

between revenues or core business performance and the cadence of

investments? Thank you.

Mark Zuckerberg: Yes. In terms of what to look at, I mean what I’m going to look at internally, the

quality of the people on the teams, the quality of the models that we’re

producing, the rate of improvement of our other AI systems across the

company and the extent to which the leading kind of foundation models that

we’re building contribute to improving all of the other AI systems and kind of

everything that we’re doing around the company.

Then I think you just get into our standard product and business playbook,

which is translating that technology into new products, which will first scale to

billions of people and then over time we will monetize.

But I think that there’s going to be some lag in that, right? And that, I think, is

kind of always the way that we work is, whether we’re building some new social

product or this something like Meta AI or a new product around this that we’re

going to work on getting to leading scale, building the highest quality product,

focused on that for a few years. And then once we’re really confident in that

position, then we’ll focus on ramping up the business around it.

So it’s -- I mean going back to the last question a little bit, it’s sort of when you

compare this business to some of the cloud businesses, it’s like we do have this

delay where we focus on building research and then doing research and then

ramping consumer products, and it often does take some period of time before

we really are ramping up the business around it.

I think that’s kind of a known property of our business and the cycle around it.

But I guess, on the flip side, we believe that if you are building

superintelligence, you should use all of your GPUs to make it so that you’re

serving your customers really well with that.

And we think that there’s going to be a much higher return than we can do by

generating that directly rather than just kind of renting or leasing out the

infrastructure at other companies.

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Susan Li: On the second part of your question, we’ve said in the past that our primary

focus from a profitability perspective is driving consolidated operating profit

growth over time. And it won’t be linear.

In some years, we’ll deliver above-average profit growth. And in years where

we’re making big investments, I think we will see that impact the amount of

operating profit growth that we can deliver. And at the moment, we see a lot of

attractive investment opportunities that we believe are going to set us up to

deliver compelling profit growth in the coming years for all of our investors.

And so we’re focused on constraining investments elsewhere as we pursue

those investments. But we really believe that this is a time for us to really make

investments in the future of AI as I think it will open up both new opportunities

for us in addition to strengthen our core business.

Operator: Your next question comes from the line of Ron Josey with Citi. Please go

ahead.

Ronald Josey: Great, thanks for taking the question. Mark, I wanted to ask you on Meta AI and

I think you talked about in the call just growing engagement overall, particularly

on WhatsApp and now you have 1 billion users on the platform and the focus is

now on driving personalization.

drive adoption here, particularly with Behemoth coming online at some point.

And then as people are using Meta AI with WhatsApp, thoughts on search and

queries and potentially monetizing that.

Mark Zuckerberg: Yes. I’m not going to get super deep into the roadmap on this, but the basic --

we do see that as we continue improving the models behind Meta AI and post

training and just engagement increases and as we swap in the updated models,

when we go from Llama 4 to Llama 4.1 when we have that, we expect that just

-- the models are inherently pretty general.

So it’s -- yes, you focus on specific areas, but in general, just sort of gets better

at a lot of different things that people want to ask it or want to do with it. And I

think with each version, both like what we’re doing on a week-to-week basis in

terms of continuing to train it. And when we drop kind of new generations or

big dot releases of each generation, that will improve engagement, too.

So we’re focused on that. I’m not going to go into the specific research areas or

capabilities that we’re planning on dropping in the future. But obviously I’m

pretty excited about it.

Operator: Our last question comes from the line of Youssef Squali with Truist Securities.

Youssef Squali: Great, thank you guys for taking the questions. I have two. So Mark, the Ray-

Ban initiative has been a homerun for you guys so far. Where are we on the

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development of glasses? Is that new computational platform that you’ve talked

about in the past? Is it moving faster or slower than you thought? And as you

leverage Meta AI, do you believe glasses will ultimately replace smartphones?

Or do you need a new form factor that’s AI first? And then, Susan, just quickly,

how do you guys see SBC progressing over the next couple of years? Is it fair to

assume it will grow materially faster than revenue and OpEx? And how do you

minimize shareholder dilution? Thank you.

Mark Zuckerberg: Yes. I can talk a bit about the glasses. Yes. I mean I’m very excited about the

progress that we’re making. I think both the Ray-Ban Metas and I’m very

excited about the Oakley Meta, the HSTN’s too and other things that we have

planned.

Yes. I mean this product category is clearly doing quite well. And I think it’s

good for a lot of things. It is stylish eyewear, so people like wearing them just as

glasses.

It has a bunch of interesting functionality. And then the use of Meta AI in them

just continues to grow, and the percent of people who are using it for that on a

daily basis is increasing, and that’s all good to see.

I mean I continue to think that glasses are basically going to be the ideal form

factor for AI because you can let an AI see what you see throughout the day,

hear what you hear, talk to you. Once you get a display in there, whether it’s the

kind of wide holographic field of view like we showed with Orion or just a

smaller display that might be good for displaying some information.

And that’s also going to unlock a lot of value where you can just interact with

an AI assistant throughout the day in this multimodal way. It can see the

content around you. It can generate a UI for you, show you information and be

helpful.

I mean I personally think that -- I wear contact lenses, I feel like if I didn’t have

my vision corrected, I’d be sort of at a cognitive disadvantage going through

the world. And I think in the future, if you don’t have glasses that have AI or

some way to interact with AI, I think you’re kind of similarly probably be at a

pretty significant cognitive disadvantage compared to other people who you’re

working with, or competing against.

So I think that this is a pretty fundamental form factor. There are a lot of

different versions of it. Right now we’re building ones that I think are stylish,

but aren’t focused on the display.

I think that there’s a whole set of different things to explore with displays. This

is kind of what we’ve been maxing out with Reality Labs over the last 5 to 10

years is basically doing the research on all of these different things.

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And it’s a -- I don’t know 10 years ago, I would have -- like the other thing that’s

awesome about glasses is, they are going to be the ideal way to blend the

physical and digital worlds together.

important, too, and AI is going to accelerate that, too.

It’s just that if you’d asked me five years ago, whether we’d have kind of

holograms that created immersive experiences or superintelligence first, I think

most people would have thought that you’d get the holograms first. And it’s

this interesting kind of quirk of the tech industry that I think we’re going to end

up having really strong AI first.

But because we’ve been investing in this, I think we’re just several years ahead

on building out glasses. And I think that, that’s something that we’re excited to

keep on investing in heavily because I think it’s going to be a really important

part of the future.

Kenneth Dorell: Youssef, we didn’t quite catch your second question, do you mind just

repeating it?

Youssef Squali: Sure. Just as you look at the spend on stock-based compensation over the next

couple of years with all these hires, I’m assuming that we’re going to see that

materially or grow materially faster maybe than revenue and OpEx. And I just

want to know how -- what you guys are doing to plan to minimize shareholder

dilution? Is it mostly buybacks or anything else? Thank you.

Susan Li: Thanks, Youssef. So I mean the impact of the sort of increased compensation

costs including SBC, of our AI hires this year is reflected in the revised 2025

expense outlook and in the comments I made about sort of the 2026, expense

outlook.

Those are obviously a big driver of 2026 expense growth as we recognize the

full year of compensation for the additional talent we’re bringing on. Having

said that, so we factored that into our sort of expense outlook. Having said

that, we certainly -- we are very focused on making sure, on keeping an eye on

dilution.

And we generally believe that our strong financial position is going to allow us

to support these investments while continuing to repurchase shares as part of

the sort of buyback program that offsets equity compensation and as well as

provide quarterly cash dividend distributions to our investors.

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.

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