Meta Q3 2026 Earnings Call: Muse Tops 10 Million Users in North America, Plans to Boost Compute Efficiency 10x Within a Year

Cailianshe Financial News Agency

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Cailianshe, Liu Rui, Editor Lin Qi, 2026-10-22 08:48

① Meta's total revenue in Q3 2026 was $64.6 billion, up 26% year over year, with earnings per share of $3.83; ② The company expects Reality Labs' losses to narrow to about $15 billion next year, and full-year capital expenditures to narrow to $135–145 billion.

Cailianshe, October 22 (Editor Liu Rui) — After the U.S. market closed on Wednesday, Meta released its Q3 2026 financial results and held an earnings call. The company's overall revenue and fourth-quarter guidance both exceeded market expectations.

The biggest highlight of this earnings report is clearly Meta's personal AI agent Muse. After delivering impressive early user and commercialization data, CEO Mark Zuckerberg said directly on the earnings call that the company plans to increase Muse's compute efficiency tenfold within a year and is expected to achieve high profitability within a few years.

However, while looking at the AI outlook, compute spending, Muse's path to profitability, and the pace of narrowing Reality Labs' losses also became the core issues that Wall Street institutional investors pressed on. Key points from the earnings call:

I. Core Financials

1. Q3 total revenue was $64.6 billion, up 26% year over year, with earnings per share of $3.83. Expenses rose sharply due to a one-time $9.9 billion litigation settlement expense.

2. Advertising remains the foundation of Meta's revenue, with advertising revenue of $63.1 billion, rising in both volume and price. Reality Labs revenue was $443 million, and losses are expected to narrow to about $15 billion next year.

3. Guidance: Q4 revenue of $73–76 billion; full-year capital expenditures narrowed to $135–145 billion; free cash flow was negative this quarter.

1. User numbers: In the U.S. and Canada, more than 10 million users had set up the product within a month and a half of launch, 25% of users use it daily, and retention is better than similar products; shopping is the largest use case, with average orders including travel exceeding $200.

2. Current status: Compute costs are high, and it has not yet been rolled out globally; the company plans to increase compute efficiency tenfold within a year and is expected to achieve high profitability within a few years.

3. Model: Basic features are currently free, with monetization through transaction commissions and premium/enterprise subscriptions; the company is advancing enterprise APIs and adaptation for small and medium-sized businesses, with the long-term goal of building an interconnected network of personal and enterprise agents.

1. Daily active users across the company's family of products reached 3.64 billion, and Threads' monthly active users have surpassed X.

2. On the advertising side, AI tools continue to improve conversion, and annualized revenue from creator partnership ads has exceeded $15 billion.

3. Hardware: Smart glasses will integrate Muse next month; VR is slowing its iteration pace, with resources prioritized toward AI.

IV. Capital and Q&A Highlights

1. A new Class C non-voting common stock was introduced, equivalent to a 2-for-1 split, effective in January 2027, to be used for financing, employee incentives, and M&A, while safeguarding the founder's control; there are no specific financing plans at present.

2. Analysts focused on Muse's compute bottleneck and commercial friction in agent-based e-commerce; Meta has joined the industry to launch an open protocol for agent interaction; the AI business may begin contributing incremental revenue in 2027, but large-scale commercialization will still take time.

Full transcript of Meta's Q3 2026 earnings call (AI-assisted translation, some content abridged)

Rob Bhavani (Director of Investor Relations, Meta): Good afternoon, and welcome to Meta Platforms' Q3 2026 earnings call. Joining me today to share results are Chief Executive Officer Mark Zuckerberg and Chief Financial Officer Susan Li. I'll now turn it over to Mark Zuckerberg.

Mark Zuckerberg (CEO, Meta): All right, hello everyone, and thanks for joining. The past few months have been busy and exciting. Daily active users across our family of apps have reached 3.64 billion; thanks to rapid growth in video, engagement on both Facebook and Instagram has hit all-time highs. Threads' monthly active users have surpassed X and it is still expanding quickly. WhatsApp messaging volume hit a record, and it is increasingly becoming an important platform for running AI agents.

Over the past few months, we have hit several important milestones toward our goal of bringing personal superintelligence to everyone. The Muse personal agent has become one of the most successful product launches in our history; we launched our first frontier model, Muse Wonder; and we released multiple smart glasses and hardware devices with built-in Muse capabilities, with all-new audio glasses coming soon.

We have been discussing our vision for personal superintelligence for more than a year. We want everyone to have a powerful personal agent: one that understands your goals and helps you achieve them and improve your life—whether that is maintaining relationships, saving time and money, running a business, creating, or bringing ideas to life. Muse is the product embodiment of that vision. The underlying foundational technology is now ready, there is still a lot of work ahead, but we are off to a very strong start.

In just a month and a half since launch, more than 10 million users in the U.S. and Canada have completed setup of the Muse personal agent, user growth momentum is strong, and it has held the top spot on the App Store almost every day.

Product engagement and retention are off to a strong start: among users who complete Muse setup, about 25% use the product on any given day. The longer early users have been using it, the better retention gets. For reference, that retention level is more than twice that of Threads in its early days, and higher than the starting level of other AI apps on the market.

Muse's most mainstream use case is shopping. More than a quarter of Muse users perform shopping-related actions, with an average order value of over $200. Payment provider Plaid reported that it has never seen connectors generate business traffic on such a scale on any other service. In the first week after launch, we even had to work with Stripe to raise our credit limit; actual business volume exceeded everyone's expectations.

Beyond shopping, other high-frequency use cases include personal productivity tools and connecting Muse to third-party services such as email and calendars. This also highlights the value of our in-house Muse secure virtual machine, which provides industry-leading privacy and security protection. We will soon launch the Muse confidential virtual machine as well; once enabled, even Meta itself will not be able to read user content.

Muse Wonder further improved answer quality, user engagement and retention. This signals that as model intelligence continues to iterate, Muse's product performance will keep improving. Muse has now validated product-market fit, and we are entering a new, more operations-focused phase of scaling. This new phase has three major goals: expanding the product's user base, growing revenue, and optimizing compute costs.

Our goal is to serve billions of users with Muse. This is both a mission and a strategy. On the mission side, we believe personal superintelligence should be placed in everyone's hands; individual empowerment through technology is the source of social prosperity, and checks on power are the cornerstone of safety—this is the path to a broadly shared, better future. In terms of values, we believe spreading this technology is our most important undertaking.

On the business side: the annual ARPU (average revenue per user) of U.S. users across our existing apps is in the hundreds of dollars. Based on the e-commerce transaction volume already emerging, we expect Muse's long-term ARPU to be higher. Reaching that goal will take time, but we have already begun laying the groundwork.

The business model has multiple components: the vast majority of users can use the product for free; we build e-commerce tools and take a commission on transactions we facilitate, with merchants bearing that cost. Users who need top-tier models and large amounts of token compute can pay for a subscription directly. Business operators who want to grow their business with Muse, or companies that need to use Muse in work settings, can also pay for a subscription. Users can also connect Muse to the Meta ecosystem to optimize personalization and content quality for content and ads.

A note: the ARPU figure mentioned above refers to revenue generated by Muse users across Meta's family of apps. On compute, over the coming years we are building a compute foundation capable of serving more than 1 billion users.

Running Muse consumes enormous compute in the short term, but we expect compute efficiency to improve more than 10x over the next year. Once revenue scales up, the product can become profitable. If compute efficiency can continue improving rapidly over the next several years—which we judge highly likely—Muse can achieve high profitability within a few years.

In the short term, strong cash flow from our core business lets us fund Muse's network expansion first, and we believe Meta is the company best suited to execute this strategy.

One more point on compute economics: model training is a large fixed cost. Given that we hold top-tier models, we should take on as much high-margin inference business as possible. That is why we launched the Meta enterprise platform led by CJ Desai.

Muse Wonder and stronger models trained later can, beyond serving the Muse network, also be offered externally through API interfaces, Muse Code, and other enterprise services—all high-margin businesses. One of our advantages is that compute can serve a wide variety of business needs.

It is hard to precisely predict the growth rate of the Muse network and the enterprise platform, or to quantify how much models like Muse Wonder will drive the core business. But by releasing advanced model capabilities through multiple channels, at least some of these businesses will likely outperform expectations.

Beyond serving enterprise customers externally, we have not yet hit the ceiling of what compute can do for our own core business. We have already begun testing the use of Muse Wonder to autonomously iterate and optimize our recommendation and ads systems. As the next generation of models grows more capable, such systems will iterate even faster.

Next, let me talk about models. Muse Wonder reaches top-tier levels on many of the key dimensions required for personal superintelligence. This confirms that training our own models is the only way to give users the best experience. Beyond Muse, since the new models rolled out in April, average time spent per user on Meta AI has doubled.

Muse Wonder is the most important version iteration, and I am also satisfied with the overall execution pace. Over the past few months we released Muse Spark 1.2 and 1.3, Muse Code, two open-weight models, Muse Glimmer and Muse Spark Open, as well as a Muse voice version.

The industry is developing rapidly. Going forward, on one hand we need to push to the frontier of intelligence capabilities, and on the other hand distill frontier capabilities into smaller, more efficient product models for personal agents. My judgment: continued scaling can achieve superintelligence, and we are methodically advancing step by step.

Larger-scale models beyond Muse Wonder are already in development, and their capabilities will be very strong; at the same time, we are also developing new lightweight models that will be released soon, achieving Muse Wonder-level intelligence with higher efficiency.

Now, personal superintelligence has also become a core focus of the Reality Labs division. Muse will launch on Meta smart glasses next month, which is an important step.

Users already love the glasses' existing features: music, calls, photo and video capture, hearing assistance, and more. But in the long run, Muse, the personal superintelligence you carry with you, will be the most core part of the hardware experience. Muse is also key to the glasses business model.

Our mission and strategy remain bringing benefits to everyone. We do not price hardware products to earn high profits on the hardware itself; the goal is to build a massive software and superintelligence business on top of the hardware. Glasses sales are growing rapidly, and with Muse added, paid subscriptions are expected to grow significantly, which will greatly improve the operating performance of this segment.

I mentioned the VR business strategy adjustment at Connect: we will release new hardware only when there is a major technological breakthrough. This year's Meta VR glasses are that kind of product, the first ordinary glasses with an integrated VR experience. I still believe that the holographic experience blending virtual and reality will start with VR technology. But we will adjust our investment pace and prioritize personal superintelligence.

Taking all these factors together, Reality Labs' operating performance will improve. We expect operating losses to narrow to about $15 billion next year and continue moving toward profitability.

Finally, let me talk about overall iteration speed. Fast iteration, fast learning, and serving users at scale have always been our methodology, and iteration has accelerated further recently. Over the past year, Edits launched and monthly active users have already surpassed 50 million; new products such as Forum and Seller were built by small teams and quickly serve deeply engaged users. Muse went from an idea on a whiteboard to a mature product in only about 8 months. The upcoming Muse Charm took less than a year from concept to mass-produced hardware.

I am proud of what the team has achieved. Views on the future differ, but Meta believes technology should serve everyone, and putting capabilities in the hands of ordinary people is the way forward. This is crucial; the world needs companies that uphold this philosophy. It is not just about ideas, but about execution. Thank you to all my colleagues for working to bring products to billions of people around the world. As always, thank you to everyone on this journey. I will now hand it over to Susan Li.

Susan Li (Chief Financial Officer): Thank you, Mark, and good afternoon, everyone. First, let's look at performance by segment; unless otherwise noted, all comparisons are year over year.

Q3 Family of Apps revenue was $64.2 billion, up 26% year over year; Family of Apps advertising revenue was $63.1 billion, up 26% year over year on both a reported and constant currency basis. Ad impressions across our platforms rose 10% year over year this quarter. Benefiting from growth in users and engagement as well as optimized ad load strategy, ad impressions grew solidly in all regions. Driven by improved ad performance and a macro environment that improved from a year ago, the global average price per ad rose 14% year over year; however, significantly higher impressions in some lower-monetizing placements and regions partly offset the increase in price.

Other revenue in Family of Apps was $1.1 billion, up 59% year over year, mainly from WhatsApp paid messaging and subscriptions.

Reality Labs: Q3 revenue was $443 million, down 6% year over year, as Quest headset sales declined, but revenue from AI smart glasses continued to grow strongly, partly offsetting the pressure.

On a consolidated basis: Q3 total revenue was $64.6 billion, up 26% year over year on both a reported and constant currency basis. Total expenses were $51.6 billion, up 68% year over year, including a $9.9 billion charge corresponding to the multistate attorneys general settlement reached in August 2026. The year-over-year increase in expenses was mainly driven by legal-related expenses, infrastructure investments, and third-party AI token costs. Higher infrastructure costs came from third-party cloud purchases, depreciation, and data center operating expenses.

Headcount was 66,000 at the end of Q3, down 12% from Q2 following the layoffs in May 2026. GAAP operating income was $13 billion, down 37% year over year, with an operating margin of 20%. Excluding the one-time $9.9 billion legal settlement charge, Q3 operating income rose 11% year over year. The tax rate this quarter was 24%, above the expected range, mainly due to the non-deductible portion of legal expenses. Net income was $9.9 billion, or $3.83 per share.

Capital expenditures, including principal payments on finance leases, were $34.9 billion, concentrated in servers, data centers, and network infrastructure. Free cash flow was -$2.1 billion. Cash and marketable securities were $82.4 billion at quarter end, and debt was $83.7 billion.

Business performance is driven by two core pillars: first, building highly engaging product experiences for the community; second, continuing to monetize user engagement.

First, content recommendation initiatives continue to deliver significant returns. Instagram's average time spent per user hit an all-time high, with double-digit year-over-year growth in Q3, as both Reels short videos and the feed grew stronger. Facebook's Reels time spent reached new highs globally and in the US, up double digits year over year. Threads average time spent per user grew more than 20% year over year.

Rising engagement across products stems from continuous improvements in content relevance, personalization, and timeliness. The two major recommendation engines—retrieval and ranking—continue to iterate, delivering positive results for both Instagram and Facebook. For example: Instagram Reels launched relevance optimization in Q3, lifting Reels watch time by more than 4%; we upgraded the model architecture to no longer score individual short videos separately, but instead score a sequence of short videos together, while also optimizing for watch-time signals.

Facebook Feed and Reels completed the global rollout of a generative retrieval architecture, drawing on large language model approaches to tag content with semantic labels and proactively generate candidate recommendations. This allows us to surface high-quality new posts before they have accumulated much engagement data. The architecture currently covers about a quarter of non-friend Feed content and half of Reels traffic, driving a 1% increase in Reels watch time. The recommendation algorithm also makes content fresher and more personalized.

In the Instagram Reels recommendation feed, nearly 45% of content was posted less than a day earlier, up more than 11 percentage points from Q2. The Facebook Reels ranking model can now read twice as much historical content, lifting Reels watch time in the US by 1%.

Large language models also deepen content understanding. We use large models daily to process public Instagram posts, and by the end of the quarter this signal had lifted engagement in Instagram search results by 1%. This quarter we began switching our content understanding models to our in-house Muse model family. Offline evaluations show the new models outperform the previous production models; at the same time, per-unit content processing costs fell more than 80% across Instagram, Threads, Facebook, and the ads business. Building our own full model stack also lets us flexibly iterate models around the signals that matter most for personalization.

The second major driver: improving monetization efficiency. We continue to help businesses on our platforms improve their results. The ads system deploys more prediction models to deeply understand user preferences, match more relevant ads, and optimize infrastructure efficiency. Last quarter we launched the Meta generative recommender, which uses large models to interpret both ad creative and user preferences and predict the best ad. In Q3, Facebook Feed launched a generative retrieval module, lifting conversion rates in direct-response conversion scenarios by nearly 2%.

The GEM ads ranking model expanded to more scenarios, lifting conversion rates by 1.7% for off-site conversion ads and 9.6% for app download ads. The Andromeda retrieval system was iterated to increase model complexity and throughput, lifting Facebook ad conversions by 1.4%.

AI also lowers the operating barrier for advertisers, helping businesses plan campaigns, generate creative, and capture opportunities. The Meta AI business assistant has become a valued tool for advertisers. Advertisers who adopt the assistant's suggestions increased conversions by nearly 5% while keeping cost per conversion flat, and increased their ad spend by 9% compared with similar non-users. Additional capabilities such as creative insights, business analytics, and intelligent task execution will be added.

Marketers can complete planning, asset creation, and optimization through conversation, with approvals and previews, all fully controllable.

One example: Simply Organic Maple, a family-owned maple syrup business in Vermont, used the Meta AI business assistant to optimize ad copy; after following its suggestion to test longer, more descriptive copy, purchase conversions rose 57% and cost per purchase fell 36%; the best-performing new ad delivered a 52% higher return on ad spend, three times the original.

Generative AI ad creative tools continue to spread, with nearly 10 million advertisers using at least one of them in Q3. Adoption of the video generation feature among advertisers rose 25% quarter over quarter, letting them generate a finished video from a single uploaded image in seconds.

The creator ad partnership business is growing rapidly, allowing advertisers to run ads in collaboration with creators and brands; the business grew 97% year over year, with annualized revenue exceeding $15 billion. In Q3, the Creator Marketing Hub launched globally for all advertisers, bringing together creator search, asset management, and partnership ad delivery, with more than 10 million creators searchable on the platform. The Meta AI business assistant will also be embedded in the hub, letting advertisers search for creators, discover assets, and get end-to-end guidance through conversation.

Beyond ads, there are more monetization paths across the ecosystem. In September we launched Meta One, a unified subscription service rolling out gradually worldwide for everyday users, creators, and businesses, covering more than 50 features across Instagram, Facebook, and WhatsApp, including Meta Business Agent and Meta AI; subscriptions and trials have already surpassed 15 million. The core experience of all our apps and Meta AI remain free, with subscriptions unlocking advanced capabilities and more AI compute.

Beyond monetizing existing products, we are building new experiences and revenue streams around consumer AI agents. Mark described Muse's strong early performance. Usage scenarios are diverse, with the top three being shopping, personal productivity, and creative work; shopping is used by more than a quarter of Muse weekly active users.

At this stage, the primary goal is to expand Muse's user coverage, while also having multiple monetization paths, with subscriptions being the starting point. The opportunity for Muse among small and medium-sized businesses is equally huge. According to research, an estimated at least 500 million SMBs use Meta's family of apps to reach customers and generate revenue. Muse can help businesses acquire customers, improve operational efficiency, and also facilitate user transactions. Over the long term, consumer agents and enterprise agents can transact directly with each other, bringing multiple opportunities across paid usage, e-commerce, and enterprise services.

Beyond growth, we are pursuing a healthy unit economic model. Muse consumes a large amount of compute, and user expansion will push up total cost of service; however, with idle-time compute scheduling, model lightweighting, and inference optimization, we expect comprehensive compute efficiency to improve by more than 10x over the next year. As revenue scale grows, efficiency gains give us confidence in building a highly profitable business over the long term.

Now for the business outlook. For Q3 2026, we guide total revenue to $73-76 billion. This guidance assumes foreign exchange will create roughly a 1% headwind to year-over-year revenue.

Expense and capital expenditure outlook: expense guidance is raised, incorporating a $9.9 billion one-time legal charge in the third quarter. Full-year 2026 total expenses are expected in the range of $175-179 billion. Excluding this legal expense, full-year operating income is expected to be higher than in 2025. By segment, Reality Labs' full-year loss is expected to narrow compared with 2025, landing in the $18-19 billion range. Full-year capital expenditures (including principal payments on finance leases) narrow to $135-145 billion, versus the prior range of $130-145 billion. Assuming no change in the tax environment, the Q4 tax rate is expected to be 15%-17%.

We continue to monitor various legal and regulatory risks. Regulatory scrutiny related to teens continues in multiple markets, and several teen-related court hearings in the U.S. will take place in the coming months.

Beyond 2026, next year's budget and capacity plans are still being formulated, but we will continue to invest heavily to support our core business and emerging AI projects. Existing and upcoming compute allows us to allocate resources flexibly: model training, core business growth, and new product development. AI investments are already generating incremental revenue for the advertising business by increasing engagement, optimizing recommendations, and improving monetization. Investment in MSL labs will unlock a large number of new revenue opportunities and strengthen product capabilities across the company. The payback period for AI investment is long, but multiple monetization paths and internal efficiency gains support our confidence in the long-term potential.

Although we are not disclosing full-year 2026 revenue guidance, the gains AI brings to the core business, combined with model iterations and new products, are sufficient to support strong overall revenue growth in 2027. In addition, we will continue to drive improved profitability at Reality Labs; as Mark said, next year's loss will narrow to about $15 billion, with resources prioritized toward AI.

Today we are also announcing a new non-voting common stock proposal: to be distributed to all existing shareholders in the form of a stock dividend, with each share of existing stock entitled to one new non-voting share, equivalent to a 2-for-1 stock split, expected to begin trading in late January 2027.

In summary, in Q3 the core advertising and product execution momentum was strong, and we are steadily advancing on the path to universal personal superintelligence. Krista, let's open it up for questions.

Operator: Thank you. We will now open the line for questions. The first question goes to Brian Nowak of Morgan Stanley.

Brian Nowak (Morgan Stanley): Thanks. Congratulations on Muse's success. From a user behavior and product perspective, what do you see as the bottlenecks that constrain Muse's user growth slope and need to be addressed first? For example, capabilities, model efficiency, compute capacity. Second question, Mark, could you describe the vision for Meta's enterprise platform and the core value for enterprise customers?

Mark Zuckerberg: I'll take both questions first, and Susan can add. Currently 10 million users in the U.S. and Canada have completed Muse setup, but the biggest constraint comes from the enormous compute consumption, and we have not yet launched globally. On one hand, we need to adapt the product for each region and connect with local third-party connectors; on the other hand, we need to first solve operating efficiency to ensure commercial viability.

And it is precisely because we have only launched in a few markets that this 10 million result is even more impressive. If we opened globally, the user scale would be far higher than it is now. But the efficiency optimization work must be implemented. Compute efficiency is the top priority; we need enough compute, and only after achieving our efficiency goals will we be able to serve more than 1 billion users. This is a major constraint for next year.

Commercialization buildout must advance in sync with improvements in compute efficiency, and I remain optimistic about that. The product's own capabilities must also continue to iterate, driven by new models. The free version uses Muse Spark by default, while the paid version can experience Muse Wonder, which is much more powerful. We are developing an efficient version of Muse Wonder to roll out broadly later.

After two to three generations of iteration, model intelligence will improve substantially while costs decline, which will unlock enormous user value.

Turning to the enterprise business. The two are connected: in the near term, enterprise products are mainly model APIs and Muse Code. Enterprise-facing business agents are already scaling, and all three are growing well. Over the long term, we want to build a complete Muse business agent network.

Enterprise agents can handle sales and customer service; today a large number of SMBs are already using Muse to run their operations. We want to build dedicated enterprise agents to automate a large amount of tedious, non-core business processes for SMBs. Launching a version of Muse adapted to enterprises' internal environments is equally significant.

Going further, building the Muse network: it is no longer for single-person use; personal agents can collaborate with one another. When shopping, a user's agent connects directly to a merchant's agent instead of visiting the merchant's webpage. Once this system is proven out, it will unlock enormous value at scale. Enterprise scenarios are an important part, and it will likewise enhance the value of consumer products. I am excited about both sides.

Susan Li: Let me add on the enterprise business. AI is profoundly reshaping the enterprise market. Traditional enterprises do not have a monopoly on this track; many of today's leading players did not exist a few years ago, and they are investing heavily in model R&D. Meta is the same, and our models are now competitive for both SMBs and large enterprises. 500 million businesses worldwide already use our platform. AI will completely change how enterprises do development, productivity, and marketing and customer service.

We have multiple differentiated assets: models, compute infrastructure, global distribution, and long-term merchant relationships. We want to build diversified, durable revenue streams, covering subscriptions, usage-based API fees, messaging services, and more. This opportunity will take years to cultivate, and we will gradually increase investment based on real customer needs and product-market fit.

Conference operator: Next question, Eric Sheridan from Goldman Sachs.

Eric Sheridan (Goldman Sachs): Thanks for taking my question. First question: agentic commerce has friction, and some aggregator platforms and suppliers are unwilling to have their services accessed by AI agents. How does Meta view this kind of conflict? How do you reduce friction and balance existing consumer services with the industry changes that agents will bring in the medium and long term? Second question: with the new share class announced today, how do you view future sources of capital and capital structure optimization to ensure you can seize investment opportunities in the AI era?

Mark Zuckerberg: Let me answer the first question first.

Susan Li: It is about the friction points in agentic commerce.

Mark Zuckerberg: Got it. Overall, the vast majority of businesses are enthusiastic about this new model; they see that it can not only improve the existing customer experience and expand sales, but also bring in new customers. Different players in the industry will adopt it at different paces, and our ecosystem strategy will adjust accordingly. A large number of merchants have reached out proactively, hoping to connect with and adapt to Muse, which exceeded my expectations. Of course, there will also be businesses unwilling to try early on, and we will negotiate reasonable partnership terms. In the long run, this system will be valuable to all parties, and overall progress is positive.

Susan Li: To add. Agents are part of the future reality, and everyone needs to adapt to the era of agentic commerce. Many merchants are actively seeking cooperation, but some businesses want to protect their own customer relationships. We believe that in the future agents will no longer browse webpages like real people; instead, it will move toward authorized, structured interactions: certified APIs, official agent partnerships, and consumer agents communicating directly with enterprise agents. We are working with partners such as Sierra, Walmart, Stripe, and Shopify to launch an open standard, the "Personal Agent Protocol," which defines rules for secure interaction between consumer agents and merchant agents while balancing the permissions of merchants and users.

Back to your second question, the original intent behind the new share class: to create a publicly traded non-voting share class for fundraising, employee compensation, acquisitions, and other corporate purposes, while protecting the voting rights of existing Class A and B shareholders. This both maintains the founder-led structure and broadens our financing tools, giving us ample flexibility to seize AI-driven opportunities across the entire business. There are no specific financing plans at present, but building a complete capital framework in advance makes it easier to draw on diverse funding in the future.

Conference operator: Next question, Doug Anmuth from JPMorgan.

Doug Anmuth (JPMorgan): Great, thank you. Following up on authorized agent transactions. When a large number of businesses adopt agent-to-agent interactions, what changes will that unlock over the long term? Second, on expenses and capex, although the 2027 plan is not yet finalized, looking back at historical statements: full-scale compute expansion in 2026-2027, greater freedom in 2028; a year ago you mentioned that the 2026 expense increase would be significantly larger than 2025, while also pursuing annual operating profit growth. Are there any clues to help understand 2027?

Susan Li: The agent ecosystem is hard to predict precisely, and the pace of development will be uneven. But using Muse today already shows that if you can connect directly to enterprise-side agents, the experience improves by a large margin. For example, when Muse calls a merchant on a user's behalf, if the other side is answered by a merchant agent, the whole process is much smoother.

One of Muse's defining traits is proactivity: after receiving a task, when it encounters obstacles, it finds its own path to complete the goal. The proactive capabilities of consumer and enterprise agents will generate more transactions and business activity than today. It is no longer the case that agents act only when users explicitly state a need; agents can continuously monitor user needs and periodically push results; the same applies to enterprise-side agents. Over the long term, this will form an entirely new proactive interaction paradigm.

On your second question about 2027. Next year's budget and capacity planning are still at an early evaluation stage, and a large number of investment plans with different payback periods are being evaluated. We want to execute as many high-quality projects as possible within the framework of our budget and financial targets. There are many variables, and we will not disclose quantitative guidance for next year until the Q4 earnings call.

Conference operator: Next question, Justin Post from Bank of America.

Justin Post (Bank of America): One question for Mark and one for Susan. Mark, beyond external products, the new large model also has great potential internally. The ads system has already been rebuilt on AI; after deploying the new generation of models, how much room for improvement remains internally? Susan, you mentioned strong revenue next year. Does that include only advertising, or does it already factor in some of the new AI revenue?

Susan Li: I'll answer both together. The advertising business has benefited for years from both traditional machine learning and the new generation of large models.

There are three main threads: First, the traditional machine learning iteration pipeline remains ample. Even if we don't disclose guidance for 2027, product iteration gives us confidence in growth of our core business, and we have ample reserves in ranking and recommendation systems.

Second, we are migrating to large-model infrastructure, using large models to deeply understand user interests, and organic content and the recommendation engine can continue to release dividends.

Third, generative AI advertising tools are already delivering value to advertisers, and we will continue to iterate the Advantage+ suite to rapidly generate creatives, image-to-video, and audio, enabling highly personalized ads. All three of these still have a lot of room for improvement.

The second question, next year's growth. We will continue to invest in core advertising and user engagement projects; at the same time, multiple new AI businesses are being built, and 2027 will begin to contribute incremental revenue, but larger-scale release will come later. Revenue sources include AI subscriptions, transaction commissions, advertising, and enterprise services. We are still in the early stage of polishing products and validating product-market fit. But we have the experience of serving billions of users, and in the long run AI will become an important source of revenue diversification.

Conference operator: Next question, Mark Shmulik from Bernstein.

Mark Shmulik (Bernstein): Thank you. The Connect conference gave the feeling that the company has entered a new era, with Muse at the center of all businesses. Is that really how it is positioned internally? If so, what changes have been made to business assessments and goals? Susan, the enterprise product was released earlier than the small and medium business version. Talk about what needs to be done to bring Muse to small and medium businesses?

Mark Zuckerberg: More than a year ago I proposed that personal superintelligence is the company's North Star. It continues Meta's core of more than twenty years: empowering individuals and giving people tools for expression and connection. Extending into the AI era, the most important undertaking of the next generation is to put superintelligence into the hands of ordinary people.

To that end, we restarted the MSL lab, with the goal of building a full set of end-to-end in-house technologies for personal agents, including models and agent products. The smart glasses hardware vision follows the same logic: building the next-generation hardware platform and delivering a complete end-to-end experience.

These are all long-term bets. Meta's mission is to make long-term technology bets and polish the best products for billions of people around the world. As long as execution is in place, commercial results will follow naturally, and I am proud of the team's progress.

Susan Li: Muse for small and medium businesses launched at the end of September. It is not a standalone app, but a new set of capabilities and connectors added within the existing Muse. It helps small and medium business operators run their businesses and acquire customers; with one click it connects to the Instagram analytics backend, Facebook Pages, and Meta ad accounts, and also integrates third-party tools such as Shopify, QuickBooks, and Canva. The agent understands the merchant's products, brand tone, and customer needs; it can automatically analyze ad and content performance, write marketing copy, reply to customer messages, and identify business risks.

We continue to collect feedback from small and medium businesses and identify the most frequent needs: ad optimization, message organization, expanding sales, syncing inventory and product updates to websites, and workforce scheduling. We closely track usage and iterate on features. There are 500 million small and medium businesses worldwide, and much of the technology we have built for the consumer side can be reused in small and medium business scenarios. We have mature channels to reach small and medium businesses, and our foundational platform is strong. AI also opens up entirely new enterprise market opportunities for us.

Conference operator: Next question, Ross Sandler from Barclays.

Susan Li: There are no specific figures we can disclose publicly. As Mark said, compute costs are high. Compute efficiency is the core lever for achieving scale and meeting business goals. Short-term measures: use idle compute during off-peak hours to run some tasks, optimize inference across multiple dimensions, improve GPU utilization, and pair this with various product optimizations. Long term, after large-model iterations, completing the same task consumes fewer tokens; at the same time, AI models themselves will optimize the underlying operating software. The efficiency roadmap still has a large number of projects to implement, and technological evolution will continue to bring costs down.

Conference operator: Next question, Mark Mahaney from Evercore ISI.

Mark Mahaney (Evercore ISI): Meta's past logic for new products: first build scale and engagement, then layer on monetization. Muse's compute costs are far higher than past products. Does that old playbook still apply, or does monetization need to move faster? Second, does the e-commerce data on 25% of users and average order values above $200 include travel spending, or is it retail only?

Mark Zuckerberg: Given the scale of costs and the market opportunity, Muse's monetization pace will be earlier than our traditional playbook. Second, that statistic includes travel, and travel itself is a high-frequency use case for Muse, with users using it for all kinds of trip planning.

Conference operator: That concludes the Q&A session. This concludes the entire Meta earnings call. Thank you for participating, and you may now disconnect.

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