Meta Platforms, Inc. (META) 2026 Q2 法說會逐字稿

Good afternoon. My name is Krista, and I will be your conference operator today. At this time, I would like to welcome everyone to Meta's second-quarter 2026 earnings conference call. (Operator Instructions) And this call will be recorded. Thank you very much.

下午好。我叫 Krista，今天將擔任本次電話會議的接線員。此刻，我想歡迎各位參加 Meta 2026 年第二季財報電話會議。(接線員指示) 本次通話將會錄音。非常感謝。

Chad Heaton, Meta's Vice President of Finance, you may begin.

Meta 財務副總裁 Chad Heaton，您可以開始了。

Thank you. Good afternoon, and welcome to Meta Platforms second-quarter 2026 earnings conference call. Joining me today to discuss our results are Mark Zuckerberg, CEO; and Susan Li, CFO. Our remarks today will include forward-looking statements, which are based on assumptions as of today. Actual results may differ materially as a result of various factors, including those set forth in today's earnings press release and in our quarterly report on Form 10-Q filed with the SEC. We undertake no obligation to update any forward-looking statement.

謝謝。下午好，歡迎參加 Meta Platforms 2026 年第二季財報電話會議。今天與我一同討論業績的有執行長 Mark Zuckerberg，以及財務長 Susan Li。我們今天的發言將包含前瞻性陳述，係基於截至今日的假設。實際結果可能因各種因素而與前述陳述存在重大差異，包括今日財報新聞稿以及我們向美國證券交易委員會（SEC）提交之 10-Q 季度報告中所列因素。我們不承擔更新任何前瞻性陳述之義務。

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.

在本次通話中，我們將同時呈現 GAAP 以及若干非 GAAP 財務衡量指標。GAAP 與非 GAAP 指標之調節表已包含於今日的財報新聞稿中。財報新聞稿及隨附的投資人簡報可於我們網站 investor.atmeta.com 取得。

And now I'd like to turn the call over to Mark.

現在我想把電話交給 Mark。

All right. Hey, everyone. Thanks for joining today. We had a strong quarter for our community and business with 3.6 billion people using at least one of our apps each day. We reached several milestones. Instagram reached 2 billion daily actives. Threads crossed 500 million monthly actives, making it the fastest-growing conversation app ever. Facebook has reached more than 2 billion daily actives for a while now. WhatsApp just hit an all-time messaging record peaking at 30 million messages sent per second during the World Cup final.

好。各位好。感謝今天加入。本季我們的社群與業務表現強勁，每天有 36 億人至少使用我們其中一款應用程式。我們達成了幾個里程碑。Instagram 的每日活躍用戶達到 20 億。Threads 的月活躍用戶突破 5 億，成為史上成長最快的對話型應用程式。Facebook 的每日活躍用戶超過 20 億已經有一段時間了。WhatsApp 則剛創下歷史訊息量紀錄，在世界盃決賽期間每秒傳送訊息峰值達 3,000 萬則。

And also, Kunal Shah just joined as our new Head of WhatsApp. He built one of India's most important payment companies and will be a great addition to the team. We also shipped some strong new models from Meta Superintelligence Labs and released new Glasses. Overall, the scale and reach of our community across our apps is pretty remarkable, and it gives us a strong platform for delivering new innovations to billions of people.

另外，Kunal Shah 剛加入，擔任我們新的 WhatsApp 負責人。他打造了印度最重要的支付公司之一，將會是團隊的絕佳補強。我們也從 Meta Superintelligence Labs 推出了幾個強勁的新模型，並發布了新款眼鏡。整體而言，我們社群在各款應用程式上的規模與觸及相當驚人，這也讓我們擁有強大的平台，能把新的創新帶給數十億人。

The opportunity in front of us is massive. First, we are now at a point where our investments in AI are accelerating every major part of our core business. They're improving the experience for people using our apps, driving better performance for advertisers and helping our teams build new experiences and ship faster.

我們面前的機會非常龐大。第一，我們已經來到一個階段：我們在 AI 上的投資正在加速推動核心業務的每一個主要部分。這些投資正在改善人們使用我們應用程式的體驗、為廣告主帶來更好的成效，並協助我們的團隊打造新體驗、加快產品推出速度。

Second, we are developing new personal agents that will be the foundation for our next wave of products and revenue lines in the months and years ahead. And third, we see a large enterprise opportunity to sell to businesses, including APIs, business agents, potentially selling compute directly and other services that we're building for large customers.

第二，我們正在開發新的個人代理（personal agents），它們將成為未來幾個月與幾年我們下一波產品與營收線的基礎。第三，我們看到一個龐大的企業級機會，可向企業銷售，包括 API、企業代理，可能也包括直接銷售算力，以及我們為大型客戶打造的其他服務。

And I want to spend some time today laying out exactly how our investments are delivering results today and the opportunities that we see over time. For accelerating our core business, we are seeing strong and promising results in several areas. In Instagram and Facebook, I'm very optimistic about our work to integrate large language models into our recommendation systems. LLMs add a first principles understanding of what the content is about and why it is compelling as well as a deeper understanding of what people are interested in and what their goals are when they're using our apps.

我也想花一些時間，具體說明我們的投資如何在今天就帶來成果，以及我們隨時間推進所看到的機會。在加速核心業務方面，我們在幾個領域看到了強勁且令人振奮的成果。在 Instagram 與 Facebook 上，我對我們將大型語言模型整合進推薦系統的工作非常樂觀。LLM 能從第一性原理理解內容在講什麼、為何吸引人，也能更深入理解人們的興趣，以及他們使用我們應用程式時的目標。

So this means that we can show more relevant and engaging content that better reflects people's goals and interests. Our new Muse Image and Muse Video models will also dramatically expand the universe of content that people can discover across our platforms. There are already two large sets of content to draw from, first, from your friends and the people you follow and second, from creators that you don't follow. But now there's going to be a whole new and nearly infinite universe of personalized content. This is going to make our services a lot more useful and engaging for people.

這表示我們能展示更相關、更有吸引力的內容，更貼近人們的目標與興趣。我們新的 Muse Image 與 Muse Video 模型，也將大幅擴展人們在我們平台上可探索的內容宇宙。目前已經有兩大內容來源可供取用：第一，來自你的朋友與你追蹤的人；第二，來自你未追蹤的創作者。但接下來將會出現一個全新、幾乎無限的個人化內容宇宙。這將使我們的服務對人們而言更有用、也更具黏著度。

For ads, we are using LLMs to improve how our systems predict and rank the ads that we show. We've expanded the context that we can take into account around a person's organic and ads activity to determine an ad's relevance, driving significant increases in relevance and conversions on both Facebook and Instagram.

在廣告方面，我們正使用 LLM 改善系統預測與排序所展示廣告的方式。我們擴大了可納入考量的脈絡範圍，涵蓋一個人在自然內容與廣告活動上的行為，以判斷廣告的相關性，進而在 Facebook 與 Instagram 上帶動相關性與轉換的顯著提升。

And on a dollar basis, our ads business is reporting faster year-over-year revenue growth than any other company's reported ad business. So these AI investments are paying off. We are also seeing a lot of demand for our new AI-powered creative tools. 9 million small businesses on our platforms are now using at least one of our AI ad creative tools, and we're rolling out new end-to-end creative solutions that help advertisers translate performance data into their creative decisions.

以金額計，我們的廣告業務所呈現的年對年營收成長速度，快於任何其他公司所公布的廣告業務。因此，這些 AI 投資正在帶來回報。我們也看到市場對新一代 AI 驅動創意工具的強勁需求。我們平台上已有 900 萬家小型企業正在使用至少一項 AI 廣告創意工具，而我們也正在推出新的端到端創意解決方案，協助廣告主把成效數據轉化為創意決策。

Muse image is going to supercharge this. The model can analyze images, improve its own work and produce better ad variations based on advertiser input. We're getting great feedback on this so far.

Muse Image 將會大幅強化這一點。該模型能分析圖片、改進自身作品，並依據廣告主輸入產出更好的廣告變體。截至目前，我們收到非常好的回饋。

We also just launched Meta One, a new subscription offering that provides more tools and AI features across our apps. As demand grows, we're going to offer a variety of different tiers and pricing options there as well. And I'm also excited about how AI is helping our teams speed up product development. Earlier this year, we shipped Instagram Instants. We also just launched Forum, a stand-alone group's app and Seller, a stand-alone marketplace app. And I expect it to become a lot easier to ship new apps. So we are planning to build out more ideas and use our recommendation systems to scale them to the people who will find them interesting as we've done with Threads.

我們也剛推出 Meta One，這是一項新的訂閱方案，能在我們的各款應用程式中提供更多工具與 AI 功能。隨著需求成長，我們也將提供多種不同的等級與定價選項。我也很期待 AI 如何協助我們的團隊加速產品開發。今年稍早，我們推出了 Instagram Instants。我們也剛推出 Forum（獨立的社團應用程式）以及 Seller（獨立的市集應用程式）。我預期推出新應用程式將會變得容易得多。因此，我們計畫孵化更多想法，並像 Threads 一樣，運用推薦系統把它們擴展到會覺得有興趣的人群。

It's been a little more than a year since we launched Meta Superintelligence Labs and our trajectory is strong. In the last month, we shipped Muse Spark 1.1 and Muse Image. Since we rebuilt Meta AI and integrated Muse Spark, we have seen a 60% increase in the number of people interacting with the assistant each day, and that continues to grow quickly week-over-week.

自從我們成立 Meta Superintelligence Labs 以來已經一年多一點，而我們的發展軌跡相當強勁。在過去一個月，我們推出了 Muse Spark 1.1 與 Muse Image。自從我們重建 Meta AI 並整合 Muse Spark 之後，我們看到每天與助理互動的人數增加了 60%，而且每週仍在快速成長。

Muse Spark 1.1 is a strong agentic encoding model that is very efficient and excels at computer use, tool use and multimodal understanding. It's available through our new public API, and we are ramping up distribution through partner channels and more coding agents over the coming weeks.

Muse Spark 1.1 是一個強大的代理式（agentic）編碼模型，效率非常高，並且在電腦操作、工具使用與多模態理解方面表現出色。它可透過我們新的公開 API 取得，我們也將在未來幾週透過合作夥伴通路以及更多程式撰寫代理加速擴大分發。

We are also building out features to make it easier for enterprises to adopt a Muse Spark, and that is an area that we expect to continue focusing on. One reason that we are so focused on making Muse Spark great at agentic capabilities is that we think that there's a very big opportunity to ship a few types of agents that are aligned with our mission and business. The first is personal agents. Soon, we will have agents that can work 24/7 on your behalf to help you achieve your goals and improve your life, your health, your relationships, your finances, whatever you want.

我們也正在建置更多功能，讓企業更容易採用 Muse Spark，而這也是我們預期會持續聚焦的領域。我們之所以如此專注於讓 Muse Spark 在代理式能力上表現卓越，其中一個原因是我們認為，推出幾種類型、且與我們使命與業務一致的代理，存在非常大的機會。第一種是個人代理。很快，我們將推出能夠 24/7 代表你運作的代理，協助你達成目標並改善你的生活、健康、人際關係、財務——任何你想要的面向。

The first domain that agents have really taken off in is coding, but engineers are more technical and willing to spend time making those agents work. So to build great personal agents, this needs to be a great consumer product that just works out of the box and is easy enough for billions of people to adopt and use. I'm very excited about this, and we're going to have more to share soon.

代理真正大幅起飛的第一個領域是程式開發（coding），但工程師更具技術背景，也更願意花時間讓這些代理運作起來。因此，要打造出色的個人代理，這必須是一個開箱即用、能順暢運作的優秀消費級產品，而且要簡單到足以讓數十億人採用並使用。我對此非常興奮，我們很快也會分享更多內容。

As we move towards a future where we're all interacting with multiple agents, I think that WhatsApp and our other messaging surfaces are going to become increasingly important. WhatsApp is already the leading surface where people engage with Meta AI. And as we build out a platform for more agents across our messaging apps, we're going to innovate on how to deliver a private and secure AI experience. We launched Incognito mode this quarter on WhatsApp and the Meta AI app, allowing people to have private conversations with their assistant that even Meta can't see. We are planning to make strong privacy and security a fundamental part of the agents that we're building as well.

隨著我們邁向一個人人都會與多個代理互動的未來，我認為 WhatsApp 以及我們其他的訊息介面將會變得愈來愈重要。WhatsApp 已經是人們與 Meta AI 互動的主要介面。而當我們在各個訊息應用上打造一個支援更多代理的平台時，我們將在如何提供私密且安全的 AI 體驗方面持續創新。我們在本季於 WhatsApp 與 Meta AI 應用推出了「無痕模式」（Incognito mode），讓人們能與助理進行連 Meta 都看不到的私密對話。我們也計畫把強大的隱私與安全性，作為我們正在打造的代理的基本核心。

I'm also very excited about our progress with business agents. We made Meta business agents available globally this quarter on WhatsApp and Messenger, and there are already more than 1 million businesses using them to talk to their customers or complete sales every week. We're rolling business agents out on Instagram now too. One interesting thing about having an agent talk to your customers every day is that it learns over time and can bring all of those insights back to you. So we're building more agentic capabilities to summarize all these conversations, digest what happened overnight and surface what customers are asking for.

我也對我們在商務代理方面的進展感到非常振奮。本季我們已在 WhatsApp 與 Messenger 上將 Meta 商務代理全球上線，目前每週已有超過 100 萬家企業使用它們與客戶對話或完成銷售。我們也正在把商務代理推廣到 Instagram。讓代理每天與你的客戶對話的一個有趣之處在於，它會隨時間學習，並把所有這些洞察回饋給你。因此，我們正在打造更多代理式能力，用於彙總這些對話、消化一夜之間發生的事情，並呈現客戶正在詢問的需求。

And soon, it will go further, including suggesting ways to grow your business, giving you competitive intelligence and real-time insights into what's working and what's not. And over time, we'd like to build this into a business-in-a-box service that can help you start and run a whole business using Meta's platforms.

而且很快，它還會更進一步，包括建議擴大業務的方式、提供競爭情報，以及對哪些有效、哪些無效給出即時洞察。隨著時間推進，我們希望把這發展成一種「開箱即用的企業」（business-in-a-box）服務，能協助你使用 Meta 的平台來創辦並經營一整個企業。

In terms of how we will monetize these, we have a mix of subscriptions, volume-based pricing, and I expect that we're going to continue to evolve more of these products to be like our ad systems where businesses only pay us when we achieve results for them.

就我們將如何將這些變現而言，我們會採用訂閱制、按用量計價等多種方式；我也預期我們會持續把更多產品演進得更像我們的廣告系統——也就是企業只有在我們為其達成成果時才付費給我們。

Over time, that will let us run an efficient auction over our compute, similar to how we do that for advertisers today. As AI usage in our products and businesses continues to ramp, we continue to invest aggressively in infrastructure to meet the demand. Yesterday, as part of our meta compute effort, we announced a new strategic venture with BlackRock to develop a new 1 gigawatt data center in El Paso, Texas. Overall, we expect that a significant portion of our compute is going to go towards training our models, growing our core business and delivering personal agents and new products.

長期來看，這將使我們能在運算資源上進行高效率的競價拍賣，類似於我們今天為廣告主所做的方式。隨著 AI 在我們的產品與企業中的使用持續攀升，我們也持續積極投資基礎設施以滿足需求。昨天，作為我們 Meta 運算（meta compute）工作的一部分，我們宣布與 BlackRock 展開一項新的策略性合資計畫，在德州埃爾帕索（El Paso）開發一座新的 1 吉瓦資料中心。整體而言，我們預期相當大一部分運算資源將用於訓練模型、擴大核心業務，以及提供個人代理與新產品。

But we also expect to grow a large business serving large customers as well. We've built our API. We're rolling out business agents. We're getting a lot of offers for compute at a significant premium over what we paid for it. And we have more coding and productivity tools on our road map as well. And we'll have more to share on all of this soon.

但我們也預期會成長出一個服務大型客戶的大型業務。我們已建立 API。我們正在推出商務代理。我們也收到許多以顯著高於我們成本的溢價來購買運算資源的提案。此外，我們的路線圖上也有更多程式開發與生產力工具。我們很快也會就這些分享更多內容。

As we get closer to personal superintelligence, we are also going to need hardware that allows you to seamlessly interact with it. Glasses are the ideal form factor since they can be with you throughout the day, and they can assist you without pulling you away from the moment. Our Glasses remain one of the fastest-growing consumer electronics of all time, and we continue adding to the lineup. We just released our own line of Meta Glasses in collaboration with EssilorLuxottica, including a style that we designed with Kylie Jenner. They're the first glasses to ship with Muse Spark out of the box so that they can understand what you're seeing and give even more helpful answers.

隨著我們更接近個人超級智慧（personal superintelligence），我們也需要能讓你無縫與其互動的硬體。眼鏡是理想的外形規格（form factor），因為它們可以整天隨身佩戴，並且能在不讓你脫離當下情境的情況下提供協助。我們的眼鏡產品仍是史上成長最快的消費性電子產品之一，我們也持續擴充產品陣容。我們剛與 EssilorLuxottica 合作推出自有的 Meta 眼鏡系列，其中包含一款由我們與 Kylie Jenner 共同設計的款式。它們是第一款開箱即搭載 Muse Spark 的眼鏡，因此能理解你所看到的內容並提供更有幫助的答案。

Early sales have been strong, exceeding our expectations, and we're going to have more to share on our Glasses lineup at this year's Connect Conference on September 23. So I encourage you to tune in then.

早期銷售表現強勁，超出我們的預期；我們也將在 9 月 23 日今年的 Connect 大會上，分享更多關於我們眼鏡產品陣容的資訊。因此我鼓勵大家到時收看。

Before I wrap, I want to mention that I just published an op-ed about why I'm so optimistic that we are building a positive future for everyone. At Meta, we have always built technology to put power in people's hands, so that they can connect with the people they care about and shape the world in the ways that they want. It's why we've always focused on making our products affordable and accessible for everyone, and it's a strategy that has served both our community and our business well.

在我結束之前，我想提到我剛發表了一篇評論文章（op-ed），談我為何如此樂觀，相信我們正在為所有人打造一個正向的未來。在 Meta，我們一直打造能把力量交到人們手中的科技，讓他們能與在乎的人連結，並以他們想要的方式塑造世界。這也是為什麼我們始終專注於讓產品對所有人都負擔得起且易於取得；而這項策略也一直同時良好地服務了我們的社群與業務。

As we enter this next chapter, that same philosophy is guiding how we approach developing AI. We're the only major company building AI with the primary goal of putting superintelligence directly into people's hands. Rather than centralizing superintelligence, we are focused on distributing it widely and giving everyone the ability to direct it towards what matters to them. That's the way that society has always made progress. I think that these are the right values for building a positive AI future. And if we help build this, then I think that we will continue to build a very strong business as well.

當我們進入下一個篇章，同樣的理念也在引導我們如何開發 AI。我們是唯一一家以「把超級智慧直接交到人們手中」為主要目標來打造 AI 的大型公司。我們不是要把超級智慧集中化，而是專注於廣泛分散，讓每個人都能把它導向對自己重要的事。這一直是社會取得進步的方式。我認為，這些是打造正向 AI 未來的正確價值觀。而如果我們能協助建立這一切，我也相信我們將能持續建立非常強健的業務。

So that's everything I wanted to cover upfront today. AI is improving our core business. It's making our apps more relevant and delivering better results for businesses. We're starting to deliver more novel products, and we'll have a lot more there soon as well. And we are investing aggressively because the potential is huge, and we know that there are many ways to deliver value here. As always, I'm grateful to all of you for being on this journey with us.

以上就是我今天一開始想先涵蓋的所有內容。AI 正在改善我們的核心業務。它讓我們的應用更貼近使用者需求，並為企業帶來更好的成果。我們也開始推出更多新穎的產品，而且很快還會有更多。我們正積極加大投資，因為潛力巨大，而且我們知道在這裡有許多創造價值的方式。一如既往，我很感謝各位與我們一同踏上這段旅程。

And now here's Susan.

接下來交給 Susan。

Thanks, Mark, and good afternoon, everyone. Let's begin with our segment results. All comparisons are on a year-over-year basis, unless otherwise noted. Q2 total Family of Apps revenue was $60.4 billion, up 28% year-over-year. Q2 Family of Apps ad revenue was $59.4 billion, up 27% or 26% on a constant currency basis.

謝謝你，Mark，各位下午好。我們先從各業務分部的業績開始。除非另有說明，所有比較皆以年對年（YoY）為基礎。第二季（Q2）應用程式家族（Family of Apps）總營收為 604 億美元，年增 28%。第二季應用程式家族廣告營收為 594 億美元，年增 27%；若以固定匯率（constant currency）計算則年增 26%。

In Q2, the total number of ad impressions served across our services increased 14%. Impression growth was healthy across all regions, driven by growth in engagement and users as well as ad load optimizations. The global average price per ad increased 12% year-over-year, driven by ad performance gains, improvements in macro conditions relative to Q2 of last year and currency tailwinds. This was partially offset by strong impression growth, particularly from lower monetizing surfaces and regions.

在第二季，我們各項服務投放的廣告曝光總量增加了 14%。在所有地區，曝光成長都維持健康，主要由互動與用戶成長，以及廣告載入量（ad load）最佳化所帶動。全球平均每則廣告價格年增 12%，主要受惠於廣告成效提升、相較去年第二季宏觀環境改善，以及匯率順風。不過，這部分被強勁的曝光成長所部分抵銷，尤其是來自變現能力較低的介面與地區。

For the first time, quarterly Family of Apps other revenue reached $1 billion and grew 73% year-over-year, driven primarily by WhatsApp paid messaging and subscriptions revenue. Within our Reality Labs segment, Q2 revenue was $431 million, up 16% year-over-year due to strong growth in AI Glasses revenue, partially offset by lower Quest headset sales.

我們的應用程式家族（Family of Apps）其他收入在單季首次達到 10 億美元，年增 73%，主要由 WhatsApp 付費訊息與訂閱收入帶動。在我們的 Reality Labs 部門，第二季營收為 4.31 億美元，年增 16%，主要因 AI 眼鏡營收強勁成長，部分被 Quest 頭戴式裝置銷售下滑所抵銷。

Moving now to our consolidated results. Q2 total revenue was $60.8 billion, up 28% or 27% on a constant currency basis. Q2 total expenses were $42 billion, up 55% compared to last year and included $2.4 billion in charges related to legal proceedings and $1.2 billion in severance expenses in connection with the May 2026 headcount reduction.

接下來談我們的合併財務結果。第二季總營收為 608 億美元，年增 28%；以固定匯率計算年增 27%。第二季總費用為 420 億美元，較去年增加 55%，其中包含與法律程序相關的 24 億美元費用，以及與 2026 年 5 月人力縮減相關的 12 億美元資遣費用。

Year-over-year growth was primarily driven by increases in employee compensation, infrastructure costs, legal-related costs and third-party AI token costs. Excluding the previously mentioned severance expense, growth in employee compensation was driven by technical hires we've added over the past year, particularly AI talent. The growth in infrastructure costs was driven by higher depreciation, data center operating costs and third-party cloud spend.

年對年成長主要由員工薪酬、基礎設施成本、法律相關成本以及第三方 AI token 成本增加所帶動。若排除前述資遣費用，員工薪酬的成長主要來自我們過去一年新增的技術人員招募，尤其是 AI 人才。基礎設施成本的成長則來自較高的折舊、資料中心營運成本以及第三方雲端支出。

We ended Q2 with over 75,000 employees, down 3% from Q1. This total includes approximately 8,000 employees impacted by the May 2026 headcount reduction. We expect the majority of impacted employees will no longer be captured in our headcount by the end of Q3 2026.

我們在第二季結束時員工人數超過 75,000 人，較第一季下降 3%。此總數包含約 8,000 名受 2026 年 5 月人力縮減影響的員工。我們預期到 2026 年第三季末，多數受影響員工將不再計入我們的員工人數。

Second quarter GAAP operating income was $18.8 billion, representing an 8% decline year-over-year and a 31% operating margin. Excluding the Q2 legal charges and severance expenses, our second quarter operating income would have increased 9% year-over-year.

第二季 GAAP 營業利益為 188 億美元，年減 8%，營業利益率為 31%。若排除第二季法律費用與資遣費用，我們第二季營業利益將年增 9%。

Our tax rate for the quarter was 16%. Net income was $15.8 billion or $6.18 per share. Capital expenditures, including principal payments on finance leases, were $31.1 billion, driven by investments in servers, data centers and network infrastructure. Free cash flow was $784 million. We ended the quarter with $90.3 billion in cash and marketable securities and $83.7 billion in debt.

本季稅率為 16%。淨利為 158 億美元，或每股 6.18 美元。資本支出（包含融資租賃的本金償付）為 311 億美元，主要由伺服器、資料中心與網路基礎設施投資所驅動。自由現金流為 7.84 億美元。本季結束時，我們持有現金與有價證券 903 億美元，負債為 837 億美元。

Turning now to the business performance. There are two primary factors that drive our revenue performance: our ability to deliver engaging experiences for our community and our effectiveness at monetizing that engagement over time. On the first, we continue to see significant gains from our content recommendation initiatives. On Instagram, global time spent this quarter grew double digits year-over-year this quarter, largely driven by improvements to our feed and reels recommendations.

接著談業務表現。推動我們營收表現的主要有兩個因素：我們為社群提供引人入勝體驗的能力，以及我們隨時間將這些互動有效變現的能力。在第一點上，我們持續看到內容推薦計畫帶來顯著提升。在 Instagram 上，本季全球使用時間年對年呈雙位數成長，主要由我們對動態消息（feed）與 Reels 推薦的改善所帶動。

On Facebook, video time spent increased 9% globally year-over-year and over 10% within the US and Canada, where it was driven by ranking improvements. We are finding that LLMs are increasingly capable of delivering ranking and recommendations gains. First, they make our existing systems smarter by understanding what the content is actually about and generating better training data. Second, LLM-powered agents are also helping with engineering development by evaluating content quality, detecting trends and testing ranking changes.

在 Facebook 上，影片使用時間全球年對年增加 9%，在美國與加拿大更超過 10%，主要由排序（ranking）改善所驅動。我們發現 LLM 在帶來排序與推薦提升方面的能力正日益增強。首先，它們能透過理解內容實際在講什麼並產生更好的訓練資料，使我們既有系統更聰明。其次，LLM 驅動的代理（agents）也在工程開發上提供協助，包括評估內容品質、偵測趨勢以及測試排序變更。

Earlier this year, we reached a milestone of every public reels and feed post on Instagram being automatically processed through an LLM and analyzed across dimensions from topics to tone, and we're working towards including more surfaces on Facebook as well.

今年稍早，我們達成一項里程碑：Instagram 上每一則公開的 Reels 與動態消息貼文都會自動經由 LLM 處理，並從主題到語氣等多個面向進行分析；我們也正朝著在 Facebook 納入更多介面（surfaces）邁進。

These signals can then be passed to downstream applications across ranking, recommendations and content policy enforcement, which is a key building block toward greater personalization. This quarter, we also began using our Muse family of models to conduct content understanding across signals like video topic classification and summarization, and we've seen positive early results. Finally, our recommendations are also becoming more personalized, surfacing more fresh content while giving people more direct control over what they see. On Reels, we shipped our largest single release ranking improvement to date, combining faster inference with a new architecture that draws on deeper user history to improve predictions. This drove a 15 basis point increase in sessions on Instagram with particular strength in reshares and time spent, which are both strong indicators of better content-to-user matching.

這些訊號接著可傳遞到下游應用，涵蓋排序、推薦與內容政策執行，這是邁向更高程度個人化的關鍵基石。本季我們也開始使用 Muse 模型家族，針對影片主題分類與摘要等訊號進行內容理解，並已看到正向的初期成果。最後，我們的推薦也正變得更個人化，在提供更多新鮮內容的同時，也讓使用者能更直接掌控所看到的內容。在 Reels 上，我們推出迄今最大的一次單次排序改進版本，結合更快的推論與新的架構，利用更深層的使用者歷史來提升預測。這使 Instagram 的使用場次（sessions）提升 15 個基點，且在轉分享（reshares）與使用時間方面表現尤為突出；兩者都是內容與使用者匹配更佳的強力指標。

We are now bringing this to feed where early results look comparable. We are also getting new content to people more quickly. Investments we've made in more real-time infrastructure and modeling improvements on new videos are allowing our largest ranking models to now identify high-quality new Reels at creation. On Instagram feed, over half of all recommended content is now less than one day old, more than double from a year ago.

我們現在正把這套能力帶到動態消息（feed），初期結果看起來相當可比。我們也讓新內容能更快觸及使用者。我們在更即時的基礎設施與新影片建模改進上的投資，使我們最大的排序模型如今能在創作當下就辨識出高品質的新 Reels。在 Instagram 動態消息中，目前超過一半的推薦內容都不到一天前發布，較一年前增加逾一倍。

We're also giving people more direct control of the content they see. Today, Instagram users can visit the Your Algo page, which lets users write natural language prompts to tune their recommendations. Similarly, on Facebook, we launched Shape Your Feed. Early results show over 80% retention among users who engage with it.

我們也讓使用者能更直接掌控他們看到的內容。目前 Instagram 使用者可以前往「Your Algo」頁面，透過自然語言提示（prompts）來調整其推薦結果。同樣地，在 Facebook 上我們推出了「Shape Your Feed」。初期結果顯示，與其互動的使用者留存率超過 80%。

Looking forward, we're executing on our longer-term efforts to develop the next generation of our recommendation systems. This includes building foundation models that are designed to power organic content and ads recommendations simultaneously as well as developing LLM native recommender systems. We hit our first research milestone this half by continuously pretraining a large-scale model with recommendations data and observing healthy scaling laws in the process. We're encouraged by this milestone and expect continued progress in the second half of the year.

展望未來，我們正推進更長期的工作，以開發下一代推薦系統。這包括打造可同時驅動自然內容與廣告推薦的基礎模型（foundation models），以及開發 LLM 原生的推薦系統。本半年我們達成第一個研究里程碑：以推薦資料對大規模模型進行持續預訓練，並在過程中觀察到良好的擴展定律（scaling laws）。我們對此里程碑感到鼓舞，並預期在下半年將持續取得進展。

Turning to the second driver of our revenue performance, increasing monetization efficiency. The first part of this work is optimizing the level of ads within organic engagement. Here, we continue to enhance our systems to show ads at the optimal time and location.

接著談推動我們營收表現的第二個因素：提升變現效率。這項工作的第一部分，是在自然互動（organic engagement）中最佳化廣告量。在這方面，我們持續強化系統，以在最佳時間與位置展示廣告。

In Q2, we also expanded availability of ads on our newer surfaces, including completing our global ads expansion on Threads. On WhatsApp, we have introduced support for more types of ad destinations and advertiser performance goals and status and continue on track toward our global rollout.

在第二季，我們也擴大在較新介面（surfaces）上的廣告供給，包括完成 Threads 的全球廣告擴張。在 WhatsApp 上，我們已新增對更多廣告導流目的地類型的支援，以及更多廣告主成效目標與狀態（status），並持續按計畫推進全球上線。

Moving to the second part of increasing monetization efficiency, improving performance for the businesses who use our services. Within our ad systems, we're delivering performance gains as we deploy more complex and predictive models. This quarter, we introduced Meta Generative Recommender, a paradigm shift in how our ad system works. Rather than scoring every possible ad individually, we are now using LLMs to reason about ad content and user preferences together and predict the best ad for each person. This makes our ad matching more intelligent and more precise, which compounds performance gains for advertisers. We deployed the first generative model into our ads retrieval system and saw notable improvements in ads performance.

談到提升變現效率的第二部分：改善使用我們服務的企業成效。在我們的廣告系統中，隨著部署更複雜且更具預測性的模型，我們正帶來成效提升。本季我們推出 Meta Generative Recommender，這代表我們廣告系統運作方式的典範轉移。我們不再逐一為每個可能的廣告單獨評分，而是改用 LLM 同時推理廣告內容與使用者偏好，並為每位使用者預測最佳廣告。這使我們的廣告匹配更聰明、更精準，進而為廣告主帶來複利式的成效提升。我們已將第一個生成式模型部署到廣告檢索（retrieval）系統中，並看到廣告成效出現顯著改善。

Early pilots using LLMs to better understand user preferences drove a 1% increase in app event conversions on Instagram. In Q2, we also advanced our user understanding models to analyze ads and organic activity and simultaneously improve both user experience and advertiser performance. Combined with our GEM model for ads ranking and sequence learning, these advancements generated an 8.3% increase in ad clicks and a 15.7% uplift in conversions on Facebook.

早期試點顯示，使用 LLM 更好地理解使用者偏好，使 Instagram 的應用程式事件轉換（app event conversions）提升 1%。在第二季，我們也推進使用者理解模型，以分析廣告與自然活動（organic activity），並同時改善使用者體驗與廣告主成效。結合我們用於廣告排序與序列學習的 GEM 模型，這些進展使 Facebook 的廣告點擊增加 8.3%，轉換提升 15.7%。

We're also leveraging AI to empower businesses to more easily manage their campaigns, develop ad creative and engage with their customers.

我們也正在運用 AI，協助企業更輕鬆地管理其行銷活動、開發廣告創意，並與客戶互動。

Our AI-powered Advantage+ end-to-end solutions continue to grow, reaching over $75 billion in annual revenue run rate this quarter. We're working to deepen adoption as advertisers who leverage multiple tools see compounding performance gains. I'll share one example of how Advantage+ is making performance marketing meaningfully easier and more effective for SMBs.

我們以 AI 驅動的 Advantage+ 端到端解決方案持續成長，本季年化營收運行率已超過 750 億美元。我們正致力於深化採用，因為同時運用多種工具的廣告主會看到績效增益的疊加效應。我將分享一個例子，說明 Advantage+ 如何讓中小企業的成效型行銷顯著更容易且更有效。

Underneat, an online apparel brand in India, had been setting up each campaign manually across Facebook and Instagram. After adopting Advantage+ sales campaigns layered with Advantage+ audience placements and budget optimization, they saw a 13% incremental lift in purchases and a 16% increase in add-to-cart conversions.

Underneat 是印度的一個線上服飾品牌，過去一直在 Facebook 和 Instagram 上以手動方式逐一設定每個行銷活動。在採用 Advantage+ 銷售行銷活動，並搭配 Advantage+ 受眾版位與預算最佳化後，他們的購買量提升了 13%（增量），加入購物車轉換也增加了 16%。

Adoption of our GenAI ad creative tools continues to scale with over 9 million small businesses using at least one AI creative tool. Image generation, which now lets advertisers produce more creatives at scale from existing content, including a new ability to create images from video assets saw adoption more than double this quarter.

我們的 GenAI 廣告創意工具採用持續擴大，已有超過 900 萬家小型企業使用至少一項 AI 創意工具。影像生成功能如今可讓廣告主以既有內容大規模產出更多創意素材（包含新增可由影片素材生成影像的能力），本季採用量成長超過一倍。

We also introduced a new end-to-end creative solution that gives advertisers the AI infrastructure to translate real performance signals into their next creative decision while preserving brand identity and tone. We're building with agency integrations from day one, so teams can diagnose, generate and scale high-performing creative without leaving their existing workflows.

我們也推出全新的端到端創意解決方案，為廣告主提供 AI 基礎設施，能在維持品牌識別與語氣的同時，將真實的成效訊號轉化為下一個創意決策。我們從第一天起就與代理商整合建置，讓團隊不必離開既有工作流程，就能診斷、生成並擴大高成效的創意素材。

Looking ahead with the rollout of Muse image, we expect to further expand advertisers' ability to generate high-quality on-brand creatives at scale. With Meta Business Agent, businesses are better able to serve their customers through our messaging apps by responding to inquiries, recommending products and handling support around the clock.

展望未來，隨著 Muse image 的推出，我們預期將進一步擴大廣告主以規模化方式生成高品質、符合品牌調性的創意素材之能力。透過 Meta Business Agent，企業能在我們的訊息應用程式上更好地服務客戶，包括回覆詢問、推薦產品並提供 24 小時不間斷的支援。

Earlier this month, we also introduced the Meta Business Agent platform, which gives enterprises the infrastructure to build, customize and deploy their business agent at scale on WhatsApp. The platform provides larger businesses with enterprise-grade controls, guardrails and measurement built in so they can define rules and offer personalized experiences, starting within the messaging apps that their customers already use.

本月稍早，我們也推出 Meta Business Agent 平台，為企業提供基礎設施，可在 WhatsApp 上以規模化方式建置、客製化並部署其商務代理。該平台內建企業級控制、護欄與衡量機制，讓大型企業能制定規則並提供個人化體驗，並從客戶已在使用的訊息應用程式開始。

Movida, one of Brazil's largest rental car companies with nearly 400 locations, deployed a business agent on WhatsApp to handle the entire booking flow from vehicle selection and pricing to payment in a single conversation. Returning customers could complete a reservation in as few as three messages. In a one-month period, Movida reported a 44% increase in daily bookings through WhatsApp when compared to the same period in the prior year and that 85% of conversations in the channel were resolved entirely by the AI agent without human assistance.

Movida 是巴西最大的租車公司之一，擁有近 400 個據點；其在 WhatsApp 上部署商務代理，在單一對話中處理從車輛選擇與定價到付款的完整訂車流程。回訪客戶最快只需三則訊息即可完成預訂。在一個月期間內，Movida 表示，與前一年同期相比，透過 WhatsApp 的每日訂單量增加 44%，且該通路中 85% 的對話完全由 AI 代理在無需人工協助下解決。

We're also building out additional ways to monetize our ecosystem. Two additional revenue streams are subscriptions and monetizing our competitive models through an API. Meta One is an evolution of our subscription portfolio to create more value for everyday users, businesses and creators so they get more features and AI tools to create, connect and stand out. We are excited to bring this to more users and continue to build enhanced tools for our subscribers.

我們也正在打造更多方式來將我們的生態系變現。另外兩項收入來源是訂閱，以及透過 API 將我們具競爭力的模型變現。Meta One 是我們訂閱產品組合的進化，旨在為一般使用者、企業與創作者創造更多價值，讓他們獲得更多功能與 AI 工具，以便創作、連結並脫穎而出。我們很期待將其帶給更多使用者，並持續為訂閱者打造更強化的工具。

We also recently launched a high intelligence model API at a competitive price and are encouraged by the initial results. We recently made Muse Spark available on OpenRouter for US-based developers, broadening its distribution and making it easier for developers to adopt the model. We soon expect to roll out the model API to more distribution channels, make it available in more countries and open it up for enterprises.

我們近期也以具競爭力的價格推出高智慧模型 API，初步成果令人鼓舞。我們最近已在 OpenRouter 上向美國開發者提供 Muse Spark，擴大其發佈範圍並讓開發者更容易採用該模型。我們預期不久後將把模型 API 推向更多發佈通路、在更多國家提供，並向企業開放。

Our approach to building capacity is strongly influenced by several key elements. First, the broad environment for building infrastructure is dynamic and uncertain in both near-term and longer-term time horizons. The industry has underbuilt historically for the wave of AI adoption, making existing capacity, including our own, extremely valuable. Longer term, the supply chains need to be built out to support the capacity that we anticipate we and others will need for AI-powered experiences.

我們的產能建置方法深受幾個關鍵要素影響。首先，建置基礎設施的整體環境在短期與長期都充滿動態變化與不確定性。產業過去對 AI 採用浪潮的建置不足，使得既有產能（包含我們自身的）極具價值。從更長期來看，供應鏈需要擴建，以支援我們與其他業者預期在 AI 驅動體驗上所需的產能。

Second, we have high confidence in our ability to utilize capacity to scale and build on top of our existing experiences as well as continue to invest in foundational models that will create substantial new opportunities. Consequently, our current plans are geared towards maximizing 2026 and 2027 capacity. When we have had incremental capacity in the past, it has proven extremely valuable in scaling experiences like Reels, and we are confident that this will be true in this time frame as well.

第二，我們對自身運用產能進行擴張的能力，以及在既有體驗之上持續打造並投資基礎模型（可創造可觀的新機會）充滿信心。因此，我們目前的規劃著重於最大化 2026 與 2027 年的產能。過去當我們擁有額外產能時，已證明其在擴大如 Reels 等體驗上極具價值；我們也有信心在此期間同樣如此。

Longer term, it's harder to predict the exact usage scaling curves, but we believe that our distribution advantages will give us the opportunity to serve AI products that are valuable for everyone, both our 3.6 billion users and millions of businesses.

從更長期來看，精確的使用量擴張曲線較難預測，但我們相信，我們的分發優勢將讓我們有機會提供對所有人都有價值的 AI 產品，涵蓋我們的 36 億使用者以及數百萬家企業。

This should be true regardless of whether our models are on the frontier, but we believe that being on the frontier will unlock new markets and opportunities for which we may need additional compute. Therefore, our longer-term capacity strategy aims to give us the flexibility to continue growing compute in 2028 and beyond by laying down data center and network foundations to accommodate future server decisions. The long-lived nature of these assets inherently provides the flexibility that will make it possible to adjust our investment to the pace of AI adoption.

無論我們的模型是否處於最前沿，這點都應成立；但我們相信，站在最前沿將開啟新的市場與機會，而這可能需要額外的運算資源。因此，我們的長期產能策略旨在透過奠定資料中心與網路基礎，以容納未來伺服器決策，讓我們在 2028 年及以後仍能彈性地持續擴增運算資源。這些資產的長期使用特性，本質上提供了彈性，使我們能依 AI 採用速度調整投資步調。

In addition, we have been making strategic investments in areas like our internal custom silicon effort, which will provide long-term strategic flexibility and supply chain leverage. This will be helpful in driving better returns on those long-term investments.

此外，我們也在例如內部客製化晶片等領域進行策略性投資，這將在長期提供策略彈性與供應鏈議價能力。這將有助於提升這些長期投資的報酬。

Finally, we believe that overall industry capacity is going to remain tight for the foreseeable future. As we've said earlier, we strongly believe that the models, consumer experiences and enterprise offerings that we are building will be the best and highest ROI use of our infrastructure.

最後，我們認為整體產業產能在可預見的未來仍將維持緊俏。如同我們先前所說，我們堅信我們正在打造的模型、消費者體驗與企業產品，將是我們基礎設施最佳且投資報酬率（ROI）最高的用途。

Those enterprise offerings have the potential to take multiple forms, as Mark mentioned, agentic tools, our API or monetizing compute directly given outsized market demand. We expect that remaining nimble about these opportunities will help us fund our build-out more efficiently while preserving our strategic flexibility to have the compute when we need it and provide us multiple pathways to generate returns on expected on invested capital.

如 Mark 所提，這些企業產品可能以多種形式呈現，包括代理型工具、我們的 API，或在市場需求異常強勁的情況下直接將運算資源變現。我們預期，對這些機會保持敏捷，將有助於我們在維持策略彈性（在需要時擁有運算資源）的同時，更有效率地為擴建計畫提供資金，並為預期投入資本報酬提供多條實現路徑。

In funding these infrastructure investments, the strength of our balance sheet gives us the ability to attract capital from a wide range of markets to supplement the cash flow generated by our business. Our announcement with BlackRock yesterday is an example of the partnerships that we can structure to complement our approach to building infrastructure capacity.

在為這些基礎設施投資籌資方面，我們資產負債表的強勁實力，使我們能從廣泛的市場吸引資金，以補充我們業務所產生的現金流。我們昨日與 BlackRock 的公告，就是我們可設計之合作夥伴關係的例子，用以配合我們建置基礎設施產能的方法。

Moving now to our financial outlook. We expect third quarter 2026 total revenue to be in the range of $61 billion to $64 billion. Our guidance assumes foreign currency is an approximately 1% headwind to year-over-year total revenue growth based on current exchange rates.

接著談我們的財務展望。我們預期 2026 年第三季總營收將落在 610 億至 640 億美元之間。我們的指引假設以目前匯率計算，外匯因素對總營收年增率約造成 1% 的不利影響。

Turning to the expense and CapEx outlook. We are raising the lower end of our expense outlook to incorporate the $2.4 billion charge related to legal proceedings recognized in Q2. We now expect full year 2026 total expenses to be in the range of $165 billion to $169 billion. We continue to expect to deliver operating income this year that is above 2025 operating income. We anticipate 2026 capital expenditures, including principal payments on finance leases, to be in the range of $130 billion to $145 billion, narrowed from our prior outlook of $125 billion to $145 billion.

接著是費用與資本支出（CapEx）展望。我們上調費用展望的下緣，以納入第二季認列、與法律程序相關的 24 億美元費用。我們目前預期 2026 年全年總費用將落在 1,650 億至 1,690 億美元之間。我們仍預期今年的營業利益將高於 2025 年的營業利益。我們預期 2026 年資本支出（包含融資租賃的本金償付）將落在 1,300 億至 1,450 億美元之間，較先前 1,250 億至 1,450 億美元的展望區間有所縮窄。

Absent any changes to our tax landscape, we expect our tax rate for the remaining quarters of 2026 to be between 15% to 17%, an increase from our prior outlook of 13% to 16%. Finally, we continue to monitor active legal and regulatory matters that could significantly impact our business and financial results. For example, we continue to see scrutiny on youth-related issues in several markets and have a number of youth-related trials scheduled for this year in the US, which may ultimately result in a material loss.

在我們的稅務環境未出現任何變化的情況下，我們預期 2026 年剩餘季度的稅率將介於 15% 至 17% 之間，較先前 13% 至 16% 的展望有所上調。最後，我們持續關注可能對我們的業務與財務結果造成重大影響的進行中法律與監管事項。例如，我們在多個市場仍看到對青少年相關議題的審視，且今年在美國安排了多起與青少年相關的審判，最終可能導致重大損失。

In closing, our business momentum continued in Q2 with strong execution across our core ads and engagement initiatives. We're also progressing in our efforts to bring personal super intelligence to everyone with exciting model releases, and we expect to build on that momentum over the course of this year with new products.

總結來說，我們在第二季延續了業務動能，並在核心廣告與互動參與計畫上展現強勁執行力。我們也在推進讓每個人都能擁有個人超級智慧的努力，推出令人振奮的模型版本，並預期在今年接下來的時間裡，透過新產品在此動能基礎上再接再厲。

With that, Krista, let's open up the call for questions.

那麼，Krista，我們開放提問。

(Operator Instructions) Brian Nowak, Morgan Stanley.

（接線員指示）Brian Nowak，摩根士丹利。

I have two, one for Mark, one for Susan. Mark, I appreciate all the color on the big pipeline for new products across consumer and business agents, API tools and compute rental. There's a lot of opportunities here. My question is, as you look at these opportunities and the state of the current offerings, the compute capacity, which of them do you expect to be able to scale first sort of in '26 and '27 to sort of showcase quantifiable material ROIC for investors?

我有兩個問題，一個問 Mark，一個問 Susan。Mark，感謝你就消費者與企業代理、新產品管線、API 工具以及算力租賃等提供了很多細節。這裡有很多機會。我的問題是，當你看這些機會、目前的產品供給與算力容量時，你預期其中哪些會最先在 2026 與 2027 年擴大規模，從而向投資人展現可量化且具實質性的 ROIC？

And then Susan, there's been some public comments about capacity in '27, doubling capacity, and I appreciate your color about CapEx. Any early comments on '27 CapEx, even the philosophy around sources of upside or sources of downward pressure as you sort of think through different ways to finance this multiyear build and have '27 CapEx.

接著 Susan，市場上有一些公開評論談到 2027 年的容量、容量翻倍，我也感謝你對資本支出（CapEx）的說明。對於 2027 年 CapEx 是否有任何初步看法——甚至是當你們思考以不同方式為這個多年期建置融資、並規劃 2027 年 CapEx 時，關於上行來源或下行壓力來源的理念？

I can take the first one. So in terms of the different opportunities and how we think about the compute, overall, a substantial amount of the compute goes towards training models to be a leading lab. And I think that that's an important investment. But then the rest of it goes towards a set of different products and revenue opportunities, which spans from optimizing and improving our core business to building new consumer products that we're releasing soon to the API, to the business agents work, to the developer tools work on the road map that I alluded to and then also the opportunity to sell compute directly where -- I mean, I mentioned that we have quite a number of offers at a meaningful premium over what we paid for the compute.

我先回答第一個。就不同機會以及我們如何看待算力而言，整體來說，相當大一部分算力會用於訓練模型，以維持我們作為領先實驗室的地位。我認為這是一項重要投資。其餘算力則用於一系列不同的產品與營收機會，範圍涵蓋從優化與改善我們的核心業務、到我們即將推出的新消費者產品、到 API、到企業代理的工作、到我先前提到的開發者工具路線圖，另外也包括直接出售算力的機會——我的意思是，我提到過，我們已收到相當多報價，其價格相較我們購買算力的成本有顯著溢價。

The question in thinking about this is we believe that there will continue to be a significantly higher margin on selling intelligence rather than selling compute directly. But we think that there is a big opportunity, obviously, to sell compute as well. So we're thinking through -- I think you asked what one area would likely scale the most. But I mean I'm actually quite optimistic that we're going to see meaningful growth in all of these areas. And I think we will have more to share soon on a number of them.

在思考這個問題時，我們相信，出售智慧（intelligence）相較於直接出售算力，將持續擁有顯著更高的毛利率。但我們也認為，出售算力同樣存在很大的機會。因此我們正在推演——我想你問的是哪一個領域最可能擴張得最多。不過我的確相當樂觀，我們會在所有這些領域看到有意義的成長。而且我認為我們很快會就其中若干項分享更多資訊。

Brian, on your second question, we aren't providing a specific outlook for 2027 CapEx at this time. Infrastructure planning remains highly dynamic. And even this year, there are a range of outcomes embedded in our outlook. I alluded in my main remarks to our focus on being -- on gearing our current infrastructure plans towards maximizing capacity in 2026 and '27 and giving us the flexibility to continue to grow in '28 and beyond, but also giving us the ability to make server decisions when we come to our -- being able to evaluate our actual needs in '28 and beyond. So we're still working through what our capacity needs are going to be over the coming years. Generally, we believe near-term capacity is more valuable than long-term capacity, and it remains a very dynamic planning process.

Brian，關於你的第二個問題，我們目前不提供 2027 年資本支出（CapEx）的具體展望。基礎設施規劃仍然高度動態。即便在今年，我們的展望中也包含一系列可能結果。我在主要發言中提到，我們的重點是——將目前的基礎設施規劃導向在 2026 與 2027 年最大化容量，並讓我們在 2028 年及之後仍具備持續成長的彈性，同時也讓我們能在做出伺服器決策時——能夠評估我們在 2028 年及之後的實際需求。因此，我們仍在釐清未來幾年的容量需求會是什麼。整體而言，我們認為短期容量比長期容量更有價值，而這仍是一個非常動態的規劃流程。

(Operator Instructions) Eric Sheridan, Goldman Sachs.

（接線員指示）Eric Sheridan，高盛。

Maybe two, if I can. When you frame up the enterprise opportunity, how much of that opportunity do you think is available to you today based on what you've built out in terms of go-to-market strategy as extensions of the advertising and the marketing business you have already versus go-to-market strategies that might have to be built to capitalize on the opportunity?

如果可以的話，我也許有兩個問題。當你們界定企業端機會時，基於你們目前已建立的上市策略（go-to-market strategy）——作為既有廣告與行銷業務的延伸——你認為今天你們能掌握的機會有多少？相較之下，又有多少需要建立新的上市策略才能把握？

And then maybe, Susan, if I could just squeeze a second one in. When you think about sources of capital for the business for the next couple of years, you referenced the deal that was announced yesterday as an example of looking at ways to finance forward obligations. We continue to get questions from investors about how to think about the mix of debt and equity and sources of capital. Philosophically, how are you guys thinking about wanting to be ambitious on the spend, but then marrying that with the need for capital?

另外 Susan，如果我可以再塞第二個問題。當你思考未來幾年業務的資金來源時，你提到昨天宣布的交易，作為尋找方式為未來義務融資的例子。我們持續收到投資人提問，想了解如何看待負債與股權的組合以及資金來源。從理念上，你們如何思考：一方面希望在支出上保持進取，另一方面又要與資金需求相匹配？

Sure. I can talk about the first part. I think for enterprise, there's going to be a combination of extending the current business, which is effectively selling to marketers and businesses that are like -- basically customer-facing, trying to reach customers and sell to them directly. Obviously, that's the vast majority of the business across Facebook and Instagram.

好的。我可以先談第一部分。我認為在企業端，會是延伸現有業務與開拓新方向的組合；現有業務本質上是向行銷人員與企業銷售——也就是面向消費者、試圖觸達客戶並直接向其銷售的企業。顯然，這是 Facebook 與 Instagram 上絕大多數的業務。

And we believe that there is an opportunity to extend this with business agents across messaging apps and other surfaces to continue to interact with customers. And just like the ad system, effectively, we will get paid when we deliver results for those businesses.

我們相信，有機會透過訊息應用程式與其他介面上的企業代理（business agents）來延伸這一點，持續與客戶互動。而且就像廣告系統一樣，基本上，我們在為這些企業帶來成果時就會獲得報酬。

So we view this as an extension of the sales and the partnerships that we have with many millions of advertisers and hundreds of millions of small businesses that use our platforms. So that one, I think, should be quite a natural opportunity for us. And we're focused on delivering it in a way where we're not trying to kind of maximize the sale in the near term. We're trying to maximize the results for people and build out a robust auction, and that's what we've seen -- has served the business well over time.

因此，我們將其視為延伸我們與數百萬廣告主以及數億使用我們平台之中小企業之間的銷售與合作關係。所以我認為這對我們而言會是相當自然的機會。我們也專注於以一種方式來交付它：我們不是試圖在短期內把銷售最大化。我們是試圖為人們把成果最大化，並建立一個健全的拍賣機制；而這正是我們所看到——長期以來對業務很有幫助的做法。

There are other enterprise customers who I think we're increasingly going to serve too. We're building coding and developing an internal productivity tools partially because we need to build them ourselves, and we need to make sure that we have tools that are tuned for ourselves. And now that we have those, we feel like there's a large opportunity to serve whether that's small businesses or larger businesses, that is a somewhat different muscle than we have historically had, and we will share more soon on how we're planning to build that out.

我認為還有其他企業客戶，我們也會愈來愈多地服務。我們正在打造並開發內部生產力工具，部分原因是我們自己就需要打造它們，並確保這些工具是為我們自身調校的。而現在我們已具備這些工具後，我們覺得無論是服務中小企業或大型企業，都存在很大的機會；這在某種程度上是我們過去較少具備的一種能力（muscle），我們也會很快分享更多我們打算如何建置這一塊。

But I think that there is just a very large opportunity on this. And the way that I think about this is there's obviously been a bunch of news about the compute side, but I think that the enterprise opportunity is kind of the sum of all of these different things. It's not just the selling compute, also the API services, the productivity services, the kind of business agents for other parts of the business beyond marketing are all parts of the overall offering. And I think that there's just a very, very large opportunity there.

但我認為這裡確實存在非常大的機會。我看待這件事的方式是：顯然外界對算力端有很多新聞，但我認為企業端機會其實是所有這些不同事項的總和。不只是出售算力，API 服務、生產力服務、以及用於行銷以外其他業務領域的各類企業代理，都是整體供給的一部分。我認為那裡有非常、非常大的機會。

So we're quite focused on that. That's going to be somewhat of a new muscle that we build as a company, but I think it's a very important one that we build so that way we can make sure that we can maximize the opportunity ahead of us.

所以我們對此相當專注。這將會是我們作為一家公司要建立的一項相對新的能力，但我認為這是一項非常重要、必須建立的能力，如此一來我們才能確保把握並最大化眼前的機會。

Eric, on your second question in terms of sources of capital, this is something that we have been looking at thoughtfully as we think about financial planning for the future. Obviously, our strong operating cash flow certainly has put us in a position of strength as it pertains to funding our infrastructure build-out. But we've also been evolving our capital structure in recent years to include a greater mix of debt as we work to bring down our cost of capital, and we have generally found it prudent to continue adding cost-efficient long-duration sources of capital as we make investments in initiatives that themselves have long time horizons, especially AI infrastructure projects. We've also broadened our aperture there to include partnerships like the one we announced with BlackRock yesterday. And we'll continue to be thoughtful about evaluating the appropriate different sources of capital over time as we evaluate future projects.

Eric，關於你第二個問題、也就是資金來源方面，這是我們在思考未來財務規劃時一直審慎評估的事項。顯然，我們強勁的營運現金流，確實讓我們在為基礎設施擴建提供資金方面處於有利位置。但近年來我們也在調整資本結構，納入更高比例的債務，以降低資金成本；而且在投資本身具有長期時間跨度的計畫時——尤其是 AI 基礎設施專案——我們普遍認為持續增加具成本效率、期限較長的資金來源是審慎之舉。我們也擴大了視野，把像我們昨天與 BlackRock 宣布的那類合作夥伴關係納入考量。隨著我們評估未來專案，我們也會持續審慎評估在不同時間點採用哪些適當的資金來源。

(Operator Instructions) Mark Shmulik, Bernstein.

(接線員指示) Mark Shmulik，Bernstein。

Mark, everyone's got a story of kind of someone coming back from Silicon Valley, deep writing code, building agents with AI and then they head home and they try to tell their parents they're using AI wrong as just kind of like a glorified search tool. Consumer behavior is always pretty difficult to predict, but kind of reading your op-ed on AI for everyone, how do you think about whether consumer adoption can close this AI utility gap? Like are we on the cusp of something kind of breaking through? Or do we just need to be a bit more patient?

Mark，每個人似乎都有一個故事：有人從矽谷回來，埋頭寫程式、用 AI 建代理（agents），然後回到家試著跟父母說，他們把 AI 用錯了——只是把它當成一個被美化的搜尋工具。消費者行為一向很難預測，但讀了你那篇「AI for everyone」的評論文章後，你怎麼看消費者採用是否能縮小這個 AI 實用性落差？我們是否正處在某種即將突破的臨界點？還是我們只需要再更有耐心一點？

Well, I think that some things have already broken through. And I think one of the interesting things about AI compared to other technologies is that every year or so and the cycle may accelerate, but every year or so, you get new capabilities that create new possible product lines. So there's obviously the AI assistant market for consumers where that's what we're doing with Meta AI and then there's the other products that competitors have in that space.

我認為有些事情其實已經突破了。而且我覺得 AI 相較於其他技術有趣的一點是：大約每一年左右——而且這個週期可能還會加速——你就會獲得新的能力，進而創造新的可能產品線。所以很明顯地，面向消費者的 AI 助理市場已經存在，我們在做的就是 Meta AI，而競爭對手在這個領域也有其他產品。

And then in the last year, I think the coding agent market has really grown very quickly. And that's the first real agentic market. And I think that there's a number of reasons why coding is first, right? You have technical customers who are willing to do the work to make it work. Coding is an inherently digital and closed-loop activity. So it's sort of -- in some ways, it is somewhat of kind of an easier system to train on.

另外在過去一年，我認為「程式碼代理」市場確實非常快速地成長。而那是第一個真正的代理式（agentic）市場。我認為程式撰寫會先發有幾個原因，對吧？你有願意投入心力讓它運作起來的技術型客戶。寫程式本質上是數位化且閉環的活動。所以在某些方面，它算是一個相對更容易用來訓練的系統。

But our bet is that -- or one of the bets here that we're making is that we think that consumer personal agents is going to end up being an extremely important and massive market. I think that it's extremely unlikely if you look out five years from now, for example, that whatever period of time you want that you don't have billions of people with a personal agent that understands your goals and it is just working on your behalf 24/7 to achieve your goals. And whatever the domain is that you care about, whether it's helping you with your health or your hobbies or your personal finances or your productivity and running your home better or improving and enhancing your relationships, helping with your career, helping -- just all these different things. This is like a very, very deep set of use cases.

但我們的押注是——或者說我們在這裡做的一個押注是——我們認為面向消費者的個人代理，最終會成為一個極其重要且規模龐大的市場。我認為如果你把時間拉到五年後（或任何你想看的時間尺度），幾乎不可能出現這樣的情況：沒有數十億人擁有一個理解你目標的個人代理，並且 24/7 代表你工作以達成你的目標。而且不論你在意的領域是什麼——無論是協助你的健康、嗜好、個人理財、提升生產力與把家裡打理得更好，或是改善並增進你的人際關係、協助職涯發展，協助——各式各樣的事情。這是一組非常、非常深的使用情境。

But like you said, I think that building for consumers is a little bit different than building for developers. If you're building for consumers, especially if you're trying to build something that isn't used by millions of people, but is used by billions of people, it needs to just work, right? It needs to be simple.

但就像你說的，為消費者打造產品，和為開發者打造產品有點不同。如果你是為消費者打造，特別是你想打造的不是被數百萬人使用、而是被數十億人使用的東西，它就必須「就是能用」，對吧？它必須很簡單。

And I think a lot of what we see with these agents today, a lot of the kind of proto agents is it takes a bunch of fiddling, you have to get into a terminal to get it set up, you get a use case going. It kind of seems magical, but then maybe it breaks down over time. And I think that the companies that can deliver the personal agents that just work, I think that this is going to be one of the next major opportunities in AI, and we are very excited about that in addition to what we're doing with Meta AI, in addition to what we're doing with business agents, in addition to all these other things, while also developing the models that we think are going to continue to advance new capabilities to create new product lines as well. So this is why we're very optimistic.

而我認為我們今天看到的很多代理、很多所謂的原型代理（proto agents），都需要一堆調整；你得進到終端機去設定，才能把某個使用情境跑起來。它看起來很神奇，但之後可能會隨著時間推移而失靈。我認為能夠交付那種「就是能用」的個人代理的公司，這將會是 AI 下一個重大機會之一；我們對此非常興奮——除了我們在 Meta AI 上做的事、除了我們在商業代理上做的事、除了所有其他事情之外——同時我們也在開發我們認為將持續推進新能力、並創造新產品線的模型。所以這也是為什麼我們非常樂觀。

Now I get we're going to ship this at some point soon, and that's going to be very exciting. And we haven't done that yet. So this is -- there's kind of only so much that I can say on an earnings call about this. But I think this is a very large opportunity and one that I think is almost inevitable that someone does. And I think it really plays to Meta's strengths as a company. We build consumer products that reach billions of people. We're great at once we get something working, scaling it to a large number of people, and we're good at building the infrastructure to be able to support these intensive applications. So I feel very good about this. I kind of understand that we need to deliver it for our community, and that's what we're very focused on.

我知道我們會在不久的某個時間點推出這個，屆時會非常令人興奮。而我們目前還沒有做到。所以——在財報電話會議上，關於這件事我能說的其實也有限。但我認為這是一個非常大的機會，而且幾乎可以說是必然會有人做到的。我也認為這非常符合 Meta 作為一家公司的優勢。我們打造能觸及數十億人的消費者產品。我們很擅長在把某件事做出來、讓它運作之後，把它擴展到大量人群；我們也擅長建置能支撐這些高強度應用的基礎設施。所以我對此感覺非常好。我也理解我們需要把它交付給我們的社群，而這正是我們非常專注的事情。

Doug Anmuth, JPMorgan.

Doug Anmuth，JPMorgan。

One for Susan, one for Mark. Susan, you talked about how LLMs are increasingly capable of delivering ranking and recommendation gains. Can you just talk more about the road map here and how far along you are in just leveraging better models and more compute?

一題給 Susan，一題給 Mark。Susan，你提到 LLM 的能力正越來越能帶來排序與推薦的提升。你能否多談談這裡的路線圖，以及你們在運用更好的模型與更多算力方面目前進展到什麼程度？

And then, Mark, just in terms of the number of offers to monetize your compute externally, you also, at the same time, are purchasing capacity from a number of third parties. So just hoping you can help us understand some of the differences here. Is it just timing and stopgap issues? Or is it training versus inference and leveraging the chips that are best suited for each?

另外，Mark，關於把你們的算力對外變現的提案數量，同時你們也在向多家第三方購買容量。希望你能幫我們理解這裡的一些差異。這只是時點與過渡性（stopgap）問題嗎？還是與訓練對比推論（inference），以及針對各自最適合的晶片來加以運用有關？

Thanks, Doug. I'll take that first question about where we are on the recommendations road map. First of all, we certainly see further headroom to continue improving recommendations over the rest of the year and into 2027. And we expect that will help us drive additional gains on both engagement on Facebook and Instagram.

謝謝，Doug。我先回答第一個關於推薦路線圖進展的問題。首先，我們確實看到在今年剩餘時間以及一路到 2027 年，推薦系統仍有進一步改善的空間。我們預期這將有助於推動 Facebook 與 Instagram 在互動度上的額外提升。

A couple of things I would highlight. First, we'll continue to make recommendations even more personalized and relevant to user interest. And that's in part by advancing our recommendation models and architectures to further capture user interest more precisely and respond faster to what people care about in a given moment.

我想強調幾點。第一，我們會持續讓推薦更個人化、也更貼近使用者興趣。其中一部分是透過推進我們的推薦模型與架構，更精準地捕捉使用者興趣，並更快速回應人們在某個當下所關心的事。

Our AI investments are going to play a significant role in delivering on this vision, including the expansion of LLM-based content understanding to develop a deeper understanding of posts and creators that people value to capture user interest more precisely and respond more quickly to what they care about in the moment and using AI to surface high-quality, fresh and trending content and reduce the share of low-quality content.

我們在 AI 方面的投資將在實現這一願景中扮演重要角色，包括擴展以 LLM 為基礎的內容理解能力，以更深入理解人們重視的貼文與創作者，從而更精準地捕捉使用者興趣、在當下更快速回應他們所關心的事，並運用 AI 推送高品質、最新且具趨勢的內容，同時降低低品質內容的占比。

Second, we're continuing to improve our data infrastructure to allow our models to train on more data and leverage that data more effectively. We're adding more detail to how we describe content that users have engaged with in the past and enriching past user interaction sequences with more granular content. That allows our models to more precisely learn which engagements are more or less valuable to users. We're continuing to scale up both the length of user interaction sequences we use during training as well as the complexity of our model architectures across Facebook and Instagram to take advantage of the larger data sets. And then we've already made significant strides leveraging LLMs for content understanding.

第二，我們持續改善資料基礎設施，讓模型能以更多資料進行訓練，並更有效地運用這些資料。我們正在更細緻地描述使用者過去曾互動過的內容，並以更細粒度的內容來豐富過往的使用者互動序列。這讓我們的模型能更精準地學習哪些互動對使用者而言更有價值、哪些較沒有價值。我們也持續擴大訓練時所使用的使用者互動序列長度，並提升 Facebook 與 Instagram 上模型架構的複雜度，以善用更大的資料集。此外，我們在運用 LLM 進行內容理解方面已取得顯著進展。

We're going to further incorporate them into both our recommendations and content policy enforcement stacks given their capability to more deeply understand content. And I would say, broadly, I think we're very optimistic about that body of work. We've also invested in using LLM-based agentic approaches to transforming our recommendation system, and we grew the number of launches from our ranking agents this half. That also helps make our engineers more productive, and that's another path that we're very excited about as well.

鑑於它們能更深入理解內容的能力，我們將進一步把它們納入推薦系統與內容政策執行的技術堆疊中。整體而言，我想我們對這一系列工作非常樂觀。我們也投資於採用以 LLM 為基礎的代理式（agentic）方法來改造推薦系統，並在本半年度增加了由排名代理（ranking agents）帶來的上線次數。這也有助於提升工程師的生產力，而這也是我們同樣非常期待的另一條路徑。

I can answer the second part of that. I mean I think your question was about how we think about offers that we're getting to sell the compute, but then we're also buying the compute. I mean, look, the high-level observation is that there's just nowhere near enough compute for all the demand. So that is why we see that like basically, we are getting a large number of offers for the compute that we have, but also we have a lot of internal uses that we think are going to be quite valuable.

我可以回答第二部分。我想你的問題是關於：我們如何看待收到要我們出售算力的提案，但同時我們也在購買算力。你看，從高層次來說，算力供給遠遠不足以滿足所有需求。因此我們確實看到：基本上，我們收到大量針對我們現有算力的收購／採購提案，但同時我們也有許多內部用途，我們認為這些用途將相當有價值。

Now in terms of running the business, obviously, a common trade-off that we need to make is around how much do you monetize something today versus develop future assets for the future. And I think that it's always a portfolio, right? It's not like you don't want to only do long-term things and do no -- like -- and not kind of prove the markets out that exist in the near term, but I also think it would be foolish to basically just sell all of the compute and take a short-term profit, but when you have the opportunity to build intelligence on top of it, which will be a kind of a multiple and that compounds the value of the compute on top of that.

就經營業務而言，顯然我們常見的一個取捨是：今天要把某項資產變現到什麼程度， versus 為未來培育資產。而我認為這永遠是一個投資組合的問題，對吧？你不可能只做長期的事、完全不做——也就是——不去驗證短期內存在的市場；但我也認為，如果只是把所有算力都賣掉、賺取短期利潤，那基本上是愚蠢的，因為你其實有機會在其上建立智慧能力，而那會形成一種倍數效應，並在其上複利式地提升算力的價值。

So I think the answer is what we're doing, which is to basically use a lot of our capital to build out compute, having confidence that we have the ability to monetize the compute directly when that makes sense, but also knowing that we have quite a number of different use cases to monetize the intelligence on top of the compute, including the enterprise cases that we talked about and including some of the consumer and cases that we talked about and including just the core business, which is not even necessarily new products that we haven't talked about, but just in terms of using that to be able to further add intelligence and improve the ranking and recommendations and ads in the core services.

所以我認為答案就是我們正在做的事：基本上投入大量資本來擴建算力，並有信心在合適時機能直接將算力變現；同時也知道我們有相當多不同的使用情境，可以把建立在算力之上的智慧能力加以變現，包括我們談到的企業情境，也包括我們談到的一些消費者情境，還包括核心業務——這甚至不一定是我們尚未談到的新產品，而是就利用這些能力進一步增加智慧、改善核心服務中的排序、推薦與廣告而言。

So I think all of that is true. And it creates this dynamic where there is a lead time where we're investing in building out these data centers now. They come online at some point in the future. You obviously are not getting value out of them until they're online. But that's -- we basically see a very large demand for all of this and want to go maximize the opportunity to build out all of these different businesses.

所以我認為這些都是真的。而這也形成一種動態：我們現在投資建置這些資料中心，存在一段前置期。它們會在未來某個時間點上線。顯然在上線之前，你無法從中獲得價值。但我們基本上看到對這一切都有非常龐大的需求，並希望把握機會，最大化拓展這些不同業務的可能性。

Justin Post, Bank of America.

Justin Post，美國銀行。

Great. Mark, you hired the senior leadership of your AI Labs about a year ago. Could you just give us your thoughts on how the lab is performing? And do you think the Street will really see an uptick in product velocity as far as models or chips or other things coming forward? And then what kind of sustainable competitive advantages do you think the lab is building?

很好。Mark，你大約一年前聘請了 AI Labs 的資深領導團隊。你能否談談你對實驗室表現的看法？以及你是否認為華爾街會真正看到產品推進速度的提升，例如模型、晶片或其他成果的推出？另外，你認為該實驗室正在建立哪些可持續的競爭優勢？

Yes. So I mean, I'm quite happy with the trajectory that we're on. We've released a few models that I think are quite impressive on our early scaling ladder. And as I've said, we're in the process of scaling much larger and more advanced models, and we're excited about those, too. I think that there's the intelligence aspect of this and then there's the data aspect of it.

是的。我對我們目前的發展軌跡相當滿意。我們已發布了幾個模型，我認為在我們早期的擴展階梯（scaling ladder）上表現相當令人印象深刻。而如我所說，我們正在擴展到更大、更先進的模型，我們也對那些感到興奮。我認為這裡既有智慧（intelligence）的面向，也有資料（data）的面向。

And I think it is true that in any kind of product category that you want to go into, there is going to be some kind of flywheel where you learn from the behaviors and how people in that community are using the product. So in kind of the better that you can do there, the more feedback you get and then that inevitably makes the product better.

而我認為確實如此：在你想進入的任何產品類別中，都會存在某種飛輪效應，你會從該社群的人們如何使用產品、以及他們的行為中學習。因此，你在這方面做得越好，就能獲得越多回饋，而這也不可避免地讓產品變得更好。

So I think this is a field where people understandably are obsessed with intelligence, but the data and the knowledge part of it to serve people well is really important. If you're talking about personal super intelligence, then obviously, having a very clear mental model of everything that is going on in the person's life and their goals is going to be a very important aspect of that.

所以我認為這是一個人們可以理解地著迷於智慧能力的領域，但要把人服務好，資料與知識的部分其實非常重要。如果你談的是個人超級智慧（personal super intelligence），那麼顯然，對一個人生活中正在發生的一切以及其目標，擁有非常清晰的心智模型，將會是其中非常重要的一環。

And certain companies start with different advantages in different markets and different parts of that. But also, I think part of the reason for working on a number of these different things upfront is that, first of all, the technology is general. So when you build the intelligence out, it can apply to a number of different uses. But then you want to invest in building up the flywheel on them because I think that that's how you build the sustainable advantage over time.

而不同公司在不同市場、以及其中不同部分，起點上會有不同的優勢。但同時，我認為之所以要一開始就投入做多個不同方向的部分原因在於：首先，這項技術是通用的。因此當你把智慧能力建構出來後，它可以應用到多種不同用途。但接著你也會想投資去建立其飛輪，因為我認為這正是你隨時間建立可持續優勢的方式。

But I think that we've definitely shown at Meta that, when we have a product and a format that works, we -- I would go as far as to say that I think we're probably the best company in the world at scaling those experiences to billions of people. So I feel quite good about the personal agent work. I feel very good about the business agent work where we kind of already have our base of advertisers and small businesses that we serve and the ability to scale that out and roll it out around those. I think that those are durable advantages as well as the data flywheels that we're building on them.

但我認為我們在 Meta 已經清楚展現：當我們有一個有效的產品與形式時，我們——我甚至可以說——我們可能是全世界最擅長把這些體驗擴展到數十億人規模的公司。所以我對個人代理（personal agent）的工作相當有信心。我對商業代理（business agent）的工作也非常有信心，因為我們已經有我們所服務的廣告主與中小企業基礎，以及把它擴大規模並圍繞它們推廣落地的能力。我認為這些都是持久的優勢，連同我們正在其上建立的資料飛輪也是如此。

And then obviously, on the research side, you want to build good culture. I mean this is just kind of the simple stuff, but it's -- I think that it's -- you just want to make sure that you're building the team in a way where you're managing things well and kind of consistently with low drama compounding over time. And that's sort of the goal that you aspire to. And I think if you can do that well, then that is hopefully may not be kind of a clear mathematical explanation of something, but I think that that's kind of how business works. So yes, that's what we do.

然後很明顯，在研究端，你會希望建立良好的文化。我的意思是，這些是相對基本的事情，但——我認為——你就是要確保以一種能把事情管理好、並且長期一致、低戲劇性（low drama）地累積的方式來打造團隊。這就是你所追求的目標。而我認為如果你能把這件事做好，那麼這或許不是某種可以用清楚數學方式解釋的東西，但我認為商業運作大致就是如此。所以是的，這就是我們在做的事。

Ross Sandler, Barclays.

Ross Sandler，巴克萊。

Yes. Mark, sticking with the comments on the AI Lab. So Muse Spark 1.1 is pretty close to the Pareto frontier, but it's at the lower end of the intelligence spectrum or lower cost end, I should say. And it sounds like from your last answer, you think it's important to compete at both the lower cost end and the more expensive performance end. Can you just talk about that a little bit?

是的。Mark，延續你剛才對 AI Lab 的評論。所以 Muse Spark 1.1 非常接近帕累托前緣，但它位於智慧光譜的較低端，或者我應該說是成本較低的一端。而且聽起來從你上一個回答來看，你認為在較低成本端與較昂貴的高效能端都要競爭很重要。你能否稍微談談這點？

And then the leadership of your lab has also talked about going back into open source kind of where you were a couple of years ago. So how does that fit into the strategy and all this discussion around monetization of AI products and models?

另外，你們實驗室的領導層也談到要回到開源，有點像你們幾年前的做法。那這如何融入策略，以及與 AI 產品與模型變現相關的這些討論？

Yes. So I mean, just to get into the kind of scientific process on this a little bit, basically, training large models at each stage of training a large model, you see novel behaviors. So basically, the way that you want to do this is you build up from training smaller models to building larger models. And the models that we've released so far are based on, there are a certain scale and climbing up the scaling ladder, and we're continuing to scale larger models. So I think for Muse Spark 1 and Muse Spark 1.1, I think that they are very impressive models for the scale of the model, the kind of stage of development of the lab, and I feel quite good about them.

是的。我想稍微從科學流程的角度談一下：基本上，在訓練大型模型的每個訓練階段，你都會看到新的行為出現。所以基本上，你想要做的是從訓練較小的模型逐步建立到更大的模型。而我們目前已發布的模型，是基於某個規模，並且正在沿著擴展階梯往上爬，我們也會持續擴展到更大的模型。所以我認為 Muse Spark 1 和 Muse Spark 1.1，以它們的模型規模、實驗室的開發階段來看，都是非常令人印象深刻的模型，我對它們感覺相當不錯。

Now we want to have models that are more advanced as well, and that's why we're scaling larger models, and that's why we're building out a bunch of the research infrastructure around that. But at the same time, we also want to have good more efficient models that will be a lot of what we serve to consumers at scale. But if you're serving billions of people, you want the ability to have more advanced models for things that are very hard problems, and you want the ability to serve the vast majority of prompts from simpler and more efficient models. So I think both are going to be very important. The efficiency really matters, but I also think that we want to be able to solve the hardest problems for businesses and customers around the world.

現在我們也希望擁有更先進的模型，這也是為什麼我們要擴展更大的模型，以及為什麼我們要圍繞這件事建置一整套研究基礎設施。但同時，我們也希望有更好、更高效率的模型，因為在大規模面向消費者提供服務時，這會是我們提供的很大一部分。但如果你要服務數十億人，你需要有能力用更先進的模型來處理非常困難的問題，同時也要能用更簡單、更高效率的模型來處理絕大多數的提示（prompts）。所以我認為兩者都會非常重要。效率確實很關鍵，但我也認為我們希望能夠為全球的企業與客戶解決最困難的問題。

So we care about both of them. On open source, I think we have always felt like open source was an important part of the ecosystem, and it's good for the world. And it creates its own feedback loops that are positive for us around getting the community invested in our infrastructure stack and our work and contributing improvements. But we've always basically said, that we were going to do a mix of open and closed. And that continues to be true.

所以我們兩者都重視。關於開源，我認為我們一直覺得開源是生態系的重要一部分，對世界也有益。而且它會形成對我們有利的正向回饋循環，讓社群投入我們的基礎設施堆疊與工作，並貢獻改進。但我們基本上一直都說，我們會採取開放與封閉並行的方式。這點仍然不變。

Now in ramping up Meta Superintelligence Labs, in some ways, actually counterintuitively, it takes some more work to do open source models because you're not necessarily -- if you're doing something as a closed system that only -- you're only building for your own use cases, it can be a little more jagged. Whereas if you release it as open and it's going to be used for a lot of things, you want to make it more well-rounded.

現在在加速推進 Meta Superintelligence Labs 的過程中，某種程度上其實有點反直覺：做開源模型反而需要更多工作，因為你不一定——如果你做的是封閉系統，只——只為你自己的使用情境打造，它可以做得比較不那麼圓滑、比較有稜角。但如果你以開放形式發布，會被用在很多事情上，你就會希望它更全面、更均衡。

And I just wanted to make sure that the MSL team was uninhibited in building out the most intelligent models that we could. And we expect that we will get back to releasing some open source models at some point soon. But like we've always said, we're not dogmatic about this. We think open source is important. We want to contribute to that ecosystem. We plan to do a combination of open and closed models.

而我只是想確保 MSL 團隊在打造我們所能做到的最聰明模型時不受拘束。我們預期在不久的某個時間點會回到發布一些開源模型。但就像我們一直說的，我們不會在這件事上教條化。我們認為開源很重要。我們希望為那個生態系做出貢獻。我們計畫採取開放與封閉模型的組合。

Ken Gawrelski, Wells Fargo.

Ken Gawrelski，富國銀行。

Two, if I may, please. I just want to maybe, Mark, just touch on the last point again around open weight models. There's been a lot of discussion you've weighed in on the topic. Why or why not does that change Meta's view of developing closed proprietary frontier models? Is there an opportunity if open weight models proliferate that you don't have to develop your own frontier models? So that's question one.

如果可以的話，兩個問題。我想再碰一下你剛才關於開放權重模型（open weight models）的最後一點，Mark。這個議題你也參與了很多討論。為什麼或為什麼不，這會改變 Meta 對於開發封閉、專有的前沿模型（frontier models）的看法？如果開放權重模型大量普及，是否存在一個機會，使你們不必開發自己的前沿模型？這是第一個問題。

And the second one, a clarification, if I may, for Susan. You noted that in your prepared remarks that you plan to maximize '26 and '27 capacity. Is that a demand or supply comment? Meaning, are you suggesting that Meta plans for internal use of all the capacity built through '27? Or are you saying that you will evaluate '28 and beyond builds based on demand for Meta products and services?

第二個問題，如果可以的話，想請 Susan 澄清一下。你在事先準備的發言中提到，你們計畫把 '26 和 '27 的產能最大化。這是需求面的評論還是供給面的評論？也就是說，你是在暗示 Meta 計畫把到 '27 為止建置的所有產能都用於內部使用嗎？還是說，你們會根據 Meta 產品與服務的需求，來評估 '28 以及之後的建置？

I can take the open source question. And let's see. So do we -- basically, the question is, do we think that because there are some open-weight models that we can just rely on those. I mean, right now, the open source models are not as strong as the frontier models. So no is the basic answer.

我可以回答開源的問題。我想想。所以我們是否——基本上問題是：因為有一些開放權重模型，我們就可以只依賴那些嗎？我的意思是，目前開源模型的能力還不如前沿模型。所以基本答案是否定的。

And then there's also just always the perpetual, both policy debate and question around other companies' actions and whether that's actually a thing that we can -- that we -- that a company like Meta can rely on. I think that that's very tricky. So I think on both fronts, we believe we're going to be able to do better work, and we think that there's some risk in that reliance.

另外，還一直存在一個長期的政策辯論與疑問：其他公司的行動，以及那是否真的是我們——我們——像 Meta 這樣的公司可以依賴的東西。我認為那非常棘手。所以我認為從這兩個面向來看，我們相信我們能做得更好，而且我們也認為依賴那樣做存在一些風險。

I don't believe that, that is the right thing to do. I think that we're a company that -- if you look at Meta from -- take a step back on this, a lot of people view the surface layer of we build some social media apps and we have an ad business. We are really a full-stack technology company. We built our own data centers, our own infrastructure, our own chips, our own low-level software. When we got started, like my background in engineering, like I wrote a lot of the systems code.

我不認為那是正確的做法。我認為我們是一家——如果你退一步看 Meta，很多人看到的是表層：我們做了一些社群媒體 App，並且有廣告業務。但我們其實是一家全棧（full-stack）的科技公司。我們建自己的資料中心、自己的基礎設施、自己的晶片、自己的底層軟體。我們剛開始時，以我工程背景來說，我寫了很多系統程式碼。

A lot of the reason why Facebook worked was because it actually -- it just worked, right? Like it literally worked when other social networks did not work fast and efficiently. And I think we just have the ability to build things that can be more personalized, more optimized, more efficient. Some qualitative experiences are just not even possible for others to build because we go all the way down the stack.

Facebook 之所以能成功，很大一部分原因是它真的——它就是能用，對吧？就字面上來說，當其他社群網路無法快速且高效率運作時，它能運作。而我認為我們具備打造更個人化、更最佳化、更高效率事物的能力。有些質性的體驗，其他人甚至不可能做出來，因為我們一路做到整個技術堆疊的最底層。

And it just seems to me pretty clear that having kind of sovereignty over building your own models is going to be an important part of that stack going forward, which is why it is important for Meta, but it is also important -- is also why other people care about open source and why open source matters overall because like other companies, if they -- even if they don't have the ability to build these models, do not want to have to just rely on a small number of closed labs. Like there is a very important place in the world for there to be open source models. And to be clear, that doesn't take away from the API opportunity or any of the things that I'm talking about because someone still needs to run the models and run inference on them and be able to kind of run those models efficiently and have the compute to do that is going to continue to be a source of business advantage.

在我看來相當明確的是，未來在這個技術堆疊中，對於自行建構模型擁有某種主權（sovereignty）會是重要的一環；這也是為什麼這對 Meta 很重要。同時，這也——也是為什麼其他人關心開源、以及為什麼開源整體很重要：因為像其他公司，即使他們——即使他們沒有能力建這些模型，也不希望只能依賴少數幾家封閉的實驗室。世界上非常需要有開源模型的一席之地。而且要說清楚，這並不會削弱我所談到的 API 機會或任何事情，因為仍然需要有人去運行模型、對其進行推論（inference），並且能夠高效率地運行這些模型；而具備足夠算力來做到這點，將會持續成為商業優勢的來源。

So I don't really think that these things are necessarily at odds. But when I look at like what a lot of discerning customers and companies are going to want around the world, they know that -- they're going to want to know that they have control of their destiny and that they can trust the models that they're using and that their data is safe and that they're not sending it to competitors and all that.

所以我並不真的認為這些事情必然是相互衝突的。但當我觀察全球許多眼光挑剔的客戶與企業會想要什麼時，他們知道——他們會想要確信自己能掌控自身的命運，並且能信任他們正在使用的模型，確保其資料是安全的，而且不會把資料送到競爭對手那裡等等。

So I think open source is going to be important, but I think for us, also building the models is going to be a critical part of it. It also -- for what it's worth, and I know that like the models all get evaluated on like a common set of evals and they get chalked up to like, okay, some models within like a couple of points of another model or whatever. But they all really do have different combinations of skills, kind of like people, right? It's like people have spikes in certain areas and have different personalities and are better and worse at different things.

所以我認為開源會很重要，但我也認為對我們而言，自行打造模型也會是其中關鍵的一環。另外——僅供參考，我也知道模型通常都會用一套共同的評測集來評估，然後被歸類為：好吧，某些模型可能只比另一個模型差個幾分之類的。但它們確實都有不同的技能組合，就像人一樣，對吧？就像人會在某些領域特別突出、個性不同，對不同事情各有擅長與不擅長。

And if you're trying to build personal super intelligence for people or you're trying to build a business agent for small businesses that needs to have potentially some different skills than what the other labs are tuning. And just like being able to do the full-stack work on our -- on like Instagram recommendations on our ad system is how we've gotten the results there over time. I believe that building the kind of full-stack model is going to be a lot of the advantage over time in how we build personal super intelligence agents, business agents, all these different use cases for all of the different customers that we want to serve.

而如果你想為個人打造個人超級智慧，或是想為小型企業打造商業代理（business agent），那它可能需要具備一些不同於其他實驗室正在調校的技能。而且就像我們能在 Instagram 推薦與廣告系統上做端到端（full-stack）的工作，才讓我們隨時間推進取得那些成果一樣。我相信，打造這種端到端的模型，將會是我們在長期建立個人超級智慧代理、商業代理，以及面向我們想服務的各類客戶、各種不同使用情境時，重要的優勢來源。

So in addition to the distribution that we have and the ability to reach all these people and scale products that work, I think this is a lot of the durable advantages that you build something that is kind of specific to that use case and excellent at those use cases. And I mean, look, I get that this is a big investment, and it's a big bet. We see the technology working. We're happy with the trajectory of the lab. I'm excited about the products that are coming, and we believe that this is going to be a big thing. So I mean, I get that this is sort of a big bet across the industry. My personal bet is that the people who invest in this are going to be rewarded and feel very good over time.

因此，除了我們既有的分發能力、觸達這些人的能力，以及把有效的產品規模化之外，我認為這也是一項相當持久的優勢：你能打造出某種針對特定使用情境、並且在那些使用情境上表現卓越的東西。而且我的意思是，你看，我理解這是一筆很大的投資，也是一個很大的押注。我們看到這項技術正在奏效。我們對實驗室的發展軌跡感到滿意。我對即將推出的產品感到興奮，我們也相信這會是一件大事。所以我的意思是，我理解這在整個產業中算是一個相當大的押注。我個人的押注是：投資這件事的人，長期來看會獲得回報，並且會覺得非常值得。

And, I'll just quickly answer your second question. When we refer to focusing on 2026 and '27 capacity, there are really two factors. One is we are today and expect to be in the sort of foreseeable future demand constrained. And that really includes our core business, too, where there are -- we still have numerous ROI-positive places that we would put compute toward if we had it. And the second, of course, is just the uncertainty over long-term constraints on building capacity, and we talked about some of the sort of the need to build out more of the supply chain earlier in my comments.

另外，我也快速回答你第二個問題。當我們提到聚焦在 2026 與 2027 年的產能時，主要有兩個因素。第一是，我們目前以及可預見的未來，預期都會處於需求受限（demand constrained）的狀態。這其實也包含我們的核心業務，因為——如果我們有算力，我們仍然有許多投資報酬率為正（ROI-positive）的地方會把算力投入進去。第二，當然就是對於長期擴建產能所面臨限制的不確定性；我在前面的發言中也談到一些需要更早擴展供應鏈的必要性。

So I think beyond '27, when we look into '28 and further than that, the world is going to evolve a lot. Our own internal demand will evolve. We'll have turned over a lot of cards by then. And so when we think about planning today for '28, we're really focusing on flexibility. That's just giving us kind of the ability to have land and power, but to really make the actual decisions about buying chips and other big-ticket items further in the future. So for now, I think we know we have a lot of good use cases for capacity in '26 and '27, and that's really what we're building toward.

所以我認為在 2027 年之後，當我們看向 2028 年以及更遠的未來時，世界會有很大的變化。我們自身的內部需求也會演變。到那時我們會翻開很多牌、掌握更多資訊。因此，當我們思考今天要如何為 2028 年做規劃時，我們真正聚焦的是彈性。也就是先讓我們具備土地與電力等條件，但把實際採購晶片與其他高單價項目（big-ticket items）的決策，留到更往後的時間點再做。所以就目前而言，我認為我們知道在 2026 與 2027 年的產能有很多很好的使用情境，而這也正是我們正在建置與努力的方向。

Great. Thank you for joining us today, and we look forward to speaking with you again soon.

很好。感謝您今天加入我們，我們期待不久後再次與您交流。

This concludes today's conference call. Thank you for your participation, and you may now disconnect.

今天的電話會議到此結束。感謝您的參與，您現在可以中斷連線。