Meta Platforms, Inc. (META) 2026 Q1 法說會逐字稿

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

午安。我叫 Christa，今天將擔任各位的電話會議接線員。此刻，我謹代表公司歡迎各位參加 Meta 2026 年第一季財報電話會議。（接線員指示）本次通話將被錄音。非常感謝。

Kenneth Dorell, Meta's Director of Investor Relations, you may begin.

Meta 投資人關係總監 Kenneth Dorell，您可以開始了。

Thank you. Good afternoon, and welcome to Meta Platform's first-quarter 2026 earnings conference call. Joining me today to discuss our results are Mark Zuckerberg, CEO; and Susan Li, CFO.

謝謝。午安，歡迎各位參加 Meta Platforms 2026 年第一季財報電話會議。今天與我一同出席並討論本季業績的有執行長 Mark Zuckerberg，以及財務長 Susan Li。

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 annual report on Form 10-K filed with the SEC. We undertake no obligation to update any forward-looking statement.

我們今天的發言將包含前瞻性陳述，係基於截至今日的各項假設。由於多項因素，實際結果可能與前瞻性陳述存在重大差異，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 Form 10-K 年度報告中所載之因素。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.

在本次電話會議中，我們將同時呈現 GAAP 與若干非 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.

接下來我想把電話交給 Mark。

All right. Hey, everyone. Thanks for joining today. We had a strong quarter for our community, our business, and our progress towards AI. More than 3.5 billion people use at least one of our apps every day. We saw a small decrease in total family dailies due to Internet outages in Iran and blocks in Russia. But otherwise, trends across our apps are strong.

好的。各位好，感謝今天加入。我們在社群、業務以及 AI 進展方面都交出強勁的一季。每天有超過 35 億人使用我們至少一款 App。由於伊朗的網路中斷以及俄羅斯的封鎖，我們的整體家族日活用戶（family dailies）略有下降；但除此之外，我們各款 App 的趨勢依然強勁。

Daily and monthly actives on Instagram and Facebook continue to grow with video driving all-time high engagement across both apps. WhatsApp continues to see strong momentum, too, including in the US. And Threads continues on its trajectory to be the leading app in its category.

Instagram 與 Facebook 的日活與月活持續成長，影片帶動兩款 App 的互動創下歷史新高。WhatsApp 也持續展現強勁動能，包括在美國市場。Threads 也持續沿著軌道前進，朝成為其類別的領先 App 邁進。

Our biggest milestone so far this year has been the release of our Muse family of models and our first model Muse Spark along with a significantly upgraded new version of Meta AI. This was the first release from Meta Super Intelligence Labs, and it shows that our work is on track to build a leading lab.

今年迄今我們最大的里程碑，是發布 Muse 模型家族，以及我們的第一個模型 Muse Spark，並同步推出大幅升級的新版 Meta AI。這是 Meta Super Intelligence Labs 的首次發布，顯示我們打造領先實驗室的工作正按計畫推進。

Over the past 10 months, we have built the strongest research team in the industry and established the scientific and technical foundations to scale very advanced models. Spark is just one step on that scaling ladder, and we are already training even more advanced models. But Spark has already made Meta AI, a world-class assistant that leads in several areas related to our vision of personal super intelligence, including visual understanding, health, shopping, social content, local, creating games and more.

在過去 10 個月，我們建立了業界最強的研究團隊，並奠定可擴展至非常先進模型的科學與技術基礎。Spark 只是這個擴展階梯上的一步，我們已在訓練更先進的模型。不過，Spark 已讓 Meta AI 成為世界級助理，並在多個與我們「個人超級智慧」願景相關的領域領先，包括視覺理解、健康、購物、社群內容、本地資訊、創作遊戲等。

We're hearing very positive feedback on it so far. We've seen large increases in Meta AI use since releasing the updates, and the Meta AI app has consistently been near the top of the app stores as well.

到目前為止，我們收到非常正面的回饋。自從發布這些更新後，Meta AI 的使用量大幅增加，而 Meta AI App 也持續名列各大 App 商店前茅。

Now that we have a strong model, we can develop more novel products as well. Since I first wrote about our vision for personal Super Intelligence last year, we've been focused on delivering personal and business agents to billions of people around the world. Our goal is not just to deliver Meta AI as an assistant, but to deliver agents that can understand your goals and then work day and night to help you achieve them.

現在我們已擁有強大的模型，也能開發更具新意的產品。自從我去年首次撰文談到我們對「個人超級智慧」的願景以來，我們一直專注於為全球數十億人提供個人與商務代理（agents）。我們的目標不只是把 Meta AI 作為助理交付給大家，而是提供能理解你的目標，並日以繼夜協助你達成目標的代理。

My view of AI is very different from many others in the industry. I hear a lot of people out there talk about how AI is going to replace people. Instead, I think that AI is going to amplify people's ability to do what you want, whether that's to improve your health, your learning, your relationships, your ability to achieve your personal career goals and more. My view is that human progress has always been driven by people pursuing their individual aspirations. And I believe that this will continue to be true in the future.

我對 AI 的看法與產業中許多人非常不同。我聽到很多人談 AI 將取代人類；相反地，我認為 AI 會放大人們做自己想做之事的能力，無論是改善健康、學習、人際關係、達成個人職涯目標等。我認為人類的進步一直是由人們追求各自的志向所驅動，而我相信未來也將如此。

People will be more important in the future, not less. Meta believes in empowering individuals. And those are the kinds of products that we're going to build, and I believe that they're going to be some of the most important and valuable products of all time.

未來人將更重要，而不是更不重要。Meta 相信賦能個人。這正是我們要打造的產品類型，我也相信它們將成為史上最重要、最有價值的一些產品。

We are building a personal agent focused on helping people achieve the diverse goals in their lives. We're also building a business agent focused on helping entrepreneurs and businesses across the world, use our tools and others to grow their efforts, reach new customers and serve existing customers better. These agents will work together to form an ecosystem.

我們正在打造一個以協助人們達成生活中多元目標為核心的個人代理。我們也在打造一個以協助全球創業者與企業為核心的商務代理，讓他們運用我們的工具以及其他工具來擴大經營、觸及新客戶，並更好地服務既有客戶。這些代理將彼此協作，形成一個生態系。

And whether you use our personal or business agents to achieve your goals, I believe that the future will see a massive increase in entrepreneurship from people creating new things that they've always wanted to exist, but previously didn't have the tools to bring into the world. We're already testing an early version of business AIs and weekly conversations have grown 10 times since the start of this year.

無論你使用我們的個人或商務代理來達成目標，我相信未來將出現創業活動的大幅增加：人們將創造那些一直希望存在、但過去缺乏工具將其帶到世界上的新事物。我們已在測試商務 AI 的早期版本，自今年年初以來，每週對話量已成長 10 倍。

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

我們也在研究於即將推出的模型中使用 Spark，以改善 Facebook、Instagram 與廣告的推薦系統與核心業務。目前，我們的 App 主要協助人們達成三個重要目標：與人連結、了解世界、以及娛樂。但我們一直希望 App 能更理解人們的目標，從而以人們想要的各種方式改善生活。這些新的 AI 模型將讓我們能更細緻地理解這一點。

So instead of just looking at statistical patterns of what types of people engage with what content, for the first time in Meta's history, we're going to be able to develop a first principles understanding of what you care about and what each piece of content in our system is about -- is that way we can show you more useful things for what you're trying to accomplish.

因此，我們不再只是觀察「哪些類型的人會與哪些內容互動」的統計模式；在 Meta 歷史上第一次，我們將能從第一性原理（first principles）出發，理解你在意什麼，以及我們系統中每一則內容的主題——如此一來，我們就能向你展示對你想完成的事情更有用的內容。

And we'll also be able to create personalized content specifically for people to help you achieve your goals as well. Since our recommendation systems are operating at such a large scale, we'll phase in this new research and technology over time. But the trend over the last few years seems clear that we are seeing an increasing return on the amount that we can improve engagement for people and value for advertisers. This encourages us to continue investing heavily in what we expect will provide increasing value over the coming years as well.

我們也將能為個人創作量身打造的內容，專門協助你達成目標。由於我們的推薦系統運作規模極大，我們會隨時間逐步導入這些新的研究與技術。不過，過去幾年的趨勢似乎很明確：我們看到，隨著我們提升互動的能力增加，對使用者的價值與對廣告主的價值也呈現更高的回報。這也鼓勵我們持續大力投資，因為我們預期未來幾年將帶來更高的價值。

On that note, we are increasing our infrastructure CapEx forecast for this year. Most of that is due to higher component costs, particularly memory pricing, but every sign that we're seeing in our own work and across the industry gives us confidence in this investment. That said, we are very focused on increasing the efficiency of our investments, and as part of that, we are rolling out more than 1 gigawatt of our own custom silicon that we're developing with Broadcom, as well as significant amount of AMD chips to complement the new NVIDIA systems that we're rolling out as well.

基於此，我們上調今年的基礎設施資本支出（CapEx）預測。主要原因是零組件成本上升，尤其是記憶體價格；但我們在自身工作以及整個產業所看到的各項跡象，都讓我們對這項投資充滿信心。即便如此，我們仍高度專注於提升投資效率；作為其中一部分，我們正推出超過 1 吉瓦（gigawatt）的自研客製化矽晶片（silicon），由我們與 Broadcom 共同開發，同時也將部署大量 AMD 晶片，以補足我們正在推出的新 NVIDIA 系統。

One of the primary goals of our Meta compute initiative is to lead the industry in efficiency of building compute, and we expect that will be a strategic advantage over time.

我們 Meta 運算（compute）計畫的主要目標之一，是在建置運算能力的效率上領先業界；我們預期這將隨時間成為一項策略優勢。

Talking about building physical goods at scale, our AI glasses continue to perform well with the number of people using them, daily tripling year over year. This continues to be one of the fastest-growing categories of consumer electronics ever. We released Ray-Ban Meta optics this quarter designed for all day wear rather than primarily of sunglasses. And building on our release of Oakley last year, we have some exciting new partnerships and styles that I think are going to have the potential to reach even more people coming later this year.

談到大規模打造實體產品，我們的 AI 眼鏡持續表現良好，使用者人數的日使用量（daily）年增三倍。這仍是消費性電子史上成長最快的類別之一。本季我們發布了 Ray-Ban Meta optics，設計重點是可全天配戴，而不僅主要作為太陽眼鏡。延續我們去年推出的 Oakley，我們在今年稍晚還會有一些令人振奮的新合作夥伴與款式，我認為有潛力觸及更多人。

All of our glasses are designed to easily update to use our newest AI models and features. I'm also really excited to see the glasses evolve from being able to answer questions to being able to be a personal agent that's with you all day long, helping you remember things and achieve your goals.

我們所有眼鏡的設計都能輕鬆更新，以使用我們最新的 AI 模型與功能。我也非常期待看到眼鏡從「能回答問題」進化到「能成為全天候陪伴你的個人代理」，協助你記住事情並達成目標。

Beyond glasses, I am excited for more of our Metaverse efforts to be powered by the AI models we're training as well. We remain the biggest investors in the VR space across the industry, but we are focused on making our VR business sustainable as we invest more in other areas like AI and glasses.

除了眼鏡之外，我也期待我們更多的元宇宙（Metaverse）工作能由我們正在訓練的 AI 模型所驅動。我們仍是全產業在 VR 領域最大的投資者，但在加大對 AI 與眼鏡等其他領域投資的同時，我們也專注於讓 VR 業務具備可持續性。

Before wrapping, I want to talk for a moment about how AI is transforming our work. We are seeing more and more examples where one or two people are building something in a week that would have previously taken dozens of people months. And I want to make sure that Meta is the best place in the world for these types of people to come and make an impact.

在結束之前，我想花點時間談談 AI 如何改變我們的工作方式。我們看到越來越多例子：一到兩個人能在一週內完成過去需要數十人花數月才能完成的事情。我也希望確保 Meta 是全世界這類人才最適合加入並產生影響力的地方。

We're building the next evolution of our company around these people. And there's a lot that we can do to enable this, building the best infrastructure for creating and delivering products at scale, streamlining our teams so they aren't bigger than they need to be. recognizing and rewarding the people who are having outsized impacts and setting ourselves up to try many more ideas and take on many new projects in the future.

我們正在圍繞這些人打造公司下一階段的演進。而我們可以做很多事情來促成這一點：建置用於大規模創建與交付產品的最佳基礎設施；精簡團隊，讓團隊規模不超過實際所需；辨識並獎勵那些帶來超常影響的人；並讓我們在未來能夠嘗試更多想法、承接更多新專案。

Of course, we will continue pushing to increase our efficiency as well. But overall, I think the future is about building many more higher-quality things than we've ever built before.

當然，我們也會持續推動提升效率。但整體而言，我認為未來在於打造比我們過去任何時候都更多、且品質更高的事物。

All right. That is what I wanted to cover today. We are living through a historic technological transformation. We are among the few companies positioned to shape the future, and we are on track to do that. I'm looking forward to delivering personal super intelligence to billions of people. And as always, I am grateful for the hard work of our teams and to all of you for being on this journey with us.

好。以上就是我今天想要涵蓋的內容。我們正身處一場歷史性的科技轉型之中。我們是少數有能力塑造未來的公司之一，而且我們正朝著這個方向穩步前進。我期待把個人超級智慧帶給數十億人。一如既往，我感謝我們團隊的辛勤付出，也感謝各位與我們一同踏上這段旅程。

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.

謝謝你，Mark，各位下午好。讓我們先從各部門的業績結果開始。除非另有說明，所有比較皆以年對年為基礎。

We estimate 3.56 billion people used at least one of our family of apps on a daily basis in March, which declined slightly from December due to Internet disruptions in Iran and a restriction on access to WhatsApp in Russia. Absent these impacts, growth in family daily active people would have been positive quarter over quarter.

我們估計在 3 月，每日有 35.6 億人至少使用我們應用程式家族中的一款，較 12 月略有下降，原因是伊朗的網路中斷以及俄羅斯對 WhatsApp 存取的限制。若排除這些影響，應用程式家族的每日活躍人數將呈現季對季正成長。

Q1 total family of apps revenue was $55.9 billion, up 33% year over year. Q1 family of apps ad revenue was $55 billion, up 33% or 29% on a constant currency basis. In Q1, the total number of ad impressions served across our services increased 19%. Impression growth was healthy across all regions, driven primarily by growth in engagement and users as well as ad load optimizations.

第一季應用程式家族總營收為 559 億美元，年增 33%。第一季應用程式家族廣告營收為 550 億美元，年增 33%，若以固定匯率計則年增 29%。第一季，我們各項服務投放的廣告曝光總量增加 19%。各地區的曝光成長皆維持健康，主要由互動度與用戶成長，以及廣告載入量（ad load）最佳化所帶動。

The global average price per ad increased 12% year over year in Q1, with broad-based growth as we benefited from ad performance improvements, better macro conditions versus Q1 of last year, and currency tailwinds in international regions. This was partially offset by strong impression growth, including from lower monetizing regions.

第一季全球平均每則廣告價格年增 12%，呈現廣泛成長，受惠於廣告成效改善、相較去年第一季更佳的總體環境，以及國際地區的匯率順風。這部分被強勁的曝光成長（包含來自變現能力較低地區的曝光）所部分抵銷。

Family of Apps Other revenue was $885 million, up 74% driven primarily by WhatsApp paid messaging and subscriptions revenue.

應用程式家族的其他營收為 8.85 億美元，年增 74%，主要由 WhatsApp 付費訊息與訂閱營收所帶動。

Within our Reality Labs segment, Q1 revenue was $402 million, down 2% year over year due to lower Quest headset sales, which were partially offset by continued strong growth in AI glasses revenue.

在 Reality Labs 部門方面，第一季營收為 4.02 億美元，年減 2%，原因是 Quest 頭戴式裝置銷售較低；部分被 AI 眼鏡營收持續強勁成長所抵銷。

Moving now to our consolidated results. Q1 total revenue was $56.3 billion, up 33% or 29% on a constant currency basis. Q1 total expenses were $33.4 billion, up 35% compared to last year. Year-over-year growth was driven mainly by infrastructure costs and employee compensation.

接著看合併財務結果。第一季總營收為 563 億美元，年增 33%，若以固定匯率計則年增 29%。第一季總費用為 334 億美元，較去年增加 35%。年對年成長主要由基礎設施成本與員工薪酬所驅動。

The growth in infrastructure costs was due to higher depreciation, data center operating costs and third-party cloud spend. The growth in employee compensation was driven by technical hires we've added over the past year, particularly AI talent. We ended Q1 with over 77,900 employees, down 1% from Q4 as the impact of headcount optimization efforts in certain functions was partially offset by hiring in priority areas of monetization and infrastructure.

基礎設施成本的成長來自更高的折舊、資料中心營運成本以及第三方雲端支出。員工薪酬的成長則來自我們在過去一年新增的技術人員招募，尤其是 AI 人才。第一季末我們共有超過 77,900 名員工，較第四季下降 1%；部分職能的人力優化影響，部分被在變現與基礎設施等優先領域的招募所抵銷。

First-quarter operating income was $22.9 billion, representing a 41% operating margin. Q1 interest and other income was negative $1.1 billion, driven by unrealized losses on our equity investments. Our tax rate for the quarter was negative 23%, which was favorably impacted by a tax benefit of $8.03 billion. This benefit partially relieves the $15.93 billion noncash tax charge we recorded in the third quarter of 2025, which reflects updated guidance from the US Treasury issued in February 2026 regarding the tax treatment of previously capitalized R&D expenditures in the United States.

第一季營業利益為 229 億美元，營業利益率為 41%。第一季利息及其他損益為負 11 億美元，主要由我們股權投資的未實現損失所致。本季稅率為負 23%，主要受惠於 80.3 億美元的稅務利益。此項利益部分抵銷我們在 2025 年第三季認列的 159.3 億美元非現金稅務費用；該費用反映美國財政部於 2026 年 2 月發布的最新指引，內容涉及在美國對先前資本化研發支出的稅務處理方式。

Absent the tax benefit, our Q1 tax rate would have been 14%. Net income was $26.8 billion or $10.44 per share. Absent the tax benefit, our net income and EPS would have been $18.7 billion and $7.31, respectively.

若不計入該稅務利益，我們第一季稅率將為 14%。淨利為 268 億美元，或每股 10.44 美元。若不計入該稅務利益，我們的淨利與每股盈餘（EPS）將分別為 187 億美元與 7.31 美元。

Capital expenditures, including principal payments on finance leases were $19.8 billion, driven by investments in servers, data centers and network infrastructure. Free cash flow was $12.4 billion. We ended the quarter with $81.2 billion in cash and marketable securities and $58.7 billion in debt.

資本支出（包含融資租賃的本金償付）為 198 億美元，主要由伺服器、資料中心與網路基礎設施投資所帶動。自由現金流為 124 億美元。本季末我們持有 812 億美元的現金與有價證券，以及 587 億美元的負債。

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're continuing to see significant gains from our content recommendation initiatives.

接著談業務表現。有兩個主要因素驅動我們的營收表現：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。

On Instagram, the ranking improvements that we made in Q1 drove a 10% lift in real time spent. On Facebook, total video time increased more than 8% globally in Q1, the largest quarter-over-quarter gain in four years. Within the US and Canada, ranking improvements we made drove a 9% increase in video watch time on Facebook in Q1. These games are benefiting from advances we're making across the full stack.

在 Instagram 上，我們第一季所做的排序（ranking）改進，使即時使用時間提升 10%。在 Facebook 上，第一季全球影片總觀看時間增加超過 8%，為四年來最大的季對季增幅。在美國與加拿大，我們所做的排序改進使第一季 Facebook 的影片觀看時間增加 9%。這些成果受惠於我們在整個技術堆疊（full stack）上的進展。

Starting with data, we doubled the length of user interaction sequences we use for training on Instagram in Q1 and increase the richness of how each user interaction is described, enabling our systems to develop a deeper understanding of user interests. Within our models, we've significantly increased the speed with which our ranking models index new posts, which is enabling us to recommend them sooner after they are published.

從資料開始，我們在第一季將 Instagram 用於訓練的使用者互動序列長度加倍，並提升對每次使用者互動描述的豐富度，使系統能更深入理解使用者興趣。在模型方面，我們大幅提升排序模型索引新貼文的速度，讓我們能在貼文發布後更快推薦給用戶。

We're also applying more advanced content understanding techniques, which is enabling us to quickly identify posts that may be interesting to someone even if they haven't engaged with a lot of similar content. These and other improvements have enabled us to increase the diversity and recency of recommended content with same-day posts now representing more than 30% of recommended reels on both Instagram and Facebook more than double the levels one year ago.

我們也在採用更先進的內容理解技術，使我們即使在某人尚未與大量相似內容互動的情況下，也能快速辨識其可能感興趣的貼文。這些以及其他改進，使我們能提升推薦內容的多樣性與即時性；目前在 Instagram 與 Facebook 上，當日貼文已占推薦 Reels 的 30% 以上，較一年前的水準增加逾一倍。

We're also using AI to unlock more inventory by auto translating and dubbing videos into a viewer's local language, enabling us to recommend a more diverse set of content. Over 0.5 billion users on each of Facebook and Instagram are now watching AI translated videos weekly. Looking forward, we're making several investments we expect will deliver more valuable recommendations.

我們也運用 AI 透過自動翻譯與配音，將影片轉換為觀眾的本地語言，以釋放更多可供投放的內容庫存（inventory），讓我們能推薦更多元的內容。現在 Facebook 與 Instagram 各自每週都有超過 5 億用戶在觀看 AI 翻譯的影片。展望未來，我們正在進行多項投資，預期將帶來更有價值的推薦。

This year, we will continue scaling up our models in several dimensions, including their size and complexity, while incorporating LLM to deepen content understanding across our platform. This will enable us to better match people to a wider variety of content aligned to their interests. At the same time, we are executing on our longer-term efforts to develop the next generation of our recommendation systems. This includes building foundation models that power organic content and ads recommendations as well as developing LLM based recommender systems.

今年，我們將在多個面向持續擴大模型規模，包括模型大小與複雜度，同時納入大型語言模型（LLM），以加深我們平台上的內容理解。這將使我們能更好地把人們與更廣泛、且符合其興趣的內容配對。同時，我們也在推進更長期的工作，以開發下一代推薦系統，包括建置可驅動自然內容與廣告推薦的基礎模型，以及開發以 LLM 為基礎的推薦系統。

Our focus this year is validating the model architectures and techniques in these domains before we scale them out in future years. Aside from our recommendations work, we are focused on deploying the models from Meta super intelligence labs to enable a new set of product experiences. We're seeing encouraging results within Meta AI since we began powering responses with the first model from MSL, Muse Spark.

我們今年的重點是在未來幾年擴大部署之前，先在這些領域驗證模型架構與技術。除了推薦相關工作外，我們也專注於部署 Meta 超級智慧實驗室（MSL）的模型，以帶來一系列新的產品體驗。自從我們開始以 MSL 的第一個模型 Muse Spark 驅動回覆以來，我們在 Meta AI 內部看到了令人鼓舞的成果。

In tests we ran leading up to the launch, we saw meaningful engagement gains that accelerated week-over-week with each new iteration of the model. We're seeing similar games within Meta AI following the broad rollout of our new model with double-digit percent increases in Meta AI sessions per user.

在上線前的測試中，我們看到顯著的互動提升，且隨著模型每次迭代，週對週加速成長。在新模型廣泛推出後，我們也在 Meta AI 看到類似的成果：每位用戶的 Meta AI 會話數出現兩位數百分比的增加。

Muse Spark is now powering Meta AI in direct chat threads across our family of apps as well as the stand-alone Meta AI app and website, giving billions of people globally access to our latest model. Overall, we're very encouraged by the momentum within our research and product road map and look forward to sharing more detail on what we're building over the course of the year.

Muse Spark 目前已在我們應用程式家族中的直接聊天對話串、以及獨立的 Meta AI App 與網站上驅動 Meta AI，讓全球數十億人都能使用我們最新的模型。整體而言，我們對研究與產品路線圖的動能感到非常振奮，並期待在今年內分享更多我們正在打造的細節。

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.

接著談我們營收表現的第二個驅動因素：提升變現效率。這項工作的第一部分，是在自然互動（organic engagement）中最佳化廣告量水準。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 Q1, we also expanded availability of ads on our newer services, including bringing ads on Threads to people in more markets. On WhatsApp, we're making good progress with the rollout of ads and status with hundreds of millions of people now viewing them daily.

在第一季，我們也擴大了在較新服務上的廣告供給，包括將 Threads 的廣告推廣到更多市場的用戶。在 WhatsApp 上，我們在廣告與狀態（Status）的推出方面進展良好，目前每天已有數億人觀看。

Moving to the second part of increasing monetization efficiency, improving performance for the businesses who use our services. To do so, we're deploying AI more deeply across each layer of our systems and tools. Within our ad systems, we're delivering performance gains as we deploy more complex and predictive models.

接著談提升變現效率的第二部分：改善使用我們服務的企業之成效。為此，我們正把 AI 更深入地部署到系統與工具的每一層。在廣告系統方面，隨著我們部署更複雜且更具預測性的模型，我們正帶來成效提升。

In Q1, enhancements we made to Lattice's modeling and learning techniques, along with advances in our GEM model architecture, drove a more than 6% increase in conversion rate for landing page view ads. In addition, we've been investing in more performing inference models for one more serving ads.

在第一季，我們對 Lattice 的建模與學習技術所做的強化，加上 GEM 模型架構的進展，使落地頁瀏覽（landing page view）廣告的轉換率提升超過 6%。此外，我們也持續投資於更高效能的推論模型，以用於廣告投放服務。

In the second half of last year, we began rolling out our new adaptive ranking model, which is an LLM scale adds recommender model that we use for inference. This model improves our inference ROI by routing requests to more compute-intensive inference models when it determines there is a higher probability of conversion.

在去年下半年，我們開始推出新的自適應排序模型（adaptive ranking model），這是一個 LLM 規模的廣告推薦模型，用於推論。當模型判斷轉換機率較高時，會將請求導向需要更多算力的推論模型，從而提升我們的推論投資報酬率（ROI）。

In Q1, we expanded coverage of our adaptive ranking model to support off-site conversions, which drove a 1.6% increase in conversion rates across the major surfaces on Facebook and Instagram.

在第一季，我們擴大了自適應排序模型的覆蓋範圍，以支援站外轉換（off-site conversions），帶動 Facebook 與 Instagram 主要版位的整體轉換率提升 1.6%。

We're also leveraging AI to make it easier for businesses to manage their customers, develop ad creative and engage with customers. The Meta AI business assistant has now been fully rolled out to all eligible advertisers on supported meta buying services, providing personalized recommendations to advertisers, resolving account issues, and servicing campaign insights to help optimize results. Performance has been strong since we began testing the assistant in Q4 with common account issues being resolved at a 20% higher rate.

我們也運用 AI 讓企業更容易管理客戶、開發廣告素材並與客戶互動。Meta AI 商務助理目前已在支援的 Meta 購買服務上，全面向所有符合資格的廣告主推出，為廣告主提供個人化建議、解決帳戶問題，並提供行銷活動洞察以協助最佳化成效。自從我們在第四季開始測試該助理以來，表現一直很強勁，常見帳戶問題的解決率提高了 20%。

This week, we're also introducing meta ads AI connectors in open beta, providing advertisers the ability to connect their meta ad account directly to an AI agent. We've always supported advertisers both on our platform and through tools like the marketing API. And now we're extending that to AI so businesses and agencies can analyze and optimize campaigns with the tools they're already using.

本週，我們也在公開測試版（open beta）推出 Meta Ads AI 連接器，讓廣告主能將其 Meta 廣告帳戶直接連接到 AI 代理（AI agent）。我們一直透過平台本身以及行銷 API 等工具支援廣告主；現在我們把這種支援延伸到 AI，讓企業與代理商能用他們既有的工具來分析並最佳化行銷活動。

Usage of our ad creative tools is also scaling with more than 8 million advertisers using at least one of our Gen AI ad creative tools and particularly strong adoption among small- and medium-sized advertisers. These tools are benefiting performance as well with advertisers using our video generation feature seeing more than 3% higher conversion rates in tests.

我們的廣告素材工具使用量也在擴大，目前已有超過 800 萬名廣告主使用至少一項我們的生成式 AI（Gen AI）廣告素材工具，且在中小型廣告主之間的採用尤其強勁。這些工具也有助於提升成效；在測試中，使用我們影片生成功能的廣告主，其轉換率提高超過 3%。

We're also seeing good traction in using AI to facilitate customer engagement. In Q1, we expanded business AIs on WhatsApp to SMBs across Latin America and Indonesia as well as on Messenger in Asia Pacific. We now have more than 10 million conversations each week being facilitated through business AIs, up from 1 million at the start of the year. We'll further expand access to more countries this quarter while adding more capabilities to the AIs.

我們也看到使用 AI 促進客戶互動方面有良好進展。在第一季，我們將 WhatsApp 上的商務 AI 擴展到拉丁美洲與印尼的中小企業（SMB），並在亞太地區的 Messenger 上推出。我們目前每週透過商務 AI 促成的對話已超過 1,000 萬次，較年初的 100 萬次大幅成長。本季我們將進一步擴大到更多國家，同時為這些 AI 增加更多能力。

We also continue to invest in the value optimization suite, which helps advertisers maximize their return on ad spend by prioritizing the highest value conversions rather than optimizing solely for the most conversions at the lowest cost. Adoption by businesses has been strong following performance improvements we've made over the past year with the annual revenue run rate of our value optimization suite now over $20 billion, more than doubling year over year.

我們也持續投資於價值最佳化套件（value optimization suite），該套件透過優先鎖定最高價值的轉換，而非僅以最低成本追求最多轉換，來協助廣告主最大化其廣告支出報酬（ROAS）。在過去一年我們改善成效後，企業採用情況強勁；目前價值最佳化套件的年化營收運行率（annual revenue run rate）已超過 200 億美元，年增逾一倍。

Last, I want to touch on our commerce efforts. People discover products on our platforms through ads and organic posts with brands increasingly turning to creators to promote their products. This is contributing to rapid growth in our partnership's ads product with its revenue run rate more than doubling year over year in Q1 to $10 billion.

最後，我想談談我們的電商（commerce）工作。人們透過我們平台上的廣告與自然貼文發現商品，而品牌也愈來愈常透過創作者來推廣產品。這推動了我們合作夥伴廣告產品的快速成長：在第一季，其營收運行率年增逾一倍，達到 100 億美元。

To support the product discovery and purchasing happening through creators, we're expanding our solutions beyond ads. Last month, we rolled out our affiliate partnerships offering on Facebook to more test partners, so creators can tag products from participating retailers on their posts and earn a commission when someone makes a purchase.

為了支援透過創作者進行的商品發現與購買，我們正把解決方案從廣告延伸到更多面向。上個月，我們在 Facebook 上將聯盟合作（affiliate partnerships）方案擴大到更多測試合作夥伴，讓創作者能在貼文中標註參與零售商的商品，並在有人購買時賺取佣金。

We have also started testing similar experiences on Instagram. We see a real opportunity to help people more easily discover and buy products within our services, particularly as we incorporate AI deeply across our platforms.

我們也已開始在 Instagram 上測試類似體驗。我們看到一個實質機會：協助人們在我們的服務內更容易發現並購買商品，尤其是在我們把 AI 深度整合到各平台之際。

Next, I would like to discuss our approach to capital allocation. Compute is becoming increasingly important as it determines the quality of services we can provide including powering more capable models and delivering innovative new products. It is also becoming more critical to how we work at a -- as we are entering a world where employees are managing agents to help them generate new ideas, run experiments, execute tasks and build products.

接下來，我想討論我們的資本配置（capital allocation）方法。算力（compute）正變得愈來愈重要，因為它決定了我們能提供的服務品質，包括支援更強大的模型並推出創新的新產品。它也正變得對我們的工作方式更為關鍵——因為我們正進入一個世界：員工將管理代理（agents）來協助他們產生新想法、進行實驗、執行任務並打造產品。

We are investing aggressively to meet our infrastructure needs, and ensure we maximize our strategic flexibility over the coming years. This includes substantially expanding our own data center footprint and striking deals throughout the supply chain to secure necessary components for future capacity. We're also signing cloud deals that will come online over the course of this year and 2027, allowing us to scale more quickly.

我們正積極投資以滿足基礎設施需求，並確保在未來幾年最大化我們的策略彈性。這包括大幅擴張我們自有資料中心的佈局，並在供應鏈各環節達成協議，以確保未來產能所需的關鍵元件。我們也正在簽署雲端合約，將在今年以及 2027 年期間陸續上線，使我們能更快速擴張。

These multiyear cloud deals and our infrastructure purchase agreements drove a $107 billion step-up in our contractual commitments this quarter. Our investments will support our training needs for future models and most importantly, provide us the inference capacity necessary to deliver personal and business agents to billions of people around the world, along with several other AI product experiences we're developing.

這些多年期雲端合約與我們的基礎設施採購協議，使我們本季的合約承諾（contractual commitments）增加了 1,070 億美元。我們的投資將支援未來模型的訓練需求，更重要的是，提供必要的推論產能（inference capacity），以便向全球數十億人提供個人與商務代理，以及我們正在開發的其他多項 AI 產品體驗。

As we grow our infrastructure spend, we remain committed to operating efficiently, and we recently shared internally that we plan to reduce the size of our employee base in May. We believe a leaner operating model will allow us to move more quickly while also helping to offset the substantial investments we're making.

在我們擴大基礎設施支出的同時，我們仍致力於高效率營運；我們最近也在內部分享，計畫在 5 月縮減員工規模。我們相信更精實的營運模式將讓我們行動更快，同時也有助於抵銷我們正在進行的大量投資。

Moving to our financial outlook. We expect second-quarter 2026 total revenue to be in the range of $58 billion to $61 billion. Our guidance assumes foreign currency is an approximately 2% tailwind to year-over-year total revenue growth based on current exchange rates.

接著談我們的財務展望。我們預期 2026 年第二季總營收將落在 580 億至 610 億美元之間。我們的指引假設以目前匯率計算，外幣因素將對總營收年增率帶來約 2% 的順風（tailwind）。

Turning to the expense and CapEx outlooks. We expect full year 2026 total expenses to be in the range of $162 billion to $169 billion, unchanged from our prior outlook. We continue to expect to deliver operating income this year that is above 2025 operating income.

再來看費用與資本支出（CapEx）展望。我們預期 2026 全年總費用將落在 1,620 億至 1,690 億美元之間，與先前展望一致。我們仍預期今年的營業利益（operating income）將高於 2025 年的營業利益。

We anticipate 2026 capital expenditures including principal payments on finance leases to be in the range of $125 billion to $145 billion, increased from our prior range of $115 billion to $135 billion. This reflects our expectations for higher component pricing this year and to a lesser extent, additional data center costs to support future year capacity. Absent any changes to our tax landscape, we expect our tax rate for the remaining quarters of 2026 to be between 13% and 16%.

我們預估 2026 年資本支出（包含融資租賃的本金償付）將落在 1,250 億至 1,450 億美元之間，高於先前的 1,150 億至 1,350 億美元區間。這反映我們對今年零組件價格較高的預期，以及在較小程度上，為支援未來年度產能而增加的資料中心成本。在稅務環境未發生變化的前提下，我們預期 2026 年剩餘季度的稅率將介於 13% 至 16% 之間。

Finally, we continue to monitor active legal and regulatory matters, including headwinds in the EU and the US that could significantly impact our business and financial results. For example, we continue to see scrutiny on youth-related issues and have additional trials scheduled for this year in the US, which may ultimately result in a material loss.

最後，我們持續關注仍在進行中的法律與監管事項，包括歐盟與美國可能對我們業務與財務結果造成重大影響的逆風。例如，我們仍面臨對青少年相關議題的審視，且今年在美國還安排了額外的審判，最終可能導致重大損失。

In closing, Q1 was a solid start to the year with strong execution across our core ads and engagement initiatives. We're also making exciting progress on our AI research and product efforts and expect to build that momentum over the course of this year.

總結來說，第一季為今年帶來穩健的開局，我們在核心廣告與互動（engagement）計畫上執行有力。我們在 AI 研究與產品工作上也取得令人振奮的進展，並預期在今年接下來的時間持續累積動能。

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

接下來，Christa，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 wanted to ask you just about the level of investment you're making in sort of the signposts you're watching to ensure you're going to generate ROIC and all these investments - us and the other products. So if you could just sort of let us know some of the key factors you're watching over the next 12 to 24 months, whether it's Meta AI, Muse advances, chloralgorithm, what are you sort of watching foremost just to make sure that you're on the right path to generating healthy ROIC on all this CapEx and infrastructure spend?

我想請教你們目前的投資強度，以及你們正在觀察哪些「路標」來確保能產生投入資本報酬率（ROIC），涵蓋這些投資——我們以及其他產品。能否請你分享未來 12 到 24 個月你們會重點追蹤的一些關鍵因素？不論是 Meta AI、Muse 的進展、chloralgorithm，你們最優先關注的是哪些，以確保在這些資本支出與基礎設施投入上，走在能產生健康 ROIC 的正確道路上？

That's a very technical question for basically where -- the things that we're watching are to make sure that we're on track building leading models and leading products. The formula for our company has always been build experiences that can get to billions of people and focus on monetizing them once you get to scale. That's -- we're seeing a little bit of that here where basically we invest in advance to build leading models, and we convert that into leading products.

這是一個非常技術性的問題。基本上，我們在觀察的重點，是確保我們正按計畫打造領先的模型與領先的產品。我們公司的公式一直是：打造能觸及數十億人的體驗，並在達到規模後再專注於變現。我們在這裡也有點類似：我們會提前投資以打造領先模型，並把它轉化為領先產品。

And then we think that these are going to be some of the most important products that get built over the next decade. So I think just like anything else that we've done over time, the basic milestones that I look at are around, first, technically, are we delivering the quality to enable a great product; then second, when you have the product, how is it scaling; and then third, you look at the monetization and then you drive up the efficiency of it towards increasing profitability.

接著我們認為，這些將會是未來十年內打造出來的一些最重要的產品。因此我覺得就像我們過去隨時間所做的任何事情一樣，我所看的基本里程碑大致是：第一，在技術層面，我們是否交付了足以支撐一個優秀產品的品質；第二，當你有了產品之後，它的擴張（scale）情況如何；第三，你再看變現，並推動其效率提升，以朝向提高獲利能力。

I don't -- I mean, like I don't think we have a very precise plan for exactly how each product is going to scale month-over-month or anything like that. But I think we have a sense of the shape of where these things need to be.

我不——我的意思是，我不認為我們對於每個產品究竟要如何逐月擴張之類的事情，有一個非常精準的計畫。但我想我們對於這些事情需要呈現的樣貌，心裡是有概念的。

And I think if you look at the usage of these and the quality of the products and the quality of the models that are out there and the use that other frontier models are getting and the trajectory of that, I'm quite comfortable that a, the lab that we're building is on track to be a leading lab in the world. I think MuSpark was a very high-quality model. It powers Meta AI, which I think is now a world-class assistant. We have an ability to be able to grow that and have a large amount of engagement.

而且我認為，如果你看看這些產品的使用情況、產品品質、現有模型的品質、其他前沿模型所獲得的使用程度以及其發展軌跡，我相當有信心：a，我們正在打造的實驗室正按計畫成為世界領先的實驗室之一。我認為 MuSpark 是一個非常高品質的模型。它驅動 Meta AI，而我認為 Meta AI 現在已是世界級的助理。我們有能力把它做大，並帶來大量互動參與。

And over the coming quarters, we're just going to be tracking how do our next set of training runs go. How do our products scale how excited are we about the products in the pipeline, where right now, we're very excited. And then we'll also ramp up monetization over that period of time as well. So those are the set of things that I look at.

在接下來幾個季度，我們會追蹤：下一輪訓練跑得如何；我們的產品如何擴張；我們對產品管線中的產品有多興奮——而目前我們非常興奮。然後我們也會在這段期間逐步加速變現。所以這些就是我會看的那一組事項。

I think for the kind of specific financial questions, I think Susan can jump in if there's anything more to add.

我想針對比較具體的財務問題，如果還有什麼要補充的，Susan 可以加入說明。

Mark Shmulik, Bernstein.

Bernstein 的 Mark Shmulik。

Mark, I guess now that we've got Muse Spark kind of out there launched -- how are you thinking about the team's focus here kind of divided on to further model training runs and kind of further specialization in that personal intelligence goal versus product launches and kind of shipping more product out the door?

Mark，我想既然我們已經把 Muse Spark 推出並上線了——你們如何看待團隊的重心分配：一方面是進一步的模型訓練與在「個人智慧」目標上的更深度專精；另一方面是產品發布、把更多產品交付到市場？

And Susan, I guess, kind of as a follow-up to Brian's question, I know it's too early to discuss 2027 CapEx. But we've had peers mention tonight a potential significant step-up. Any way to think about dimensionalizing kind of how we think about some of the returns or traction this year and how it might affect the 2027 spend?

Susan，另外作為對 Brian 問題的追問，我知道現在談 2027 年的資本支出（CapEx）還太早。但我們今晚聽到同業提到可能會有顯著的上調。你能否談談，我們如何從今年的回報或牽引力（traction）來量化思考，並推估它可能如何影響 2027 年的支出？

I mean, I think the road map from the team is -- has been pretty consistent. So we have the research team, which is focused on scaling increasingly intelligent models with capabilities for the specific things that we're focused on, which are business and personal agents. So we're -- we just released our first model, and I talked about in my comments how we're climbing the scaling ladder towards greater capabilities and scale for the models.

我的意思是，我認為團隊的路線圖——一直都相當一致。所以我們有研究團隊，專注於擴展（scale）更高智慧的模型，並針對我們聚焦的特定事項打造能力，也就是商業與個人代理（agents）。因此我們——剛發布了第一個模型，而我在發言中也提到，我們正沿著擴展階梯往上爬，讓模型具備更強的能力與更大的規模。

That work continues. We have our next set of more advanced models in training now. And that is -- that work will, I think, just continue. I mean, that's a loop. I don't think you were going to be done with that anytime soon. We're going to have teams that are just consistently focused on training more intelligent and more capable models and the way that we want.

這項工作會持續。我們下一批更先進的模型目前正在訓練中。而那——我想這項工作就會一直延續。我的意思是，那是一個循環。我不認為你短期內會把它做完。我們會有團隊持續專注於訓練更聰明、更有能力、且符合我們需求的模型。

Then we have our product team, and that team is now really unlocked to be able to build things on top of our models because we now have a very strong model. So before this, we have been prototyping a bunch of things using other different models, whether it was our previous older models or kind of using the APIs from other companies. And now we're unlocked to be able to go build things and get them to scale on top of our own models.

接著我們有產品團隊，而這個團隊現在真的被解鎖了，能在我們的模型之上打造各種東西，因為我們現在有一個非常強的模型。在此之前，我們一直用其他不同的模型來做大量原型，不管是我們先前較舊的模型，或是使用其他公司的 API。現在我們被解鎖了，可以在自家模型之上去打造東西並把它們擴張起來。

So I think you will see that over some period of time. I tried in my opening remarks to give a bit of a sense of where we're going, but I think that more of the details of that will become clear over the coming months. And I think that these are just both loops that we'll iterate on. We'll keep on iterating on the intelligence. We'll keep on working on building new products and scaling the products.

所以我想你會在一段時間內看到這些。我在開場致詞中試著稍微說明我們要往哪裡走，但我認為更多細節會在接下來幾個月逐漸清楚。而我也認為這兩者都是我們會反覆迭代的循環。我們會持續迭代智慧能力；也會持續打造新產品並擴張產品。

And then as we get to product market fit, we're also going to increasingly focus on building the businesses around them and decreasing the costs. And this is kind of how we've done everything over the last 20 years of running the company, and that is basically the plan.

然後當我們達到產品市場契合（product market fit）時，我們也會愈來愈專注於圍繞它們建立商業模式並降低成本。這大致就是我們過去 20 年經營公司時做所有事情的方式，而這基本上就是計畫。

Mark, on your second question, we aren't providing a specific outlook for 2027 CapEx. And we are, frankly, undergoing a very dynamic planning process ourselves as we're working through what our capacity needs will be over the coming years. Our experience so far has been that we have continued to underestimate our compute needs even as we have been ramping capacity significantly as the advances in AI have continued and our teams continue to identify compelling new projects and initiatives.

Mark，關於你的第二個問題，我們不會提供 2027 年資本支出（CapEx）的具體展望。坦白說，隨著我們在推演未來幾年的產能需求，我們自己也正在進行一個非常動態的規劃流程。到目前為止，我們的經驗是：即使我們已大幅提升產能，隨著 AI 持續進步、團隊不斷提出具吸引力的新專案與倡議，我們仍持續低估了運算（compute）需求。

And now to -- there are very compelling internal use cases. So our expectation is that compute will become even more central to the business going forward. And it will be critical to determining the quality of the models we develop, the types of products we can introduce, how productive we can be as an organization. So we're going to continue building out our infrastructure with flexibility in mind.

而現在——也有非常有吸引力的內部使用案例。因此我們預期，未來運算將在業務中變得更加核心。它將是決定我們所開發模型品質、我們能推出哪些產品、以及我們作為組織能有多高生產力的關鍵。所以我們會在兼顧彈性的前提下，持續擴建基礎設施。

And if we end up not needing as much as we anticipate, we can choose to bring it online more slowly or reduce our spending in future years as we grow into the capacity that we're building now.

如果最後我們不需要像預期那麼多，我們可以選擇更慢地把產能上線，或在未來年度降低支出，讓我們隨著時間成長並消化我們現在正在建置的產能。

Eric Sheridan, Goldman Sachs.

Goldman Sachs 的 Eric Sheridan。

Maybe if I can build out on one of the topics that was discussed in the prepared remarks. But just the opportunity set that sits in front of the company with respect to putting agentic compute in front of both consumers and enterprises. You've long been associated with sort of the consumer landscape. And I am curious about how you're thinking about extensions of the media engagement parts of your business model and the commerce part of the business model to become more agentic over time.

也許我可以延伸一下事先準備的發言中討論到的一個主題。也就是公司面前的機會集合：把具代理性的運算（agentic compute）帶到消費者與企業面前。你們長期以來更常被視為偏向消費者版圖。我很好奇，你們如何思考把商業模式中的媒體互動（media engagement）部分與商務（commerce）部分，隨時間推進變得更具代理性？

But what do you see also as the opportunity set that sits in front of you across SMEs and enterprises where historically, you maybe haven't had as much product velocity?

另外，你也如何看待在中小企業（SMEs）與大型企業（enterprises）領域的機會集合？在這些領域，過去你們的產品推進速度（product velocity）可能沒有那麼快。

Thanks, Eric. So I would say in the near term, obviously, the sort of biggest focuses are some of the areas that you mentioned around deepening sort of engagement, obviously, with our existing community and user base, making ad experiences meaningfully more personalized, more engaging, more valuable, helping SMBs find and engage with customers across our platform. Those are some of the, I think, most intuitive and adjacent opportunities to the business that we have today.

謝謝你，Eric。我會說在短期內，顯然最大的重點會是你提到的一些方向：深化我們既有社群與用戶基礎的互動；讓廣告體驗在個人化、互動性與價值上都有顯著提升；協助中小企業（SMBs）在我們的平台上找到並與客戶互動。我認為這些是目前業務最直觀、也最相鄰的機會。

And then, of course, as we are able to build out more agentic capabilities enabling agents to help people be more productive, but also agents for businesses and enabling, frankly, those agents to interact with each other and build what we hope will be a thriving commerce ecosystem on our platform. So I would say some of these are a little bit further out, especially in that latter category of things.

然後，當然，隨著我們能打造出更多具代理性的能力，讓代理能幫助人們提升生產力，同時也為企業提供代理，並且坦白說，讓這些代理彼此互動，進而在我們的平台上建立我們希望能蓬勃發展的商務生態系。我會說其中一些會更偏中長期，尤其是後面那一類。

Again, the focus is on building personal super intelligence, building a consumer agent that can work for you and help you get things done. That right now is a consumer experience that we're focused on, but we think there will be clear monetization opportunities over time. You can imagine commission structures or a premium offering. And on the business side, we're seeing a large opportunity, of course, around agents and scaling our business AI initiatives.

再說一次，重點是打造個人超級智慧（personal super intelligence），打造一個能替你工作、幫你把事情完成的消費者代理。這在目前是一個我們聚焦的消費者體驗，但我們認為隨時間會有明確的變現機會。你可以想像抽成（commission）結構或是高級訂閱（premium）方案。而在企業端，我們當然看到代理以及擴大我們企業 AI 計畫方面的巨大機會。

I think I mentioned earlier in my remarks that there are over 10 million weekly conversations between people and business AIs on our messaging platforms. That's up from $1 million at the start of the year, and we're going to continue expanding globally in Q2. And business AIs today are currently free for most businesses on our messaging apps.

我想我在先前的發言中提到，我們的訊息平台上，每週人們與企業 AI 之間的對話超過 1,000 萬次。這比年初的 100 萬次有所增加，而我們會在第二季持續在全球擴張。而目前在我們的訊息應用程式上，企業 AI 對大多數企業仍是免費的。

But as we make more progress, we expect that we will also work towards establishing a longer-term monetization model. And we'll also consider other services that we can offer to businesses in the future, but we don't have anything more to share today.

但隨著我們取得更多進展，我們預期也會朝建立更長期的變現模式努力。我們也會考慮未來能提供給企業的其他服務，但今天沒有更多內容可以分享。

Youssef Squali, Truist Securities.

Youssef Squali，Truist Securities。

Maybe one for Mark and one for Susan. Mark, ray-Ban, Oakley AI glasses continue to perform really well for you guys, but Essilor-Luxottica owns and manages a lot more brands. What are the gating factors to see the launch of additional classes under these other brands this year? And what would be a successful year for you as you look back at 2026, maybe in terms of units sold?

也許一題給 Mark、一題給 Susan。Mark，Ray-Ban、Oakley 的 AI 眼鏡持續為你們帶來很好的表現，但 Essilor-Luxottica 擁有並管理更多品牌。今年要看到在這些其他品牌之下推出更多類別產品，主要的門檻因素是什麼？另外，當你回頭看 2026 年時，對你而言什麼會是一個成功的一年——也許以銷售台數來衡量？

And then Susan, on that 10% risk, how much of that is due to efficiencies for maybe AI implementation versus just the need to stay fit? And as you look at your employee needs over time, how do you see that growing maybe relative to your overall top line growth?

然後 Susan，關於那 10% 的風險，其中有多少是來自例如 AI 導入帶來的效率提升，而不是單純需要保持精實？並且當你長期看員工需求時，你認為它相對於整體營收成長會如何變化？

I can go ahead and take both of those. I might answer your second question first. And I'm just trying to make sure I got all of the parts of the question. So in terms of what the sort of kind of the optimal size of the company, I think, over time, we don't really know the optimal size of the company will be in the future. I think there's a lot of change right now with AI capabilities advancing rapidly.

我可以兩題都回答。我可能先回答你的第二個問題。我只是想確認我把問題的各個部分都理解到了。就公司「最佳規模」而言，我認為隨著時間推移，我們其實不太知道未來公司的最佳規模會是什麼。因為現在 AI 能力快速進展，變化非常大。

We're very focused on leveraging AI tools to substantially increase our productivity, and we're seeing that reflected in the accelerating output from our engineers. And we're generally approaching -- we're approaching this with a bias towards wanting to use these tools to build even more products and services than we would have before.

我們非常專注於運用 AI 工具來大幅提升生產力，而我們也看到這反映在工程師產出加速上。整體而言，我們在做這件事時帶著一種偏好——希望用這些工具去打造比過去更多的產品與服務。

At the same time, we're making very significant investments in infrastructure, and we are very focused on continuing to operate efficiently. So I think we will be continuously evaluating how we're structured just to make sure we're best set up to deliver against our priorities over the coming years. So that is, I think, your second question.

同時，我們也在基礎設施上做非常重大的投資，並且非常專注於持續高效率地營運。因此我認為我們會持續評估組織架構，確保在未來幾年能以最佳方式配置，以交付我們的優先事項。我想這就是你第二個問題的回答。

The first question was about the AI glasses. We have -- we're continuing to see strong growth in, obviously, the AI glasses sales over the course of Q1. Demand for the expanded portfolio lineup has generally been quite strong, and we're seeing sales shift now from the prior generation of Ray-Ban metas to the latest generation, which I think speaks to the value of the improved features like extended battery life and higher features like higher resolution video capture.

第一個問題是關於 AI 眼鏡。我們——顯然在第一季期間，AI 眼鏡銷售持續呈現強勁成長。對擴充後的產品組合需求整體相當強勁，我們也看到銷售正從上一代 Ray-Ban Meta 轉向最新一代；我認為這反映了改良功能的價值，例如更長的電池續航，以及更高階的功能，例如更高解析度的影片拍攝。

So we're pretty excited about the progress we've made with glasses. We see strong interest now in the Meta Ray-Ban displays with the Meta neurlbands. So that's an encouraging sign that there is consumer appetite for display glasses, which is kind of the next generation of how this product evolves. And yes, so I think this is an area that we will continue to be -- that we continue to be excited about and are investing in.

所以我們對眼鏡的進展感到相當振奮。我們現在也看到市場對 Meta Ray-Ban 顯示款（搭配 Meta neuralbands）的強烈興趣。這是一個令人鼓舞的訊號，顯示消費者對顯示型眼鏡有需求，而這也算是這個產品演進的下一代方向。是的，所以我認為這會是我們持續感到興奮並且持續投資的領域。

Justin Post, Bank of America.

Justin Post，美國銀行。

Mark, it took about 10 months to get you Spark out. I think it's a pretty good pace. Just help us understand what kind of unlock that is for some of the new products you're developing? And how is the product cadence going to be over the next nine months on either consumer or business enterprise products built on top of that model?

Mark，你們大約花了 10 個月推出 Spark。我覺得這速度相當不錯。能否幫我們理解，這對你們正在開發的一些新產品而言，解鎖了哪些能力？另外，接下來九個月，基於該模型之上的消費者或企業產品，產品推出節奏會是如何？

I mean, the field is moving pretty quickly. So I mean, I'm very happy that we're -- I think the lab that has gone the fastest from standing up the lab to having a very kind of widely accepted a strong model. So that's good. I think that is a very significant validation of the effort that the team is working well together, that the infrastructure is working, that that the effort is on track. And I think that that's basically the main thing that we've learned over the last quarter that I would take away is like where and we started what is this pretty big bet, and it's on track for our plan.

我的意思是，這個領域進展非常快。所以我很高興我們——我認為我們的實驗室從成立到推出一個被廣泛接受的強模型，速度是最快的之一。這很好。我認為這是對團隊協作良好、基礎設施運作到位、整體投入按計畫推進的一個非常重要的驗證。我想，過去一季我最大的收穫就是：我們下了一個相當大的賭注，而它正按我們的計畫在軌道上。

In terms of what exactly the cadence is going to be? It's tough for me to say both because I don't really want to share competitively sensitive information and because I think some of the stuff we are more focused on quality than hitting a specific date. I mean, on the research side, this is research, right? We are trying novel things. You don't exactly know when they're going to land.

至於具體的節奏會是什麼？我很難說，一方面我不太想分享具競爭敏感性的資訊，另一方面我們有些事情更重視品質，而不是卡在某個特定日期。我的意思是，在研究端，這就是研究，對吧？我們在嘗試新穎的東西，你不可能精準知道它什麼時候會落地。

And on the product side, I think we care a lot about just having. Let me put it this way. There's a lot of agents out there, right, that people are building for different things. And there aren't that many that I would want to give to my mother. And I think getting to like that quality bar is something that I care about more than hitting a specific week for launching or something like that.

而在產品端，我認為我們非常在意的是——我換個說法。外面有很多代理（agents），人們在為不同用途打造它們。但真正多到我願意拿給我媽媽用的，其實不多。我認為把品質拉到那個門檻，比起為了上線而命中某一週之類的時間點，更是我在意的事。

But with that said, I mean, we're in a zone here where the teams don't check in with me like once a quarter, like we make meaningful progress like day over day. I think that's part of the fun of developing in this world is that people can make very rapid progress. small groups of people and teams can make very rapid progress. So I think we're going to see a lot of innovation.

不過話說回來，我們現在處在一個區間：團隊不是像每季才跟我 check in 一次——我們是日復一日都有實質進展。我覺得在這個世界做開發的樂趣之一，就是人們可以非常快速地推進；小團隊也能非常快速地推進。所以我認為我們會看到很多創新。

The timing of this call is it's good in some ways because the Muse Spark release, I think, was was positive. The Meta AI first released, I think, is positive. I think that, that shows that we're on track. I'm trying to kind of paint a picture of the very high-level direction that we're going in, but I think that the picture is going to come into focus a lot more over the subsequent quarters.

這通電話的時間點在某些方面很好，因為 Muse Spark 的發布我認為是正面的；Meta AI 的首次發布我認為也是正面的。我認為這顯示我們在軌道上。我試著描繪我們前進的非常高層次方向，但我想在接下來幾季，這個圖像會變得更清晰。

Ross Sandler, Barclays.

Ross Sandler，Barclays。

Yes. Mark, just sort of related to that last answer, but there's a lot of new consumer applications kind of cropping up everything from like an open claw to something a little bit more consumer friendly than you would build for your mom, like you said, with like Pak or Dreamer, which you recently acquired. So how are these new ideas, I guess, changing your view around the direction that core Meta AI or Dream or kind of your overall agentic strategy needs to go?

是的。Mark，算是延伸你剛才的回答，但現在有很多新的消費者應用正在冒出來，從像 Open claw 到一些更偏消費者友善的東西——就像你說的，比你會做給你媽媽用的更友善——例如 Pak 或 Dreamer（你們最近收購的）。所以我想問，這些新想法如何改變你對核心 Meta AI 或 Dream、或整體代理（agentic）策略方向的看法？

And then the second part of it would be, do you think the lab will stay in this consumer lane? Or do you think you need -- or you want to go down the route that others are going down with code writing and like the recursive self-improvement loop and in that direction kind of in parallel just thoughts on that.

第二部分是，你認為實驗室會一直留在這條消費者路線嗎？還是你覺得你需要——或你想要——像其他人那樣走向寫程式碼、以及類似遞迴式自我改進迴圈那條路，並行推進？想聽聽你的想法。

Yes. So look, on the Open Claude and other agents, I think that they give you a very exciting glimpse of what types of things should be possible. Now they're pretty rough systems today. And to set up Open claw you need to like install a computer locally and then get into a terminal and configure a bunch of things that, again, like there's -- maybe there's hundreds of thousands of people or small numbers of millions of people who can do that.

是的。所以你看，關於 Open Claude 和其他代理，我認為它們讓你非常興奮地一瞥：哪些事情應該是可能的。只是它們今天仍是相當粗糙的系統。要架設 Open claw，你得在本機安裝一台電腦，然後進到終端機，配置一堆東西——再一次，可能只有幾十萬人或少數幾百萬人能做到。

But what we're talking about is delivering personal super intelligence for billions of people around the world. So how do you make a version of that experience that is a lot more polished and dialed and easy and that has all the infrastructure basically done for people already and that just works. And that's kind of what we're focused on, on the consumer side. and I'm really excited about that.

但我們談的是要為全球數十億人提供個人超級智慧。所以要怎麼把那種體驗做成更精緻、更到位、更容易使用的版本？讓所有基礎設施基本上都先替人們做好，然後它就是能用、開箱即用。這就是我們在消費者端專注的方向。我對此非常興奮。

I think if you had something like that, that worked quite a bit better than those systems and was easy enough that people could just get then I think you go from having something that hundreds of thousands or millions of people are going to use to something that is going to be addressable to billions of people. And that has been our primary focus from day one of the lab is being able to deliver something like that as a product, and I think it's just going to be very exciting.

我認為如果你能做出那樣的東西——比那些系統好用得多，而且簡單到人們可以直接上手——那你就能從「只有幾十萬或幾百萬人會用」變成「可觸及數十億人」的產品。這一直是我們實驗室從第一天起的主要重點：把這樣的能力以產品形式交付。我認為這會非常令人興奮。

By the way, the same thing is true for businesses, right? I mean, there's the personal version of this -- but there's also a lot of people's goals are they want to create things, right? They want to create websites. They want to create products. They want to grow their products. These are all things that good agents are going to be able to help people do, which I think is partially why this is so exciting.

順帶一提，對企業也是一樣，對吧？我的意思是，這有個人版本——但也有很多人的目標是想要創造東西，對吧？他們想做網站、想做產品、想把產品做大。這些都是好的代理能夠幫助人們完成的事情，我想這也是為什麼這件事如此令人興奮的一部分原因。

And in my opening comments, I talked about how today we can handle a few goals for people, they're big goals, right? We can help people stay connected with people they care about, learn about the world. These are big things that people care about. But they're not the only things that people care about.

在我開場的評論中，我談到今天我們可以為人們處理幾個目標——這些都是很大的目標，對吧？我們可以幫助人們與他們在乎的人保持連結、了解世界。這些都是人們很在意的大事。但它們並不是人們在意的唯一事情。

And one of the things that I would love for our products to be able to do is just understand people's goals specifically and then be able to just go work on them for them, and check back in and whenever you have questions that you need to answer it. So whether those are personal goals or you're trying to create a business or do work. I think that this is like -- this is stuff that I think literally every person in the world is going to want some version of it.

而我非常希望我們的產品能做到的一件事，就是能具體理解人們的目標，然後就能替他們去推進這些目標，並在需要時回來確認進度；而且每當你有需要解答的問題時，它也能回答。所以不論是個人目標，或是你想創辦一家公司、或是要完成工作。我認為這類東西——我覺得世界上幾乎每個人都會想要某種版本的它。

And also, I think it is something that scales where the more you want to get out of it, I think people are going to also be willing to pay a lot of money to have premium or high compute versions of it. So I think that this is like it's a very exciting area. But I think what you all should be waiting to see is like whether we can build the version that really like just works and how effective we are at converting people who are using our products into being hundreds of millions and then billions of people using this stuff.

另外，我也認為這是一種可擴展的東西：你越想從中獲得更多，我想人們也會願意花很多錢去使用高階版或高算力版本。所以我覺得這是一個非常令人興奮的領域。但我認為你們接下來應該觀察的是：我們是否能打造出那個真正「就是能用」的版本，以及我們在把使用我們產品的人轉化為數以億計、進而數十億人使用這些東西方面，到底有多有效。

And then over time, how can we effectively convert that into something that's increasingly profitable by monetizing it and getting the costs down. So I think that that's the road map of what we need to do. You asked about whether we're primarily focused on consumers or also recursive self-improvement.

然後隨著時間推進，我們要如何透過變現並降低成本，把它有效地轉化為越來越高的獲利能力。我認為這就是我們需要做的路線圖。你問到我們是否主要聚焦在消費者，或也聚焦在遞迴式自我改進。

I think that we've talked about two main goals for the team. I mean, one is this kind of agents version vision of what we're doing. The other is that self-improvement is really important because you can't build a leading AI product if you don't have leading models. And you're not going to have leading models in the future if your models can't improve themselves, right? So you're getting to a point where today, the models are still able to learn from people -- and then I think at some point, the models will have to improve themselves.

我想我們談過團隊的兩個主要目標。其一是我們正在做的這種「代理（agents）」版本的願景。其二是自我改進非常重要，因為如果你沒有領先的模型，就不可能打造領先的 AI 產品。而如果你的模型無法自我改進，你未來也不會有領先的模型，對吧？所以你會走到一個點：今天模型仍然能向人類學習——但我認為在某個時點，模型將必須自我改進。

And that's how the growth is going to -- an improvement in the models is going to happen. And if you don't -- if we don't have an ability to do that, then we or anyone else, I think the companies that don't do that are not going to be leading labs, then they're not going to produce leading products. So I think at that like that is a table stakes thing that we are focused on.

而模型的成長——模型的改進——就會以這種方式發生。如果你沒有——如果我們沒有這種能力，那麼我們或任何其他人，我認為那些做不到的公司就不會是領先的實驗室，也就不會產出領先的產品。所以我認為在那個層面上，這是一個基本門檻（table stakes），也是我們正在專注的事情。

Now, does that make us a developer tools company? Not necessarily. I mean, I'm not against having an API or coding tools or anything like that. But it's not our primary focus. But I actually think people conflate coding with self-improvement more than they should. Coding is one ingredient for the model self-improving. It's not the only thing. And we are focused on all of the parts that are going to be necessary for self-improvement in service of the personal super intelligence vision that we have for people and businesses.

那這會讓我們變成一家開發者工具公司嗎？不一定。我並不反對提供 API 或程式設計工具之類的東西，但那不是我們的主要重點。不過我其實認為，人們把「寫程式」和「自我改進」混為一談的程度超過了應有的程度。寫程式是模型自我改進的一個要素，但不是唯一要素。而我們專注於所有為了自我改進所必需的部分，以服務於我們為個人與企業所描繪的「個人超級智慧」願景。

Ron Josey, Citigroup.

Ron Josey，花旗集團。

Mark, maybe a quick follow-up to a prior question around personal agency and business agents. And with Sparks now live and more models in development, do you look at the personal agent opportunity, which we talked about earlier on the in the call, more of a short-term, medium-term, long-term goal, I'm sure it's a never-ending goal, but when we see a product, is the question short or medium term?

Mark，或許針對先前關於個人代理與商業代理的問題做個簡短追問。現在 Sparks 已經上線、也有更多模型在開發中，你怎麼看我們先前在電話會議中談到的個人代理機會？它比較像是短期、中期、還是長期目標？我知道這可能是個永無止境的目標，但我們何時會看到產品——問題是偏短期還是中期？

And then Susan, I think the ranking recommendation model improvements are are very impressive to see, given the size and scale of both Instagram and Facebook. Could you help us understand just how doubling the length of these interaction sequences can drive greater usage? There's a thesis out there that maybe some of the rating recommendation improvements are long in the two. So it seems if there's a lot more room to go. So any help there would be helpful.

另外 Susan，我覺得排名與推薦模型的改進非常令人印象深刻，考量到 Instagram 和 Facebook 的規模與體量。你能否幫我們理解，僅僅把這些互動序列的長度加倍，為什麼就能帶動更高的使用量？外界有個論點認為，也許某些排名推薦的改進已經接近尾聲了。所以看起來如果還有很大空間可做，任何說明都會很有幫助。

I mean, I think that the agents work, there's going to be short-term versions of it, but then I think that there's going to be massive upside for delivering more intelligence and more capabilities in the models. And you're kind of seeing this across the industry. Each month, each generation of models, they just have more capabilities and can do more things and people absorb it and are able to get more superpowers and it's awesome.

我的意思是，我認為代理（agents）的工作會有短期版本，但我也認為，隨著模型提供更多智慧與更多能力，將會有巨大的上行空間。你在整個產業都能看到這點：每個月、每一代模型，它們都有更多能力、能做更多事情，人們吸收這些能力後就能獲得更多「超能力」，這很棒。

It's like the most exciting time in the industry. So I think of the agents as the product vehicle for delivering that capability to people. And we certainly -- I think this year is going to be a key period for establishing that as the vehicle for how people are going to use this, but then the model improvement, I think, is going to be something that's going to go on for a very long time. So there's a lot to do here in both the short, medium and long term.

這就像是這個產業最令人興奮的時刻。所以我把代理視為把這些能力交付給人們的產品載體。我們當然——我認為今年會是把它確立為人們使用這些能力之載體的關鍵時期；但模型改進，我認為會持續非常長一段時間。因此在短期、中期與長期都有很多事情要做。

And then on your second question, which I think is about the ranking and recommendations improvements that we talked about in our -- that I talked about in my earlier remarks, I think they're -- first of all, there is still a lot of room to continue improving recommendations over the rest of the year, and we expect we'll be able to do that to drive additional engagement on both Facebook and Instagram.

至於你的第二個問題，我理解是關於我們在先前發言中提到的排名與推薦改進。首先，接下來一年我們仍有很大空間持續改進推薦，我們也預期能藉此在 Facebook 與 Instagram 上帶動更多互動。

A couple of the things. First, we're going to continue to improve our data infrastructure that's going to allow our models to train on more data. And we're adding more detail to how we describe the content that users have engaged with in the past and scaling up the complexity of our model architecture to take advantage of those larger data sets like using even longer histories of content interactions, and that should all be in service of improving the overall quality of recommendations.

有幾件事。第一，我們會持續改善資料基礎設施，讓模型能用更多資料訓練。我們也會在描述使用者過去互動過的內容時加入更多細節，並擴大模型架構的複雜度，以利用更大的資料集——例如使用更長的內容互動歷史——而這些都將用來提升整體推薦品質。

We also are focused on making the recommendations even more personalized and more relevant to any given users interest. There's work we're doing to redesign our content retrieval system to show more content that matches the full range of a user's interest and to tailor the diversity of the topics we recommend to the broadness of someone's interest.

我們也專注於讓推薦更個人化、並更貼近特定使用者的興趣。我們正在重新設計內容檢索系統，以展示更多符合使用者完整興趣範圍的內容，並依照某人興趣的廣度來調整我們推薦主題的多樣性。

So someone with particularly concentrated interest might see relatively more of that content while people with a broader set of interest might see kind of a greater range in the topics that we show them.

因此，興趣特別集中的人可能會看到相對更多那類內容；而興趣較廣的人，則可能會看到我們展示給他們的主題範圍更大。

And then finally, we're continuing to make improvements to our sort of LLM based two neuro algorithm features that allow users to provide more granular natural language feedback on what they want to see more of or less of in their feed. So the sort of the kind of the sequence length, which is the thing that you called out is one of really many improvements we made in Q1, and there is a big road map of further improvements going forward.

最後，我們也持續改進我們某種以 LLM 為基礎的「雙神經（two neuro）」演算法功能，讓使用者能以更細緻的自然語言回饋，告訴我們他們希望在動態消息中看到更多或更少的內容。所以你點出的那個「序列長度」其實只是我們在第一季做的眾多改進之一，未來還有一個很大的改進路線圖。

Doug Anmuth, JPMorgan.

Doug Anmuth，摩根大通。

Mark, how do you think about the step up as you go from leveraging smaller models in the ad business to use Spark and future large language models going forward, where are some of the key unlocks across engagement and monetization?

Mark，當你從在廣告業務中運用較小模型，走向使用 Spark 以及未來的大型語言模型時，你如何看待這個升級的過程？在互動與變現方面，有哪些關鍵的解鎖點？

And then on Manus, can you just talk at all about the strategic importance and the role in developing genetic products for Meta and then just current status around the tech and the deal?

另外關於 Manus，你能否談談它對 Meta 開發基因（genetic）產品的策略重要性與角色，以及目前技術與交易的最新狀態？

I'll take that question. On Manus, we're still working through the details. So we don't have an update right now. On your first question, which is about sort of the -- going from leveraging smaller ads businesses -- smaller models in the ads business to kind of the ads sort of models growing.

我來回答這個問題。關於 Manus，我們仍在處理細節，所以目前沒有更新。至於你的第一個問題，也就是從在廣告業務中運用較小模型，走向某種——廣告模型逐步變大的這件事。

There's already some work underway, and I think I alluded to some of this in my earlier remarks, even kind of in the current landscape of the ads road map, where we're basically trying to advance the architecture here to allow sort of -- to allow us to leverage the abilities of larger models. Historically, we haven't used larger model architectures like GEM for inference. -- because their size and complexity would make them too cost prohibitive.

其實已經有一些工作在進行中，我想我在先前的發言中也提到過一些，即使是在目前的廣告路線圖格局下，我們基本上正嘗試推進這裡的架構，以便——讓我們能利用更大模型的能力。過去我們沒有使用像 GEM 這樣較大的模型架構來做推論——因為它們的規模與複雜度會讓成本高到難以負擔。

And the way we drive performance from those models is by using them to transfer knowledge to smaller, more lightweight models that are used at run time. The inference models are bound by strict latency requirements since they need to find the right ad within milliseconds, and that has, again, historically prevented us from meaningfully sizing up -- scaling up their size and complexity.

而我們從這些模型中驅動績效的方式，是把它們用來將知識遷移到更小、更輕量、在執行時使用的模型上。推論模型受限於嚴格的延遲要求，因為它們需要在毫秒內找到正確的廣告；而這也再次在歷史上阻礙了我們有意義地擴大它們的規模——提升其尺寸與複雜度。

But in the second half of last year, we introduced a new adaptive ranking model, which enables us to leverage LLM scale model complexity of 1 trillion parameters, and we made advances in the model architecture and codesign the system with the underlying silicon, so it maintains the sub-second speed that is required to serve ads at scale.

但在去年下半年，我們推出了一個新的自適應排序模型，使我們能夠運用具備 1 兆個參數的 LLM 規模模型複雜度；同時我們在模型架構上取得進展，並與底層矽晶片進行系統協同設計，因此它仍能維持在大規模投放廣告所需的次秒級速度。

We also developed an approach that intelligently routes request more compute-intensive inference models if it determines that there is a higher probability of conversion and that lets us drive both better performance and increased inference ROI. So there's a lot of work being done there before we even sort of incorporate more of the LLM work into our underlying ads ranking models.

我們也開發了一種方法：若判斷轉換機率較高，便會智慧地將請求導向需要更多算力的推論模型，從而同時帶來更好的表現與更高的推論投資報酬率（ROI）。因此，在我們甚至還沒把更多 LLM 的工作納入底層廣告排序模型之前，這方面就已經做了大量工作。

Ken Gawrelski, Wells Fargo.

Ken Gawrelski，富國銀行。

Two, if I may. First, if I -- you talked on the Mu Spark launch. You've talked about two categories or two verticals. You talked about health and wellness and shopping. Can I dive a little bit -- ask you to dive a little deeper into the latter on the shopping and commerce side? And maybe if you could -- were there any learnings and the 2021, '22 phase where you push deeper into commerce on Instagram and on Facebook? Any learnings from that period that you might apply? Is there an opportunity for a next-gen marketplace-type business in commerce?

如果可以的話，我有兩個問題。第一個，關於你提到的 Mu Spark 發表。你談到兩個類別或兩個垂直領域：健康與保健，以及購物。我能否更深入一點——請你更深入談談後者，也就是購物與商務這一側？另外，也許你可以分享——在 2021、2022 年你們在 Instagram 與 Facebook 上更深入推進商務的那個階段，有沒有任何學習或心得？那段期間的經驗是否有可應用之處？在商務領域是否存在下一代、類似市集（marketplace）型態的業務機會？

And then the second, please, Maybe, Susan, can you talk a little bit about -- based on your model improvements and the content recommendations, where -- how much visibility do you think you have to kind of the growth trajectory on the core business? You continue to grow at basically double the pace of the industry despite being a very large share of the industry.

第二個問題，麻煩了。也許 Susan，你能否談談——基於你們的模型改進與內容推薦，你認為自己對核心業務的成長軌跡有多少能見度？儘管你們已占產業相當大的份額，你們仍以基本上是產業兩倍的速度持續成長。

Could you just talk about a little bit about your visibility into that continued performance?

你能否談談你對這種持續表現的能見度？

Yes. So I might give you a somewhat loftier answer to the question. You're asking about shopping. I think it's sort of an interesting example of the way in which the work that we're doing is different than what I think others are doing out there. These products, they -- AI agents get better when you fully optimize the stack. That's why we believe that we need to be a company that builds frontier models in addition to building the agents.

好的。我可能會給你一個比較宏觀的回答。你問的是購物。我認為這是一個很有意思的例子，能說明我們正在做的工作，與我認為外界其他人正在做的事情有何不同。這些產品——當你把整個技術堆疊（stack）完全最佳化時，AI 代理（agents）會變得更好。這也是為什麼我們相信，除了打造代理之外，我們也需要成為一家建構前沿模型（frontier models）的公司。

And then in order to do that, you, of course, need to build your infrastructure in order to be able to do that well. So we're undertaking this large investment to be able to do that top to bottom. And I think a lot of the way to think about the investment that we're making is a bet that the individual things that people care about and that people are going to be more important in the future. And that's sort of like -- and I think it should be a pretty obvious thing to say.

而要做到這一點，你當然也需要建置相應的基礎設施，才能把它做好。因此，我們正在進行一項自上而下的大型投資，以便把整體做完整。我認為理解我們這項投資的一個方式，是把它視為一個押注：人們在意的個別事物，以及人們本身，在未來會變得更重要。這有點像——我覺得這應該是個相當顯而易見的說法。

But I think so much of the rhetoric around AI in the industry is around like a company trying to build some kind of centralized thing that like does all the productive work in society in some way or something like that. And that just is very different from how we see the world. Like our vision for the future is one where society makes progress by individuals pursuing their own aspirations.

但我認為，產業裡圍繞 AI 的許多論述，都是在談某家公司試圖打造某種集中式的東西，彷彿能以某種方式包辦社會中所有具生產力的工作之類的。而那與我們看待世界的方式非常不同。我們對未來的願景，是社會透過個人追求自身抱負而取得進步。

And some people care about big grand things like curing diseases. And a lot of people care about personal things like finding the right for my daughter. And I just think that we want to -- we're going to build things that help deliver this vision for personal agents for people. And I think that part of the lane and what is interesting and differentiated about what we're doing is that that's just so different from how I hear everyone else talking about the work that we're doing.

有些人在意宏大而崇高的事情，例如治癒疾病；也有很多人在意個人的事情，例如替我女兒找到合適的（東西）。我只是認為——我們將打造能幫助人們實現「個人代理」願景的產品。我認為這條路徑中有趣且具差異化的一點在於：這與我聽到其他人談論我們所做工作的方式截然不同。

So even though I think some of these ideas, they seem like they should be so obvious. I actually think that our approach of trying to empower individuals and building consumer things is just in the details extremely different from what others are doing. And shopping might be one kind of specific example that I think is going to have interest in commercial implications.

所以即使我認為其中一些想法看起來應該非常顯而易見，我其實覺得，我們試圖賦能個人、打造面向消費者的產品這種做法，在細節上與其他人正在做的事情極其不同。而購物可能就是一個具體例子，我認為它會帶來商業層面的影響與意涵。

And I think people -- consumers are going to like it. But I don't hear any other labs out there talking about how they're building an AI that's really good at shopping. And I think that the reason for that is like not because shopping is the most important thing by itself, but because like empowering people to do the things that matter in their lives, whether that's local or understanding social context, or shopping or personal health things or understanding what's going on around them visually, which is going to be really important on the glasses.

而我認為消費者會喜歡它。但我沒有聽到其他實驗室在談他們如何打造一個真的很擅長購物的 AI。我認為原因並不是因為購物本身是最重要的事，而是因為：賦能人們去做生活中重要的事情——不論是在地化、理解社交情境、購物、個人健康相關的事，或是視覺上理解周遭正在發生什麼（這在眼鏡上會非常重要）——這些都是「個人超級智慧」願景的要素。

These are all elements of the personal super intelligence vision. I think like a lot of this, and when you're thinking about kind of the investment in Meta over time, I think you should think about it as coming down to these set of values around what do we want AI to do in society. And if what you want it to do is empower individuals and build a world where the AI is in service to individuals' goals, then that is what we are going to build, and I think it's going to be incredibly valuable.

這些都是個人超級智慧願景的組成元素。我認為其中很大一部分——當你思考 Meta 隨時間的投資時——你應該把它視為回到一組價值觀：我們希望 AI 在社會中扮演什麼角色。如果你希望它做的是賦能個人，並打造一個 AI 服務於個人目標的世界，那麼這就是我們要打造的東西，而我認為它將極具價值。

Gosh, I almost wish we could end on that answer, but I will answer the second question, which I think kind of is two versions. One is a version of like, what's the revenue outlook? And obviously, we gave the Q2 guide, which embeds, I think, both a range of kind of macro outcomes, but also the work that we've -- the ongoing work that we have to continue improving both the sort of usage and engagement on our family of apps and then our ability to continue making the ads better and more performance.

天啊，我幾乎希望我們可以用這個回答作結，但我還是會回答第二個問題；我認為它大概有兩種版本。一種是「營收展望如何？」的版本。顯然，我們已提供第二季（Q2）指引，其中我認為同時納入了一系列可能的總體經濟結果，以及我們正在做的工作——持續改善我們應用程式家族的使用與互動，並持續讓廣告變得更好、更具成效。

I think the second question is maybe more of a -- the second version of that question is more of a higher-level question about kind of the overall trajectory of the road map here. And one of the things I will say, having been working on this for a very long time, I'm always really impressed by the team's ability to continue to advance the state of the art here.

我認為第二個問題也許更像是——這個問題的第二種版本更偏高層次，關於整體路線圖的走向。有一點我想說：我在這上面投入很長時間後，仍然總是對團隊持續推進最先進水準（state of the art）的能力感到非常印象深刻。

And our planning process now is, I think, really fine-tune around this. So I've mentioned on a couple of calls, the budgeting process in which we run a very sort of ROI-based process to make sure that we are funding all of the ads initiatives that we think will drive growth in future years. And that's something that is both quite dialed in.

而我們現在的規劃流程，我認為也已非常精細地圍繞這點進行調校。我在幾次電話會議中提過，我們的預算流程採用非常以投資報酬率（ROI）為基礎的方式，以確保我們為所有我們認為能在未來年度帶動成長的廣告計畫提供資金。這件事也已相當到位。

And I think that our ability to measure the impact of that has been pretty robust, and it's been a very important driver of our ads revenue growth, and that continues to be a process that again, we ran in this past budget and -- as far as we can -- as we have line of sight, we feel good about the investment opportunities ahead of us.

而且我認為，我們衡量其影響的能力一直相當穩健，這也一直是我們廣告營收成長非常重要的驅動因素；而這仍將持續是一個流程——同樣地，我們在這次的預算中也跑過一遍——就我們所能掌握的範圍而言、在我們有清晰視線（line of sight）的地方，我們對未來的投資機會感到樂觀。

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

很好。感謝各位今天加入我們。我們期待很快再次與各位交流。

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

今天的電話會議到此結束。感謝您的參與，您現在可以掛線。