Meta Platforms Inc (META) 2024 Q1 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 the Meta First Quarter Earnings Conference Call. (Operator Instructions) This call will be recorded. Thank you very much.

午安.我叫克里斯塔，今天我將擔任你們的會議操作員。現在，我歡迎大家參加 Meta 第一季財報電話會議。 （操作員說明）此通話將會被錄音。非常感謝。

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

Meta 的投資者關係總監 Ken Dorell，您可以開始了。

Thank you. Good afternoon, and welcome to Meta Platform's First Quarter 2021 Earnings Conference Call. Joining me today to discuss our results are Mark Zuckerberg, CEO; and Susan Li, CFO.

謝謝。下午好，歡迎參加 Meta Platform 2021 年第一季財報電話會議。今天與我一起討論我們的結果的是執行長馬克·祖克柏 (Mark Zuckerberg)；和財務長李蘇珊。

Before we get started, I would like to take this opportunity to remind you that our remarks today will include forward-looking statements. Actual results may differ materially from those contemplated by these forward-looking statements. Factors that could cause these results to differ materially are set forth in today's earnings press release and in our annual report on Form 10-K filed with the SEC.

在我們開始之前，我想藉此機會提醒您，我們今天的言論將包含前瞻性陳述。實際結果可能與這些前瞻性陳述預期的結果有重大差異。今天的收益新聞稿以及我們向 SEC 提交的 10-K 表格年度報告中列出了可能導致這些結果出現重大差異的因素。

Any forward-looking statements that we make on this call are based on assumptions as of today, and we undertake no obligation to update these statements as a result of new information or future events.

我們在本次電話會議中所做的任何前瞻性陳述均基於截至目前的假設，我們不承擔因新資訊或未來事件而更新這些陳述的義務。

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.fb.com.

在本次電話會議中，我們將介紹 GAAP 和某些非 GAAP 財務指標。今天的收益新聞稿中包含了 GAAP 與非 GAAP 指標的調整表。收益新聞稿和隨附的投資者簡報可在我們的網站 Investor.fb.com 上取得。

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

現在我想把電話轉給馬克。

All right. Thanks, Ken, and everyone, thanks for joining. It's been a good start to the year, both in terms of product momentum and business performance. We estimate that more than 3.2 billion people use at least one of our apps each day, and we're seeing healthy growth in the U.S. And I want to call out WhatsApp specifically, where the number of daily actives and message sends in the U.S. keeps gaining momentum, and I think we're on a good path there. We've also made good progress on our AI and metaverse efforts, and that's where we're going to focus most of my comments today.

好的。謝謝肯，還有大家，謝謝你們的加入。無論是在產品動力還是業務表現方面，今年都有一個好的開始。我們估計，每天有超過 32 億人至少使用我們的一款應用程序，而且我們在美國看到了健康的增長。強勁，我認為我們正走在一條良好的道路上。我們在人工智慧和元宇宙方面也取得了良好進展，這就是我們今天大部分評論的重點。

So let's start with AI. We are building a number of different AI services, from Meta AI, our AI assistant that you can ask any question across our apps and glasses, to creator AIs that help creators engage their communities and that fans can interact with, to business AIs that we think every business eventually on our platform will use to help customers buy things and get customer support to internal coding and development AIs to hardware like glasses for people to interact with AIs and a lot more.

那麼就讓我們從人工智慧開始吧。我們正在建立許多不同的人工智慧服務，從我們的人工智慧助理Meta AI（您可以透過我們的應用程式和眼鏡提出任何問題），到幫助創作者參與社群並讓粉絲與之互動的創作者人工智慧，再到我們提供的商業人工智慧。人工智慧互動的眼鏡等）。

Last week, we had the major release of our new version of Meta AI that is now powered by our latest model, Llama 3. And our goal with Meta AI is to build the world's leading AI service, both in quality and usage. The initial rollout of Meta AI is going well. Tens of millions of people have already tried it. The feedback is very positive. And when I first checked in with our teams, the majority of feedback we were getting was people asking us to release Meta AI for them wherever they are.

上週，我們發布了新版本 Meta AI 的主要版本，現在由我們的最新模型 Llama 3 提供支援。 Meta AI 的首次推出進展順利。數以千萬計的人已經嘗試過。反饋非常正面。當我第一次與我們的團隊聯繫時，我們得到的大部分回饋都是人們要求我們為他們發布 Meta AI，無論他們身在何處。

So we've started launching Meta AI in some English speaking countries, and we'll roll out in more languages and countries over the coming months. You all know our product development playbook by this point. We released an early version of a product to a limited audience to gather feedback and start improving it. And then once we think it's ready, then we make it available to more people. That early release was last fall. And with this release, we are now moving to that next growth phase of our playbook. We believe that Meta AI with Llama 3 is now the most intelligent AIS system that you can freely use. And now that we have the superior quality product, we are making it easier for lots of people to use it within WhatsApp, Messenger, Instagram and Facebook.

因此，我們已經開始在一些英語國家推出 Meta AI，並將在未來幾個月內推出更多語言和國家。到目前為止，你們都知道我們的產品開發手冊了。我們向有限的受眾發布了產品的早期版本，以收集回饋並開始改進它。一旦我們認為它已經準備好了，我們就會將其提供給更多的人。早期發布是在去年秋天。隨著這個版本的發布，我們現在正在進入我們劇本的下一個成長階段。我們相信，Meta AI 與 Llama 3 是現在您可以自由使用的最聰明的 AIS 系統。現在，我們擁有優質的產品，我們正在讓許多人更輕鬆地在 WhatsApp、Messenger、Instagram 和 Facebook 中使用它。

Now in addition to answering more complex queries, a few other notable and unique features from this release. Meta AI now creates animations from still images and now generates high-quality images so fast that it can create and update them as you are typing, which is pretty awesome. I've seen a lot of people commenting about that experience online, how they've never seen or experienced anything like it before.

現在，除了回答更複雜的查詢之外，此版本還提供了一些其他值得注意和獨特的功能。 Meta AI 現在可以從靜態圖像創建動畫，並且生成高品質圖像的速度如此之快，以至於它可以在您打字時創建和更新它們，這真是太棒了。我看到很多人在網路上評論了這次經歷，他們以前從未見過或經歷過類似的事情。

In terms of the core AI model and intelligence that's powering Meta AI, I'm very pleased with how Llama 3 has come together so far. The 8 billion and 70 billion parameter models that we released are best-in-class for their scale. The 400-plus billion parameter model that we're still training seems on track to be industry leading on several benchmarks. And I expect that our models are just going to improve further from open source contributions.

就支援 Meta AI 的核心 AI 模型和智慧而言，我對 Llama 3 迄今為止的表現感到非常滿意。我們發布的 80 億和 700 億參數模型的規模是同類中最好的。我們仍在訓練的 400 多個參數模型似乎在多個基準測試中處於行業領先地位。我預計我們的模型將透過開源貢獻進一步改進。

Overall, I view the results our teams have achieved here as another key milestone in showing that we have the talent, data and ability to scale infrastructure to build the world's leading AI models and services. And this leads me to believe that we should invest significantly more over the coming years to build even more advanced models and the largest scale AI services in the world.

總的來說，我認為我們的團隊在這裡取得的成果是另一個重要的里程碑，表明我們擁有人才、數據和能力來擴展基礎設施以建立世界領先的人工智慧模型和服務。這讓我相信，我們應該在未來幾年進行更多投資，以建立更先進的模型和世界上最大規模的人工智慧服務。

As we're scaling CapEx and energy expenses for AI, we'll continue focusing on operating the rest of our company efficiently. But realistically, even with shifting many of our existing resources to focus on AI, we'll still grow our investment envelope meaningfully before we make much revenue from some of these new products. I think it's worth calling that out that we've historically seen a lot of volatility in our stock during this phase of our product playbook, where we're investing in scaling a new product but aren't yet monetizing it. We saw this with Reels, Stories as newsfeed transition to mobile and more. And I also expect to see a multiyear investment cycle before we fully scale Meta AI, business AIs and more into the profitable services I expect as well.

隨著我們擴大人工智慧的資本支出和能源支出，我們將繼續專注於高效地經營公司其他部門。但實際上，即使將我們許多現有的資源轉移到人工智慧上，在我們從這些新產品中獲得大量收入之前，我們仍然會有意義地擴大我們的投資範圍。我認為值得指出的是，在我們的產品策略的這個階段，我們在歷史上看到了我們的股票的巨大波動，我們正在投資擴展新產品，但尚未將其貨幣化。隨著新聞源向行動裝置的過渡，我們在 Reels、Stories 中看到了這一點。我還預計，在我們將元人工智慧、商業人工智慧等全面擴展到我期望的獲利服務之前，會經歷一個多年的投資週期。

Historically, investing to build these new scaled experiences in our apps has been a very good long-term investment for us and for investors who have stuck with us. And the initial signs are quite positive here, too. But building the leading AI will also be a larger undertaking than the other experiences we've added to our apps, and this is likely going to take several years.

從歷史上看，投資在我們的應用程式中建立這些新的規模體驗對於我們和一直關注我們的投資者來說是一項非常好的長期投資。這裡的初步跡像也非常積極。但與我們添加到應用程式中的其他體驗相比，建立領先的人工智慧也將是一項更大的任務，而且這可能需要幾年的時間。

On the upside, once our new AI services reach scale, we have a strong track record of monetizing them effectively. There are several ways to build a massive business here, including scaling business messaging, introducing ads or paid content into AI interactions and enabling people to pay to use bigger AI models and access more compute. And on top of those, AI is already helping us improve app engagement, which naturally leads to seeing more ads and improving ads directly to deliver more value.

從好的方面來看，一旦我們的新人工智慧服務達到規模，我們就擁有有效將其貨幣化的良好記錄。在這裡建立大規模業務有多種方法，包括擴展業務訊息傳遞、將廣告或付費內容引入人工智慧交互，以及使人們能夠付費使用更大的人工智慧模型和存取更多計算。最重要的是，人工智慧已經在幫助我們提高應用程式參與度，這自然會導致看到更多廣告並直接改進廣告以提供更多價值。

So if the technology and products evolve in the way that we hope, each of those will unlock massive amounts of value for people and business for us over time.

因此，如果技術和產品按照我們希望的方式發展，隨著時間的推移，每項技術和產品都將為我們的人們和企業釋放巨大的價值。

We're seeing good progress on some of these efforts already. Right now, about 30% of the posts on Facebook feed are delivered by our AI recommendation system. That's up 2x over the last couple of years. And for the first time ever, more than 50% of the content that people see on Instagram is now AI recommended.

我們已經看到其中一些努力取得了良好進展。目前，Facebook feed 上大約 30% 的貼文是由我們的人工智慧推薦系統提供的。過去幾年增長了兩倍。在人們在 Instagram 上看到的內容中，有史以來第一次超過 50% 是人工智慧推薦的。

AI has also been a huge part of how we create value for advertisers by showing people more relevant ads. And if you look at our 2 end-to-end AI-powered tools, Advantage+ shopping and Advantage+ app campaigns, revenue flowing through those has more than doubled since last year.

人工智慧也是我們透過向人們展示更相關的廣告來為廣告商創造價值的重要組成部分。如果您查看我們的 2 個端到端人工智慧工具（Advantage+ 購物和 Advantage+ 應用程式行銷活動），您會發現自去年以來，透過這些工具產生的收入增加了一倍多。

We're also going to continue to be very focused on efficiency as we scale Meta AI and other AI services. Some of this will come from improving how we train and run models. Some improvements will come from the open source community, and we're improving cost efficiency is one of the main areas that I expect that open sourcing will help us improve similar to what we saw with Open Compute. We'll also keep making progress on building more of our own silicon. Our Meta training and inference accelerator chip has successfully enabled us to run some of our recommendations-related workloads on this less expensive stack. And as this program matures over the coming years, we plan to expand this to more of our workloads as well. And of course, as we ramp these investments, we will also continue to carefully manage head count and other expense growth throughout the company.

當我們擴展元人工智慧和其他人工智慧服務時，我們也將繼續非常關注效率。其中一些將來自改進我們訓練和運行模型的方式。一些改進將來自開源社區，我們正在提高成本效率，這是我預計開源將幫助我們改進的主要領域之一，類似於我們在開放運算中看到的。我們還將在製造更多我們自己的晶片方面不斷取得進展。我們的元訓練和推理加速器晶片成功地使我們能夠在這個較便宜的堆疊上運行一些與建議相關的工作負載。隨著該計劃在未來幾年的成熟，我們也計劃將其擴展到更多的工作負載。當然，隨著我們加強這些投資的力度，我們也將繼續仔細管理整個公司的員工人數和其他費用成長。

Now in addition to our work on AI, our other long-term focus is the metaverse. It's been interesting to see how these 2 themes have come together. This is clearest when you look at glasses. I used to think that AR glasses wouldn't really be a mainstream product until we had full holographic displays. And I still think that, that's going to be awesome and is the long-term mature state for the product. But now it seems pretty clear that there's also a meaningful market for fashionable AI glasses without a display.

現在除了人工智慧方面的工作之外，我們的另一個長期關注點是元宇宙。看到這兩個主題如何結合在一起很有趣。當您觀看眼鏡時，這一點最為清晰。我曾經認為，在我們擁有全息顯示器之前，AR 眼鏡不會真正成為主流產品。我仍然認為，這將是非常棒的，並且是該產品的長期成熟狀態。但現在看來很明顯，不帶顯示器的時尚人工智慧眼鏡也有一個有意義的市場。

Glasses are the ideal device for an AI assistant because you can let them see what you see and hear what you hear. So they have full context on what's going on around you as they help you with whatever you're trying to do. Our launch this week of Meta AI with a vision on the glasses is a good example where you can now ask questions about things that you're looking at.

眼鏡是人工智慧助理的理想設備，因為你可以讓他們看到你所看到的，聽到你所聽到的。因此，他們對您周圍發生的事情有完整的了解，因為他們可以幫助您完成您想做的任何事情。我們本週推出的帶有眼鏡視覺的 Meta AI 就是一個很好的例子，您現在可以就您正在查看的事物提出問題。

Now one strategy dynamic that I've been reflecting on is that an increasing amount of our Reality Labs work is going towards serving our AI efforts. We currently report on our financials as if Family of Apps and Reality Labs were 2 completely separate businesses. But strategically, I think of them as fundamentally the same business with the vision of Reality Labs to build the next generation of computing platforms in large part so that we can build the best apps and experiences on top of them. Over time, we'll need to find better ways to articulate the value that's generated here across both segments so it doesn't just seem like our hardware costs increase as our glasses ecosystem scales, but all the value flows to a different segment.

現在，我一直在思考的一個策略動態是，我們現實實驗室越來越多的工作正在為我們的人工智慧工作服務。我們目前報告的財務狀況就好像應用程式家族和現實實驗室是兩個完全獨立的業務一樣。但從策略上講，我認為它們與 Reality Labs 的願景本質上是相同的，即在很大程度上建立下一代運算平台，以便我們可以在它們之上建立最好的應用程式和體驗。隨著時間的推移，我們需要找到更好的方法來闡明這兩個細分市場產生的價值，因此，隨著眼鏡生態系統的擴展，我們的硬體成本似乎不僅會增加，而且所有價值都會流向不同的細分市場。

The Ray-Ban Meta glasses that we built with EssilorLuxottica continue to do well and are sold out in many styles and colors. So we're working to make more and release additional styles as quickly as we can. We just released the new cat-eye Skyler design yesterday, which is more feminine. And in general, I'm optimistic about our approach of starting with the classics and expanding with an increasing diversity of options over time.

我們與 EssilorLuxottica 合作打造的 Ray-Ban Meta 眼鏡繼續表現出色，多種款式和顏色均已售罄。因此，我們正在努力盡快製作更多款式並發布更多款式。我們昨天剛發布了新款貓眼Skyler設計，更加女性化。總的來說，我對我們從經典開始並隨著時間的推移以越來越多樣化的選擇進行擴展的方法感到樂觀。

If we want everyone to be able to use wearable AI, I think eyewear is a bit different from phones or watches and that people are going to want very different designs. So I think our approach of partnering with the leading eyewear brands will help us serve more of the market.

如果我們希望每個人都能夠使用穿戴式人工智慧，我認為眼鏡與手機或手錶有點不同，人們會想要非常不同的設計。因此，我認為我們與領先眼鏡品牌的合作方式將幫助我們服務更多的市場。

I think a similar open ecosystem approach will help us expand the virtual and mixed reality headset market over time as well. We announced that we're opening up Meta Horizon OS, the operating system we've built to power Quest.

我認為類似的開放生態系統方法也將幫助我們隨著時間的推移擴大虛擬和混合實境耳機市場。我們宣布開放 Meta Horizo​​n OS，這是我們為 Quest 打造的作業系統。

As the ecosystem grows, I think there will be sufficient diversity in how people use mixed reality, that there will be demand for more designs than we'll be able to build. For example, a work-focused headset may be slightly less designed for motion but may -- you want to be lighter by connecting to your laptop; a fitness-focused headset may be lighter with sweat-wicking materials; an entertainment-focused headset may prioritize the highest resolution displays over everything else; a gaming-focused headset may prioritize peripherals and haptics or a device that comes with Xbox controllers and a game pass subscription out of the box.

隨著生態系統的發展，我認為人們使用混合實境的方式將會有足夠的多樣性，對設計的需求將會超出我們所能建構的範圍。例如，專注於工作的耳機可能不太適合運動，但可能 - 您希望透過連接到筆記型電腦來變得更輕；專注於健身的耳機可能會因採用吸汗材質而變得更輕；專注於娛樂的耳機可能會優先考慮最高解析度的顯示器；專注於遊戲的耳機可能會優先考慮週邊設備和觸覺或附帶 Xbox 控制器和開箱即用的遊戲通行證訂閱的設備。

Now to be clear, I think that our first-party Quest devices will continue to be the most popular headsets as we see today, and we'll continue focusing on advancing the state-of-the-art tech and making it accessible to everyone. But I also think that opening our ecosystem and opening our operating system will help the overall mixed reality ecosystem grow even faster.

現在需要澄清的是，我認為我們的第一方 Quest 設備將繼續成為我們今天看到的最受歡迎的耳機，我們將繼續專注於推進最先進的技術並使每個人都能使用它。但我也認為開放我們的生態系統和開放我們的作業系統將有助於整個混合實境生態系統更快發展。

Now in addition to AI and the metaverse, we're seeing good improvements across our apps. I touched on some of the most important trends already with WhatsApp growth in the U.S. and AI-powered recommendations in our feeds and reels already. But I do want to mention that video continues to be a bright spot. This month, we launched an updated full-screen video player on Facebook that brings together reels, longer videos and live content into a single experience with a unified recommendation system.

現在，除了人工智慧和元宇宙之外，我們還看到我們的應用程式有了良好的改進。隨著 WhatsApp 在美國的成長，以及我們的資訊流和捲軸中人工智慧驅動的推薦，我已經談到了一些最重要的趨勢。但我確實想提一下，影片仍然是一個亮點。本月，我們在 Facebook 上推出了更新的全螢幕視訊播放器，透過統一的推薦系統將捲軸、較長的視訊和直播內容整合為單一體驗。

On Instagram, reels and video continue to drive engagement, with reels alone now making up 50% of the time that's spent within the app. Threads is growing well, too. There are now more than 150 million monthly actives. And it continues to generally be on the trajectory that I hope to see. And of course, my daughters would want me to mention that Taylor Swift is now on Threads. That one was a big deal in my house.

在 Instagram 上，影片和影片繼續推動參與度，目前僅影片就佔據了應用程式內花費時間的 50%。線程也增長良好。目前每月活躍人數超過 1.5 億人。它總體上繼續沿著我希望看到的軌跡發展。當然，我的女兒們希望我提到泰勒絲現在在 Threads 上。這在我家裡是一件大事。

All right. That is what I wanted to cover today. I am proud of the progress we've made so far this year. We've got a lot more execution ahead to fulfill the opportunities ahead of us. A big thank you to all of our teams who are driving all these advances and to all of you for being on this journey with us.

好的。這就是我今天想要介紹的內容。我對今年迄今的進展感到自豪。我們還有更多的執行力來抓住眼前的機會。非常感謝我們所有推動所有這些進步的團隊，也感謝你們所有人與我們一起踏上這段旅程。

And now here is Susan.

現在蘇珊來了。

Thanks, Mark, and good afternoon, everyone. Let's begin with our consolidated results. All comparisons are on a year-over-year basis unless otherwise noted. Q1 total revenue was $36.5 billion, up 27% on both a reported and constant currency basis. Q1 total expenses were $22.6 billion, up 6% compared to last year.

謝謝，馬克，大家下午好。讓我們從我們的綜合結果開始。除非另有說明，所有比較均按年比較。第一季總營收為 365 億美元，按報告和固定匯率計算成長 27%。第一季總支出為 226 億美元，比去年增長 6%。

In terms of the specific line items, cost of revenue increased 9% as higher infrastructure-related costs were partially offset by lapping Reality Labs' inventory-related valuation adjustments. R&D increased 6%, driven mostly by higher head count-related expenses and infrastructure costs, which were partially offset by lower restructuring costs.

就具體項目而言，收入成本增加了 9%，因為基礎設施相關成本的增加被 Reality Labs 的庫存相關估值調整部分抵消。研發成長 6%，主要是因為人員數量相關費用和基礎設施成本增加，但部分被重組成本下降所抵銷。

Marketing and sales decreased 16% due mainly to lower restructuring costs, professional services and marketing spend. G&A increased 20% as higher legal-related expenses were partially offset by lower restructuring costs.

行銷和銷售額下降 16%，主要是由於重組成本、專業服務和行銷支出下降。由於較高的法律相關費用被較低的重組成本部分抵消，一般管理費用增加了 20%。

We ended the first quarter with over 69,300 employees, up 3% from Q4. First quarter operating income was $13.8 billion, representing a 38% operating margin. Our tax rate for the quarter was 13%. Net income was $12.4 billion or $4.71 per share.

第一季末，我們擁有超過 69,300 名員工，比第四季成長 3%。第一季營業收入為 138 億美元，營業利益率為 38%。我們本季的稅率為 13%。淨利潤為 124 億美元，即每股 4.71 美元。

Capital expenditures, including principal payments on finance leases, were $6.7 billion, driven by investments in servers, data centers and network infrastructure. Free cash flow was $12.5 billion. We repurchased $14.6 billion of our Class A common stock and paid $1.3 billion in dividends to shareholders, ending the quarter with $58.1 billion in cash and marketable securities and $18.4 billion in debt.

受伺服器、資料中心和網路基礎設施投資的推動，資本支出（包括融資租賃本金支付）為 67 億美元。自由現金流為 125 億美元。我們回購了 146 億美元的 A 類普通股，並向股東支付了 13 億美元的股息，本季末我們擁有 581 億美元的現金和有價證券以及 184 億美元的債務。

Moving now to our segment results. I'll begin with our Family of Apps segment. Our community across the Family of Apps continues to grow, with approximately 3.2 billion people using at least one of our Family of Apps on a daily basis in March. Q1 total Family of Apps revenue was $36 billion, up 27% year-over-year. Q1 Family of Apps ad revenue was $35.6 billion, up 27% or 26% on a constant currency basis. Within ad revenue, the online commerce vertical was the largest contributor to year-over-year growth, followed by gaming and entertainment and media.

現在轉向我們的細分結果。我將從我們的應用程式系列部分開始。我們的應用程式系列社群持續成長，3 月每天約有 32 億人至少使用我們的應用程式系列之一。第一季應用系列總營收為 360 億美元，較去年同期成長 27%。第一季應用程式系列廣告營收為 356 億美元，成長 27%，以固定匯率計算成長 26%。在廣告收入中，線上商務垂直領域是年成長的最大貢獻者，其次是遊戲、娛樂和媒體。

On a user geography basis, ad revenue growth was strongest in Rest of World and Europe at 40% and 33%, respectively. Asia Pacific grew 25% and North America grew 22%. In Q1, the total number of ad impressions served across our services increased 20%, and the average price per ad increased 6%. Impression growth was mainly driven by Asia Pacific and Rest of World. Pricing growth was driven by advertiser demand, which was partially offset by strong impression growth, particularly from lower-monetizing regions and services.

從用戶地理來看，全球其他地區和歐洲的廣告收入成長最為強勁，分別為 40% 和 33%。亞太地區成長 25%，北美成長 22%。第一季度，我們服務中的廣告展示總數增加了 20%，每個廣告的平均價格增加了 6%。印象數成長主要由亞太地區和世界其他地區推動。定價成長是由廣告商需求推動的，但廣告商需求的強勁成長部分抵消了廣告商的需求，尤其是來自貨幣化程度較低的地區和服務的印象增長。

Family of Apps other revenue was $380 million in Q1, up 85%, driven by business messaging revenue growth from our WhatsApp business platform. We continue to direct the majority of our investments toward the development and operation of our Family of Apps. In Q1, Family of Apps expenses were $18.4 billion, representing approximately 81% of our overall expenses. Family of Apps expenses were up 7% due mainly to higher legal and infrastructure costs that were partially offset by lower restructuring costs.

第一季應用程式系列的其他收入為 3.8 億美元，成長 85%，這主要得益於 WhatsApp 業務平台的業務訊息收入成長。我們繼續將大部分投資用於應用程式系列的開發和營運。第一季度，應用程式系列支出為 184 億美元，約占我們整體支出的 81%。應用程式系列支出增加了 7%，主要是由於法律和基礎設施成本上升，但重組成本下降部分抵消了這一成本。

Family of Apps operating income was $17.7 billion, representing a 49% operating margin. Within our Reality Labs segment, Q1 revenue was $440 million, up 30%, driven by Quest headset sales. Reality Labs expenses were $4.3 billion, down 1% year-over-year as higher head count-related expenses were more than offset by lapping inventory-related valuation adjustments and restructuring costs. Reality Labs operating loss was $3.8 billion.

Family of Apps 營業收入為 177 億美元，營業利益率為 49%。在我們的 Reality Labs 部門中，受 Quest 耳機銷售的推動，第一季營收為 4.4 億美元，成長 30%。 Reality Labs 支出為 43 億美元，年減 1%，因為與人員數量相關的支出增加被庫存相關估值調整和重組成本所抵消。 Reality Labs 營運虧損為 38 億美元。

Turning now to the business outlook. There are 2 primary factors that drive our revenue performance: our ability to deliver engaging experiences for our community and our effectiveness at monetizing that engagement over time. On the first, we remain pleased with engagement trends and have strong momentum across our product priorities. Our investments in developing increasingly advanced recommendation systems continue to drive incremental engagement on our platform, demonstrating that people are finding added value by discovering content from accounts they are not connected to. The level of recommended content in our apps has scaled as we've improved these systems, and we see further opportunity to increase the relevance and personalization of recommendations as we advance our models.

現在轉向業務前景。推動我們收入表現的主要因素有兩個：我們為社區提供引人入勝的體驗的能力，以及隨著時間的推移，我們透過這種參與獲利的有效性。首先，我們對參與趨勢仍然感到滿意，我們的產品優先事項具有強勁的勢頭。我們在開發日益先進的推薦系統方面的投資繼續推動我們平台上的增量參與，這表明人們正在透過從他們未連接的帳戶中發現內容來發現附加價值。隨著我們改進這些系統，我們的應用程式中推薦內容的水平也隨之提高，隨著我們改進模型，我們看到了進一步提高推薦的相關性和個性化的機會。

Video also continues to grow across our platform, and it now represents more than 60% of time on both Facebook and Instagram. Reels remains the primary driver of that growth, and we're progressing on our work to bring together Reel's longer-form video and live video into one experience on Facebook.

影片在我們的平台上也持續成長，目前在 Facebook 和 Instagram 上佔據了超過 60% 的時間。 Reels 仍然是這一成長的主要推動力，我們正在努力將 Reel 的較長影片和直播視訊整合為 Facebook 上的一種體驗。

In April, we rolled out this unified video experience in the U.S. and Canada, which is increasingly powered by our next-generation ranking architecture that we expect will help deliver more relevant video recommendations over time.

今年4 月，我們在美國和加拿大推出了這種統一的視訊體驗，我們的下一代排名架構越來越支持這種體驗，我們預計隨著時間的推移，該架構將有助於提供更相關的影片推薦。

We're also introducing deeper integrations of generative AI into our apps in the U.S. and more than a dozen other countries. Along with using Meta AI within our chat surfaces, people will now be able to use Meta AI in search within our apps as well as feed and groups on Facebook. We expect these integrations will complement our social discovery strategy as our recommendation systems help people to discover and explore their interests, while Meta AI enables them to dive deeper on topics they're interested in. Threads also continues to see good traction as we continue to ship valuable features and scale the community.

我們還將生成式人工智慧更深入地整合到我們在美國和其他十幾個國家的應用程式中。除了在我們的聊天介面中使用 Meta AI 之外，現在人們還可以在我們的應用程式以及 Facebook 上的提要和群組中使用 Meta AI 進行搜尋。我們預計這些整合將補充我們的社交發現策略，因為我們的推薦系統可以幫助人們發現和探索他們的興趣，而 Meta AI 使他們能夠更深入地研究他們感興趣的主題。的功能並擴展社區。

Now to the second driver of our revenue performance, increasing monetization efficiency. There are 2 parts to this work. The first is optimizing the level of ads within organic engagement. Here, we continue to advance our understanding of users' preferences for viewing ads to more effectively optimize the right time, place and person to show an ad to.

現在我們的收入表現的第二個驅動因素是提高貨幣化效率。這項工作有 2 個部分。首先是優化有機參與中的廣告層面。在這裡，我們不斷加深對用戶觀看廣告偏好的了解，以更有效地優化廣告展示的正確時間、地點和人群。

For example, we are getting better at adjusting the placement and number of ads in real time based on our perception of a user's interest and ad content and to minimize disruption from ads as well as innovating on new and creative ad formats. We expect to continue that work going forward, while surfaces with relatively lower levels of monetization, like video and messaging, will serve as additional growth opportunities.

例如，我們越來越擅長根據對使用者興趣和廣告內容的感知來即時調整廣告的展示位置和數量，最大限度地減少廣告的干擾以及創新新的創意廣告格式。我們預計未來將繼續這項工作，而視訊和訊息等貨幣化水平相對較低的表面將成為額外的成長機會。

The second part of improving monetization efficiency is enhancing marketing performance. Similar to our work with organic recommendations, AI is playing an increasing role in these efforts. First, we are making ongoing ads modeling improvements that are delivering better performance for advertisers. One example is our new ads ranking architecture, Meta Lattice, which we began rolling out more broadly last year. This new architecture allows us to run significantly larger models that generalize learnings across objectives and surfaces in place of numerous smaller ad models that have historically been optimized for individual objectives and surfaces. This is not only leading to increased efficiency as we operate fewer models but also improving ad performance.

提高變現效率的第二部分是提升行銷績效。與我們在有機推薦方面的工作類似，人工智慧在這些工作中發揮著越來越大的作用。首先，我們正在不斷改進廣告建模，為廣告商提供更好的效果。一個例子是我們的新廣告排名架構 Meta Lattice，我們去年開始更廣泛地推出它。這種新架構使我們能夠運行更大的模型，這些模型可以概括跨目標和表面的學習，而不是歷史上針對各個目標和表面進行最佳化的許多較小的廣告模型。這不僅可以提高效率（因為我們經營的模型更少），還可以提高廣告效果。

Another way we're leveraging AI is to provide increased automation for advertisers. Through our Advantage+ portfolio, advertisers can automate one step of the campaign setup process, such as selecting which ad creative to show, or automate their campaign completely using our end-to-end automation tools, Advantage+ shopping and Advantage+ app ads. We're seeing growing use of these solutions, and we expect to drive further adoption over the course of the year while applying what we learned to our broader ads investments.

我們利用人工智慧的另一種方式是為廣告主提供更高的自動化程度。透過我們的 Advantage+ 產品組合，廣告主可以自動化廣告活動設定流程的一個步驟，例如選擇要顯示的廣告創意，或使用我們的端對端自動化工具、Advantage+ 購物和 Advantage+ 應用程式廣告完全自動化其廣告活動。我們看到這些解決方案的使用越來越多，我們希望在一年內推動進一步採用，同時將我們學到的知識應用到更廣泛的廣告投資中。

Next, I'd like to discuss our approach to capital allocation. We continue to see compelling investment opportunities to both improve our core business in the near term and capture significant longer-term opportunities in generative AI and Reality Labs. As we develop more advanced and compute-intensive recommendation models and scale capacity for our generative AI training and inference needs, we expect that having sufficient infrastructure capacity will be critical to realizing many of these opportunities. As a result, we expect that we will invest significantly more in infrastructure over the coming years.

接下來，我想討論一下我們的資本配置方法。我們繼續看到令人信服的投資機會，既可以在短期內改善我們的核心業務，又可以在生成人工智慧和現實實驗室中抓住重要的長期機會。隨著我們開發更先進和計算密集型的推薦模型並擴展容量來滿足我們的生成式人工智慧訓練和推理需求，我們預計擁有足夠的基礎設施容量對於實現其中許多機會至關重要。因此，我們預計未來幾年將大幅增加基礎設施投資。

Our other long-term initiatives that we're continuing to make significant investments in is Reality Labs. We are also starting to see our AI initiatives increasingly overlap with our Reality Labs work. For example, with Ray-Ban Meta smart glasses, people in the U.S. and Canada can now use our multimodal Meta AI assistant for daily tasks without pulling out their phone.

我們繼續進行大量投資的其他長期計劃是現實實驗室。我們也開始看到我們的人工智慧計畫與現實實驗室的工作越來越重疊。例如，借助雷朋 Meta 智慧眼鏡，美國和加拿大的人們現在可以使用我們的多模式 Meta AI 助理來執行日常任務，而無需拿出手機。

Longer term, we expect generative AI to play an increasing role in our mixed reality products, making it easier to develop immersive experiences. Accelerating our AI efforts will help ensure we can provide the best version of our services as we transition to the next computing platform. We expect to pursue these opportunities while maintaining a focus on operating discipline, and we believe our strong financial position will allow us to support these investments while also returning capital to shareholders through share repurchases and dividends.

從長遠來看，我們預計生成式人工智慧將在我們的混合實境產品中發揮越來越大的作用，使開發沉浸式體驗變得更加容易。加快我們的人工智慧工作將有助於確保我們在過渡到下一個運算平台時能夠提供最佳版本的服務。我們希望在抓住這些機會的同時保持對營運紀律的關注，我們相信我們強大的財務狀況將使我們能夠支持這些投資，同時透過股票回購和股息向股東返還資本。

In addition, we continue to monitor an active regulatory landscape, including the increasing legal and regulatory headwinds in the EU and the U.S. that could significantly impact our business and our financial results. We also have a jury trial scheduled for June in a suit brought by the state of Texas regarding our use of facial recognition technology, which could ultimately result in a material loss.

此外，我們繼續監控積極的監管環境，包括歐盟和美國日益增加的法律和監管阻力，這可能會對我們的業務和財務表現產生重大影響。我們還計劃在 6 月就德克薩斯州就我們使用臉部辨識技術提起的訴訟進行陪審團審判，最終可能導致物質損失。

Turning now to the revenue outlook. We expect second quarter 2024 total revenue to be in the range of $36.5 billion to $39 billion. Our guidance assumes foreign currency is a 1% headwind to year-over-year total revenue growth based on current exchange rates.

現在轉向收入前景。我們預計 2024 年第二季總營收將在 365 億至 390 億美元之間。我們的指引假設，根據當前匯率，外幣對年比總收入成長有 1% 的阻力。

Turning now to the expense outlook. We expect full year 2024 total expenses to be in the range of $96 million to $99 billion, updated from our prior outlook of $94 million to $99 billion due to higher infrastructure and legal costs.

現在轉向費用前景。由於基礎設施和法律成本增加，我們預計 2024 年全年總支出將在 9,600 萬美元至 990 億美元之間，較先前預測的 9,400 萬美元至 990 億美元有所更新。

For Reality Labs, we continue to expect operating losses to increase meaningfully year-over-year due to our ongoing product development efforts and our investments to further scale our ecosystem.

對於 Reality Labs 來說，由於我們正在進行的產品開發工作以及進一步擴大生態系統的投資，我們仍然預計營運虧損將比去年同期大幅增加。

Turning now to the CapEx outlook. We anticipate our full year 2024 capital expenditures will be in the range of $35 billion to $40 billion, increased from our prior range of $30 billion to $37 billion as we continue to accelerate our infrastructure investments to support our AI road map. While we are not providing guidance for years beyond 2024, we expect CapEx will continue to increase next year as we invest aggressively to support our ambitious AI research and product development efforts.

現在轉向資本支出前景。隨著我們繼續加快基礎設施投資以支援我們的人工智慧路線圖，我們預計2024 年全年資本支出將在350 億美元至400 億美元之間，從先前的300 億美元至370 億美元增加。雖然我們不會提供 2024 年以後的指導，但我們預計明年的資本支出將繼續增加，因為我們將積極投資以支持我們雄心勃勃的人工智慧研究和產品開發工作。

On to tax. Absent any changes to our tax landscape, we expect our full year 2024 tax rate to be in the mid-teens.

到稅收。如果我們的稅收格局沒有任何變化，我們預計 2024 年全年稅率將在 15 左右。

In closing, Q1 was a good start to the year. We're seeing strong momentum within our Family of Apps and are making important progress on our longer-term AI and Reality Labs initiatives that have the potential to transform the way people interact with our services over the coming years.

最後，第一季是今年的好開始。我們在應用程式系列中看到了強勁的勢頭，並且在我們的長期人工智慧和現實實驗室計劃方面正在取得重要進展，這些計劃有可能在未來幾年改變人們與我們的服務互動的方式。

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

克里斯塔，讓我們開始提問吧。

(Operator Instructions) And your first question comes from the line of Eric Sheridan from Goldman Sachs.

（操作員說明）您的第一個問題來自高盛的埃里克·謝裡丹（Eric Sheridan）。

Maybe I'll ask a two-parter. Mark, you used the analogy of other investments cycles you've been through around products like Stories and Reels. I know you're not giving long-term guidance today, but using those analogies, how should investors think about the length and depth of this investment cycle with respect to either AI and/or Reality Labs more broadly and mixed reality?

也許我會問兩個人。馬克，您使用了您所經歷過的有關故事和捲軸等產品的其他投資週期的類比。我知道您今天不會提供長期指導，但使用這些類比，投資者應該如何考慮人工智慧和/或更廣泛的現實實驗室和混合實境的投資週期的長度和深度？

And you both talked about the impact AI is having on the advertising ecosystem. What are you watching for in terms of adoption or utility on the consumer side to know that AI adoption is tracking along with the investment cycle?

你們都談到了人工智慧對廣告生態系統的影響。您在消費者方面的採用或效用方面關注什麼，以了解人工智慧的採用是否與投資週期同步？

Yes. In terms of the timing, I think it's somewhat difficult to extrapolate from previous cycles. But I guess like the main thing that we see is that we will usually take, I don't know, a couple of years, I mean, it could be a little more, it could be less to focus on building out and scaling the products. And we typically don't focus that much on monetization of the new areas until they reach significant scale because it's so much higher leverage for us just to improve monetization on other things before these new products are at scale.

是的。就時間安排而言，我認為從先前的周期中推斷有些困難。但我想我們看到的主要事情是我們通常需要，我不知道，幾年，我的意思是，可能會多一點，可能會更少地專注於構建和擴展產品。而且，在新領域達到顯著規模之前，我們通常不會太關注它們的貨幣化，因為在這些新產品規模化之前，僅僅提高其他方面的貨幣化對我們來說就具有更高的槓桿作用。

So you enter this period where I think kind of smart investors see that the product is scaling and that there's a clear monetizable opportunity there even before the revenue materializes. And I think we've seen that with Reels and with Stories and with the shift to mobile and all these things, where basically, we build out the inventory first for a period of time and then we monetize it.

因此，進入這個時期，我認為聰明的投資者會看到該產品正在擴展，並且甚至在收入實現之前就存在明顯的貨幣化機會。我認為我們已經看到了 Reels 和 Stories 以及向行動裝置的轉變以及所有這些事情，基本上，我們首先在一段時間內建立庫存，然後將其貨幣化。

And during that time, when it's scaling, sometimes it's not just the case that we're not making money from that thing. It can often actually be the case that it displaces other revenue from other things. So like you saw with Reels, I mean, it scaled and there was a period where it was not profitable for us as it was scaling before it became profitable. So I think that's more the analogy that I'm making on this.

在那段時間，當它擴展時，有時不僅僅是我們沒有從中賺錢。實際上，經常出現的情況是它取代了其他事物的其他收入。所以就像你在 Reels 上看到的那樣，我的意思是，它規模化了，但有一段時間它對我們來說沒有盈利，因為它在盈利之前就在擴大規模。所以我認為這更像是我對此所做的類比。

But I think it's -- what that suggests is that what we should all be focused on for the next period is as the consumer products scale, Meta AI really just launched in a meaningful way so we don't have any kind of hard stats to share on that. But I'd say that's the main thing that I'm focused on for this year and probably a lot of next year is growing that product and the other AI products and the engagement around them. And I think we should all have quite a bit of confidence that if those are on a good track to scale, then they're going to end up being very large businesses. So that's the main point that I was trying to make there.

但我認為，這表明我們下一階段應該關注的是隨著消費產品規模的擴大，Meta AI 確實以一種有意義的方式推出，所以我們沒有任何硬性統計數據來衡量分享。但我想說，這是我今年關注的主要事情，明年的大部分時間可能是開發該產品和其他人工智慧產品以及圍繞它們的參與。我認為我們都應該有相當大的信心，如果這些公司能夠順利擴大規模，那麼它們最終將成為非常大的企業。這就是我試圖在此闡述的要點。

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

你的下一個問題來自摩根士丹利的布萊恩諾瓦克。

Thanks for taking my questions, I have 2. The first one is on sort of the recommendation engine improvements and even, Susan, when you talked about further opportunities to increase the relevance of the models. Could you just unpack that a little bit for us? Can you give us examples of where you're still running the model in a suboptimal basis or opportunities for improved signal capture use or data you're not using? Where are sort of the areas of improvement you see from here?

感謝您提出我的問題，我有兩個問題。您能為我們解壓縮一下嗎？您能否舉例說明您仍在以次優方式運行模型的地方，或改善訊號擷取使用或未使用資料的機會？您從這裡看到哪些需要改進的地方？

And then the second one, when you talk about driving incremental adoption of AI tools for advertisers, what are sort of some of the main gating factors you've encountered to get advertisers to test these tools? And how do you think about sort of addressing that throughout '24 and '25?

然後是第二個問題，當您談論推動廣告商逐步採用人工智慧工具時，您在讓廣告商測試這些工具時遇到的一些主要限制因素是什麼？您如何看待在 24 世紀和 25 年間解決這個問題？

Thanks, Brian. So to your first question, where are there more opportunities for us to leverage and improve our recommendations models to drive engagement? One of the things I would say is, historically, each of our recommendation products, including Reels, in-feed recommendations, et cetera, has had their own AI model.

謝謝，布萊恩。那麼對於你的第一個問題，我們在哪裡有更多機會利用和改進我們的推薦模型來提高參與度？我想說的一件事是，從歷史上看，我們的每個推薦產品，包括 Reels、資訊流推薦等，都有自己的人工智慧模型。

And recently, we've been developing a new model architecture with the aim for it to power multiple recommendations products. We started partially validating this model last year by using it to power Facebook Reels. And we saw meaningful performance gains, 8% to 10% increases in watch time as a result of deploying this.

最近，我們一直在開發一種新的模型架構，旨在為多種推薦產品提供支援。我們去年開始部分驗證這個模型，用它來驅動 Facebook Reels。部署此功能後，我們看到了顯著的效能提升，觀看時間增加了 8% 到 10%。

This year, we're actually planning to extend the singular model architecture to recommend content across not just Facebook Reels, but also Facebook's video tab as well. So while it's still too early to share specific results, we're optimistic that the new model architecture will unlock increasingly relevant video recommendations over time. And if it's successful, we'll explore using it to power other recommendations.

今年，我們實際上計劃擴展單一模型架構，不僅可以在 Facebook Reels 上推薦內容，還可以在 Facebook 的影片標籤上推薦內容。因此，雖然現在分享具體結果還為時過早，但我們樂觀地認為，隨著時間的推移，新的模型架構將解鎖越來越相關的影片推薦。如果成功，我們將探索使用它來支持其他建議。

And analog exists, I would say, on the ad side. We've talked a little bit about the new model architecture Meta Lattice that we deployed last year that consolidates smaller and more specialized models into larger models that can better learn what characteristics improve ad performance across multiple services, like feed and Reels and multiple types of ads and objectives at the same time. And that's driven improved ad performance over the course of 2023 as we deployed it across Facebook and Instagram to support multiple objectives.

我想說，模擬存在於廣告方面。我們已經討論了去年部署的新模型架構Meta Lattice，該架構將更小、更專業的模型整合到更大的模型中，這些模型可以更好地了解哪些特徵可以提高跨多種服務的廣告效能，例如feed 和Reels 以及多種類型的廣告。我們在 Facebook 和 Instagram 上部署了它來支援多個目標，這推動了 2023 年廣告成效的提升。

And over the course of 2024, we expect to further enhance model performance and include support for even more objectives like web and app and ROAS. So there's a lot of work that we're investing in, in the underlying model architecture for both organic engagement and ads that we expect is going to continue to deliver increasing ads performance over time.

到 2024 年，我們預計將進一步提高模型效能，並支援更多目標，例如 Web、應用程式和 ROAS。因此，我們在有機參與和廣告的底層模型架構方面投入了大量工作，我們預計隨著時間的推移，這些工作將繼續提供不斷提高的廣告效果。

The second question you asked was around getting advertisers to test and adopt gen AI tools. There are 2 flavors of this. The more near-term version is around the gen AI ad creative features that we have put into our ads creation tools. And it's early, but we're seeing adoption of these features across verticals and different advertiser sizes.

您問的第二個問題是關於讓廣告商測試和採用新一代人工智慧工具。這個有2種口味。更近期的版本是圍繞著我們已放入廣告創建工具中的 gen AI 廣告創意功能。雖然現在還為時過早，但我們已經看到這些功能在各個垂直行業和不同規模的廣告商中被採用。

In particular, we've seen outsized adoption of image expansion with small businesses, and this will remain a big area of focus for us in 2024, and I expect that improvements to our underlying foundation models will enhance the quality of the outputs that are generated and support new features on the road map. But right now, we have features supporting text variations, image expansion and background generation, and we're continuing to work to make those more performant for advertisers to create more personalized ads at scale.

特別是，我們已經看到小型企業大規模採用圖像擴展，這將仍然是我們 2024 年關注的一大領域，我預計對我們底層基礎模型的改進將提高生成的輸出的質量並支持路線圖上的新功能。但目前，我們擁有支援文字變化、圖像擴展和背景生成的功能，我們正在繼續努力使這些功能更加高效，以便廣告商大規模製作更個人化的廣告。

The longer-term piece here is around business AIs. We have been testing the ability for businesses to set up AIs for business messaging that represent them in chats with customers, starting by supporting shopping use cases such as responding to people asking for more information on a product or its availability. So this is very, very early. We've been testing this with a handful of businesses on Messenger and WhatsApp, and we're hearing good feedback with businesses saying that the AIs have saved them significant time while customer -- consumers noted more timely response times. And we're also learning a lot from these tests to make these AIs more performant over time as well. So we'll be expanding these tests over the coming months, and we'll continue to take our time here to get it right before we make it more broadly available.

這裡的長期部分是圍繞著商業人工智慧。我們一直在測試企業為業務訊息設定人工智慧的能力，這些人工智慧在與客戶的聊天中代表他們，首先是支援購物用例，例如回應人們詢問有關產品或其可用性的更多資訊。所以現在還非常非常早。我們已經在 Messenger 和 WhatsApp 上與少數企業進行了測試，我們聽到了企業的良好反饋，稱人工智能為他們節省了大量時間，而客戶——消費者則注意到響應時間更及時。我們也從這些測試中學到了很多東西，隨著時間的推移，這些人工智慧也會變得更有效率。因此，我們將在接下來的幾個月內擴大這些測試，並且在將其更廣泛地提供之前，我們將繼續花時間來完善它。

Your next question comes from the line of Mark Shmulik from Bernstein Research.

您的下一個問題來自伯恩斯坦研究中心的馬克·什穆里克（Mark Shmulik）。

I guess back to that product playbook that we talked about a few times, with kind of Reels now such a large share of kind of time spent on Instagram and Facebook, how do we think about the next leg of kind of monetization growth from here? In particular, as we kind of get back to kind of shopping on platform or other ways to monetize, any color there on the road map kind of just beyond ad insertion from here?

我想回到我們幾次討論過的產品劇本，現在 Reels 的時間在 Instagram 和 Facebook 上花費的時間如此之大，我們如何考慮下一步的貨幣化增長？特別是，當我們回到在平台上購物或其他貨幣化方式時，路線圖上是否有任何顏色超出了廣告插入的範圍？

And then, Susan, just on the ad market, in particular, previously, we heard a lot about kind of Chinese-based advertiser contribution. Any color you could share there on kind of how that spend is trending?

然後，蘇珊，就廣告市場而言，特別是之前，我們聽到了很多有關中國廣告商貢獻的信息。您可以分享一下有關支出趨勢的任何顏色嗎？

Sure. Thanks, Mark. So Reels revenue continued to grow across Instagram and Facebook in Q1, and that's driven both by higher engagement and increased monetization efficiency through our ads ranking and delivery improvements. And we -- as we've mentioned before, we don't plan on quantifying the impact from Reels going forward, but it remains a positive contributor to overall revenue. And we expect that there are going to be opportunities for us to continue improving performance and growing supply.

當然。謝謝，馬克。因此，Reels 在第一季在 Instagram 和 Facebook 上的收入持續成長，這是由於我們的廣告排名和投放改進提高了參與度和變現效率。正如我們之前提到的，我們不打算量化 Reels 未來的影響，但它仍然對整體收入做出積極貢獻。我們預計我們將有機會繼續提高業績和增加供應。

So on the performance improvements, we are investing in ongoing ranking improvements. We're continuing to make ads easier and more intuitive to interact with through work like optimizing call to actions and post-click experiences, which are especially important for DR performance. And we're also optimizing ads to feel more native to Reels.

因此，在效能改進方面，我們正在投資持續的排名改進。我們將繼續透過優化號召性用語和點擊後體驗等工作，使廣告互動變得更容易、更直觀，這對於災難復原表現尤其重要。我們也優化了廣告，讓感覺更貼近 Reels。

In Q1, we rolled out our gen AI image expansion tools across Facebook and Instagram Reels after having introduced it to Instagram feed in Q4, and we're seeing, again, outsized adoption with small businesses. So we're excited about the opportunities to continue making these ads more performant. And even though ads -- the Reels ad loads, sorry, has increased over the last year, it remains lower on a per time basis than both Feed and Stories. So we're going to continue to look for opportunities to thoughtfully grow it in the future and invest in creative ways to address the structural supply constraints of the Reels format being more video-heavy, including higher density experiences and formats and increasingly personalizing ad loads, which we think will make sure that we're really putting ads in front of people when they're most likely to be interested and engaged with them.

在第四季度將一代 AI 圖像擴展工具引入 Instagram feed 後，我們在第一季在 Facebook 和 Instagram Reels 上推出了它，我們再次看到小型企業的大規模採用。因此，我們很高興有機會繼續提高這些廣告的效果。儘管廣告——抱歉，捲軸廣告加載量在去年有所增加，但按時間計算仍然低於 Feed 和 Stories。因此，我們將繼續尋找機會，在未來深思熟慮地發展它，並投資於創造性的方法，以解決視頻密集型 Reels 格式的結構性供應限制，包括更高密度的體驗和格式以及日益個性化的廣告負載，我們認為這將確保我們真正在人們最有可能感興趣並與之互動的時候將廣告展示在他們面前。

The second question you asked was around China. Growth in spend from China advertisers remained strong in Q1. This was driven by online commerce and gaming, and it's reflected in our Asia Pacific advertisers segment, which remained the fastest-growing region, at 41% year-over-year in Q1. Now we did see strength across other geographies as well, including a 6-point acceleration in total revenue growth from North America advertisers.

你問的第二個問題是關於中國的。第一季中國廣告商的支出成長依然強勁。這是由線上商務和遊戲推動的，這反映在我們的亞太廣告商細分市場上，該地區仍然是成長最快的地區，第一季同比增長 41%。現在我們確實看到了其他地區的實力，包括北美廣告商的總收入增加了 6 個百分點。

So I would say that we aren't quantifying the Q1 contribution from China, and we don't have forward-looking expectations to share on quarterly China-based ad revenue, but I will say that we are lapping periods of increasingly strong demand over the course of 2024 given the recovery of China-based advertisers in 2023 from their prior pandemic-driven headwinds.

因此，我想說，我們沒有量化中國第一季的貢獻，也沒有分享中國季度廣告收入的前瞻性預期，但我想說，我們正在經歷需求日益強勁的時期考慮到中國廣告商將在2023 年從先前的疫情驅動的逆風中恢復過來，我們預計2024 年的進程將是如此。

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

您的下一個問題來自摩根大通的 Doug Anmuth。

Can you just talk about what's changed most in your view in the business and the opportunity now versus 3 months ago? And is there anything you're more cautious about in revenue in the ad market? And is the AI opportunity just even bigger, and therefore, requiring more investment than expected?

您能否談談您對業務的看法以及現在與 3 個月前相比最大的變化是什麼？對於廣告市場的收入，您還有什麼比較謹慎的嗎？人工智慧的機會是否更大，因此需要比預期更多的投資？

And then, Susan, can you also just comment on how you're thinking about that ability to sustain growth rates over the next few quarters as you face tougher comps off a big base of ad dollars?

然後，蘇珊，您能否評論一下，當您面臨大量廣告收入帶來的更嚴峻的競爭時，您如何看待未來幾季維持成長率的能力？

Yes, I can speak to the first one. I think we've gotten more optimistic and ambitious on AI. So previously, I think that our work in this -- I mean when you were looking at last year, when we released Llama 2, we were very excited about the model and thought that, that was going to be the basis to be able to build a number of things that were valuable that integrated into our social products. But now I think we're in a pretty different place. So with the latest models, we're not just building good AI models that are going to be capable of building some new good social and commerce products. I actually think we're in a place where we've shown that we can build leading models and be the leading AI company in the world. And that opens up a lot of additional opportunities beyond just ones that are the most obvious ones for us.

是的，我可以和第一個人談談。我認為我們對人工智慧變得更加樂觀和雄心勃勃。所以之前，我認為我們在這方面的工作 - 我的意思是當你看到去年，當我們發布 Llama 2 時，我們對該模型非常興奮，並認為這將成為能夠的基礎構建一些有價值的東西並融入我們的社交產品。但現在我認為我們處於一個完全不同的地方。因此，透過最新的模型，我們不僅僅是建立良好的人工智慧模型，這些模型將能夠建立一些新的良好的社交和商業產品。事實上，我認為我們已經證明我們可以建立領先的模型並成為世界領先的人工智慧公司。這為我們帶來了許多額外的機會，而不僅僅是對我們來說最明顯的機會。

So that's -- this is what I was trying to refer to in my opening remarks where I just view the success that we've seen with the way that Llama 3 and Meta AI have come together as a real validation technically that we have the talent, the data and the ability to scale infrastructure to do leading work here.

這就是我在開場白中試圖提到的內容，我只是將 Llama 3 和 Meta AI 結合在一起所取得的成功視為技術上真正的驗證，即我們擁有人才、數據和擴展基礎設施的能力在這裡發揮主導作用。

And with Meta AI, I think that we are on our path to having Meta AI be the most used and best AI assistant in the world, which I think is going to be enormously valuable. So all of that basically encourages me to make sure that we're investing to stay at the leading edge of this.

透過 Meta AI，我認為我們正在努力讓 Meta AI 成為世界上最常用、最好的人工智慧助手，我認為這將非常有價值。因此，所有這些基本上都鼓勵我確保我們進行投資以保持領先地位。

And we're doing that at the time when we're also scaling the product before it is making money. So that's the analogy that I was making before, which is we've gone through some of those cycles before. But fundamentally, I think if you look at the facts of what our team is able to produce, I think it just -- our optimism and ambition have just grown quite a bit, and I think that this is just going to end up being quite an important set of products for us. So it was already going to be. Now I think it has the potential to be even more important.

我們這樣做的同時，我們也在產品賺錢之前對其進行了擴展。這就是我之前所做的類比，我們之前已經經歷過其中一些週期。但從根本上來說，我認為如果你看看我們團隊能夠創造出的成果的事實，我認為我們的樂觀情緒和雄心剛剛增長了很多，而且我認為這最終會變得相當不錯。是一套重要的產品。所以它已經是這樣了。現在我認為它有可能變得更加重要。

And I can take that second question, Doug. So we aren't giving full year 2024 guidance. And obviously, our revenue for the full year will be influenced by many factors, including macro conditions and things that are harder to predict the further out you go. And of course, over the course of 2024, we will also be lapping periods of increasingly strong demand. With that said, we expect to see good opportunities to continue growing engagement across our products, driven by the investments we made in AI-based content recommendations, our ongoing video work. And we also expect that we will continue to drive ads performance gains and continue to make our ads sort of more effective and deliver increasing value to advertisers.

我可以回答第二個問題，道格。因此，我們不會提供 2024 年全年指引。顯然，我們全年的收入將受到許多因素的影響，包括宏觀條件以及越遠的情況就越難以預測。當然，在 2024 年，我們也將經歷需求日益強勁的時期。話雖如此，在我們對基於人工智慧的內容推薦和正在進行的視訊工作的投資的推動下，我們預計將看到繼續提高我們產品參與度的良好機會。我們也期望我們將繼續推動廣告效果的提高，並繼續使我們的廣告更加有效，並為廣告商提供越來越多的價值。

One thing I'd share, for example, is that we actually grew conversions at a faster rate than we grew impressions over the course of this quarter. So we are -- we're expecting to -- which basically suggests that our conversion grade is growing and is one of the ways in which our ads are becoming more performant. So I feel like there's a lot of opportunity for us, both with our organic engagement growth and with continuing to make the ads better and to continue driving more results for advertisers.

例如，我要分享的一件事是，本季我們的轉換率成長速度實際上比展示次數成長速度更快。所以我們——我們期望——這基本上表明我們的轉換等級正在成長，並且是我們的廣告變得更有效率的方式之一。因此，我覺得我們有很多機會，無論是我們的自然參與度成長，還是繼續改進廣告並繼續為廣告商帶來更多成果。

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

您的下一個問題來自美國銀行的賈斯汀·波斯特。

First on the CapEx, mostly, you're kind of talking about an investment cycle here. Is there any way you could kind of use some of the metaverse spend over into AI? Are they converging and kind of use some of the money from the other areas to kind of fund the AI?

首先關於資本支出，主要是在這裡談論投資週期。有什麼辦法可以將虛擬宇宙的一些支出用於人工智慧嗎？他們是否會融合併使用其他領域的一些資金來資助人工智慧？

And then second, longer-term investors are very focused on returns on capital. Obviously, great returns on CapEx in the past with your margins today. How do we think about the returns on the capital you're spending? How are you thinking about it, I guess, going forward 2, 3 years out?

其次，長期投資者非常關注資本報酬率。顯然，過去的資本支出回報率與今天的利潤率相當。我們如何看待您所花費的資本的回報？我想，未來 2、3 年後你會如何看待這個問題？

So on the -- I would say -- well, I can start with the second part, and then I'll defer to Mark on the first one. In terms of measuring the ROI on our CapEx investments, we've broadly categorized our AI investments into 2 buckets. I think of them as sort of core AI work and then strategic bets, which would include gen AI and the advanced research efforts to support that. And those are just really at different stages as it relates to being able to measure the return and drive revenue for our business. So with our core AI work, we continue to have a very ROI-driven approach to investment, and we're still seeing strong returns as improvements to both engagement and ad performance have translated into revenue gains.

所以，我會說，好吧，我可以從第二部分開始，然後我會聽從馬克的第一部分。在衡量資本支出投資的投資報酬率方面，我們將人工智慧投資大致分為兩類。我認為它們是人工智慧的核心工作，然後是策略賭注，其中包括人工智慧和支援這項工作的高級研究工作。這些實際上處於不同的階段，因為它關係到能夠衡量回報並增加我們業務的收入。因此，在我們的核心人工智慧工作中，我們繼續採用投資回報率驅動的投資方法，並且隨著參與度和廣告效果的改善轉化為收入收益，我們仍然看到了強勁的回報。

Now the second area, strategic bets, is where we are much earlier. Mark has talked about the potential that we believe we have to create significant value for our business in a number of areas, including opportunities to build businesses that don't exist on us today. But we'll need to invest ahead of that opportunity to develop more advanced models and to grow the usage of our products before they drive meaningful revenue.

現在，第二個領域，即策略性押注，是我們較早進入的領域。馬克談到了我們相信我們必須在許多領域為我們的業務創造巨大價值的潛力，包括建立我們今天不存在的業務的機會。但我們需要在此機會之前進行投資，開發更先進的模型並增加我們產品的使用，然後才能帶來有意義的收入。

So while there is tremendous long-term potential, we're just much earlier on the return curve than with our core AI work. What I'll say though is we're also building our systems in a way that gives us fungibility in how we use our capacity so we can flex it across different use cases as we identify what are the best opportunities to put that infrastructure toward.

因此，雖然存在巨大的長期潛力，但我們的回報曲線比我們的核心人工智慧工作要早得多。但我要說的是，我們建立系統的方式也使我們在使用能力方面具有可替代性，這樣我們就可以在不同的用例中靈活使用它，同時確定基礎設施的最佳機會是什麼。

And then on the question of shifting resources from other parts of the company. I would say, broadly, we actually are doing that in a lot of places in terms of shifting resources from other areas, whether it's compute resources or different things in order to advance the AI efforts. For Reality Labs specifically, I'm still really optimistic about building these new computing platforms long term. I mentioned in my remarks upfront that one of the bigger areas that we're investing in Reality Labs is glasses. We think that that's going to be a really important platform for the future.

然後是從公司其他部門轉移資源的問題。我想說，從廣義上講，我們實際上在很多地方都在這樣做，從其他領域轉移資源，無論是計算資源還是其他資源，以推進人工智慧的發展。特別是對於現實實驗室來說，我仍然對長期建構這些新的運算平台非常樂觀。我在前面的演講中提到，我們在現實實驗室投資的更大領域之一是眼鏡。我們認為這將成為未來非常重要的平台。

Our outlook for that, I think, has improved quite a bit because previously, we thought that, that would need to wait until we have these full holographic displays to be a large market. And now we're a lot more focused on the glasses that we're delivering in partnership with Ray-Ban, which I think are going really well. And -- so that, I think, has the ability to be a pretty meaningful and growing platform sooner than I would have expected. So it is true that more of the Reality Labs work, like I said, is sort of focused on the AI goals as well. But I still think that we should focus on building these long-term platforms, too.

我認為，我們對此的前景已經有了很大改善，因為之前我們認為，這需要等到我們擁有這些全像顯示器才能成為一個大市場。現在，我們更加關注與雷朋 (Ray-Ban) 合作提供的眼鏡，我認為合作進展非常順利。而且，我認為，它有能力比我預期的更早成為一個非常有意義且不斷發展的平台。因此，正如我所說，現實實驗室的更多工作確實也集中在人工智慧目標上。但我仍然認為我們也應該專注於建立這些長期平台。

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

您的下一個問題來自 Truist Securities 的 Youssef Squali。

Mark, with the upcoming ban or sale of TikTok signed into law earlier today, how do you think that will impact the U.S. social media landscape? And then, in particular, what do you say to people who believe that this is potentially a slippery slope in terms of the government picking up -- picking winners and losers?

馬克，隨著今天早些時候即將禁止或出售 TikTok 成為法律，您認為這將如何影響美國社交媒體格局？然後，特別是，對於那些認為政府在選擇贏家和輸家方面可能會陷入滑坡的人，您有何看法？

And Susan, how big is Advantage+ in terms of the spend on the platform and just in terms of its impact on overall CPM stabilizing?

Susan，從平台上的支出以及對整體 CPM 穩定的影響來看，Advantage+ 有多大？

Thanks, Youssef. We've obviously been following the events related to TikTok closely, but at this stage, it is just too early, I think, to assess its impact or what it would mean for our business.

謝謝，尤瑟夫。顯然，我們一直在密切關注與 TikTok 相關的事件，但我認為，現階段評估其影響或對我們業務意味著什麼還為時過早。

To your second question on Advantage+, we're continuing to see good traction across our Advantage+ portfolio, including both with solutions, I mentioned this, that automate individual steps of a campaign creation setup as well as ones that automate the full end-to-end process. So on the single-step automation, Advantage+ audience, for example, has seen significant growth in adoption since we made it the default audience creation experience for most advertisers in Q4. And that enables advertisers to increase campaign performance by just using audience inputs as a suggestion rather than a hard constraint. And based on tests that we ran, campaigns using Advantage+ audience targeting saw, on average, a 28% decrease in cost per click or per objective compared to using our regular targeting.

關於您關於Advantage+ 的第二個問題，我們繼續看到我們的Advantage+ 產品組合具有良好的吸引力，包括我提到的解決方案，這些解決方案可以自動化行銷活動創建設定的各個步驟，以及自動化整個端到端的解決方案結束進程。因此，在單步驟自動化方面，例如，自從我們在第四季度將其設為大多數廣告商的預設受眾創建體驗以來，Advantage+ 受眾的採用率就出現了顯著增長。這使得廣告主能夠透過僅使用受眾輸入作為建議而不是硬性約束來提高廣告活動績效。根據我們執行的測試，與使用常規定位相比，使用 Advantage+ 受眾群體定位的行銷活動平均每次點擊或每個目標的成本降低了 28%。

On the end-to-end automation products like Advantage+ shopping and Advantage+ app campaigns, we're also seeing very strong growth. Mark mentioned the combined revenue flowing through those 2 has more than doubled since last year. And we think there's still significant runway to broaden adoption, so we're trying to enable more conversion types for Advantage+ shopping. In Q1, we began expanding the list of conversions that businesses could optimize for. So previously, it only supported purchase events, and now we've added 10 additional conversion types. And we're continuing to see strong adoption now across verticals.

在Advantage+購物和Advantage+應用程式行銷活動等端到端自動化產品上，我們也看到了非常強勁的成長。馬克提到，自去年以來，這兩家公司的總收入增加了一倍以上。我們認為，擴大採用範圍仍有很長的路要走，因此我們正在努力為 Advantage+ 購物提供更多轉換類型。在第一季度，我們開始擴大企業可以優化的轉換清單。之前，它僅支援購買事件，現在我們添加了 10 種額外的轉換類型。我們現在繼續看到各個垂直領域的大力採用。

So generally, I would say we are building a lot more functionality into the Advantage+ tools over time. also where a lot of our gen AI ads creative features have been introduced and where advertisers have the opportunity to experiment with those. And we'll keep looking to apply what we learn from these products more broadly to our ads investments over the course of the year.

所以總的來說，我想說，隨著時間的推移，我們正在 Advantage+ 工具中建立更多的功能。我們也引進了許多新一代人工智慧廣告創意功能，廣告主有機會嘗試這些功能。我們將繼續尋求將從這些產品中學到的知識更廣泛地應用到我們今年的廣告投資中。

Your next question comes from the line of Ken Gawrelski from Wells Fargo.

您的下一個問題來自富國銀行的 Ken Gawrelski。

As you look out through the coming period of product investment, how should we think about the relationship between Family of Apps revenue and cost growth? Is there any insight you can give us there?

當您展望未來一段時間的產品投資時，我們該如何思考應用家族收入與成本成長之間的關係？您能給我們一些見解嗎？

And then maybe just one that's a little bit more specific to the G&A growth in 1Q. You called out legal expenses. Just wanted to see if there's anything onetime in there that would cause the elevated growth in 1Q.

然後也許只是一個更具體的第一季的一般管理費用成長。你報了法律費用。只是想看看是否有什麼因素會導致第一季的成長加快。

Yes. On the second part of your question first, so on the G&A side, that was really driven by legal expenses. We recognized some accruals in Q1 related to ongoing legal matters, and you'll see more detail on that in the 10-Q.

是的。首先，關於你問題的第二部分，在一般行政費用方面，這實際上是由法律費用所驅動的。我們在第一季確認了一些與正在進行的法律事務相關的應計費用，您將在第十季中看到更多詳細資訊。

On the first part of your question, which is really about sort of the kind of long-term margin profile of Family of Apps, we aren't giving guidance on that per se. But one of the things that we really have been very disciplined about over the course of 2023 and continuing is really operating the business in a very efficiency oriented way. So we're being very disciplined with allocation of new resources. This is a muscle that we really built over 2023 that we believe is important for us to keep carrying forward. And I think you'll see us continue to emphasize that, especially with the Family of Apps business being at the scale that it is.

關於您問題的第一部分，這實際上是關於應用程式系列的長期利潤狀況，我們本身並沒有提供指導。但在 2023 年期間，我們真正嚴格遵守並持續執行的一件事是以非常注重效率的方式經營業務。因此，我們在分配新資源時非常嚴格。這是我們在 2023 年真正建立起來的力量，我們相信這對我們繼續發揚光大很重要。我想您會看到我們繼續強調這一點，特別是考慮到應用程式系列業務的規模如此之大。

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

你的下一個問題來自巴克萊銀行的羅斯桑德勒。

Great. Mark, you partnered with Google and Bing for Meta AI organic search citations. So I guess stepping back, do you think that Meta AI longer term could bring in search advertising dollars at some point? Or do you view this as what others are doing, where you kind of attach a premium subscription tier once people kind of get going on it?

偉大的。馬克，您與 Google 和 Bing 合作進行 Meta AI 自然搜尋引文。所以我想退一步來說，您認為 Meta AI 從長遠來看是否可以在某個時候帶來搜尋廣告收入？或者您認為這是其他人正在做的事情，一旦人們開始使用，您就會附加高級訂閱等級？

And then the second question is, you mentioned that you guys are working on building AI tools for businesses and creators. So just, I guess, how do you see the business model evolving when we all get to the stage of interacting with something like Taylor Swift's custom AI for merchandise or tickets or something like that. How is that going to play out?

第二個問題是，您提到你們正在致力於為企業和創作者建立人工智慧工具。所以，我想，當我們都進入與泰勒·斯威夫特（Taylor Swift）的商品或門票等定制人工智慧進行互動的階段時，您如何看待商業模式的演變。結果會如何呢？

All right. So yes, on the Google and Microsoft partnerships, yes, I mean we work with them to have real-time information in Meta AI. It's useful. I think it's pretty different from search. We're not working on search ads or anything like that. I think this will end up being a pretty different business.

好的。所以，是的，關於谷歌和微軟的合作夥伴關係，是的，我的意思是我們與他們合作，在 Meta AI 中獲取即時資訊。這很有用。我認為這與搜索有很大不同。我們不致力於搜尋廣告或類似的事情。我認為這最終將成為一項完全不同的業務。

I do think that there will be an ability to have ads and paid content in Meta AI interactions over time as well as people being able to pay for whether it's bigger models or more compute or some of the premium features and things like that. But that's all very early in fleshing out.

我確實認為，隨著時間的推移，元人工智慧互動中將有能力提供廣告和付費內容，並且人們能夠為更大的模型、更多的計算或一些高級功能等付費。但這一切都還處於充實階段。

The thing that I actually think is probably -- the biggest clear opportunity is all the work around business messaging. That's in addition to the stuff that we're already doing, just generate to increase engagement and ads quality in the apps. But business messaging thing, I mean, whether it's a creator or one of the 100-plus million businesses on our platform, we basically want to make it very easy for all of these folks to set up an AI to engage with their community. For a business, that's going to be able to do sales and commerce and customer support. And I think it will be similar for creators, although there will be more of a kind of just fun and engaging part there, but a lot of creators are on the platform because they see this as a business too, whether they're trying to sell concert tickets or products or whatever it is that their business goal is.

我實際上認為，最大的明顯機會可能是圍繞業務訊息的所有工作。這是我們已經在做的事情的補充，只是為了提高應用程式中的參與度和廣告品質。但在商業訊息傳遞方面，我的意思是，無論是創作者還是我們平台上的1 億多企業之一，我們基本上都希望讓所有這些人都能輕鬆地設定人工智慧來與他們的社群互動。對於企業來說，這將能夠進行銷售、商務和客戶支援。我認為這對創作者來說也是類似的，儘管那裡會有更多有趣和吸引人的部分，但許多創作者都在這個平台上，因為他們也將這視為一項業務，無論他們是否想嘗試銷售音樂會門票或產品或任何他們的業務目標。

And a lot of these folks either aren't advertising as much as they could or, in business, the business messaging parts, I think, are still relatively undermonetized compared to where they will be. And I think a lot of that is because the cost of engaging with people in messaging is still very high. But AI should bring that down just dramatically for businesses and creators. And I think that, that has the potential. That's probably the -- beyond just increasing engagement and increasing the quality of the ads, I think that, that's probably one of the nearer-term opportunities, even though that will -- it's not like next quarter or the quarter after that scaling thing, but it's -- but that's not like a 5-year opportunity either.

其中許多人要么沒有盡其所能地做廣告，要么在商業領域，我認為，與他們將要達到的水平相比，商業消息部分的貨幣化仍然相對不足。我認為這很大程度上是因為在訊息中與人們互動的成本仍然非常高。但人工智慧應該會大幅降低企業和創作者的這種情況。我認為，這有潛力。這可能是 - 除了增加參與度和提高廣告品質之外，我認為，這可能是近期的機會之一，儘管這會 - 它不像下個季度或擴大規模之後的季度，但這也不像五年的機會。

So I think -- that is one that I think is going to be pretty exciting to look at. But yes, I mean, as Meta AI scales too, I think that, that will have its own opportunities to monetize, and we'll build that out over time. But like I tried to emphasize, we're in the phase of this where the main goal is getting many hundreds of millions or billions of people to use Meta AI as a core part of what they do. That's the kind of next goal, building something that is super valuable. We think this has the potential to be at a very large scale. And that's sort of the next step on the journey.

所以我認為——我認為這將是一件非常令人興奮的事情。但是，是的，我的意思是，隨著 Meta AI 的擴展，我認為這將有其自身的貨幣化機會，我們將隨著時間的推移而建立它。但正如我試圖強調的那樣，我們正處於這個階段，主要目標是讓數億或數十億人使用元人工智慧作為他們工作的核心部分。這就是下一個目標，建立超級有價值的東西。我們認為這有可能達到非常大的規模。這就是旅程的下一步。

Krista, we have time for one last question.

克里斯塔，我們有時間回答最後一個問題。

And that question comes from the line of Ron Josey from Citi.

這個問題來自花旗銀行的 Ron Josey。

Mark, I want to follow up on a prior question that you mentioned optimism has grown internally quite a bit just with all the improvements and investments and innovations you're making. And we're seeing that in the experience for a few days of Meta AI. So can you just talk to us maybe how the $400 billion parameter model just might evolve the experience on Meta or how you think things might change over the next, call it, months, years, et cetera, as maybe messaging becomes a greater focus and things along those lines? So just a vision longer term.

馬克，我想繼續回答您之前提到的一個問題，您提到，隨著您所做的所有改進、投資和創新，樂觀情緒在內部已經增長了不少。我們在 Meta AI 幾天的體驗中看到了這一點。那麼，您能否與我們談談4000 億美元的參數模型將如何改進Meta 上的體驗，或者您認為接下來的情況可能會發生怎樣的變化，稱之為幾個月、幾年等等，因為也許訊息傳遞會成為一個更大的焦點，事情是這樣的嗎？所以只是一個更長遠的願景。

Yes. I mean I think that the next phase for a lot of these things are handling more complex tasks and becoming more like agents rather than just chat bots, right? So when I say chatbot, what I mean is you send it a message and it replies to your message, right? So it's almost like almost a 1:1 correspondence.

是的。我的意思是，我認為許多這些東西的下一階段是處理更複雜的任務，並變得更像代理而不僅僅是聊天機器人，對嗎？所以當我說聊天機器人時，我的意思是你向它發送一條訊息，然後它回覆你的訊息，對吧？所以這幾乎就像是一一對應。

Whereas what an agent is going to do is you give it an intent or a goal, then it goes off and probably actually performs many queries on its own in the background in order to help accomplish your goal, whether that goal is researching something online or eventually finding the right thing that you're looking to buy. There's a lot of complexity and sort of different things. I think people don't even realize that they will be able to ask computers to do for them.

而代理要做的就是你給它一個意圖或目標，然後它就會啟動並可能實際上在後台自行執行許多查詢，以幫助實現你的目標，無論該目標是在線研究某些東西還是最終找到您想要購買的正確物品。有很多複雜性和不同的東西。我認為人們甚至沒有意識到他們將能夠要求電腦為他們做事。

And I think basically, the larger models and then the more advanced future versions that will be smaller as well are just going to enable much more interesting interactions like that. So I mean if you think about this, I mean, even some of the business use cases that we talked about, you don't really just want like sales or customer support chatbot that can just respond to what you say. And if you're a business, you have a goal, right? You're trying to support your customers well and you're trying to position your products in a certain way and encourage people to buy certain things that map to their interests and would they be interested in? And that's more of like a multiturn interaction, right?

我認為基本上，更大的模型以及更先進的更小的未來版本只會實現更有趣的互動。所以我的意思是，如果你考慮這一點，我的意思是，即使是我們討論的一些業務用例，你實際上並不只是想要像銷售或客戶支援聊天機器人那樣可以只響應你所說的內容。如果你是一家企業，你就有一個目標，對吧？您正在努力為您的客戶提供良好的支持，並嘗試以某種方式定位您的產品，並鼓勵人們購買符合他們興趣的某些東西，他們會感興趣嗎？這更像是多輪交互，對吧？

So the type of business agent that you're going to be able to enable with just a chatbot is going to be very naive compared to what we're going to have in a year even, but beyond that, too, is just the reasoning and planning abilities if these things grow to be able to just help guide people through the business process of engaging with whatever your goals are as a creator of a business.

因此，與我們一年內將擁有的業務代理類型相比，僅通過聊天機器人即可啟用的業務代理類型將非常幼稚，但除此之外，這也只是推理以及規劃能力，如果這些事情發展到能夠幫助指導人們完成業務流程，實現您作為企業創建者的任何目標。

So I think that that's going to be extremely powerful. And I think the opportunity is really big. So -- and on top of that, I think what we've shown now is that we have the ability to build leading models in our company. So I think it makes sense to go for it, and we're going to. And I think it's going to be a really good long-term investment. But I did just want to spell out on this call today, the extent to which we're focusing on this and investing in this for the long term because that's what we do.

所以我認為這將非常強大。我認為機會真的很大。因此，最重要的是，我認為我們現在所展示的是我們有能力在我們的公司建立領先的模型。所以我認為這樣做是有意義的，我們也會這樣做。我認為這將是一項非常好的長期投資。但我只是想在今天的電話會議上闡明我們對此的關注程度以及長期投資的程度，因為這就是我們所做的。

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

偉大的。感謝您今天加入我們。感謝您抽出寶貴的時間，我們期待很快再次與您交談。

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，您現在可以斷開連接。