Alphabet 2026 年第三季度财报：AI 势头与 Gemini 4

欢迎大家。感谢各位参加 Alphabet 2026 年第三季度财报电话会议。目前所有参会者均处于只听模式。在发言人陈述之后将进行问答环节。如需在会议期间提问，请在电话上按星号键 1。现在我将把会议交给今天的发言人、投资者关系主管 Jim Friedland。请开始。

Welcome everyone. Thank you for standing by for the Alphabet third quarter 2026 earnings conference call. At this time all participants are in a listen-only mode. After the speaker presentation there will be a question and answer session. To ask a question during the session, you will need to press star one on your telephone. I would now like to hand the conference over to your speaker today, Jim Friedland, Head of Investor Relations. Please go ahead.

谢谢。大家下午好，欢迎参加 Alphabet 2026 年第三季度财报电话会议。今天出席会议的有 Sundar Pichai、Philip Schindler、Thomas Kurian 和 Anat Ashkenazi。现在我先快速说明安全港声明。我们今天关于业务运营和财务业绩的一些陈述可能被视为前瞻性陈述。此类陈述基于当前预期和假设，并受到诸多风险和不确定性的影响。实际结果可能大不相同。请参阅我们的 10-K 和 10-Q 表格，包括其中的风险因素，我们不承担更新任何前瞻性陈述的义务。在本次会议中，我们将同时呈现 GAAP 和非 GAAP 财务指标。非 GAAP 与 GAAP 指标的调节表包含在今天的财报新闻稿中，该新闻稿已通过我们的投资者关系网站分发并向公众提供。除非另有说明，我们的评论将基于同比比较。现在我将把会议交给 Sundar。

Thank you. Good afternoon everyone and welcome to Alphabet’s third quarter 2026 earnings conference call. With us today are Sundar Pichai, Philip Schindler, Thomas Kurian and Anat Ashkenazi. Now I’ll quickly cover the safe harbor. Some of the statements that we make today regarding our business operations and financial performance may be considered forward-looking. Such statements are based on current expectations and assumptions that are subject to a number of risks and uncertainties. Actual results could differ materially. Please refer to our Forms 10-K and 10-Q, including the risk factors, we undertake no obligation to update any forward-looking statements. During this call we will present both GAAP and non-GAAP financial measures. A reconciliation of non-GAAP to GAAP measures is included in today’s earnings press release, which is distributed and available to the public through our investor relations website. Our comments will be on year-over-year comparisons unless we state otherwise. And now I’ll turn the call over to Sundar.

谢谢 Jim，大家好。第三季度对 Alphabet 来说是强劲的一个季度。我们深度的全栈式方法正在推动整个业务的势头，前方还有许多机会。让我快速概述一下本季度的情况。Alphabet 营收同比增长 25%。我们现在已达到 5000 亿美元的年化运行率。这非常了不起。Google 搜索势头强劲。我们的 AI 投入正在推动使用量和整体查询量的增长，包括商业查询。事实上，我们的查询量同比增长率是过去五年来最高的。Google Cloud 营收增长了 103%。我们的 AI 基础设施和解决方案是云业务增长的主要驱动力，客户视我们为这里的领先提供商。所以需求巨大。我们正在努力上线更多容量，以满足这一长期需求。与此同时，我们看到各类客户对我们的 TPU 系统有着强劲需求，随着我们扩大规模，我们预计这一趋势将持续。

Thanks Jim and hello everyone. Q3 was a strong quarter for Alphabet. Our deep full-stack approach is driving momentum across the business with many opportunities ahead. Let me give you a quick snapshot of the quarter. Alphabet revenues grew 25% year over year. We are now at a half a trillion dollar annual run rate. That's remarkable. Google Search has strong momentum. Our AI investments are driving increased usage and overall query growth, including commercial queries. In fact, our year over year query growth rate is the highest it's been in the last five years. Google Cloud revenues grew 103%. Our AI infrastructure and solutions are the major drivers for cloud growth and our customers see us as the leading provider here. So there's huge demand. We are working hard to bring more capacity online to meet this long term demand. At the same time, we are seeing strong demand for our TPU systems across a wide range of customers and we expect that to continue as we scale.

我很高兴欢迎 Thomas 参加本次电话会，他将进一步介绍我们 AI 模型的势头。客户比以往任何时候都更多地使用我们的模型。我们的第一方模型 API 现在每分钟处理近 320 亿个 token，比上一季度多 40%，是一年前处理量的四倍多。凭借 Gemini 4、Argon 以及我们今天宣布的 Carbon，我们很高兴能引领前沿。Gemini 应用现在拥有 10 亿用户，是 Google 历史上增长最快的产品。最后，我们在全球的 YouTube、Music 和 Premium 订阅用户（包括试用用户）已超过 1.5 亿。

I'm excited to welcome Thomas to the call and he'll share more about its momentum on our AI models. Customers are using our models more than ever. Our first party model APIs are now processing nearly 32 billion tokens per minute, 40% more than last quarter and more than four times what we processed a year ago. And with Gemini 4, Argon and now Carbon, which we announced today, we are excited to be leading at the frontier. The Gemini app is now at 1 billion users and is the fastest growing product in Google's history. Lastly, we have surpassed 150 million YouTube, Music and Premium subscribers globally, including trials.

今天我将更多地谈谈我们的 AI 进展、以搜索和 YouTube 为首的全球消费者平台，然后是我们的长期押注，从 AI 开始，重点是我们 Gemini 4 系列前沿模型。我们看到两周前推出的 Argon 有着强劲的需求和使用量。事实上，在我们的开发者平台上，第一周的使用量增长速度比 Gemini 3 快 3.5 倍。即使存在容量限制，Argon 在金融、法律、网络安全和科学等领域也表现非常出色。开发者和云客户已经在用它来做诸如梳理遗留代码库、加速科学研究、机器人技术、发现产品改进以及需要深度推理的特定领域任务。而在 Google，就在过去几周，Chrome、广告和搜索团队已经使用 Argon 更快地编写和优化代码，交付的代码量增加了 35%，错误减少了 30%。

Today I'll talk more about our AI progress, our global consumer platforms led by Search and YouTube then our long term bets starting with AI highlighted by our Gemini 4 series of frontier models. We are seeing strong demand and usage for Argon, which we launched two weeks ago. In fact, on our developer platforms, usage in the first week ramped up three and a half times faster than Gemini 3. Even with capacity constraints, Argon is proving to do really well in areas like finance, legal, cybersecurity and science. Developers and cloud customers are already using it for things like untangling legacy code bases, accelerating scientific research, robotics, discovering product improvements and domain specific tasks requiring in depth reasoning. And at Google just in the last few weeks, teams across Chrome, ads and Search have used Argon to write and optimize code much faster, shipping 35% more code with 30% fewer errors.

Argon 只是 Gemini 4 系列的开始。今天我们宣布了 Carbon。它是我们最强的前沿模型，具备最先进的网络安全能力和长时程能力。它在最近更新的 Artificial Analysis Intelligence Index 上以 59 分排名第一。Carbon 在几乎所有领域都优于 Argon，例如全栈 Web 开发、多模态理解、复杂知识工作和科学推理。我们首先将 Carbon 开放给一小部分受信任的网络防御者，然后再扩大访问范围，目前它正在接受美国政府的测试。除了 Gemini 4 系列之外，昨天我们宣布了 Gen 4，这是我们迄今为止最先进的世界模型，能够实时模拟安全、逼真的交互式世界并带有原生音频。这是训练机器人和智能体的重要构建模块。这一快速进展凸显了我们惊人的模型势头。我们已经到了 AI 帮助我们提升训练和发布速度的阶段。由于 RSI，我预计未来 12 个月将持续加速。我们很快会推出更多模型，包括更高效的版本，帮助开发者优化速度和成本，并部署到我们的数十亿用户中。

Argon is just the start of the Gemini 4 series. Today we announced Carbon. It's our strongest frontier model with state of the art cybersecurity and long horizon capabilities. It ranks number one on the recently updated Artificial Analysis Intelligence Index at 59. Carbon outperforms Argon across almost every domain like full stack web development, multimodal understanding, complex knowledge work and scientific reasoning. We are rolling Carbon out first to a subset of trusted cyber defenders before we expand access and it's being tested by the US government. Beyond our Gemini 4 series, yesterday we announced Gen 4, our most advanced world model yet that simulates safe, realistic interactive worlds with native audio in real time. This is an important building block for training robots and agents. This rapid progress underscores our incredible model momentum. We are at the point where AI is helping us increase our train and launch velocity. I would expect the next 12 months to see continued acceleration because of RSI. We have more models coming soon, including more efficient versions to help developers optimize for speed and cost and to deploy across our billions of users.

接下来谈谈搜索，它正在将 AI 的好处带给世界上比任何其他产品都多的人。就像十多年前我们向移动端转型一样，我们专注于打造最佳的搜索用户体验，然后再进行变现。我们正在 AI Overviews、AI Mode 和传统搜索之间构建无缝体验，人们更多地使用我们的 AI 功能，以全新的方式搜索。他们的查询更长、更个性化、更多模态。AI Mode 中的查询平均比传统搜索长三倍，全球每五次 AI Mode 搜索中就有一次使用语音、图像或文档。我们现在每月视觉搜索量已超过 500 亿次。

Let's turn to Search, which is bringing the benefits of AI to more people than any other product in the world. Just like our shift to mobile over a decade ago, we are focused on building the best Search user experience followed by monetization. We are building one seamless experience across AI Overviews, AI Mode and traditional Search, and people are using our AI features more, searching in entirely new ways. Their queries are longer, personalized and multimodal. Queries in AI Mode are on average three times longer than traditional searches and one in five AI Mode searches globally use voice, images or documents. We have now crossed over 50 billion visual searches per month.

我感到兴奋的一个领域是帮助人们把事情办成，无论是预订、填写表格、安排预约还是购物。随着 AI 变得更加个性化并开始为个人和企业采取行动，信任和安全比以往任何时候都更加重要。人们已经信任 Google 来帮助他们完成日常生活中的事情，我们在这方面的领导地位仍然是一个重要的差异化优势。我们首先专注于通过 Gemini Agent for Enterprise 把这些智能体式体验做好，因为安全、保障和治理的标准极高。下一步，我们将把这些功能引入搜索，当然也会带给 Gemini 应用上的十亿用户。人们已经在使用 Gemini 应用做的不只是提问。他们用它来做事，包括复杂的多步骤智能体查询。我们的 Gemini 4 系列在这方面迈出了一大步，已经很好地处理了这些复杂任务。智能体将作为统一层贯穿我们的产品，因此你会看到我们将这些智能体式体验从搜索和 Gemini 应用扩展到 Chrome、Android 以及我们的 Workspace 产品。

One area I'm excited about is helping people get things done, be it booking reservations, filling in forms, scheduling appointments or making purchases. As AI becomes more personal and starts taking action for people and businesses, trust and security matter more than ever. People already put their trust in Google to get things done in their daily lives and our leadership here remains an important differentiator. We focus first on getting these agentic experiences right with the Gemini Agent for Enterprise because the bar for security, safety and governance is extremely high. As a next step, we are bringing these features to Search and also of course to our billion users on the Gemini app. People are already using the Gemini app to do more than ask questions. They're using it to do things including complex multi step agent queries. Our Gemini 4 series has been a really big step forward here and has already been handling these complex tasks really well. Agents will operate as a unifying layer across our products, so you will see us extend these agentic experiences beyond Search in the Gemini app to Chrome, Android and our Workspace products as well.

接下来是 YouTube，它继续在客厅场景中领先，按观看时长计算是美国排名第一的流媒体平台。它的订阅用户增长也很好。正如我上个月在 Made on YouTube 上提到的，我们为创作者和观众宣布了 30 多项产品和功能。一个很好的例子是自定义信息流，你可以直接选择一个你喜欢的主题，然后获得基于该主题量身定制的视频首页信息流。Philip 会进一步介绍 YouTube。

On to YouTube, which continues to lead in the living room as the number one streaming platform in the US in terms of watch time. It's also seeing good subscriber growth. As I mentioned at Made on YouTube last month, we announced more than 30 products and features for creators and viewers. One great example is custom feeds, where you can simply pick a topic you love and get a tailored video home feed based on that topic. Philip will say more on YouTube.

接下来是我们的长期押注，Waymo 正处于很好的发展轨道上。我们现在已经行驶了超过 3 亿英里的完全自动驾驶里程。Waymo 目前在美国 15 个主要城市运营，最近在圣安东尼奥、圣迭戈和坦帕向公众开放了服务，并计划扩展到加拿大、德国、新加坡和西班牙。我们的其他长期计划也取得了强劲进展。Isomorphic 正在使用 Argon 支持药物设计研究。我们在量子计算中首次展示了魔法态培育，并通过 Project Suncatcher 将我们的 TPU 送入太空。所以这是一个忙碌的季度，还有更多值得期待。我要感谢员工们本季度的辛勤工作，接下来交给

On to our long term bets, Waymo is on a great trajectory. We have now driven more than 300 million fully autonomous miles. Waymo now operates in 15 major US cities and just recently opened up service to the public in San Antonio, San Diego and Tampa, with planned expansions into Canada, Germany, Singapore and Spain. We are also seeing strong progress across our other long term initiatives. Isomorphic is using Argon to support drug design research. We demonstrated magic state cultivation for the first time in quantum computing and launched our TPUs into space with Project Suncatcher. So it's been a busy quarter and there's much more to come. I want to thank our employees for their work this quarter and over to

谢谢 Sundar，大家好。我将重点介绍我们在搜索、YouTube 和合作伙伴关系方面的势头。我们继续加速在整個广告基础设施中部署 Gemini 模型，以提升我此前强调的三个领域的表现：广告质量、广告主工具和 AI 用户体验。

Thanks Sundar, and hello everyone. I'll highlight our momentum across Search, YouTube, and partnerships. We continue to accelerate the deployment of Gemini models across our entire ads infrastructure to boost performance in the three areas I've highlighted before: ads quality, advertiser tools, and AI user experiences.

第一，广告质量。将 Gemini 模型整合进我们的广告基础设施，正在改善我们解读用户意图和提升广告相关性的方式。例如，在第三季度，我们对产品列表广告进行了十年来最大的一次更新，加入新的 AI 能力，帮助用户更有信心地做出购买决策。目前，满意度已提升 19%，不满意度下降超过一半。

First, ads quality. The integration of Gemini models into our ads infrastructure is improving how we decode user intent and drive ads relevance. For example, in Q3 we made the biggest update to product listing ads in a decade, adding new AI capabilities to help users make more confident purchase decisions. Already, we've seen satisfaction increase by 19% and dissatisfaction drop more than half.

我们还看到，AI 模型能够更好地预测点击和转化、带来更强的整体表现，并实现更深入的广告个性化。例如，通过个性化建模方面的多项创新，我们在 2026 年为广告主带来了 18% 的增量价值，并使 Discover 广告的用户情绪提升了 8%。总体而言，AI 正在加速我们的广告开发，并驱动了我们大多数的产品发布。

We're also seeing AI models enable better prediction of clicks and conversions, stronger overall performance, and deeper personalization of ads. For instance, through several innovations in personalization modeling, we delivered 18% incremental advertiser value and an 8% increase in user sentiment on Discover ads in 2026. Overall, AI is accelerating our ads development and drives the majority of our product launches.

第二，广告主工具。我们正在确保每一位广告主，从全球品牌到小型企业，都能无缝参与我们的 AI 服务。AI Max 和 Performance Max 的采用率持续强劲。为了让营销人员拥有更多控制权，我们现在推出一种方式，可以在单个 AI Max 中跨多个搜索广告系列测试不同的预算和 ROI 目标。

Second, advertiser tools. We're ensuring every advertiser, from global brands to small businesses, can seamlessly participate in our AI services. AI Max and Performance Max continue to see strong adoption. To give marketers even more control, we're now introducing a way to test different budgets and ROI targets across multiple Search campaigns in a single AI Max.

为了推动更好的表现，我们将 AI 创意工具直接整合进了 AI Max。仅第三季度，Gemini 模型就帮助广告主生成了超过 15 亿个广告系列素材，同比增长超过 7 倍。例如，通过使用 AI Max 捕捉长文本和多语言查询，Telstra 的转化量提升了 52%，单笔订单成本下降了 36%，无论客户如何搜索，都能展示关键时刻。

To drive better performance, we integrated our AI creative tools directly into AI Max. In Q3 alone, Gemini models helped advertisers generate over 1.5 billion campaign assets, up over 7x year over year. For example, by using AI Max to capture long-form and multilingual queries, Telstra saw a 52% increase in conversions and a 36% reduction in cost per order, showcasing key moments however their customers search.

第三，AI 用户体验。Sundar 提到了随着用户提出全新类别的问题，我们所看到的明显行为变化。我们以整体视角看待搜索——一个统一的产品，涵盖传统结果和 AI 驱动的搜索或 AI Mode。其中许多查询属于上层漏斗查询，使我们能够捕捉新的高价值商业意图。此外，AI Mode 的大多数查询是增量性的，随着时间推移创造了更多变现机会。

Third, AI user experiences. Sundar mentioned the distinct behavioral changes we're seeing as users ask entirely new categories of questions. We think of Search holistically—a unified product spanning traditional results and AI-powered Search or AI Mode. Many of these queries are upper-funnel queries, allowing us to capture new high-value commercial intent. In addition, the majority of AI Mode queries are incremental, creating more monetization opportunities over time.

此外，我们的用户研究表明，人们对这些界面上的广告感到舒适，并认为它们高度相关。在 AI Mode 内，赞助产品和自然产品列表项的点击率已经相近。基于这些综合结果，我们现在正在将 AI Mode 中 AI Overviews 内的高质量广告扩展到更多用户和查询。这包括首次将我们的变现版图推向全球。

Additionally, our user research shows that people are comfortable with ads on these surfaces and find them highly relevant. Click-through rates are already similar between sponsored and organic product list items within AI Mode. Based on these collective results, we are now scaling high-quality ads within AI Overviews in AI Mode to more users and queries. This includes taking our monetization footprint global for the first time.

今天，我们宣布将 AI Mode 中的广告扩展到国际市场，同时在美国推出 AI Mode 中的多轮广告。一如既往，我们的方法是审慎的——优先考虑用户体验和参与度，然后再扩展那些为广告主带来经过验证的 ROI 的格式。这种纪律为我们未来奠定了强大而持久的基础。

Today we're announcing the expansion of ads in AI Mode into international markets, alongside multi-turn ads in AI Mode in the U.S. As always, our approach is deliberate—prioritizing the user experience and engagement first, and then scaling the formats that drive proven ROI for advertisers. That discipline is what gives us a strong, durable foundation for the future.

正如 Sundar 所提到的，我们正处于各服务中智能体式体验的早期阶段，并对未来的商业机会感到兴奋。Universal Commerce Protocol（UCP）目前正被零售商迅速采用。UCP 为 Google 上数十万个品牌提供直接结账功能，包括 Chewy、Nike、Tapestry 等众多品牌。我们正将其与 Agent Payments Protocol 结合，为商业的未来而构建。

As Sundar mentioned, we're in the early stages of agentic experiences across our services and excited about the commercial opportunities ahead. The Universal Commerce Protocol, UCP, is being rapidly adopted by retailers today. UCP enables direct checkout for hundreds of thousands of brands across Google, including Chewy, Nike, Tapestry, and many more. We're combining this with the Agent Payments Protocol as we build for the future of commerce.

我们的重点横跨两个方面：释放代表消费者行事的智能体式能力，以及设计实用工具，让购物者拥有信心和控制权。我们在本季度推出了 Universal Cart 以支持第二条路线，反映人们今天在 Google 上实际的购物方式。这使零售商能够维持与客户的关系，同时帮助捕获销售并避免购物车被放弃。

Our focus spans two fronts: unlocking agentic capabilities that act on behalf of the consumer, and designing practical tools that give shoppers confidence and control. We launched Universal Cart this quarter to support that second track, mirroring how people actually shop today across Google. This allows retailers to maintain their relationships with customers while helping to capture sales and avoid abandoned carts.

接下来是 YouTube。我们将 YouTube 视为创作者、观众和广告主的飞轮。首先是创作者。每天有超过 2000 万个视频被上传。我们正通过 YouTube Studio 中新的生成式 AI 工具，以及 Shorts 和 YouTube 中的对话式编辑助手，让创作变得更轻松。

Moving to YouTube, we view YouTube as a flywheel of creators, viewers, and advertisers. First, creators. Every day, more than 20 million videos are being uploaded. We are making creation easier with new generative AI tools in YouTube Studio, plus a conversational editing assistant in Shorts and YouTube.

在观看方面，在 Made on YouTube 活动上，我们推出了 Shorts 系列，创作者可以将他们的 Shorts 组织成季和集。顺序反馈让观众能够无缝发现新的 Shorts 并持续观看。

Looking at viewership, at Made on YouTube, we introduced Shorts series where creators can structure their Shorts into seasons and episodes. Sequential feedback makes it seamless for viewers to discover new Shorts and keep watching.

第三部分是变现。从广告开始，我们的重点仍然是覆盖整个漏斗交付价值。在第三季度，客厅场景占 YouTube 广告总收入的超过三分之一。中小广告主继续采用 Demand Gen，推动 YouTube 上的直接响应。Wayfair 的 Demand Gen 广告系列相比传统视频触达广告系列，广告支出回报率高出 2.3 倍。

The third part is monetization. Starting with ads, our focus remains on delivering value across the entire funnel. In Q3, living room accounted for over one-third of total YouTube ads revenues. Small and medium advertisers continue to adopt Demand Gen, driving direct response on YouTube. Wayfair's Demand Gen campaigns drove a 2.3 times higher return on ad spend compared with traditional video reach campaigns.

我们的 YouTube Creator Partnerships 项目将品牌与超过 300 万名创作者连接起来，推动高效的广告系列。像 Lexus 这样的品牌继续与 Colin 和 Samir 等创作者合作，推动品牌考虑度提升 11%。全球有超过 130 万个频道加入了 YouTube Shopping。在此基础上，我们最近宣布 Amazon 加入美国 YouTube Shopping。创作者现在可以在他们的视频、Shorts 和直播中直接标记 Amazon 产品，推动直接转化。

Our YouTube Creator Partnerships program connects brands with over 3 million creators to drive highly effective campaigns. Brands like Lexus continue to partner with creators like Colin and Samir, driving an 11% lift in brand consideration. More than 1.3 million channels are enrolled in YouTube Shopping globally. Building on this momentum, we recently announced that Amazon is joining YouTube Shopping U.S. Creators can now tag Amazon products directly in their videos, Shorts, and live streams, driving direct conversions.

重要的是，我们的订阅生态系统继续补充我们的广告业务。订阅增长继续快于广告，由 YouTube Music Premium 引领。YouTube Premium 拥有超过 1.5 亿订阅用户。通过 Premium 和 Premium Lite，观众拥有选择权。随着更多人选择高级体验，这对观众、创作者和我们的业务都是共赢——订阅帮助为创作者生态系统提供资金，并推动平台向前发展。

Importantly, our subscription ecosystem continues to complement our advertising business. Subscriptions continue to grow faster than ads, led by YouTube Music Premium. YouTube Premium has more than 150 million subscribers. With Premium and Premium Lite, viewers have choice. As more people opt into premium experiences, it's a win for viewers, creators, and our business—subscriptions help fund the creator ecosystem and drive the platform forward.

最后，战略合作伙伴关系。企业选择 Google，是因为我们将云、AI、广告和消费者平台结合在一起，加速他们的转型。我们扩大了与 American Express 的合作，支持其联络中心的持续现代化，并在搜索和钱包领域就智能体式商务展开合作。Unity 签署了一项扩大的多年期云协议，使用我们的完整 AI 技术栈为下一代游戏引擎提供动力。

Finally, strategic partnerships. Businesses are choosing Google because we bring together Cloud, AI, ads, and consumer platforms to accelerate their transformation. We expanded our partnership with American Express to support the ongoing modernization of their contact centers and collaborate on agentic commerce across Search and Wallet. And Unity signed an expanded multi-year Cloud deal to use our full AI stack to power the next-generation game engine.

我们的商业优先事项很明确：扩大广告主 ROI，系统性地提升我们的 AI 变现效率，并扩展我们的生态系统合作伙伴关系，以支持下一个发现时代。最后，我要感谢各地 Google 员工的出色执行，以及客户和合作伙伴的持续信任。Thomas，交给你了。

Our commercial priorities are clear: expanding advertiser ROI, systematically advancing our AI monetization efficiency, and scaling our ecosystem partnerships to support the next era of discovery. In closing, I want to thank Googlers everywhere for the incredible execution and our customers and partners for their continued trust. Thomas, over to you.

我们强劲的势头得益于我们集成的 AI 产品组合——芯片、模型、数据安全和智能体平台——所有这些都设计为协同工作——并在我们的产品线、行业和客户中实现了强大的多元化。Google Cloud 现在拥有 18 条产品线，每条年收入均超过 10 亿美元，本季度的增长由 Google Cloud Platform 的强劲增长推动，AI 和核心 GCP 均表现强劲。TPU 系统销售也是一个重要的贡献因素。

Our strong momentum is driven by our integrated AI portfolio of chips, models, data security, and agent platforms—all designed to work together—with strong diversification across our product lines, industries, and customers. Google Cloud now has 18 product lines with more than $1 billion each in annual revenue, and our growth this quarter was driven by strong growth in Google Cloud Platform, with strength in both AI and core GCP. TPU system sales were also a meaningful contributor.

在过去 12 个月中，超过 2,600 家客户在 Gemini Enterprise 上至少使用了 1000 亿个 token，近 75 家客户至少使用了 10 万亿个 token。近 90% 的财富 100 强企业使用 Gemini Enterprise，2025 年第三季度使用 Gemini Enterprise 的客户在我们这里的支出同比翻了两番多。

Over the past 12 months, more than 2,600 customers have used at least 100 billion tokens, and nearly 75 at least 10 trillion tokens on Gemini Enterprise. Nearly 90% of the Fortune 100 use Gemini Enterprise, and customers who used Gemini Enterprise in Q3 2025 more than quadrupled spend with us year over year.

客户使用 Gemini 的重要方式之一是知识工作。在我们所有的 AI 服务中，我们已经售出了超过 1 亿个知识工作席位。Gemini Enterprise 现在包括 Gemini Agent，它深度集成在我们整个 Cloud 产品组合中。它可以研究并回答问题、编写和运行代码，并自动化流程。通过一个智能体、一个 API 和一个单一的提示窗口，它为企业提供了强大的安全和成本控制，以及最广泛的前沿模型选择。

One of the important ways in which customers are using Gemini is for knowledge work. Across all our AI services, we have sold more than 100 million seats for knowledge work. Gemini Enterprise now includes the Gemini Agent that is deeply integrated across our entire Cloud portfolio. It can research and answer questions, write and run code, and automate process flows. With one agent, one API, and a single prompt window, it gives companies strong security and cost controls and the widest choice of frontier models.

客户包括法国巴黎银行，该银行正在向超过 65,000 名员工部署 Gemini Enterprise，以简化工作流程，同时保持严格的企业级安全和数据治理；塔塔钢铁，在短短九个月内部署了 300 多个专业 AI 智能体，以提高制造安全性、优化供应链，并将客户服务时间缩短了 50%；Ulta Beauty，构建了 Ulta AI 购物助手，其转化率比其网站上的传统浏览高出 3 倍。

Customers include BNP Paribas, who is deploying Gemini Enterprise to more than 65,000 employees to streamline workflows while maintaining strict enterprise-grade security and data governance; Tata Steel, who deployed more than 300 specialized AI agents in just nine months to enhance manufacturing safety, optimize supply chains, and cut service times for customers by 50%; Ulta Beauty, who built the Ulta AI shopping assistant, resulting in a 3x higher conversion rate than traditional browsing on their site.

Gemini 也在推动我们的数据库、分析和网络安全产品加速发展。Gemini 现在可以帮助用户从结构化和非结构化数据中查找、分析和可视化信息。Gemini 使用我们的 Knowledge Catalog 来找到正确的信息来源和定义，提高准确性、降低 token 成本，并使响应更可靠。Gemini 使用我们的 Lakehouse 以零拷贝方式访问来自多个系统的信息，以响应智能体式分析。BigQuery 的查询速度同比提高了 40% 以上，同时查询处理成本同比降低了 50% 以上。

Gemini is also driving acceleration for our database, analytics, and cybersecurity products. Gemini can now help users find, analyze, and visualize information from structured and unstructured data. Gemini uses our Knowledge Catalog to find the right sources and definitions of information, improving accuracy, lowering token cost, and making responses more reliable. Gemini uses our Lakehouse to access information from multiple systems with zero copy to respond to agentic analysis. BigQuery improved query speed over 40% year over year while reducing query processing cost by over 50% year over year.

客户包括德国电信，该公司使用 BigQuery、Cloud Storage 和 Knowledge Catalog 在 40 多个传统本地系统中构建统一的数据湖仓；Etisalat，通过使用 BigQuery 和 Iceberg 将 3 PB 数据迁移到统一湖仓——他们现在执行关键数据连接的速度提高了 60%。

Customers include Deutsche Telekom, which uses BigQuery, Cloud Storage, and Knowledge Catalog to build a unified data lakehouse across 40-plus legacy on-premise systems; Etisalat, which migrated 3 petabytes of data to a unified lakehouse by using BigQuery and Iceberg—they now perform critical data joins up to 60% faster.

网络安全威胁风险的日益增长也推动了对我们 AI 驱动的安全平台的强劲需求。我们的 Gemini Flash Cyber 模型是前沿模型，可自动化发现 20 种编程语言中的漏洞，并且补丁可靠性比其他模型高 2.6 倍。随着我们推出 Argon 和 Carbon，性能还将进一步提升。我们的安全平台集成了威胁情报、网络响应优先级排序、自动化扫描、代码修复和监控。Google Threat Intelligence 中的 Gemini 每天以 98% 的准确率自主分析数百万个外部事件，超过 95% 的 Wiz 客户正在采用 AI 驱动的功能，并且增长持续。这些安全工具保护关键基础设施，包括 Nordea 和 Jack Henry 等金融机构，以及 AT&T 等电信提供商。

The growing risk of cybersecurity threats is also driving strong demand for our AI-powered security platform. Our Gemini Flash Cyber models are frontier models that automate vulnerability discovery across 20 programming languages and patch 2.6 times more reliably than other models. And there’s even better performance to come as we roll out Argon and Carbon. Our security platform integrates threat intelligence, cyber response prioritization, automated scanning, code remediation, and monitoring. Gemini in Google Threat Intelligence autonomously analyzes millions of external events every day with 98% accuracy, and more than 95% of Wiz customers are adopting AI-powered features and growth continues. These security tools protect critical infrastructure, including financial institutions like Nordea and Jack Henry, and telecommunication providers such as AT&T.

所有这些都由我们领先的 AI 基础设施提供支持，涵盖我们的 TPU 系统、Axion V、最新的 NVIDIA GPU 以及我们专为 AI 智能体设计的 Agent Compute Engine。TPU 系统是为 AI 构建的专用系统——将硬件、算力、存储和网络与系统组件以及针对训练和推理优化的软件相结合。与上一代 TPU 相比，TPU 8T 和 8I 分别提供高达 2.7 倍的训练性能和 80% 的推理性价比提升。

All of this is powered by our leading AI infrastructure spanning our TPU systems, Axion V, the latest NVIDIA GPUs, and our Agent Compute Engine, which is specifically designed for AI agents. TPU systems are specialized systems built for AI—combining hardware, compute, storage, and networking with systems components and optimized software for training and inference. TPU 8T and 8I offer up to 2.7 times better training performance and 80% better inference price-performance, respectively, versus our previous-generation TPUs.

为了让开发者轻松将工作负载迁移到 TPU，我们最近与 Meta 密切合作推出了 TorchTPU，允许用户使用他们已经熟悉和依赖的工具在 TPU 上训练、服务和部署 PyTorch 模型。如今已有近 100 家客户使用 TPU，我们看到许多其他客户的需求强劲且不断增长。

To make it easy for developers to port workloads to TPUs, we recently worked closely with Meta to deliver TorchTPU, which allows users to train, serve, and deploy PyTorch models on TPUs using the tools that they’re already familiar with and rely on. Nearly 100 customers use TPUs today, and we’re seeing strong and growing demand from many others.

除了与 Anthropic 的合作——包括直接合作和通过 Broadcom 合作——以及像 Apple 这样的公开参考客户，Apple 正在最新一代 TPU 上扩展其基础模型的预训练，我们还与新的前沿模型公司合作，包括 Mistral 和 Physical Intelligence；资本市场公司包括 Citadel Securities 和 Jump Trading；加州理工学院等大学；以及包括 CACI 和 Zscaler 在内的公共部门服务提供商。鉴于持续强劲且加速的需求，我们最近与 CoreWeave AI 和 Oracle 建立了合作伙伴关系以交付 TPU 系统，还有更多合作正在筹备中。接下来交给你，Anant。

In addition to our work with Anthropic—both directly and via Broadcom—and public reference customers like Apple, who are scaling pre-training of their foundation models on the latest generation of TPUs, we’re also working with new frontier model companies including Mistral and Physical Intelligence; capital markets firms including Citadel Securities and Jump Trading; universities like Caltech; and public sector service providers including CACI and Zscaler. Given continued strong and accelerating demand, we’ve recently entered into partnerships to deliver TPU systems with both CoreWeave AI and Oracle, with more in the works. Over to you, Anant.

谢谢，Thomas。我的发言将聚焦第三季度的同比比较。除非我另有说明，我会先从 Alphabet 层面的业绩讲起，然后介绍各分部业绩，最后谈谈我们对第四季度和 2026 年全年的展望。我们第三季度表现强劲，实现了连续第 13 个季度两位数的营收增长。合并营收为 1276 亿美元，按报告口径和固定汇率口径均增长 25%。总营收成本为 514 亿美元，增长 24%。TAC 为 164 亿美元，增长 10%。其他营收成本为 351 亿美元，增长 32%，主要受硬件成本推动，主要与 TPU 系统销售相关，此外还有折旧增加、内容获取成本以及技术基础设施运营成本。总运营费用增长 20% 至 356 亿美元。研发费用增长 36%，主要受薪酬推动，主要是对 AI 人才的投资，以及折旧。销售和营销费用增长 22%，原因是 Gemini 相关投资和薪酬，一般及行政费用下降 17%，主要由于去年第三季度较高的法律费用形成了高基数，部分被本年度的法律费用所抵消。运营利润增长 30% 至 406 亿美元，运营利润率为 31.8%。其他收入和支出为负 82 亿美元，主要由于我们持有的 SpaceX 投资因第三季度其股价下跌而产生的未实现亏损。净利润和每股收益因其他收入和支出的下降而减少。

Thank you, Thomas. My comments will focus on year-over-year comparisons for the third quarter. Unless I state otherwise, I will start with the results at the Alphabet level and we'll then cover our segment results. I'll end with some commentary on our outlook for the fourth quarter and full year 2026. We had a strong third quarter, delivering our 13th consecutive quarter of double-digit revenue growth. Consolidated revenues were $127.6 billion, up 25% in both reported and constant currency. Total cost of revenue was $51.4 billion, up 24%. TAC was $16.4 billion, up 10%. Other cost of revenues was $35.1 billion, up 32%, driven by hardware costs primarily related to the sales of TPU systems, increase in depreciation, content acquisition costs, and technical infrastructure operating costs. Total operating expenses were up 20% to $35.6 billion. R&D expenses increased by 36%, driven by compensation primarily for investment in AI talent as well as depreciation. Sales and marketing expenses were up 22% due to investments in Gemini and compensation, and G&A expenses decreased 17% primarily due to lapping of higher legal expenses that occurred in Q3 last year, partially offset by current year legal charges. Operating income increased 30% to $40.6 billion and operating margin was 31.8%. Other income and expense was negative $8.2 billion due primarily to unrealized loss from our investment in SpaceX from a decline in its stock price in the third quarter. Net income and earnings per share decreased due to the decline in OI&E.

我们第三季度产生了强劲的经营现金流 423 亿美元，过去 12 个月为 1796 亿美元。经营现金流受到负面影响，原因是 TPU 系统销售的库存积累，以及在申诉程序结束后就 2018 年 EC 罚款支付的 52 亿美元现金。第三季度资本支出为 514 亿美元，绝大部分支出用于技术基础设施，以支持我们在 AI 方面的投资。第三季度技术基础设施投资中约 65% 与服务器和网络设备相关，其使用寿命一般为六年。其余 35% 用于数据中心，使用寿命为 7 至 40 年。第三季度自由现金流为负 91 亿美元，主要受资本支出投资推动。第三季度自由现金流还受到前述为支持 TPU 系统销售而增加的库存以及支付 EC 罚款的影响。过去 12 个月自由现金流为 197 亿美元。

We generated strong operating cash flow of $42.3 billion in the third quarter and $179.6 billion for the trailing 12 months. Operating cash flow was negatively impacted by the buildup of inventory for TPU system sales as well as a $5.2 billion cash payment for a 2018 EC fine following the conclusion of the appeal process. CapEx was $51.4 billion in the third quarter, with the vast majority of the spend in technical infrastructure to support our investments in AI. Approximately 65% of our investments in technical infrastructure in Q3 was related to servers and networking equipment, which generally have useful lives of six years. The remaining 35% was for data centers which have useful lives of seven to 40 years. We had negative free cash flow of $9.1 billion in the third quarter driven by our investments in CapEx. Free cash flow in the third quarter was also affected by the aforementioned inventory build to support TPU system sales and payment of the EC fine. Free cash flow was $19.7 billion for the trailing 12 months.

本季度末，我们持有现金及有价证券 2651 亿美元，其中包括 904 亿美元的可出售权益证券。长期债务为 1251 亿美元。我们还签订了多年期协议，以锁定技术基础设施和库存的容量，以及租赁和能源。第三季度末，我们的未来采购承诺为 9240 亿美元，租赁开始前及其他义务为 1520 亿美元。这些金额反映的是最高合同义务。不过，其中一些协议具有可变定价和数量灵活性，可能带来更低的支出。另外，正如我们在新闻稿中提到的，我们的董事会宣布派发每股 0.22 美元的普通股季度现金股息，将于 12 月支付。

We ended the quarter with $265.1 billion in cash and marketable securities which includes $90.4 billion of marketable equity securities. Long-term debt was $125.1 billion. We have also entered into multi-year agreements to secure capacity for technical infrastructure and inventory as well as leases and energy. At the end of the third quarter we had future purchase commitments of $924 billion and pre-commencement leases and other obligations of $152 billion. These dollar amounts reflect the maximum contractual obligations. However, a number of these agreements have variable pricing and volume flexibility which could result in lower spend, and as mentioned in our press release, our Board of Directors declared a quarterly cash dividend on our common stock of $0.22 per share which is payable in December.

转向分部业绩，Google 服务收入增长 11% 至 963 亿美元，反映出搜索和订阅业务的强劲增长。广告总收入增长 11%，所有主要垂直领域均实现增长。正如我们在上季度展望中所说，汇率对同比增速构成轻微逆风，对环比的影响更为显著。Google 搜索及其他广告收入增长 12% 至 633 亿美元，其中零售贡献最大，其次是金融。YouTube 广告收入增长 9% 至 112 亿美元，品牌贡献最大，客厅场景表现尤为强劲。网络广告收入为 75 亿美元，增长 1%。订阅、平台和设备收入本季度增长 11% 至 143 亿美元，主要受 YouTube 订阅业务强劲增长推动，尤其是 YouTube Music 和 Premium，以及 Google One 套餐的强劲需求。Google 服务运营利润增长 17% 至 392 亿美元，运营利润率为 40.7%。

Turning to segment results, Google Services revenues increased 11% to $96.3 billion reflecting strong growth in Search and subscriptions. Total Advertising revenues were up 11% with growth across all major verticals. As we stated in our outlook last quarter, FX was a slight headwind on year-over-year growth rates and had a more significant impact on a sequential basis. Google Search and other advertising revenues increased by 12% to $63.3 billion with retail driving the largest contribution followed by finance. YouTube advertising revenues increased 9% to $11.2 billion with brand driving the largest contribution and notable strength in living room. Network Advertising revenues of $7.5 billion were up 1%. Subscription platforms and devices revenues increased 11% this quarter to $14.3 billion driven by strong growth in YouTube subscriptions, particularly in YouTube Music and Premium, and strong demand for plans in Google One. Google Services operating income increased 17% to $39.2 billion and operating margin was 40.7%.

Google Cloud 分部第三季度实现了创纪录的增长。云收入增长 103% 至 307 亿美元，主要受 GCP 强劲增长推动，其中 AI 表现强劲，包括 AI 基础设施和 AI 解决方案，以及核心 GCP。TPU 系统销售也做出了重要贡献。GCP 占第三季度所产生收入的绝大部分。剔除 TPU 系统影响后的云收入增速依然非常强劲。云运营利润为 111 亿美元，同比增长超过两倍。运营利润率从去年第三季度的 23.7% 上升至 36.2%。我们在上次电话会上讨论过的第三方桥接容量直到第三季度末才上线，因此对本季度利润率的负面影响有限。Google Cloud 第三季度的积压订单为 5610 亿美元，其中大部分与典型的 GCP 合同相关。我们预计未来 24 个月内将确认总积压订单中略高于 55% 的收入。

The Google Cloud segment delivered record growth in the third quarter. Cloud revenues were up 103% to $30.7 billion driven by strong growth in GCP with strength in AI, including both AI infrastructure and AI solutions, and core GCP. TPU system sales were also a meaningful contributor. GCP accounted for the vast majority of the revenue generated in Q3. The Cloud revenue growth rate, excluding the impact of TPU systems, was again incredibly strong. Cloud operating income was $11.1 billion, more than tripling year over year. Operating margin increased from 23.7% in the third quarter last year to 36.2%. The third-party bridging capacity, which we discussed on our prior call, came online only at the very end of Q3 and therefore had limited negative impact to our margin in the quarter. Google Cloud's backlog was $561 billion in the third quarter, with the majority of the backlog related to typical GCP contracts. We expect to recognize just over 55% of the total backlog as revenues over the next 24 months.

在其他押注业务中，收入为 3.72 亿美元，运营亏损为 20 亿美元。随着我们继续扩大 Waymo 的业务，并投资于关键的其他押注业务和 Alphabet 层面活动，运营亏损为 77 亿美元，主要受共享 AI 费用推动。

In Other Bets, revenues were $372 million and the operating loss was $2 billion. As we continue to expand Waymo's business and invest in key Other Bets and Alphabet-level activities, the operating loss was $7.7 billion driven by shared AI expenses.

现在我来谈谈对 2026 年剩余时间的展望。首先在收入方面，按当前即期汇率，我们预计第四季度合并收入面临的汇率逆风将比上一季度略大。这一影响将主要体现在 Google 服务的搜索和 YouTube 广告中。我们将在第四季度继续面对去年第三季度搜索业务加速增长带来的高基数，这将对第四季度收入增长构成压力。在 Google Cloud 方面，我们预计 TPU 系统销售将在第四季度继续 Scaling（规模扩张），随后在 2027 年迎来有意义的收入爬坡；就云利润率而言，鉴于供应受限的环境，我们在第三季度末开始使用第三方算力容量，并将在第四季度加大使用。随着我们使用这一容量，我们预计短期内将面临利润率逆风。

I'll now turn to our outlook for the remainder of 2026. First, in terms of revenues, at current spot rates, we would expect a slightly larger FX headwind to our consolidated revenues in Q4 relative to the prior quarter. This impact will be seen primarily in Search and YouTube ads in Google Services. We will continue to lap the Search performance acceleration again in the third quarter last year which will pressure revenue growth in Q4. In Google Cloud, we expect TPU system sales to continue to scale in Q4, followed by a meaningful revenue ramp in 2027, with respect to Cloud margins. Given the supply-constrained environment, we started utilizing third-party compute capacity at the end of Q3 and will ramp usage in Q4. We expect margin headwinds in the near term as we utilize this capacity.

谈到投资，我们 2026 年全年资本支出指引区间 1950 亿至 2050 亿美元保持不变。我们仍预计 2027 年资本支出将大幅增加，并将在第四季度电话会上提供更多细节。在费用方面，技术基础设施投资的大幅增加将继续以更高的折旧费用和相关数据中心运营成本（如能源）的形式拖累损益表。

Moving to investments, our full-year 2026 CapEx guidance range of $195 to $205 billion remains unchanged. We continue to expect our CapEx to increase significantly in 2027 and will provide further details on our Q4 call. In terms of expenses, the significant increase in our investment in technical infrastructure will continue to weigh on the P&L in the form of higher depreciation expenses and related data center operating costs such as energy.

在进入问答环节之前，我想就我们如何看待投入资本回报率和自由现金流说几句。我们处于一个独特的位置，能够通过触达数十亿用户和企业的众多产品，将我们在 AI 上的投资变现。我们在搜索领域的 AI 投资正推动我们实现五年来最高的查询增长。Google Cloud 年初至今已产生数百亿美元的 AI 收入，而 Google One 是我们增长最快的订阅产品，这得益于对 AI 套餐的需求。在整个投资周期中，我们预计将产生有吸引力的 ROIC，持续显著高于我们的资本成本，并预计将产生强劲的经营现金流。随着我们扩大业务规模，我们在技术基础设施和 TPU 系统库存上的大规模投资将对自由现金流造成压力。第三季度是一个强劲的季度，亮点在于我们的创新和所有业务领域的纪律性执行，包括 Gemini 4 的发布。我想借此机会感谢我们的员工为我们的业绩所做的贡献。我们现在开始问答环节。

Before we move to Q&A, let me say a few words on how we think about return on invested capital and free cash flow. We’re in a unique position to be able to monetize our AI investments across many products that reach billions of users and enterprises. Our AI investments in Search are driving our highest query growth in half a decade. Google Cloud has generated tens of billions of dollars in AI revenues year to date and Google One is our fastest-growing subscription product driven by demand for AI plans. Throughout this investment cycle, we expect to generate an attractive ROIC that remains meaningfully above our cost of capital and anticipate generating strong cash flow from operations. Our sizable investments in technical infrastructure and inventory build of TPU systems as we scale that business will put pressure on free cash flows. Q3 was a strong quarter highlighted by our innovation and disciplined execution across all areas of the business, including the launch of Gemini 4. I want to take this opportunity to thank our employees for their contributions to our performance. We will now open the call for Q&A.

谢谢。提醒一下，要提问，您需要在电话上按星号键 1。为防止背景噪音，我们请求您在提问后静音您的线路。我们的第一个问题来自 J.P. Morgan 的 Doug Anmuth。您的线路现已开放。

Thank you. As a reminder, to ask a question, you will need to press star-1 on your telephone. To prevent any background noise, we ask that you please mute your line once your question has been stated. And our first question comes from Doug Anmuth with J.P. Morgan. Your line is now open.

感谢回答问题。一个问题给 Sundar，一个给 Philip。Sundar，在最近的一期播客中，您说 2027 年可能是 Google 生产力的黄金时代，因为新一类模型及其与您其他产品的整合方式。我希望您能详细说明一下，并分享更多想法，思考未来的机会集。然后 Philip，关于查询增长，五年来最快，但显然搜索增长有所减速。您能帮我们理解其中的脱节吗？是不是仅仅因为您在 AI 概览和 AI 模式中基本上没有展示那么多广告，而这现在正成为整体查询的更大比例？谢谢。

Thanks for taking the questions. One for Sundar and one for Philip. Sundar, on a recent podcast you said that 2027 could be the golden age of productivity for Google because of the new class of models and how they’ll integrate with your other products. I was hoping you could elaborate on that and just share more, think about the opportunity set there ahead. And then Philip, just on query growth, the fastest in five years, but obviously some deceleration in Search growth. Can you just help us understand the disconnect there? Is it simply that you haven’t been showing as many ads basically in AI overviews and AI mode and that’s now becoming a bigger percentage of the overall queries? Thanks.

谢谢，Doug。我们确实开始看到阶跃变化，比如在内部使用 3.8 Flash 时，我们的模型在长周期任务、智能体解码等方面变得更好。但自从我们在内部开始部署 ARG 以来，我确实看到了一个巨大的拐点。这是我们在软件工程领域看到的最有意义的内部生产力杠杆之一。直接跟踪正在检入的高质量代码、检入速率、减少的回滚次数。但更重要的是，人们正在解决重大问题，同时我们开始看到部署这些模型在产品 and 消费者体验方面带来的创新。但这一切仅仅发生在四周前。我们现在正在内部部署 Carbon，而 Carbon 是比 Argon 有意义的提升。因此，随着我们推出它并使其在更多算力下更广泛地可供 Googlers 使用，我预计——这就是我当时所指的，而且自从我在播客中说过之后，我更加兴奋，我认为未来一年将是黄金年，我们能够更高效。因此，对未来的创新感到兴奋。

Thanks, Doug. We definitely started seeing a step change, say with 3.8 Flash internally as our models got better at long-horizon tasks, agent decoding, etc. But I’m definitely seeing a huge point of inflection once we’ve started deploying ARG internally. It is one of the most meaningful internal productivity levers we have seen on software engineering. Directly tracking high-quality code that’s getting checked in, the rate at which it’s getting checked in, reduced number of rollbacks. But more importantly, people are tackling massive problems, as well as we are beginning to see innovation on the product and the consumer experience side from deploying those models. But all this happened only about four weeks ago. We are now deploying Carbon internally, and Carbon is a meaningful step up from Argon. So as we roll it out and make it more widely available to Googlers with more compute, I expect to—that’s what I was referring to, and I’m even more excited since I said that in the podcast and what I would view as a golden year ahead in terms of us being able to be more productive. And so, excited for the innovation ahead.

是的，Doug，关于你问题的第二部分。第三季度，搜索同比增长 12%，达到 630 亿美元。当你观察从第二季度到第三季度的增长率变化时，有几个驱动因素。首先，是汇率。这是搜索相对于第二季度减速的唯一最大驱动因素。其次，是垂直领域的特定动态。在零售领域，我们的一些亚太广告商受到欧盟取消最低免税额豁免的影响，而媒体和娱乐支出在第二季度因 FIFA 世界杯而异常强劲后趋于正常化，世界杯有更多比赛，因此 6 月分配的广告预算比 7 月实际更多。关于你问题的下一部分，值得注意的是，AI 概览的强劲增长产生了影响，因为我们在 AI 模式中看到的变现能力较低。明确地说，我们对这里的轨迹感觉非常好。我们的广告实验显示了有希望的结果，我们现在正在将 AI 概览和 AI 模式中的广告扩展到更多用户和更多查询，包括我提到的国际市场和美国的多轮广告。

Yeah, Doug, on the second part of your question. In Q3, Search grew 12% year over year to 63 billion. And when you look at the change in growth rate from Q2 to Q3, there were a few drivers here. So first, there was FX. It was the single largest driver of Search deceleration relative to Q2. Second, there were vertical-specific dynamics. In retail, some of our APAC advertisers were impacted by the EU eliminating its de minimis exemption, and media and entertainment spend normalized after an unseasonably strong Q2 driven by the FIFA World Cup, which had more matches and therefore more advertising budgets allocated in June than actually in July. To the next part of your question, it’s worth noting that the strong growth in AI overviews had an impact because of the lower monetization we were seeing in AI mode. To be clear, we feel very good about our trajectory here. Our ad experiments have shown promising results and we’re now scaling ads within both AI overviews and AI mode to more users and more queries, including, as I mentioned, international markets and multi-turn ads in the U.S.

谢谢。我们的下一个问题来自 Morgan Stanley 的 Brian Nowak。您的线路现已开放。

Thank you. Our next question comes from the line of Brian Nowak with Morgan Stanley. Your line is now open.

感谢回答我的问题。也许让我从智能体开始。你知道，你们在今年向一些用户推出了 Spark，Sundar，你谈了很多关于向更广泛的用户群推出更多智能体产品的事情。你能帮我们稍微了解一下，要向全量用户推出智能体产品，你们必须在技术上克服的一些关键障碍吗？是模型效率吗？是容量吗？是技术还没准备好吗？我们在技术上需要做什么来实现这一点？然后第二个问题，就 Doug 关于搜索的问题，你能帮我们更好地理解你提到的影响搜索的搜索性能比较因素吗？当我们进入第四季度时，我们该如何考虑这一点，只是看看同比比较？

Thanks for taking my questions. Maybe let me start with agentic. You know, you rolled out Spark to some of the users throughout the year, and Sundar, you’ve talked a lot about shipping more agentic products to the broader base of users. Can you just help us understand a little bit some of the key hurdles you have to overcome technologically to ship out agentic products to the full user base? Is it model efficiency? Is it capacity? Is it the technology isn’t ready? What do we have to do technologically to do that? And then the second one, just to Doug’s question on search, can you help us better understand the search performance comparison factor that you talked about impacting search? And how do we think about that as we kind of go into 4Q, just kind of looking at the compares?

谢谢。谢谢，Brian。首先，关于智能体，这是一个多么令人兴奋和充满活力的领域。我认为，当我看到我们的新模型，看到它们能够以自动化方式处理复杂任务并以非常非常高的质量执行时，很明显，我们所有人设想的智能体能力将会成为现实，因此我非常兴奋能将下一代智能体体验带给我们的用户。针对你的问题，我想说几点。我认为 Gemini 4 模型系列很重要。我们正在研究核心模型的蒸馏版本，以便能够高效地在终端用户规模上部署，无论是从算力和延迟的角度，还是从质量的角度。所以这一直是一个重要的投资领域。当然，要确保在规模上不仅需要一个有能力的模型，还需要一个强大的运行框架，一个高度安全的运行时。这就是 GCP 发挥作用的地方，能够为我们的用户提供安全的全天候云服务，以及正确的技能和要素生态系统。所以所有这些都是基础。我们一直在努力工作，我们决定先向企业推出，我们基本上通过 Gemini for Enterprise 宣布了这一点，因为它们对安全性、治理和控制有最高的要求，而且反响很好，我们正在扩大这一范围。今年晚些时候，我们将把同样的体验带给消费者，显然从我们的 Gemini 应用中 10 亿用户开始。我们已经看到应用中的用户超越了聊天，转向任务型和智能体式的体验，因此用户对我们产品的信任，以及他们的所有上下文都在那里，我认为这为我们奠定了良好的基础。但作为一家公司，我们真正专注于在我们的产品中提供统一的体验，你应该期待在今年晚些时候看到很多进展。

Thanks. Thanks, Brian. First of all, on agents, what an exciting and dynamic area. I think when I look at our new models and we see the capabilities of the kind of complex tasks they can take in an automated way and execute with very, very high quality, it’s clear that the agent capabilities we all envision are going to be real, and so I couldn’t be more excited to bring the next-generation agent experience to our users. To your question I would say a few things. I think the Gemini 4 model series has been important. We are working on distilled versions of our core models so that we can deploy it across end-user scale efficiently, both from a compute and a latency standpoint and from a quality standpoint. So that’s been an important area of investment. Definitely, making sure you need more than a capable model at scale, you need a robust harness, you need a highly secure runtime. And this is where GCP comes in, being able to give our users a secure 24/7 availability in the cloud, the right ecosystem of skills and factors. So all of that is the foundation. We have been hard at work and we decided to roll it out to Enterprise first, which we announced with our Gemini for Enterprise essentially because they have the highest bar for security, governance and controls, and it’s been well received, and we are in the process of expanding that. Later this year, we will bring that same experience to consumers, obviously starting with 1 billion users in our Gemini app. We already see people in the app moving beyond chat to what are task- and agent-like experiences, and so the trust users have in our products, the fact that all their context is right there, I think sets us up well. But we are really focused as a company on delivering that unified experience across our products, and you should expect to see a lot of progress later this year.

谢谢。关于第二个问题，关于我的评论，我相信你指的是搜索的前景。我提到的几个动态。一个是环比相关的汇率逆风影响，我们本季度也看到了与上一季度相比的情况。因此，按照当前即期汇率，预计这将对第四季度业绩构成一定逆风。然后，当我们看同比比较时，本季度我们开始面临 2025 年搜索增长加速的基数效应。2025 年下半年，我们在第三季度首先面临了约三个百分点的增长基数，然后我们预计第四季度将继续，即我们在 2025 年底看到的增长加速。

Thanks. And on the second question with regard to my comments, I believe you’re referring to the outlook with regard to Search. A couple of dynamics that I’ve mentioned. One is on sequential related to impact of FX headwinds, which we saw this quarter as well versus the previous quarter. So where we are today with current spot rates, expect that this will be somewhat of a headwind to Q4 results. And then when we look at the year-over-year comparisons, this quarter we started lapping the acceleration we saw in Search growth in 2025. The back end of 2025 we lapped about three points of growth first in Q3, and then we expect to see that continue in Q4, a couple that growth acceleration that we saw at the end of 2025.

很好。谢谢两位。

Great. Thank you both.

我们的下一个问题来自高盛的 Eric Sheridan。您的线路现已开放。

Our next question comes from the line of Eric Sheridan with Goldman Sachs. Your line is now open.

非常感谢回答这个问题。Sundar，基于您最近关于算力限制的一些评论，您能否为我们更新一下，随着算力在整个组织中变得可用，您对算力应用的关键优先事项是什么？第二部分，也许由您或 Thomas 回答，具体在 Google Cloud 内部，关于算力应用于云客户需求相对于 TPU 的看法如何继续演变？非常感谢。

Thanks so much for taking the question. Based on some of the comments you’ve made recently, Sundar, on compute constraints, can you refresh for us your key priorities for the application of compute as it becomes available across the organization? And the second part, maybe for you or Thomas, would be within Google Cloud specifically, how does your view continue to evolve with respect to the application of compute to cloud customer needs relative to TPU? Thanks so much.

谢谢。谢谢，Eric。我先开始，然后 Thomas 会回答第二部分。你知道，算力分配绝对是我们花费大量时间的一个领域，因为需要进行前瞻性规划。我想首先说，我们感到幸运，面前有一系列非凡的机会。你知道，我们运营着一个覆盖数十亿人的规模化消费者业务，一个势头强劲的超大规模云业务，当然还有 Google 和 Alphabet 范围内一系列其他非常重要的领域。我们总是进行长期规划，因此我们的每个业务领域都会提交长期计划，并采用非常严格的 ROIC 框架。我大致将所有这些视为一个长期规划过程，而不是短期的分配活动。显然，我们的人工智能模型是我们所做一切的基础，从搜索和 YouTube 到云再到 Waymo 等等。所以它是基础，我们将其视为一种高杠杆的方法。全栈方法带来了很多效率，并在广泛的机会中为我们提供了很多可选性。所以显然这是基础，我们也为此进行长期规划。除此之外，正如我所说，我们真正审视我们的业务，并采取严格的长期规划方法。显然，你知道，我们在广泛领域看到了非常非常强劲的需求信号，因此我们将其视为一系列非凡的机会，我们正在为那个时刻投资。

Thanks. Thanks, Eric. I’ll start and then Thomas will answer the second half. You know, definitely compute allocation is an area where we are spending a lot of time on because of the forward planning that’s needed. I would start with saying we feel fortunate to have an extraordinary set of opportunities ahead of us. You know, we are operating a scale consumer business that reaches billions of people, a hyperscale cloud business with a lot of momentum and of course a range of other highly important areas across Google and Alphabet. We always take long-range planning and so each of our business areas submit long-range plans with a very disciplined ROIC framework. I would broadly think of all of this as a long-range planning process, not a short-term allocation exercise. Obviously our AI models underlie everything we do from Search and YouTube to Cloud to Waymo and so on. So it’s the foundation and we view it as a highly leveraged approach. The full-stack approach drives a lot of efficiency and gives us a lot of optionality across a wide variety of opportunities. So obviously that is the foundation and we plan for that for the long term too. And above that, as I said, we really look at our businesses and really take a disciplined long-range planning approach. Obviously, you know, we are seeing very, very robust demand signals across a wide variety of areas and so we view it as an extraordinary set of opportunities, and we are investing for that moment.

Thomas，Eric，关于云的需求，我们一直在管理我们的组合以确保客户广度，我们有不同类型的客户，从数字原生企业、AI 实验室、传统企业到金融和资本市场客户。所以是一组广泛的客户。许多客户要么已经开始使用 TPU 系统。例如，在我们的云中，我们有超过 100 个重要的 TPU 客户，他们从我们的云开始使用 TPU。记住，TPU 系统是硬件、系统软件、编译器以及包括光网络和其他东西在内的整个组装系统的组合。我们在如何将 TPU 带给客户方面采取了非常审慎的方法。首先是构建正确的芯片。所以我们有用于训练的 8T 和用于推理的 8I。它们相对于前几代都提供了大幅的价格性能和性能改进。其次，我们优化了软件。软件的优化不仅是为了让系统运行得更好并为你提供出色的性能，也是为了实现无缝的可移植性。例如，我们最近推出了 PyTorch，通过优化 PyTorch 为我们打开了更大的市场。第三，我们正在扩大分销。所以过去你只能在我们的云中使用 TPU。现在，对于希望在其数据中心使用 TPU 系统的客户，我们正在将 TPU 系统提供到他们的数据中心。我们也在扩大对其他提供商的可及性。例如，我们宣布了与 Oracle 的合作关系，并且我们与 Blackstone 一起启动了一项协议，成立 Crux AI 合资企业，为客户提供 TPU 系统。我们看到来自 AI 实验室、资本市场、金融服务机构和企业界的非凡需求和强劲增长。如果你想象这 100 多个客户增长到更多，并且他们的使用规模扩大到 TPU 系统，我们预计未来需求强劲。

Thomas, Eric, on the demand in Cloud, we are always managing our portfolio to ensure we have a breadth of customers, and we have different types of customers from digital natives, AI labs, traditional enterprises, financial and capital markets customers. So it’s a broad set of customers. Many of them have either started with using TPU systems. For example, in our Cloud we have over 100 significant TPU customers who start in our Cloud to use TPUs. And remember, a TPU system is a combination of hardware, system software, compilers, the whole thing assembled as a system, including optical networking and other things. We’ve taken a very deliberate approach on how we’re bringing TPUs to customers. The first one is to build the right silicon. So we have 8T for training, 8I for inference. They both offer substantial price-performance and performance improvements relative to prior generations. Second, we’ve optimized software. The optimization of the software is not just to make the systems run better and to give you great performance, but also to see portability seamless. Recently we introduced, for example, PyTorch, which opens up a much larger market for us by optimizing PyTorch. Third, we are broadening distribution. So in the past you could use TPUs in our Cloud. Now, for customers who want TPU systems in their data centers, we’re making TPU systems available in their data centers. We’re also broadening availability to other providers. For example, we announced a partnership with Oracle, and we’ve launched along with Blackstone an agreement for Crux AI, a joint venture to offer customers TPU systems. We are seeing extraordinary demand and strong growth from both AI labs, capital markets, financial service institutions and enterprises. And if you imagine these hundred-plus customers growing to many more and also scaling their use into TPU systems, we see strong demand over the future.

我们的下一个问题来自巴克莱的 Ross Sandler。您的线路现已开放。

Our next question comes from Ross Sandler with Barclays. Your line is now open.

谢谢。我想继续追问您刚才提到的 TPU 转售业务话题。Anat，我记得您上季度说过，收入将按系统销售确认。那么，如果 1 吉瓦的 TPU 全系统成本约为 350 亿美元，包括算力及周边所有组件，我们实际确认的收入占其中多大比例？相对于 Google Cloud 部门目前的利润率水平，这部分业务的利润贡献如何？问题的第二部分是，当你们帮助 Anthropic 这样的公司部署更多他们自己的第一方基础设施时，这对 Alphabet 的资本支出强度意味着什么？因为你们开始将部分未来资本支出转交给 AI 实验室，自己数据中心的资本支出强度可能不需要那么高了。我觉得这很重要。

Thanks. Wanted to stick with the TPU resale business topic that you just mentioned. So, Anat, last quarter you kind of said that the revenue will be recognized on system sales, I believe. So if a gigawatt of TPU full system costs around 35 billion for the compute and all the components around it, what percent of that are we actually recognizing as revenue, and what kind of flow-through does that have in terms of margin profile relative to where the Google Cloud segment currently sits? And then I guess the second part of the question would be, as you help companies like Anthropic bring on more of their own kind of first-party infrastructure, what does that mean to the CapEx intensity of Alphabet as you start to hand off some of the future CapEx to AI labs and may not require as much CapEx intensity in your own data centers. Thought that would be great.

谢谢。我把这两个问题合并回答。首先，我们显然不会提供产品层面的具体财务数据，但让我说明一下如何理解 TPU 系统销售相关的财务。提醒一下，Thomas 也可以补充，TPU 系统不只是一颗芯片，而是一个专用系统，整合了硬件、算力、存储、网络等多种要素。所以我们卖的远不止软件。训练、推理，为购买 TPU 系统的客户提供了显著的附加价值。运作方式是这样的，这也与你问题的部分内容相关：当我们与客户签署销售 TPU 系统的协议后，首先会体现在我们的订单储备中，然后我们开始建立库存。所以要从资本支出与现金的角度来思考。我们在获得收入流之前先建立库存。我想我在上次电话会上提到过这一点。你会看到库存余额上升，而此前库存余额主要是我们的一些硬件产品。现在那些尚未售出的 TPU 系统会体现出来。所以你会看到现金流出，我们在积累库存基础。然后当我们开始交付并向客户开具 TPU 系统账单时，你会看到现金流入。所以这不是你想象的传统资本支出，我认为这是你问题的核心，而是一种不同的现金流生成周期。现在，鉴于我们正处于建立和扩大该业务的过程中，你会在我们的现金流量表中看到现金需求，以及迄今为止的库存积累。我们已确认了一些销售收入，但预计到 2020 年会有更多收入进来。

Thank you. Yeah. Let me combine these two questions. First, obviously we don’t provide specific financials at the product level, but let me walk through how to think about the financials associated with the TPU system sales. And obviously, just as a reminder, and Thomas can elaborate on that as well, a TPU system is not just a chip, but it’s a specialized system that combines multiple things including hardware, compute, storage, networking. So we are selling much more than just software. Training, inference, there is a significant added value that’s being provided to the customer that’s purchasing a TPU system. The way this works, and this is linked to your portion of the question: when we sign an agreement with a customer to sell TPU systems, once we sign that agreement, you’ll first see that showing up in our backlog, and then we start building inventory. So think about this from a CapEx versus cash perspective. We build the inventory ahead of getting the revenue stream. So I think I’ve alluded to this on the previous call. You see inventory balances go up, where previously inventory balances primarily were some of our hardware products. Now those TPU systems that are not yet sold will be showing up. So you see cash going out and we’re building up the inventory base. And then as we start delivering and billing customers for these TPU systems, then you see cash coming in. So it’s not the traditional kind of CapEx as you’re thinking about this, and I think that’s the essence of your question, but rather a different cycle of cash flow generation. Now, given that we are in the process of building up and ramping up that business, you’re seeing that cash needs in our cash flow statements and you see those inventory builds to date. We had some sales recorded as revenue but expect more of it to come in through 2020.

我们的下一个问题来自美国银行的 Justin Post。您的线路已开通。

Our next question comes from the line of Justin Post with Bank of America. Your line is now open.

好的，谢谢。我有一些关于智能体转型的问题。你们推出了通用购物车。显然人们喜欢并使用智能体用例，随着更多此类应用出现，你们计划如何管理搜索向智能体用途的转型？你认为这可能会成为逆风吗？其次，你认为智能体长期来看是否能为你们的流量带来更多变现？然后我再问一下 TPU 利润率——我们想知道，你们认为这对你们来说是一门很好的生意吗？也许为什么，如果你们不能披露利润率，为什么认为这是利用所有这些算力的好方式？

Great, thank you. I have some questions on the agentic transition. You know, you did launch the universal shopping cart. Clearly people are liking and using agent use cases, and as more of that happens, how do you plan to manage the Search transition to agentic use? Do you think that could be a headwind? And second, do you think agentic could actually drive more monetization for your traffic over the long term? And then I’ll go again at TPU margins — we’re wondering, do you think this is a really good business for you, and maybe why, if you can’t [disclose] the margins, why do you think this is a good way to use all this capacity?

谢谢，Justin。

Thank you, Justin.

我们正处于智能体的早期阶段，但也许直接回答你问题的第二部分。我们认为智能体体验显然是增量。我们的重点是建立信任，以确保用户喜爱的超大规模体验，坦率地说，这锚定于智能体商务，以及你提到的基础模块，如通用商务、卡片、智能体支付协议等。当用户——这很重要——旅程中有一部分他们不喜欢：繁琐的工作，坦率地说，用 AI 自动化这些工作非常棒。但我们也希望增强，而不仅仅是自动化掉人们真正喜爱的发现体验。在很多情况下，用户喜欢选择，他们希望考虑远超价格的因素，如品牌忠诚度、客户服务等。所以我们真正在设计这些智能体工作流时，能够有意为用户设计时间和空间来探索这些选择。举个简单的例子：智能体可以回应，这是我们找到的保险产品，这是另一个强有力的选项，其中一个有免赔额，而且他们目前提供非常特别的折扣。所以，鉴于一个决策几乎从来不会只有一个完美选项，我们实际上有信心可以提供非常有趣、引人注目的广告，更多智能体用例，你也可以想象超越我们，通过订阅和其他支付方式。但我对智能体世界中的广告机会感到非常兴奋。

We’re in the early days of agentic, but maybe directly to the second part of your question. We view agentic experiences as clearly additive. Our focus is building trust to secure super-scale experiences that users love, anchored frankly on agentic commerce, foundational blocks like you mentioned, universal commerce, the card, the agent payments protocols, and so on. When users—and that’s important—there’s a part of the journey they don’t like: the grunt work, which is frankly fantastic to automate with AI. But we also want to enhance, not just automate away, the discovery experience people frankly love. In so many cases users like choice, and they want to consider things way beyond price like brand loyalty, customer service, and so on. So we’re really architecting these agentic workflows in a way that we can intentionally design the time and the space for users to explore those choices. So, simple example: an agent could respond, here’s the insurance product we found and here’s another strong option, one of which has a deductible, and they’re currently offering a very special discount. So, given that there’s almost never only one perfect option for a decision, we’re actually confident that we can provide really interesting, compelling advertising, more agent use cases, and also you can imagine going beyond us with subscriptions and other payment methods. But I’m really excited about the advertising opportunities in an agentic world.

总的来说，我想补充一点，我会将其视为一个扩张机会。就像几年前，能够在搜索中融入 AI 聊天用例，让人们可以在搜索中提出更多问题、做更多事情，这正是我们使用量中正在发生的情况——我认为智能体将再次成为扩展我们为用户解决问题类型的机会。我认为人们总是低估为用户提供这些复杂体验所需的所有组件。搜索表面上看起来简单，但背后有极其深厚的投资和我们长期构建的东西，比如知识图谱就是一个例子，以及我们对世界的理解等等。所以我对智能体感到兴奋，我会将其视为一个新前沿，为搜索带来一系列新的用例，因此我们很兴奋地推进这方面的工作。

Overall, I would add and say look, I would view it as an expansion opportunity. Anytime—just like a few years ago, being able to incorporate AI chat use cases in Search allowed people to ask more and do more in Search, which is what is playing out in our usage—I think agents would again be an opportunity to expand the types of problems we solve for our users. I think people always underestimate all the components you need to deliver these complex experiences for users. And Search on the surface looks simple, but there are extraordinary deep levels of investment and things we have built over time, like Knowledge Graph being an example and our understanding of the world, etc. So I think I’m excited about agents and I would view it as a new frontier which brings in a new set of use cases to Search, and so we are excited to work through that.

关于 TPU 系统和利润率，我们不会单独披露单个产品或基础设施组件的利润率。不过，设计和制造 TPU 系统带来了多项好处。首先，提醒一下，TPU 系统不只是一颗芯片，而是一个完整的系统。鉴于 TPU 系统相对于竞争对手的性价比优势、加速计算需求的增长以及加速计算的稀缺性，我们非常专注于在扩大业务规模的同时持续提升利润率。还有两点。首先，当 Anant 提到我们采购组件以建立库存时，我们只在签署合同后才这样做。因此，TPU 系统的所有库存都与已承诺的合同相关。此外，通过将 TPU 系统部署到第三方设施和客户数据中心，这让我们能够：第一，扩大可服务市场。对于某些行业，他们的数据在本地，无法迁移到云端，这使我们能够将加速计算带到数据所在的地方。另外，这也抵消了我们预先建设设施来托管这些 TPU 系统的需要。

On TPU systems and margins, we don’t break out margins for individual products or infrastructure components. However, we see several benefits from designing and manufacturing TPU systems. First of all, to remind you, the TPU system is not just a chip, but it’s a complete system. Given the price-performance benefits of TPU systems relative to competitors, growing demand for accelerated compute, and the scarcity of accelerated compute, we’re very focused on continuing to improve margins as we scale the business. Two additional points. First of all, when Anant mentioned that we procure components to build inventory, we only do that after we have signed contracts. So all of the inventory for TPU systems is associated with committed contracts. Additionally, by putting TPU systems into third-party facilities and in customer data centers, it allows us, one, to broaden our addressable markets. For some industries, they have data on their premises that they cannot move to a cloud, and it allows us to bring accelerated compute to where the data sits. Additionally, it also offsets the need for us to have to pre-build facilities to host these TPU systems.

我们的下一个问题来自 AllianceBernstein 的 Mark Schmulek。您的线路已开放。

Our next question comes from the line of Mark Schmulek with AllianceBernstein. Your line is now open.

是的，感谢回答提问。我将围绕同样的几个话题。但 Philipp，关于智能体式，你刚刚看了 Gemini AI 模式下一些早期更智能体式的用例，并观察了一些与 Google 资产的第三方集成，有什么让你感到惊讶或改变了你的看法吗？我知道我们已经谈了很多关于商业的内容，但也许在消费者生产力方面也是如此。然后，Thomas 或 Anat，关于云增长，你能分享一些关于 Google Cloud 不包括 TPU 系统销售的增长速度吗？以及其中有多少受到产能限制？而且一些第三方产能将在第四季度上线——是否可以合理假设 Google Cloud 业务会加速？谢谢。

Yes, thanks for taking the questions. I’m going to be on the same couple topics. But Philipp, just on agentic, as you’ve kind of just taken a look at some of the early more agentic-style use cases on Gemini AI mode and kind of observing some of the 3P integrations with Google properties, anything that surprised you or changed your view? I know we’ve talked a lot about commerce, but perhaps on consumer productivity as well. And then either Thomas or Anat, just on kind of Cloud growth, is there anything you could share just around the rate of growth of Google Cloud excluding the TPU system sales and just how much of that’s been kind of constrained by capacity? And some of that 3P capacity comes online in Q4— is it fair to assume an acceleration of that Google Cloud business? Thank you.

也许我来回答第一部分，关于我们在这些智能体式用例上观察到的情况。我们确实看到了。最近在 Gemini 应用中，我们添加了一个 Tasks 标签页，人们实际上在那里输入智能体式查询，所以我们可以观察到这一点。看，在所有这些中，用户真正想要的是——他们想要被愉悦。所以他们实际上在寻找——他们有很高的标准。鼓励重复使用的是你处理这些更长任务并最终实际交付满意结果的能力。就像在其他领域包括编程中看到的那样，当你做到 70% 时，看起来它不起作用，直到你跨过某个阈值，它才真正起飞。因此，我们正在密切从所有这些中学习，识别差距，奠定我之前谈到的所有基础，我相信这是一个领域，随着今年晚些时候的推进，你会在我们的各个界面上看到很多进展。当我们过渡到 TPU 时，关于 TPU 系统我想说的另一点是，在半导体行业，规模经济很重要。所以我会把我们为部署在我们数据中心和他人数据中心而制造 TPU 系统的能力视为扩大产品规模的能力，这直接转化为更好的经济效益。

Maybe I’ll take the first part on what we are observing on these agentic-style use cases. We are definitely seeing it. Just recently in the Gemini app we have added a Tasks tab where people are actually typing in agentic queries, and so we can observe that. Look, in all of this, users really want—they want to be delighted. So they effectively are looking for—they have a high bar. What encourages repeat use is your ability to handle these longer tasks and actually deliver a satisfactory outcome at the end. Just like having seen this in other areas including coding, when you’re doing it at 70%, it looks like it doesn’t work until you cross a certain threshold, and it really takes off. And so we are closely learning from all of it, identifying gaps, laying all foundations I talked about earlier, and I’m confident it’s an area where you will see a lot of progress across our surfaces as we go through later this year. As we transition to TPUs, one other comment I would make on the TPU systems is in the semiconductor industry, economies of scale matter. So I would look at our ability to do TPUs both for deploying in our data centers and TPU systems in others’ data centers as ability to scale up the product, which directly translates into better economics.

回答关于云增长率的问题，我们的 Google Cloud 部门在第三季度实现了创纪录的增长，云收入增长 103% 至 307 亿美元。增长最强的贡献者是 Google Cloud Platform，在 AI 和核心 GCP 方面都很强劲。因此，GCP 占第三季度产生的收入的绝大部分，而排除 TPU 系统销售影响的云收入增长再次非常非常强劲。我们所有产品线的需求也很强劲。我们看到三件事正在发生。第一，随着人们使用 Gemini，我们看到来自 Gemini 本身的收入增长。但我们也看到，随着人们使用 Gemini，它推动了我们在其他产品组合部分的增长。例如，当他们构建智能体时，他们使用算力来执行任务，所以他们购买算力；他们使用存储来存储智能体的记忆；使用数据库来提供所谓的上下文供智能体推理。而且显然，鉴于所有的网络安全威胁，我们看到我们的网络安全产品组合强劲增长。因此，我们对我们的增长和两者的多样性充满信心。我们还看到新客户购买 GCP，新客户采用加速增长。所以我们也将其视为我们的增长向量。我们总是——在任何时间点，我们显然可以用产能服务更多需求，但我们正在谨慎管理所有这些参数和增长。

To address the question on the rate of growth of Cloud, our Google Cloud segment delivered record growth in the third quarter with Cloud revenues up 103% to $30.7 billion. The strongest growth contributor was Google Cloud Platform, with strength in both AI and core GCP. So GCP accounted for the vast majority of revenue generated in Q3, and Cloud revenue growth, excluding the impact of TPU system sales, was again very, very strong. Demand was also strong across our product lines. We see three things going on. One, as people use Gemini, we see revenue growth from Gemini itself. But we also see that as people use Gemini, it drives growth in other parts of our portfolio. For instance, as they build agents, they use compute to perform a task, and so they buy compute; they use storage to store memories of the agent; database to serve up what’s called context for the agent to reason on. And obviously, given all the cybersecurity threats, we are seeing strong growth in our cybersecurity portfolio. So we are confident about our growth and the diversity in both. We’re also seeing new customers buy into GCP and accelerating growth in new customer adoption. So we also see that as a growth vector for us. And we are always—at any point in time, we could obviously serve a lot more demand with capacity, but we are managing all these parameters and growth carefully.

我们的最后一个问题来自 MoffettNathanson 的 Michael Nathanson。线路已开放。

And our last question comes from Michael Nathanson with MoffettNathanson. Line is now open.

谢谢。我有两个问题问 Sundar。在之前回答 Eric 关于 CPU 和云的问题时，我们听到愿意与你们的一些竞争对手或在模型和云方面我们可能视为竞争对手的公司合作。我想知道你如何看待与可能与你竞争的人合作的理念，以及为什么这一策略是机会而非风险——从长远来看，这是合理的。其次，关于智能体式消费者行为，争论之一围绕安全和信任。与我们分享你迄今为止在安全和信任方面学到的东西。这些变量对消费者重要吗？你如何判断这些变量是否重要？在构建智能体式消费者工具时，你们在做什么来保护消费者信任和隐私？谢谢。

Thanks. I have two for Sundar. In an earlier answer to Eric’s question about CPU and Cloud, we heard a willingness to work with some of your competitors or companies that we may view as competitors in models and Cloud. I wonder how you think about the philosophy here about working with people that maybe are competing with you and why the strategy is an opportunity, not a risk—longer term, that makes sense. And then secondly, on agentic consumer behavior, one of the debates is around safety and trust. Share with us what you’ve learned so far about safety and trust. Are those variables that matter to consumers? How do you say if those variables matter? What are you doing to protect consumer trust and privacy as you build out agentic consumer tools? Thanks.

谢谢，Michael。很好的问题。关于第一个问题，你看，我们长期以来一直把我们构建的东西分为两类：一类是我们作为产品来构建的，另一类是我们作为平台来构建的。你必须清楚地知道哪些是产品、哪些是平台。当你把东西作为平台来构建时，你会非常努力地把平台做好，确保它对所有人都适用。我们有着运营众多平台的悠久历史——无论是浏览器、操作系统，还是极其成功的云平台——在所有这些中，我们都努力向所有参与者提供它，并成为一个出色的合作伙伴。你们已经看到我们与包括 Apple 在内的公司合作开发我们的模型，而我们在很多很多领域与 Apple 竞争。同样，我们把我们的 TPU 系统视为我们正在构建的一个重要平台。我们希望在我们构建它的方式上做到世界一流。这意味着你要把它置于市场力量之中，对它进行测试，并争夺那些会在其上运行要求最高的产品的客户。我认为，这正是确保这些团队持续保持前沿的原因。所以这就是我们的思考方式。随着时间的推移，这会带来不断扩大的机会，而我们长期以来采取这种做法一直做得很好。第二，关于智能体式行为——回到之前的问题，我想我本应强调——当人们接触这些智能体体验时，我们清楚地看到人们担心安全与信任。我的意思是，这是他们希望如何使用这些东西的一个非常非常重要的因素。几十年来，我们通过以安全的方式在众多拥有数十亿用户规模的消费产品中提供这些体验所建立的信任——在我们构建模型和智能体、通过广泛测试进行部署、以及我们大规模部署时所做的实时监控——所有这些最终都将成为巨大的差异化优势。我们已经从用户那里听到了这一点。我认为，随着这一前沿的发展，他们会用脚投票。

Thanks, Michael. Great questions. On the first one, look, we've long viewed what we build as categories of things which we build as products, and then there are categories of things which we build as platforms. And you need to have a clear view on which is which. When you're building things as platforms, you really work hard to make the platform great and make sure it works for everyone. We have a long history of operating many platforms—be it a browser, be it an operating system, be it an extraordinarily successful Cloud platform—and in all of that, we worked hard to provide it to all players and be a great partner. You've seen us work on our models with companies including Apple, with whom we compete in many, many areas. Similarly, we view our TPU systems as an important platform we are building. We want to be world-class in how we build it. That means you're putting it against market forces, testing it, and competing for people who would have the most demanding goods on top of it. And I think that's what makes sure those teams are continuing to be frontier. So that's how we think about it. And over time that comes up in expanding opportunities, and we've been able to do well taking that approach over a very long period of time.

谢谢。

Thank you.

谢谢。今天的问答环节到此结束。我想把会议交回给 Jim Friedland 做进一步发言。

Thank you. And that concludes our question and answer session for today. I'd like to turn the conference back over to Jim Friedland for any further remarks.

感谢大家今天参加。我们期待在 2026 年第四季度电话会上再次与大家交流。谢谢，祝大家晚上愉快。

Thanks everyone for joining us today. We look forward to speaking with you again on our fourth quarter 2026 call. Thank you and have a good evening.

谢谢大家。今天的电话会议到此结束。感谢您的参与。您现在可以断开连接了。

Thank you everyone. This concludes today's conference call. Thank you for participating. You may now disconnect.