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Okay. All right. I think in the interest of time, we're going to try to find our seats and keep going. Our next speaker is Thomas Kurian, the CEO of Google Cloud. Thomas joined Google Cloud as CEO in November of 2018, bringing deep enterprise experience to the company. He's grown the business in the world's largest public clouds with nearly $100 billion annual revenue run rate.

Some of the statements that Mr. Kurian may make today could be considered forward-looking. These statements involve a number of risks and uncertainties that could cause actual results to differ materially. Please refer to Alphabet's Forms 10-K and 10-Q, including the risk factors. Any forward-looking statements that Mr. Kurian makes are based on assumptions as of today, and Alphabet undertakes no obligation to update them.

I'll welcome Thomas to the stage to go through a handful of slides, and then we'll have a bit of a fireside chat. Thomas?

Thank you, Eric. Google Cloud is Alphabet's Enterprise division. We take the products that Google builds and brings it to small enterprises, large enterprises and governments around the world. Over the last several quarters, we continue to see strong growth for our business. We're winning new customers roughly twice as fast as a year ago, deepening our relationship with existing customers, capturing market share and as a result, growing both top line and operating income.

Now the core of it comes down to the fact that we have highly differentiated products, and we remain the only provider that offers first-party solutions across the entire stack. We offer our own accelerators. And as the demand for training, inference and computation grows, we're seeing lots of customers choosing us for infrastructure. On top of that, we build our own frontier model, and we've seen strong growth with Gemini in large companies as well as in small companies, small and medium enterprises.

Organizations increasingly want to connect AI to their internal systems. And they want to do 3 things with them, understand the data, secure their company and automate processes or workflows within the organization. And across our portfolio, we have strength in data management, and we connect our historical strength in data management with our products like BigQuery and machine learning tools. We connect AI to that. In the same way, we connect AI to our security portfolio to help people with defense, and we offer a suite of applications from our collaboration applications like Workspace to domain-specific applications.

What's the value of having this diversified portfolio? First of all, it allows us to play in many large markets, each with individual large total addressable market segments. Second, as monetization of AI evolves, it allows us to diversify and capture revenue and share across every way. Some people buy us for silicon. Some people use our models. Some people use our data platforms. And so it diversifies the revenue streams that we capture. Third, because organizations want all of these things to work together, it differentiates our products from the point of view of quality, performance and cost.

Today, we have over 17 product lines, each over $1 billion. They're growing quickly and shows you the diversification we have. We've seen more than 2x growth year-on-year in new customer acquisitions and also 2x growth quarter-on-quarter and year-over-year in deals over $100 million, so large deals. And when a customer gives us a commitment, say, for $100, typically, they spend more than 50% more than that. And so that's reflective of the adoption we're seeing and the growth we're seeing with our customer base.

Now let's look at each piece briefly. At the infrastructure level, even this morning, there was an article showing the total cost of ownership benefits of our own silicon. We offer the best computational infrastructure for AI, and we offer 3 types of silicon. NVIDIA GPUs, our own Tensor Processing Units. And as increasingly as models generate code and need to run code, we also offer our own Arm processors to run that. We offer 2.7x better price performance for training, 80% better price performance for inference, 30% better price performance for CPUs. All of that allows us to differentiate our portfolio from other providers. It allows us to offer solutions to financial markets and capital markets.

So a lot of hedge funds, Deutsche Borse, for example, is using us. And so beyond just the labs, capital markets firms, high-performance computing, people like Pfizer and a number of organizations in the government, the Genesis mission in the United States government, for example, for energy are using it for high-performance computing and of course, a number of labs as well. Now our infrastructure gives us 3 important advantages. First of all, these are highly differentiated products in large markets.

Second, they allow us to optimize the cost of our products on the silicon. So we codesign our products top to bottom. And so when we launch something like Gemini Flash 3.8 and people go, how come you guys are 2x better than anybody else in the market on inference per dollar? It's our tokens, real intelligence per dollar, it's because we can optimize that whole stack. And third, it allows us to support a range of different business models, all of which have strong return on invested capital. The size of our accelerator business, our CPU business is more than twice that of the next largest hyperscaler.

Second, our payback period on AI servers in aggregate is less than 2 years and on our own silicon is half that. So we have strong payback period. And the majority of our infrastructure contracts, the total contract value of the infrastructure is long-term committed 5-year contracts. Now on this platform, people have started using models beyond just answering questions or chat for task execution. You give the model an objective and you call that an agent and the agent is given an objective, it decomposes that objective, uses a set of tools to connect into a company and execute the objective for you and give you that final answer.

So our platform is called Gemini Enterprise. It is used by over 90% of the Fortune 100 and thousands of small businesses. It's used in a very specific way. People want to use it as a reasoning agent. So break the plan, understand the steps that are needed, reason on it and execute the steps. So it uses a reasoning agent to understand all the information in the company to then automate that workflow process. And when it does it, you want strong controls. What kinds of controls? Companies are worried about security. They're worried about auditing, what these agents are doing. They want to manage costs and set budget caps. We have all those controls. And we allow people to use the right model for the right task. So you don't have to always use the most expensive model, saving people a lot of money in doing so. We have a range of companies from insurance.

SIGNAL IDUNA, they're the largest insurance company in Germany. They use it for claims and underwriting analysis. We've got PepsiCo using it for supply-demand planning, Macy's is using it for retail commerce, and there's a whole range of companies doing it. Now on this infrastructure, we're also building domain-specific solutions that's built on this foundational platform. What do these domain-specific solutions do? They do the 3 important things that people want to use agents for. Help me understand my data, protect me from cybersecurity threats and transform my applications and processes. For each of these, we bring together a stack of capability.

Think of it as you're a PepsiCo running a demand plan and you want to model the forecast. So the first thing you need to know is where is all my data. So we offer a platform that stores and manages a lot of data and can connect to other clouds to get the data. Second, tell me which of my different products are seeing growth and in which countries and what metrics? Is it revenue? Is it inventory? What is growing? So you need something called a catalog that gives you the definitions of those things. You then need to execute a machine learning model to run the calculation for your forecast and will surface up the answer. We do that super well. Example is our cost to run these models, machine learning models 2.5x better than the #1 in the industry.

And second, we do that with much greater accuracy because the definitions of your financial metrics that best is looking at is in the catalog. Same thing from a security point of view. When cyber risk grows because of threats from AI models, there are 2 things people care about. How fast can you find vulnerabilities and how quickly can you repair them. We do that in the same way by connecting AI to our Wiz platform. Wiz gives you the same capabilities. It helps you define what are your applications that need to be protected, which ones are high risk. Have they been compromised? Help me remediate them and then test that I've actually fixed the issue. And we do the same in a number of application domains.

So we are not just building a base platform, we're also integrating it into the core workflows of a company to help people understand their information, to help them modernize their processes and to help them protect themselves. The benefits of these solutions, first of all, it allows us to bring the investment we're making in Gemini into many more markets. Number two, it helps us enhance the frontier. If you look at the frontier, the big challenges are, can you give the model new skills like forecasting or long horizon planning? Can you help the model understand private data? Can you help the model behave in compliance with guardrails for safety? So it allows us to build that frontier.

And lastly, it allows us to leverage the strength that we've had in Google Cloud platform for many years, the strength we have with our analytics business and our database business, the strength we have with our cybersecurity portfolio and the strength we have with our applications to bring AI along with that to a broad set of customers. 90% of the Fortune 100 use our Gemini Enterprise portfolio. Separately, 90% of the Fortune 100 also use us for cyber defense. 80% of Google Cloud customers now use our AI products. And those that do use our AI products use 1.8x as many products as those that do not, showing us being able to strongly upsell and cross-sell into this base. The lifetime value of a customer who uses our Gemini portfolio in the cloud over a 5-year period, we estimate to be 1.5x.

And so all these are examples of things that we see where we're delivering strong return on invested capital. So to close, our product differentiation stems from the fact that we are still the only provider that offers first-party solutions across the entire portfolio. Each layer of that gives us the ability to play in a large addressable market. We have strong positions in each of them. And that's what's driving our growth in new customer acquisition, deepening relationships with existing customers, growing our partner ecosystem and driving in aggregate top line and operating income growth. Thank you.

Thank you, Thomas. So Thomas, you touched upon a lot of key themes in those slides that I want to come back to. The first being a vertically-integrated technology stack. It's so critical to what you talked about today. It comes up a fair bit with respect to the company's approach to the market. Talk a little bit about that integrated stack and how it has a financial benefit for your business as well as it stands to benefit your customers.

So I'll give you a real example to illustrate. Citigroup is building their wealth adviser on our Gemini platform, Gemini Enterprise platform, and it is designed to offer people financial advice. So when you look at that, we're combining 4 important capabilities. The first one is the person who is getting financial advice sees an avatar, which is remarkably life-like, offering them financial advice. They can ask it questions, they can assign it an objective like my kids are going to college, can you tell me how to moderate my cash flow? That agent is built using Gemini Enterprise, and it uses the strength of our TPU infrastructure to provide extremely real efficient real-time streaming because you want the agents and the avatar to behave properly.

Second, they are reasoning on an objective. It is looking up financial information about the person who's asking the question, and it needs to be 100% accurate. It cannot give you wrong financial advice because you'll lose trust in it. That uses our data platform for reasoning on financial information. Third, you clearly don't want such a system hacked for obvious reasons. So it uses our security tools to protect all this. And then because it's going to scale, you need to be extremely cost efficient in ensuring that these models run and the tokens are served in a very cost-efficient way. So that's just one example.

There are thousands of these companies who are using this platform top to bottom. And our -- the financial benefit for us -- we get to play by offering customers the whole stack if they want to. We can also play in every individual market segment as it grows. As the way that AI is monetized continues to evolve, is it -- do you monetize the model through an API? Do you monetize the model through agents? Do you monetize the model by driving more cycles on your data and security platforms? Do you monetize by driving more silicon, we get to play in all those markets. And that diversification gives us a lot more capability to reach more customers and more markets as well as grow.

Okay. I want to turn to the topic of AI accelerators and how you're positioning your strategy for AI accelerators relative to traditional computing systems. What are the source of demand you're seeing? And how do you think about meeting that demand with a combination of your own TPUs and external GPUs?

So we've been in this space for a long time. This is, I think, our 12th year of building accelerators. There are a number of factors that we're focused on. First of all, there are a variety of different types of accelerators people need for different types of models. You can have sparse models, dense models, mixture of experts. You need spots cores for certain things. So one type of accelerator definitely does not solve everything that customers need. That's number one.

Number two, where we offer specific silicon for training and specific silicon for inference because the needs of these systems are quite different. For example, training typically requires a larger scale cluster with different kinds of connectivity across the different nodes of the cluster. Inference typically requires a lot more access to disk because it's loading data off disk when it reads and writes. So there's different kinds of silicon we offer for training and inference.

From a demand point of view, we see different things from -- historically, it's not just now the AI labs, but many capital markets firms because they see the efficiency with which their algorithms for numerical trading and research can run on these chips. We see them migrating historically, high-performance computing, which is solid-state, computational fluid dynamics, et cetera, are also shifting on these platforms. And so we see a range of these.

And for us, the opportunity is to offer this, obviously, to our Gemini team, and that's our most important priority is continued leadership at the frontier for our models, but we also offer these to other customers. We offer it in a range of configurations and some of it runs in our cloud, but we also offer it in other people's data centers because people want it close to their data. And across every business model, we have strong returns in this business.

Okay. Understood. Probably one of the most often asked questions I get from investors is about TPU strategy broadly. How do you, as a company, think about offering TPU systems to third parties and neoclouds? And how do you think through the return on invested capital from that activity relative to meeting your own demand needs?

So as we get into more markets, one of the things we realized was there are many places where customers are putting these machines where it needs to sit near their own systems. So for example, if you look at high-performance computing, people have collected data for years and years, and there's a ton of data. And telling them, please copy all that to the cloud in order to reason on it or build a high-performance computing application is not viable. So we put our systems in their data centers.

Similarly, capital markets firms, particularly those that are doing quantitative research, want it close to the venue where they're executing because from the research, they want to execute. So rather than saying, please bring all that to our cloud, we say we can put it in your data center. Now we offer 2 or 3 types of business models. One is a subscription in our cloud. Second is a capital purchase. When you're buying the system to run in your data center, you buy it as a capital purchase from us, similar to buying a hardware system. And third, we also offer it through neocloud. In fact, we set one up with Blackstone to offer that as neocloud.

The benefit of these things, one is we have diversification of our -- of the way that we monetize it. For example, when you buy it as hardware, we don't need to spend capital expense for data centers because you're putting it in your data center. And so we don't have the responsibility for that power and space. So we don't need to manage that capital expense. We also see strong free cash flow when you buy that to put in your premise. So we see a range of these business models, and we're diversifying it specifically to manage that. Just as importantly, we do long-term contracts. So for example, we're very smart on how we think about prepositioning inventory in a number of cases.

We may do long-term capital purchases so that the unit cost of components are very efficient for us. And third, we are also prepositioning, for example, the way that customers buy and use these systems by making sure they're signing long-term contracts with us. So all of these different elements on the infrastructure side gives us the ability to diversify our portfolio. And because of the strength we have with the product line, we're seeing a lot of demand for it.

Okay. You talked in your presentation around Gemini Enterprise and the Cloud Next. You talked about it being widely deployed as an agent platform, and you also talked about it being a stimulant for paid user growth quarter-over-quarter. Can you talk a little bit about how Gemini Enterprise is being positioned commercially, how customers are adopting it? And what you see as durable differentiation compared to model-first companies that maybe you're competing with something like Gemini Enterprise?

So we have today over 1 billion consumer users. We have millions of small businesses using Gemini through our collaboration tools and Google Cloud platform. And we also have very large customers using Gemini to run their core functions in their companies. As I said, 90% of the Fortune 100. What is it that we do uniquely? If you think about a person wanting to use an AI system for building an agent, typically, there are 4 tasks to try to do with it, write code, analyze data, change a process, for example, change how I do my back-office reconciliation for cash accounting or protect me, and protect me, meaning please protect me from external threats and please protect me from an agent running a mock within my company.

What Gemini Enterprise does is it applies -- it has 3 important differentiations. One, it gives you a single agent that provides all of these capabilities. It can analyze data, it can generate content, it can protect you from a security point of view, et cetera. Second, in order to do it well, you actually need to hook up the AI system into the internal processes and systems of your company. So as I said, we do give you tools to understand your metric definitions so that when we give you a financial answer, it's 100% right. When we give you security defense, rather than saying, I can scan all the applications in your company, which you are free to do, but will take you a lot of time and a lot of resources. We can be very precise on which applications do you prioritize and how do you fix.

And third, we also understand process definitions in lots of enterprise systems. Now the benefit also is that we give you a choice of model. So many people ask, why do you offer a choice of model? Very simple. Take cyber. No single model finds all the issues from a cyber point of view. So if you scan with just a single model, the risk you run is think of it as you will be protected from only those vulnerabilities that, that model finds. More importantly, no model finds a super set of all the other models. So if you really want to defend yourself, you have to use multiple passes, one pass with our model, one pass with another commercial model, a third pass with an open source model. And particularly, the reason we also say use open source is many times the attackers are going to use that. So the best way to defend yourself is to figure out what they're going to attack with. And so that choice gives you actually better capability than if you just had a monoculture with one model.

Understood. Maybe just building on that and sticking with cybersecurity for one quick follow-up. How do you see your strategy with respect to cybersecurity evolving specifically to address AI-driven threats?

Most people asked us when we acquired Wiz, why did you acquire Wiz? We saw in 2023 that as models learn to code because the most initial skill that models were doing was learning to write code that they could understand code and configuration of systems. And once they understand code and configuration of systems, they could become extremely proficient in finding vulnerabilities. So we acquired Wiz as a platform because it gives us 4 things. First, it connects to a lot of clouds, not just ours, but enterprise-wide, and it captures the configuration of your systems and what software is running on those systems. It also builds a risk score on which of these applications are most likely to be compromised.

So the first thing it gives you is an incredibly efficient way to prioritize what you need to look for. Second, it allows us to then scan the stuff that you are most worried about and identify vulnerabilities. And our Gemini platform hooked after Wiz is being used by many, many companies now to scan and find vulnerabilities. Finding vulnerabilities is only half the problem. In fact, it's less than half the problem because repairing it is the bigger part of the problem. So we've built with Wiz a product called CodeMender, which is repairing. The name pretty much says CodeMender. It repairs your code.

And then Wiz gives you the ability to also test it. So that cycle is what has driven over 90% of the Fortune 100 to now adopt our cyber tools to defend themselves from threats due to AI models. You can only defend a threat from an AI model by using a combination of a security platform and an AI system. An AI system by itself cannot protect you, a security platform by itself cannot protect you. You need that combination, and we're uniquely capable of offering that.

Okay. We have a few minutes left, but I do want to sneak in a couple more topics. You recently implemented a strategy that forward deploys engineers to help with implementation. Talk to me a little bit, Thomas, about the challenges of implementation and the opportunity that sits if you forward deploy engineering talent to try to help drive wider array of enterprise adoption.

So we've never been a services company. In fact, our services business as a percentage of our total business is the smallest of any hyperscaler. We are a products and solutions company. So where do we use services and our forward deployed engineers? So our forward deployed engineers are focused on 3 important things. The first one, working with the top most customers in each industry to identify where to push the frontier of our model. So for example, in capital markets, it's forecasting.

In legal, it's correctness of the actual results that are being presented by a model because in legal, you need to be 100% factual. Otherwise, you will have a problem with the lawyers. So first thing they do is find the frontier. Second, they build the tools to allow us to capture the frontier. And what we mean by that is, for example, when we go into a financial market, understanding the actual private data for that company and being able to connect the model with it, our forward deployed engineers build the tools to do that part. We make those tools available to all companies, but they are the ones because of their expertise. They understand here's a specific thing and here's how we're going to go implement it.

Third, they build the training and certification program to scale the business through partners. Even this morning, we announced an agreement with Accenture, where Accenture is building a large Gemini Enterprise business group focused on this, and they're working with us because our goal has never been -- is always to enable the partner ecosystem and our forward deployed engineers learn what customers are doing, build the tools and certifications and then we scale it to our partners.

So we've only got about 2 or 3 minutes left here. Maybe I'll jump to one last final one, which dovetails on the back of what you just said there about Accenture. Google Cloud has announced sort of these new and expanded partnerships in the way in which you're bringing all of these products and services to market. Talk a little bit about some of the initiatives or the verticals that you're most excited about that contribute to growth and you think about Google Cloud over the next couple of years.

So we're focused on partnerships in 3 different domains, okay? First one is in specific industries. We've seen that the needs of specific industries are different. For example, the way that a life sciences company wants to use our models, like what Merck is doing using Gemini Enterprise for both science and for corporate operations. So there are 8 industries that we are very focused on pursuing because we see them as having unique needs that help us advance the frontier. That's number one. And in those, we will work not just with leading companies, but also with the independent software vendor and SaaS ecosystem in that market to build solutions.

Second is the ability to scale the services community. This is both traditional system integrators, but also new breed AI specialists. And we have that down by market, by country so that we have it mapped, and we have a lot of work going on with that partner ecosystem. That's number two. Number three, we're also working with specific data providers and people building data sets for specific industries. So for example, in financial markets, Bloomberg, FactSet, there's a whole range of them MSCI, et cetera. We built -- they've put their information in our platform so that people can reason much more quickly on that information. And depending on the domain in life sciences, for example, in health care, there are different providers that offer that information. And the goal is to make that information available so that agents and models can reason on them much more quickly and easily. And that's a very focused strategy we have by industry, by country and by customer segment.

All right. Well, I think we're going to leave it there. Please join me in thanking Thomas for being part of the conference this year.

Welcome, everyone. Thank you for standing by for the Alphabet Second Quarter 2026 Earnings Conference Call. [Operator Instructions]

I would now like to hand the conference over to your speaker today, Jim Friedland, Head of Investor Relations. Please go ahead.

Thank you. Good afternoon, everyone, and welcome to Alphabet's Second Quarter 2026 Earnings Conference Call. With us today are Sundar Pichai, Philipp Schindler, 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 statement. 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 located at abc.xyz/investor. Our comments will be on year-over-year comparisons unless we state otherwise.

And now I'll turn the call over to Sundar.

Hi, everyone. Thanks for joining us. We have exciting momentum. Alphabet revenue grew 24% year-over-year. Our AI investments are redefining what's possible across every part of our business. Our momentum starts with search where people are adopting one seamless search experience across AI overviews and AI mode. We saw 17% revenue growth in search and other and YouTube ads grew 13%. Cloud revenue grew 82% powered by strong demand for AI infrastructure and AI solutions and Cloud backlog grew to $514 billion. It's great to see the wide adoption of Gemini Enterprise with nearly 90% of Fortune 100 using it. Today, I'll expand on our AI progress, our global product footprint, led by Search and YouTube. Google Cloud's leadership and our long-term bets.

First, AI. Yesterday, we announced new models, Gemini 3.6 Flash and 3.5 Flashlight, which are cost-effective and highly efficient. We are seeing tons of demand for our workhorse Gemini Flash series because it hits the sweet spot of performance and cost. We also launched Gemini 3.5 Flash Cyber, which I'm really excited about paired with our core vendor agent, it finds and fixes vulnerabilities and delivers performance at the frontier comparable to far bigger cyber models. GEMINI 3.5 Pro is currently in testing and our team is already building the next generation of models. We have started our most ambitious pretraining run yet for Gemini 4 and are excited by the progress we are seeing at the frontier. Demand for our models is translating to strong token usage across developers and enterprise customers, and we continue to be supply constrained, a sign of momentum and rapid adoption.

More than 9 million developers are building each month with our models across our APIs and key developer products. Our model APIs are now processing approximately 22 billion tokens permanent. That's up from $16 billion just a quarter ago. Additionally, this quarter, we launched Omni. It allows users to create anything from any input starting with video. Since launching at IO in May, there's been a 40% increase in daily active users, creating videos on the Gemini app. Our Gemini family of open model, small enough to run on local devices are hugely popular. These models have been downloaded over 900 million times, and our latest GM4 models have been downloaded over 300 million times since launching in April.

Our agentic development platform, antigravity allows anyone to build in the agent first era. It is more than 2.4 million weekly active users. Antigravity is a powerful tool for users and enterprises and it's completely accelerated how we build internally. As just one example, a team in Chrome is now on track to accelerate delivery by 8 times compressing a 2-year time line into 3 months through model-driven refactoring.

Moving on to our global product footprint, which brings AI to more people than any other company. Starting with Search. AI continues to drive an expansionary moment with new experiences resonating with users and driving growth inquiries. As a big football fan, I was particularly excited to see Search usage hit an all-time high during the World Cup this year. This really highlights how much people turn to Google in moments that matter. We continue to make Search more helpful and Intuitive. We recently brought together AI Overview in AI mode into one seamless search experience that combines our frontier capabilities with the best of the web. And we are continuing to incorporate more frontier capabilities into search with agents, personal intelligence and notebooks.

Our AI-powered features are driving increased search usage since expanding AI mode globally last October, we have surpassed 1 billion monthly active users. And just like AI Overviews, AI mode is driving an incremental increase in search queries overall, and we are now sending billions of clicks to websites every week through AI features in Search. As we serve more of these queries, we have continued to drive efficiencies. Thanks to our engineering and hardware optimizations this quarter, we reduced the cost of AI mode responses to its lowest level since launch even as we have brought more advanced AI capabilities. Philipp will also talk more about how we are innovating the ads experience.

Next, the Gemini app, which now has 950 million monthly active users with daily active users tripling in the last year. Users love new agent features like Daily Brief and our personalized agent, Gemini Spark, which is now available in the U.S. and internationally. We have been shipping helpful new features like this at an incredible clip. On to YouTube. month after month, when a big event happens in the world, the world turns to YouTube. It's been incredible to see that over 1.7 million unique viewers globally watched World Cup-related videos on YouTube during the FIFA World Cup 2026. We are also bringing the power of conversational AI directly into the YouTube experience. Ask YouTube uses our Gemini models to let people ask complex questions about individual videos, get quick takeaways and jump straight to moments in those videos. The early engagement is encouraging. More than 140 million users engaged with Ask YouTube on the watch page in June 2026. And we are bringing that Ask experience to the wider Search experience on YouTube.

Next, Cloud. Our continued momentum is driven by our integrated AI portfolio, consisting of chips, models, data, security and agent platforms, all designed to work together. Gemini continues to be a key driver of growth and is deeply integrated across all of our cloud products, including Gemini Enterprise, data analytics, cybersecurity and Google Workspace. We are seeing strong diversified demand across products, customers, geographies and industries. Our product differentiation is driving expansion in 3 ways. We are winning new customers, more than doubling our acquisition velocity year-over-year. We are deepening our relationships with existing customers who are expanding their usage and exceeding their commitments by more than 50%. And also an acceleration over last quarter.

We are driving growth with partners with transactions on Google Cloud Marketplace growing over 7x year-over-year. One of the strongest parts of our growth comes from the rapid adoption of our Gemini Enterprise platform. It's differentiated with easy-to-use tools to build agents and automate processes, connectivity to enterprise systems cost management and governance tools. In Q2, agent development kit, our framework for building and deploying enterprise AI agents reached nearly 70 million total downloads. As I said, nearly 90% of Fortune 100 are using Gemini Enterprise. We have customers like PepsiCo for AI and analytics solutions, Intel to streamline core processes, HSBC for wealth management, Bell Canada for customer engagement, Macy's for commerce experiences, and Signal Iduna for knowledge management.

More broadly, Gemini is transforming how millions of businesses use AI to build custom agents automate processes, improve cybersecurity, manage customer relationships, streamline data analytics, collaborate effectively and more. All of this momentum is driving growth in our paid token usage. Nearly 500 cloud customers have each processed more than 1 trillion tokens in the last year and usage is so much deeper than that. Over the last 12 months, more than 2,000 enterprises consumed over 100 billion tokens. We are also seeing strong interest in our AI-powered security platform, which is differentiated because it integrates threat intelligence, cyber response prioritization with best AI automated scanning, code remediation and monitoring.

Today, 90% of Fortune 100 are Google Cloud security users. Nearly 90% of this customers are using AI-powered security features. And we have seen a more than 45% quarter-over-quarter increase in the number of AI workloads scanned and protected by our security platform. Our security tools are being used to protect critical infrastructure including financial services organizations such as Morgan Stanley, telecommunication providers such as TELUS, health care organizations such as Texas Children's, software companies such as Atlassian and several government agencies. And with our new Google AI threat defense, we are really excited to bring our new cyber model and core vendor to help our customers defend against AI threats. All of this is powered by our leading AI infrastructure.

We offer the industry's broadest range of accelerators from Google and NVIDIA, including the new NVIDIA verarubin platform and TPU and ATI, which deliver strong price performance. On top of this, let me mention 3 unique offerings. First, our VIrgo network, which is designed to meet the needs of modern large-scale AI workloads. It allows customers to connect 1 million AI acirators across multiple data center sites into a unified supercomputer. Second, our software stack has native support for JAKs, PyTorch, BLM and SG line, enabling workload portability across GPUs and TPUs. And third, our new agent optimized Axion CPU, provides 30% better performance per dollar compared to peer offerings. We are seeing strong growth in demand for our AI infrastructure offerings from leading labs such as ineffable intelligence, next-generation AI builders, including Kakao, financial services like Deutsche Borse Group, pharmaceutical companies such as Pfizer and Roche and robotics and spatial intelligence companies such as World Labs.

We are continuing our strategy of investing in promising companies and internal efforts that bring pioneering AI research into the real world. We have seen exciting progress from our other bets. Waymo introduced its newest vehicle or high to public riders. This is the first vehicle powered by the sixth generation Waymo Driver and we'll welcome more riders in the coming months. Wing has safely completed more than 1 million home deliveries, and we continue to grow our presence through partnerships with Walmart, DoorDash and Papa Jones. In health and drug discovery, Isomorphic Labs raised over $2 billion to power its AI drug design engine, scale globally and advance its drug candidate pipeline. It's truly been an extraordinary first half of 2026 with much more to come, thanks to all of our employees and partners worldwide for their incredible work this quarter.

With that, Philipp, over to you.

Thanks, Sundar, and hello, everyone. I'll start with a review of Google services, highlighting our momentum across Search, YouTube and partnerships. Google Services revenues were $95 billion for the quarter, up 15% year-on-year, primarily driven by search. Search and other delivered 17% growth with retail and finance driving the largest contributions. YouTube advertising revenues grew 13%, driven by direct response and brand. Network advertising revenues were down 1% year-on-year. Strong performance in Search and YouTube underscores how our investments in AI translate into measurable value for our users and advertisers.

Starting with Search and other. Revenues reached over $63 billion for the quarter. Sundar highlighted the momentum we see in search, which directly impacts our advertising business. We continue to accelerate the deployment of Gemini across our entire ads infrastructure to boost performance in 3 areas mentioned before: ads quality, advertiser tools and AI user experiences. First, ads quality. At Google Marketing Life, we showcased how Gemini improves query understanding, allowing us to find relevant ads for longer searches previously difficult to monetize. The core engine of our search ads relies on a dual prediction, delivering immediate utility for the user while maximizing measurable value for the advertiser.

Gemini completely supercharges this capability. We use Gemini's advanced reasoning to decode the nuances of longer, more detailed quarries. With shopping ads, for instance, we drove a 20% improvement in showing highly relevant ads, helping shoppers immediately find the best match. Second, advertiser tools, take AI Max. It has become the core building block for advertisers to fully participate in our new AI experiences. It's out of beta and 0.5 million advertisers have already adopted it. Those who adopt our AI-powered campaigns like AI Max or PMax see an average of 15% more conversions or value on search at a similar ROAS. AAA Auto Club enterprises used AI Max to personalize creative assets and capture growth from increasingly detailed insurance searches. This led to a 17% improvement in conversion volume and an 11% decrease in cost per lead. We see strong adoption of our generative AI creative tools, which makes creative development easier, especially for SMBs.

In fact, over half of our SMB customers globally use AI to create or optimize their creative. Third, new AI user experiences. At I/O, we showed how search, including AI Overviews and AI mode works as one connected experience. As you saw from our results, our performance across the holistic system is robust. We continue to be encouraged with monetization performance on quarries that show AI Overviews, even as we've expanded overviews to more commercial queries. Across AI overviews and AI mode, people are asking more specific and detailed questions, providing opportunities for more relevant ads. Within AI mode, we continue to test and deploy a range of new ad formats. For text ads, we improved performance by adding contextual cyclings based on the conversation. Direct offers is gaining momentum with partners like IHG hotels and resorts, soon servicing special offers during trip planning and highlighted answers on latest experience playing clearly marked sponsored links inside list responses is showing early user traction.

Looking at commerce. In collaboration with the retail industry, we established the open-source universal commerce protocol, UCP, as a new standard for agentic Commerce. Merchants are rapidly adopting UCP with Target and Steve Madden now live, while new members have joined the UCP shopping and food tech councils to help steer its vision. We also announced Universal Card, allowing shoppers to add items from different retailers across Google services and buy in a single checkout.

Now on to YouTube. As a FIFA preferred platform partner, we help fans around the world tune into the games. As Sundar mentioned, over 1.7 billion unique viewers watched World Cup-related videos and over 550 million watched them on their televisions. This made the FIFA World Cup 2026, the most viewed World Cup in YouTube history. Advertisers connected with fans through FIFA channel takeovers, Gemini-powered soccer theme sponsorships and game day mass heads and, of course, a huge congratulations to Spain for taking home the trophy. More broadly, on YouTube, we introduced an exclusive slate of creator shows making it easier for brands to tap into the fandom of creators.

We launched custom sponsorships to put brands at the heart of the world's biggest moments as they unfold on YouTube using AI to dynamically surface videos tailored to a brand's desired moment. Brands continue to partner with YouTube creators to engage new audiences. Kate Spade reached Gen Z through a first-of-its-kind YouTube creator campaign and partnered with craters Li Tuan and Hanam by leveraging multiple format videos on YouTube, the brand drove a 3.25% brand lift and purchase intent. This unique conversions of brand equity and commercial action makes YouTube a powerful full funnel platform. SMBs continue to fuel our direct response growth through campaigns like demand gen, they leverage our AI to scale visual storytelling across YouTube, shorts and now Google Maps.

To attract high-value shoppers across North America and Europe, outdoor brand Arterix used demand gen to achieve a 70% better return on their ad spend compared to other paid channels. Looking at monetization across YouTube, we're driving sustained growth across our key priorities. In the living room, we see continued momentum across both brand and direct response. With the launch of Buy with Google Pay, viewers can complete purchases directly on their CTV, turning the TV screen into a stronger performance service. Shortz continues to deliver high-performing opportunities for social and video buyers alike. Beyond advertising, YouTube subscription business, which thrives across living room screens, is growing faster than ads, particularly driven by YouTube Music and Premium.

Internally, we leverage Gemini to transform how we work and how we serve our customers. 83% of our sales team uses Gemini assisted tools weekly driving up to a 20% higher win rate when using customized pitch narratives. Our ads customer support teams use Gemini-powered agentic solutions that now autonomously address 75% of support carries, freeing them to solve our customers' most complex challenges. Similarly, our new agentic solutions for SMBs have expanded our reach by hundreds of thousands of new customers year-to-date. As always, ending with partnerships. We're seeing more businesses benefit from the unified strength of Google's AI stack.

Booking Holdings expanded a multiyear cloud commitment and are partnering closely to advance our AI part ad formats. It's also deploying Google's AI technology to enable new customer experiences like agenetic dining reservations on OpenTable, helping restaurants get discovered and booked right when it matters most. In closing, I'd like to thank Google is everywhere for their contributions to our success and our customers and partners for their continued trust.

Anat over to you.

Thank you, Philipp. My comments will focus on year-over-year comparisons for the second quarter unless I state otherwise. I will start with results at the Alphabet level and will then cover our segment's results. I'll end with some commentary on our outlook for the third quarter and full year 2026. We had an outstanding second quarter, delivering our 12th consecutive quarter of double-digit revenue growth. Consolidated revenues were $119.8 million, up 24% or 23% in constant currency. Total cost of revenues was $45.9 billion, up 18%. Tech was $16.2 billion, up 10%. Other cost of revenues was $29.8 billion, up 22%, driven by increases in depreciation, inventory costs, primarily from the sales of TPU systems to customers and content acquisition costs largely for YouTube.

Total operating expenses were up 27% to $33.1 billion. R&D expenses increased by 32%, driven by compensation from investments in AI talent as well as depreciation. Sales and Marketing expenses were up 18% due to investments to support the Gemini app in Search and Compensation. And G&A expenses increased 24%, primarily driven by Compensation and charges for certain legal and other matters. Operating income increased 30% to $40.8 billion and operating margin was 34%. Other income and expenses was $98 billion representing a substantial increase from the prior year, primarily due to unrealized gains in our equity securities portfolio. Net income and earnings per share increased significantly primarily due to the unrealized gains in OI&E I just mentioned.

We generated strong operating cash flow of $39.1 billion in the second quarter and $185.7 billion for the trailing 12 months. CapEx was $44.9 billion in the second quarter, with the vast majority of the spend in technical infrastructure to support our investments in AI. Approximately 60% of our investment in technical infrastructure this quarter was in servers and 40% was in data centers and networking equipment. We had negative free cash flow of $5.9 billion in the second quarter driven by our investments in CapEx. Free cash flow was $53.3 billion for the trailing 12 months. We ended the quarter with $242.5 billion in cash and marketable securities which includes $87.1 billion of marketable equity securities. Long-term debt was $98.2 billion.

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 September. Turning to segment results. Google Services revenues increased 15% to $94.5 billion reflecting strong growth in search and subscriptions. Total advertising revenues were up 14% with growth across all major verticals. We experienced strong ad growth related to the World Cup, particularly in YouTube ads. Google Search and other advertising revenues increased by 17% to $63.3 billion with retail and finance driving the largest contributions. YouTube advertising revenues increased 13% to $11.1 billion, driven by direct response advertising as well as brand with strength in the living room, as Phillipp mentioned earlier. Network advertising revenues of $7.3 billion were down 1%. Subscription platforms and devices revenues increased 15% this quarter to $12.9 billion due to strong growth in both YouTube subscriptions, particularly YouTube Music and Premium and Google One, which was driven by demand for AI plans.

Google Services operating income increased 20% to $39.5 billion and operating margin was 41.8%. The Google Cloud segment delivered outstanding results in the second quarter, driven by our Enterprise AI products and services. Cloud revenues were up 82% to $24.8 billion driven primarily by GCP, which grew faster than Cloud overall, core GCP, AI solutions and AI infrastructure were all important drivers of growth. We also began to recognize revenues from TPU system sales, which we delivered to customer data centers for the first time in Q2. Cloud revenue growth accelerated meaningfully even after excluding the impact of TPU system sales. Cloud operating income was $8.8 billion, more than tripling year-over-year and operating margin increased from 20.7% in the second quarter last year to 35.6%.

Google Cloud's backlog increased by more than $50 billion sequentially, reaching $514 billion in the second quarter. The increase was driven by strong demand for our Enterprise AI offerings. The majority of the backlog is related to typical GCP contracts for a broad mix of customers, and we expect to recognize just over 50% of the total backlog as revenue over the next 24 months. In Other Bets, revenues were $382 million, and operating loss was $1.8 billion as we continue to spend Waymo's business and invest in key other bets. And in Alphabet level activities, the operating loss was $5.8 billion, driven by shared AI R&D expenses.

Turning to our outlook and business performance expectations in the second half of 2026. First, in terms of revenues, at the current spot rates, we would expect a slight FX headwind to our consolidated revenue in Q3 compared to a 1 percentage point FX tailwind in Q2. This impact will be seen primarily in Search and YouTube ads. In Google Services, as Philipp mentioned, we continue to benefit from innovation and search by improving the user and advertiser experience with AI. At the same time, in Q3, we will begin lapping an acceleration in Search performance that began in the third quarter last year. In Google Cloud, we're seeing significant demand for products and services which we expect to drive strong growth. As I mentioned earlier, we started delivering TPU system to customer data centers in the second quarter. We continue to expect to recognize a relatively small portion of the revenues from our existing TPU system sales agreements this year, ramping as we exit 2026.

We anticipate the vast majority of the revenues from these agreements will be realized in 2027. And given the supply constrained environment, we plan to expand the use of third-party capacity in Q3 as a bridging strategy while we build up more internal capacity. This strategy allows us to keep growing our customer base and capture greater overall value. However, it will create modest margin pressure in the near term as we utilize this capacity.

Moving to Investments. We are updating our full year 2026 CapEx guidance range to $195 billion to $205 billion. up from our previous estimate of $180 billion to $190 billion. The increase in the range is primarily due to an acceleration in the delivery of capacity to meet growing demand. As we previously shared, we continue to expect our CapEx to increase significantly in 2027 and we'll provide more details at a later dates. In terms of expenses, the significant increase in our investments in technical infrastructure will continue to put pressure on the P&L in the form of higher depreciation expense and related data center operations costs such as energy. We also expect to continue hiring in key investment areas such as AI and Cloud and we are investing in marketing to support our AI products.

And finally, we expect the free cash flow will remain under pressure, driven by our investments in technical infrastructure which enables us to capitalize on the AI opportunity and continue to drive attractive returns. Q2 represented another strong quarter. Our teams continue to deliver impressive innovation, executing with discipline and velocity. I want to take this opportunity to thank our employees for their contribution to our performance.

Sundar, Philip and I will now take your questions.

[Operator Instructions] Our first question comes from Brian Nowak with Morgan Stanley.

I have two, one for Sundar and one for Anat. Sundar with more time in GenAI products and tools in the market and investment continuing to step up, can you just kind of give us some perspective on how your view on the size of the overall GenAI ROIC opportunity and the timing of the ROIC has changed now versus 1 year ago.

And then Anat, you talked quite a bit about supply capacity constraints. Maybe a similar question for you around forward CapEx. How is your budgeting philosophy and these constraints you're putting on the forward spend? How are those changing? Do you sort of think about the right amount to spend in 2027 to close this capacity constraint?

Look, thanks, Brian. I mean it's a good question. I do think it feels like we are in very early innings of what feels like secular shift across multiple areas in our core information businesses just the possibilities when I see what all you can do with the absolute frontier capabilities, there's still a lot of work ahead to translate all that into experiences for our consumer users. You can think about end-to-end agentic experiences to really meaningfully do a lot more for them.

So all of that looks like extraordinary opportunities with extraordinary returns for executing well on those opportunities. Similarly, on the Enterprise side, as you can see from our demand, which is reflected in our growth rates, et cetera. Again, in my conversations that many CEOs, many companies, they're all still barely scratching the early stages of what's possible here. So I think we used to talk about Cloud itself very -- small percentage of overall workloads in enterprises have shifted to cloud. Now think about what percentage of workloads are really AI native and AI enabled. It again, feels like very, very early.

So I would say from an ROIC standpoint, I think we are taking a full stack approach. We are seeing momentum across consumers and enterprises and developers and so on. So it feels like, if anything, over the past year, we've gotten more bullish on the opportunities ahead.

I'll turn it over to Anat.

How do we think about forward-looking CapEx in the context of supply constraints. So we're still in a supply-constrained environment. I think we've said this now for multiple quarters in a row. And we are seeing very strong demand, both from external cloud customers as well as across the business. And our goal is to invest as long as we see an attractive return on that investment, as Sundar mentioned earlier. We do take a long-term view. So we take a multiyear view at what the needs are as well as focus on next year and the near term and building aggressively to meet those demands.

And as you've seen, while we have increased our capacity quite significantly over the past 3 years, the demand still outpaces that investment. And we are just the rest of the industry, working in a supply-constrained environment. So we're working hard to do that. We do have a benefit of having the full stack approach. So we're able to drive operational efficiencies, technological efficiencies within our technical infrastructure organization so that we can deliver more compute, but as long as we see these attractive opportunity to invest, we will continue to invest.

Our next question comes from Doug Anmuth with JPMorgan.

One for Sundar and one for Anat. Sundar, can you just talk about your confidence level that the Gemini models can remain at the frontier. I know you had the Gemini, the flash model releases this week. But Google perhaps doesn't make maybe quite as much noise as some of the other leading labs or perhaps have quite the frequency. Just curious how you'd address any concerns about that ability to continue developing leading models?

And then just related, can you talk about your plans for coding and how you look to close the gap in that part of Enterprise. And then not just following the recent equity and debt raises, can you just help us understand how you think about optimal capital structure and how you view the cost of debt versus equity?

Thanks, Doug. Look, I think the frontier is incredibly dynamic space, and it's fiercely moving forward. At any given moment, when you take a snapshot, it feels dynamic. We are -- we've had clearly frontier models. There are many attributes on which we are still at the frontier that are areas where we have acknowledged we need to improve coding and agentic coding is an example of that, and I think the teams are very, very focused on it, with 3.6 Flash. And by the way, Flash is our work house model, and we see tremendous demand for it given it generally hits a sweet spot of performance, cost, reliability, latency, et cetera and we've incorporated Gemini and Flash, particularly in our entire portfolio of solutions be it in cybersecurity, data analytics. If you think about an area like customer service, you need really good voice quality, you need live streaming, you need the ability to reason on that or if you're a professional services firm, you need high-quality summarization, content generation, et cetera.

So Flash does very well on all of that. In terms of agentic coating, we are iterating, and you will see us make continued attritions, 3.6 Flash, for example, compared to 3.5 Flash jumped over over 10 points in Deep as a benchmark. -- and it is more token efficient doing so. So we are clearly -- we are using it internally. We are testing it with many customers in coding. So we see the progress just in 6 weeks from the prior version to the new version, and you will see more continued iterations on that as well. In terms of the frontier, we are both very committed and very confident of being at the frontier. For the next generation of frontier, you're going to need a much larger base models. We are now training Gemini 4, and we're being very ambitious with it. I am very excited by the progress I'm seeing internally on Gemini 4, and I'm confident that people will be pleased when we are putting it outside.

But we will need Gemini 4 as a larger base model to compete at that frontier level. And so we are focused on executing on that well.

And on the question regarding our capital structure and recent equity and debt raise. So as we think about our investment needs and -- as I said, we look at the next year and multiple years out, we first look at how much we can support based on cash from operations. And as you've seen in our results today, we continue to generate very healthy strong cash flow from operations. So that's our first source of funding. And then we look at debt and most recently, we did the equity raise. And we have expanded our debt portfolio quite significantly over the past 12 months. If you've seen a year ago, we were at about $16 billion, and we went to about $100 billion across multiple currencies and geographies. So we're expanding that portfolio.

But we also want to make sure we have a resilient, not just growth outlook but also a resilient balance sheet and a strong balance sheet, a healthy balance sheet, which is the rationale behind expanding into the equity markets. At this point, we're not planning to go back to the equity markets with the exception of, as you recall, part of our equity offering was the ATM or at-the-market offering that we will do to address the stock-based -- or the tax on SBC, which we'll do for some period of time. But we look at those 3 dimensions, the cash flow from operations, debt levels and equity in a way to balance it in a way that ensures that we continue to maintain a healthy balance sheet.

Our next question comes from Eric Sheridan with Goldman Sachs

Maybe two, if I could, both on TPUs. Sundar, can you talk about some of your key learnings as you scale your TPU efforts in terms of both the demand for TPUs? And how you think about balancing the external demands for TPUs versus the internal demand for custom silicon inside the organization as you look over the next couple of years?

And then Anat, if I could just follow up. You made some comments in your prepared remarks about the impact of TPUs on Google Cloud. I didn't know if you could give us a little bit more granularity about the size of TPUs as a part of the revenue backlog and how to think about the revenue recognition of the potential for margin impact from TPUs in the years ahead.

Okay. I think in terms of -- first of all, we are very pleased with our TPU road map progress and the value in terms of performance and the edge it gives. And we obviously use it extensively. In terms of allocating TPUs, look, our first priority is making sure we are allocating what we need to compete at the frontier in terms of AGI development and so that is the foundation for everything we do. But given the extraordinary demand to balance the external demand even for most cloud customers, we are using both TPUs and GPUs, mainly for serving our models.

So think Vertex AI, Gemini enterprise, the momentum we see. So we're using it or agentic workloads, et cetera. So we are using it for those purposes. To the extent people want it as infrastructure, we are balancing it by increasingly looking at opportunities to put TPUs in their data centers or in other data centers like the project we are doing with Blackstone, et cetera. So we're using that as an ability to balance and make sure we are allocating as much of the available compute for frontier model development as well as serving both our consumer business and our Enterprise business in terms of first-party models.

And in terms of revenue recognition and how we look at the TPU system sales, so the way to think about it is the following. When we signed the agreements that I've mentioned, in the prepared remarks, they would be then reflected in the back of the cloud backlog. Now the vast majority of the 514 billion of cloud backlog is the GCP agreements, but the TPU system sales are reflected in that backlog. We start building inventory to be able to sell those systems. So you see that impact on the cash from operations because we built ahead obviously, as we're building that business and ramping up. And then once we start delivering the sales, generally, that's when we start recognizing revenue. This quarter was a small amount of the total -- that total agreement will continue to ramp up throughout 2026, but then you'll see the vast majority of the revenue from that agreement comes through in 2027.

Your next question comes from Ross Sandler with Barclays.

Just back to the AI model kind of war that's going on, Sundar, just wanted to ask about like the speed of model releases and how you can potentially speed up the cadence by which Gemini is releasing model. So we all saw the third-party compute deal with SpaceX. What else are we doing to kind of speed up the pace of model releases compared to some of the other folks out there?

And then you just mentioned the Flash models, which have been very successful, to your point. Do you view that as like the right end of the market to be in, given how crowded the lower cost end of the model space has become? Just any thoughts on how you see all that playing out?

Thanks, Ross. Look, I mean, 2 things I'd say, first of all, we are -- we really think about the Pareto frontier. We want to make sure for our customers we are offering best model, best models at various price points, right? So you will see us. But it's very important to us to have the best frontier models out there as well as models, which are very performant and low cost. This is why we have Flash light Flash Pro, et cetera. And with -- so we are committed to being at the full parade of frontier. On the speed of model releases, I do think you will see us continue to pick up pace.

Obviously, you've seen us come but we announced 3.5 Flash at I/O. We have followed that up at 3.6 Flash and you'll see continued iterations of that model with further progress in agentic coding, et cetera. We are really focused, putting a lot of effort into Gemini 4. It's a very ambitious effort. We wanted to compete at the frontier level of where the frontier will be when Gemini 4 comes out. And so we are applying a lot of our compute and effort in that direction.

But with that, we are creating a baseline on which -- on top of it, you will see us rapidly iterate a subsequent model model releases. And so picking up pace and releasing models almost at a monthly cadence is part of our road map as we are building Gemini for as well.

Your next question comes from Michael Nathanson with MediaBin.

Sundar, just keep up with a model theme of this call, can you talk a bit about what do you think the moats are to this new model or what is your strategic advantages that fuel your business that even if everyone gets to the same type of model capabilities, what gives you the edge to keep growing that you're doing? And then not back to Eric's question, if you could talk about the margin impact on TPUs going forward, right, is it incremental margin to the core cloud margin? Or is it a lower margin from the group .

Look, Michael, I mean, first of all, look, there are many layers at which we are providing solutions, right? I think part of what -- what the value of our full stack approach is, customers are coming to us for solutions. So if you take an area like cybersecurity or data analytics, people are deploying solutions in which models are an ingredient in that solution, right? So take cyber, people are using chronicle and Vis and with our upcoming Cormendor product, they're using it to detect and patch vulnerabilities and so on or take data analytics, people are bringing what's been in the past, siloed data sources, putting it all together, and having an intelligence layer on top of it with Gemini Enterprise.

And so the model is just an ingredient in those solutions. So I think it's important to remember that. Then even within where you're purely on a more model consumption area, people are -- there is what looks as models are increasingly becoming end-to-end orchestrated systems, right, workflows, agentic workflows and to develop all this, it's your ability not just to bring the compute, you need to train and serve the data quality, the environments you have, your ability to continually improve, provide customers with that peace of mind that all the data they have, their data, their trajectories are confidential to them.

None of that in any way flows back to the models. Their ability to configure and serve all this in a secure way and manage it and provision it and so on. So these are big end-to-end things. And so this is what our Cloud business as a whole is focused on building it and we are seeing demand across all of these components. And so obviously, having our own models allows us to really optimize these solutions and bring an integrated offering, but we will also provide other models as part of these solutions. We also bring infrastructure as part of the solutions, et cetera, and take a whole full stack approach. So I think we are very, very well positioned there.

And on the TPU margins, we don't break out margins for any specific products or infrastructure component and certainly, there are benefits from designing and manufacturing our own chips. But the way to think about it is, this is an expansion of our total addressable market. So it expands the opportunities by providing solutions to customers that need those systems in their data centers. But overall, if you think about the cloud margins, obviously, a really fantastic expansion of margin to 35.6% this quarter, outstanding strong operational discipline across the business and leveraging growth in the top line.

And now as you think about Q3 and through the remainder of the year, and I've mentioned some of it in the prepared remarks, given the supply constraint environment we're in, we are planning to expand the use of third-party capacity in Q3 as a bridging strategy while we continue to build out internal capacity. That capacity, given the cost of that capacity will put some pressure on operating margins for cloud. And then the last item we mentioned in the past was the integration of Wiz does create some headwind either in the near term in 2026 related to the acquisition .

Your next question comes from Mark -- so with Bernstein.

And just on these third-party deals and kind of the bridge in capacity, is there a particular objective where the constraints are most severe? Or is it really broad-based? And I guess stepping back a bit and thinking about capacity allocation across the businesses, has that changed at all? Or is there a way to contextualize how you're now thinking about the trade-off between allocating compute to search versus model training versus for the GCB business?

Mark, I can comment on it, like look, again, on allocation, I think, look, we -- the baseline with which we start is what it takes to continue AGI development at the frontier. Obviously, the priors on that are just based on where the model frontier moves. And so we start with that as a baseline. And beyond that, we want to obviously -- we are prioritizing our core product areas like Search, YouTube, et cetera as well as Cloud. And within Cloud, we are prioritizing the compute to make sure we can serve our models in the context of Vertex and Gemini Enterprise and our core solutions, be it data analytics and cybersecurity, et cetera, right?

So our core serving for our core products across consumers and Enterprise where the compute is primarily going and that's how we think about it. I think on the Bridge deal, the main thing I would say is, look, they are on the margin that are very, very large customers of ours on cloud, who we are trying to support them through this extraordinary moment. And the incremental opportunities they are bringing to us while a short-term cost over a few months may be very high in the lifetime of the deal as we bring more capacity on, is highly ROI positive, right? So those are factors we are taking into account. So are you willing to take upfront 6-month deal to be able to serve that customer in what is a multiyear opportunity where the margins and the returns are very, very attractive over that multiyear horizon. So hopefully, that gives some color on how we thought about those opportunities.

Your next question comes from Ron Josey with Citi.

Maybe I'll switch topics somewhat, and Philipp ask you a little bit more about monetization from a Search and YouTube perspective, just given the greater signal we have with AI searches and then you talked about YouTube strength. talk to us just a little bit more on how advertisers are leveraging the greater personalization and targeting that Google has to drive this root.

And a question we often get is Google is that size and scale. We're still growing 17%. Any reasons what's driving that all-time high on searches?

Yes. Thank you so much for the question. Look, our 17% year-over-year growth rate in the Q2 Search and other revenue was really driven by many parts of our business working well together and very, very deep Gemini integration and maybe to for a second, all the major verticals contributed to the growth, retail growth, the greatest contribution managed as followed by really meaningful contribution from finance tech media entertainment and so on. I think it's really important to understand that Gemini Supercharge is our ability, and this goes specifically to your question, to understand what people are looking for and match the right ad. So we're applying Gemini models really across our entire ad infrastructure, whether it's ad quality, advertising tools, adds and new AI experiences. And we're really deeply integrating Gemini into the customer tools to make the campaigns more efficient, which comes on top of this.

And then we have our AI part campaigns like AI Max that help advertisers actually adapt and find the opportunities beyond keyword. Again, that's an ability for us to go deeper and target better and AI Max continues to unlock we mentioned as billions of net new searches that weren't really monetizable before. And then on the YouTube side, high level, the ads growth was driven by direct response and brand advertising, I shared earlier, we continue to see a lot of fermenting in the living room. We have a really exciting ads road map as well ahead of us on the brand side, on the direct response side. Demand gen insurance remain really interesting opportunities here. Again, this has targeting components to it, obviously. And we continue to innovate really heavily in that response with shopped formats in the living room, which will help us drive strength in retail as well.

Your next question comes from Ken Goralski with Wells Fargo.

Two questions, please. First, how would you assess the forecasted returns on compute capacity investments in '27 compared to prior years '25 and '26 in light of the supply chain constraints and supply chain inflation we're seeing. I'm curious as to how you think about the return profile of '27 in kind of future investments versus past investments in capacity? And the second one, a little different topic here. When you think about Waymo, what are the key factors you would consider when evaluating a change in corporate structure for Waymo? I know you've previously been reticent to talk about this, but the business is scaling clearly, there's leadership there and has a lot of momentum. Under what conditions would it make sense for Waymo to live outside of Alphabet?

Ken, on your first question, if I understand it, look, we are working of a disciplined ROIC framework here obviously, we, to the extent that our input costs going up to us, we reflect that in our ability to price or solutions and see returns there. So all of that is factored into how we are planning. I think our compute capacity investments in '27. I think to the first question I answered, I think we are -- we are seeing strong demand indicators including long-term deals, the existing deals which we have, which are renewing with exceptional demand on a moving forward basis. And so we are using all that to plan and invest accordingly.

And so I think if anything, the dynamics look healthier than where we were about a year ago. And so that's what gives us the confidence to undertake those investments. On Waymo, look, I think we are really focused on executing and scaling up AMO. We have set it up with our bet structure and given the team a lot of support, Alphabet has continued. I think our ability to think and plan long term and invest in the business and give them that long-term road map, the confidence in undertaking a long-term road map and scaling up I think is all hugely valuable. We're really focused on scaling the business right now and executing to that extraordinary potential, and that's what we are focused on.

Our last question comes from Sridat Kajuria with Wolf Research.

Could I please ask you, too. One is on TPU long-term strategy. Longer term, is it fair to think that as TPU sales scale you'd build merchant silicon business with a software stack that goes with it? And second is on YouTube revenue, given how strong engagement is, what are some of the factors that could drive accelerating growth rate at YouTube on a go-forward basis?

Maybe I'll answer the first one, and Philipp can broadly talk about YouTube revenue growth as well. On the first one on TPUs, look, I think we are -- we've been operating with TPUs for a while. A lot of our Cloud serving is done on TPUs. And we are also seeing strong demand for TPUs stand-alone systems from other customers. And so we'll -- obviously, the industries has constraints too. So we have to plan and reallocate on a forward-looking basis along the principles I spoke about earlier.

So I don't want to project out too far into the future there. But I do think we are -- obviously, we are -- we will scale up based on the opportunities we see and the demand we see commensurate with the constraints that exist and the allocation needs we have for frontier model development, making sure we can serve our consumer businesses and Enterprise businesses well well.

And Philipp, you want to add eon YouTube monetization.

Yes. Thank you so much. So look, I mentioned the drivers of YouTube ads revenue growth. I think there's a lot of things we're actually excited about when we look at our ads road map here and brand, we see ongoing opportunities on Connected TVs in the living room. We're building tools to help advertisers find creators scale across screens and then measure the results. And we have a slate of new and returning creator shows that are coming exclusively to YouTube. We see opportunities in direct response, particularly with the Demand Gen and shorts, I mentioned it remain a really interesting opportunity here to expand our base to more small and medium advertisers across more verticals, short of videos actually, this important one create more opportunities for less disruptive ads, increasing the overall ad effectiveness.

So we're going to continue to see a lot of innovation, in my opinion, and direct response with shoppable ad formats in the living room, which, again, we mentioned that drives retail strength. We announced a few other formats here at Brandcast that we announced buy with Google Pay that enables CTV viewers to actually complete purchases directly on their TV with 2 clicks, which is a really interesting opportunity over time. We launched the affiliate partnership Boost to drive incremental sales the greater earnings, obviously, from YouTube shopping affiliate commissions. So overall, like, frankly, very, very exciting times.

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.

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

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

Hello, everyone. Thank you for joining us. It's been a terrific start to the year for Alphabet, and there's huge energy at the company. AI is the most profound platform shift of our lifetimes. It's lighting up every part of our business, driving an expansionary moment in Search, turbocharging Cloud and much more. At the same time, we are experiencing strong demand for our AI solutions and services from enterprises and consumers at levels that are meaningfully exceeding our available supply. This is a clear indicator of Alphabet's unique opportunity.

By scaling our investments, we intend to secure the foundational infrastructure necessary for the significant growth opportunity ahead. We are innovating at scale with incredible velocity. Since launching Gemini 3 last November, our momentum has accelerated. We have rolled out increasingly capable generative media models, shipped features across Chrome and the Gemini app, launched Antigravity and our first model in our Gemini 3.5 series.

Recently, at our I/O Annual Developer Conference, we showcased new advances across models, coding and agents. This progress reflects our deep focus on delivering tangible value to people in the products they use every day. Supporting all of this at scale for our users while also serving enterprises and developers around the world requires massive compute investments. In 2022, we spent approximately $31 billion in CapEx. This year, we expect that number to be 6x larger than 2022 and double last year's at $180 billion to $190 billion. And next year, we expect it to significantly increase compared to 2026.

The overwhelming majority of this spend will be in technical infrastructure. We plan to fund our investments in a balanced way, including strong operating cash flow, debt issuances and today's equity offering. Maintaining a strong balance sheet while accelerating our investments is a strategic advantage and helps us meet surging demand for our AI products and services. I want to highlight 6 key areas where our innovation and leadership are positioning us well to capture the opportunity ahead and drive shareholder value.

First, our accelerating business momentum; second, our differentiated full stack approach to AI. Third, our unmatched global product footprint; fourth, Google Cloud leadership; fifth, our visionary long-term Bets; and sixth, strong financial discipline. Let's go deeper on each. First, accelerating business momentum. We are reaching significant milestones across every area of the business. From 2020 to 2025, our annual revenue more than doubled to over $400 billion. Over the last 12 months alone, we added $63 billion to our top line. This growth continued in Q1 of this year.

Revenue climbed 22% year-over-year to $110 billion. Finally, our operating income grew 30% year-over-year to $40 billion. This momentum spans our entire portfolio. I'll call out 5 areas in particular. In Search, revenue grew 19% year-over-year last quarter. AI Overviews and AI Mode are driving user engagement and AI-powered ad tools are driving better ROI for advertisers. In Cloud, Q1 year-over-year revenue growth was 63% and backlog nearly doubled quarter-over-quarter to more than $460 billion.

YouTube's total annual revenue for 2025 surpassed $60 billion across ads and subscriptions. And Waymo has entered a new growth era valued at $126 billion in February with plans to expand to 20 additional cities this year. Subscriptions recently reached 350 million, driven by YouTube and Google One. Our AI plans are performing exceptionally well. Users are realizing the value of our most capable AI models, expanded storage and productivity tools. We had the strongest quarter ever for our consumer AI plans. We believe we are well positioned to drive profitable revenue growth.

We are reaching new AI-driven market opportunities, spanning ads and subscriptions, model development, cloud, infrastructure and services in the physical world. This momentum is a direct result of our decade-long full stack approach to AI, our unique differentiator. The foundational layer of the stack is our unrivaled AI infrastructure. This includes 10 million kilometers of terrestrial and subsea fiber, connecting over 30 data centers and over 40 Cloud regions around the world. Our recent acquisition of Intersect will help us come to market even faster.

We pride ourselves on building efficiently and sustainably and supporting local communities through job creation and workforce development programs. We offer a wide variety of compute options from our own TPUs and Axion CPUs to the latest NVIDIA GPUs, which are a core part of our AI accelerator portfolio. Let me go a bit deeper on TPUs. It's been 10 years since we introduced our first commercial TPU.

Today, they power Gemini model training and serving at scale across our APIs and biggest products like Search. Our 8th-generation TPUs, 8i and 8t offer a new dual chip design specifically for the massive training and inference needs of the agentic era. We continue to drive tremendous efficiency gains. For example, in 2025, we reduced Gemini serving costs by 78%. Security is a critical second layer of our full stack built directly into our foundational code. With the rapid adoption of AI, protecting data and privacy grows more complex every day. Our secure by default architecture automatically blocks 10 million spam e-mails every minute and protects billions of users and customers across our broad portfolio.

We are proud to keep more people safe online than anyone else in the world. For our enterprise customers, we launched Google AI Threat Defense. This is a new security platform that combines the strengths of Gemini and other frontier models, along with Wiz, CodeMender and Mandiant to stop AI-powered threats before they impact our customers. The next layer is our world-class research teams, led by Google DeepMind. These teams are the engine of our entire AI stack from inventing the transformer architecture to developing our highly capable Gemini models.

Research teams are also pioneering advances in science, medicine and climate. That includes WeatherNext and AlphaFold, which was awarded the Noble Prize in chemistry. That brings us to our fourth player, models and tooling. We offer the most extensive model portfolio in the world, and we continue to stay on the cutting edge in reasoning, multimodality and cost. We recently introduced Gemini 3.5 with improvements focused on agentic coding, long-horizon tasks and real-world capabilities. Gemini 3.5 Flash is now available, and we expect Gemini 3.5 Pro will be coming in June.

Beyond the Gemini series, our generative media models are incredibly popular. This includes Nano Banana, which people have used to generate more than 50 billion images to date, and we were excited to unveil our latest Omni model at I/O. One way to measure growth is tokens. The fundamental units of data are models process. 2 years ago, we were processing 9.7 trillion tokens a month. Today, that number has jumped to 3.2 quadrillion, more than 300 times increase across our surfaces.

The speed of adoption from enterprises and developers is incredible. More than 8.5 million developers are now building new apps and experiences. Our model APIs are now processing approximately 19 billion tokens per minute. And over the past 12 months, over 375 Cloud customers have each processed more than 1 trillion tokens. Tooling is another important part of this layer. Antigravity, our agentic development platform launched 6 months ago, and we have millions of developers building with it. We recently expanded Antigravity to a unified platform for builders to work with agents.

This includes a stand-alone desktop app that delivers the first major step towards our vision of an independent agent-focused surface. And for the first time, we are bringing the Antigravity ecosystem to Google Cloud customers. The Antigravity coding harness has accelerated how we build internally. Every few weeks, we are doubling the number of tokens we are processing across our developer tooling.

Recently, we reached more than 3 trillion tokens a day. This scale creates a powerful feedback loop to improve our current and future models. Finally, the fifth layer of our stack is our unmatched portfolio of products and platforms used by billions of people every day. Gemini is now powering all of our 13 products that have over 1 billion users, including 5 that surpassed 3 billion users. It all begins with our founding product, Search. AI overviews now have over 2.5 billion users each month. This means Google Search is delivering AI to more people than any other product in the world.

And AI mode has revolutionized Search. Launched just a year ago, it's already surpassed 1 billion monthly users. Just as the shift to mobile drove growth in Search, we see the same expansion happening with AI. When people use AI-powered features, they use Search more. In fact, queries reached an all-time high last quarter. The Gemini app is also seeing incredible adoption. It's one of our fastest-growing products with over 900 million monthly users, more than doubling in a year. Across Search and the Gemini app, we have started to roll out personal intelligence, which connects you to your Google Apps to provide personalized suggestions. It's now available in more than 190 countries.

Both Search and the Gemini app are entering a new era of agents. At I/O this year, we announced Gemini Spark, a personal agent that works 24/7 in the background right in the Gemini app. To get things done, it now connects with Google tools and soon with third parties through the model context protocol. AI agents are transforming Search into a proactive partner. Our agent-first approach means Search can deliver unprecedented value for users, moving beyond answers to actions and creating new opportunities for Google.

Turning to YouTube, which has led streaming watch time in the U.S. for 3 years running with over 200 million hours of watch time in the living room every day. At I/O, we announced an entirely reimagined conversational search experience powered by Gemini called Ask YouTube. Google Ads is a growth engine for millions of businesses worldwide. Gemini is enabling them to reach more customers with greater speed and precision. Through AI Max and Performance Max, advertisers can use gen AI to automatically customize text, synthesize creative assets and dynamically bid across all Google channels.

Retail is another big part of our business, and agentic commerce is a huge opportunity. At I/O, we introduced the Universal Cart, an intelligent shopping cart that allows you to add things to your card from Search, Gemini, YouTube or even in Gmail. This is powered by the Universal Commerce protocol, an open-source standard we co-developed with retailers for shopping agents and systems to work together.

Now let's move to Google Cloud. Google Cloud is a world-class enterprise business, and we are the only provider to offer first-party solutions across the entire enterprise AI stack, including infrastructure, the Google Cloud platform, Workspace and security. These pillars are underpinning progress. 75% of our Cloud customers are using our AI products. We saw incredible deal velocity in Q1, doubling our acquisition of new customers and signing twice as many deals in the $100 million to the $1 billion range compared to last year.

Our Enterprise AI solutions have become Cloud's primary growth driver for the first time. With unprecedented demand for AI compute, we are expanding beyond our hosted Cloud infrastructure to deliver TPUs directly to select Enterprise customers in their own data centers. This unlocks a significant previously untapped addressable market and enables the world's most advanced labs to run their heaviest workloads on the same hardware that powers Gemini.

The opportunity for Cloud has never been clearer. Our backlog nearly doubled quarter-on-quarter to over $460 billion, and we expect to recognize just over 50% of the backlog as revenue over the next 24 months. That means our customers aren't just buying services, they are committing to a long-term AI road map with us because of the unique value of our integrated stack. We are also extending our AI leadership into 2 frontiers through our long-term Bets, bringing unique services to the physical world and the life sciences.

First, the physical world. Waymo is on an extraordinary commercial trajectory. Already this year, we have launched 6 new markets. That brings our network to 11 major U.S. cities, and we are getting ready to expand internationally soon. We are surpassing 500,000 fully autonomous rides per week. Wing has incredible potential to transform the speed and cost of last-mile delivery. We've already safely completed more than 1 million home deliveries in partnership with major brands, including Walmart and DoorDash.

Intrinsic is using our foundation models to develop solutions for industrial robotics. In life sciences, "Isomorphic Labs is using our latest models to accelerate drug discovery, identifying novel compounds for previously untreatable diseases in a fraction of the time. Calico is applying deep computer science and foundational research through the biology of aging, so people can live longer, healthier lives.

Beyond AI, we are excited about our leadership in quantum computing. We are actively moving this out of the lab and into the real world through our Willow Early Access Program. This is a unique moment in our history. Between our rapid pace of innovation and the massive surge in AI demand, the opportunity ahead has never been clearer. Our performance today gives us deep confidence in the investments we are making for tomorrow. I'll now turn it over to Anat to go through our sixth area, strong financial discipline.

Thank you, Sundar, and hello, everyone. As you've seen in our results over the past several quarters and as you heard from Sundar, we're driving strong business performance. Let me dive a bit deeper into the drivers of this performance, how our capital investments are informed by our view of ROIC and share our balance sheet strategy and rationale for today's capital raise. The impact of our AI investment is evident in the momentum and scale of our business.

As Sundar mentioned, in 2025, we achieved a significant milestone, surpassing $400 billion in revenue. In the first quarter of 2026, our momentum has continued as revenue reached nearly $110 billion, up 22% year-over-year. This marks our 11th consecutive quarter of double-digit revenue growth. Importantly, we're driving this top line expansion while maintaining rigorous operational discipline. Over the last 5 years, our operating income tripled and our operating margin expanded to 33% on a trailing 12-month basis.

Our AI investments and full stack approach are being monetized and are delivering tangible value across our business as evident in our Q1 results. Beginning with Google Services, revenue grew 16% year-over-year, and operating margin expanded to 45% from 42% a year ago. In Search, we're seeing an expansionary moment powered by AI Overviews in AI Mode as well as our AI tools for advertisers.

In particular, Gemini's understanding of intent has significantly expanded our ability to deliver ads on longer, more complex searches. At the same time, since launching Gemini 3, our hardware and engineering breakthroughs have reduced the cost of core AI responses by more than 30%. Next, in subscriptions, Google One is our fastest-growing product, and our AI plans are a significant driver of revenue growth. Finally, in YouTube, AI is helping power our recommendation systems, which are improving the user experience.

Looking at Google Cloud, Cloud delivered a record $20 billion in revenue in the first quarter while expanding margins to 33% and more than tripling operating income to $7 billion from a year ago. AI Solutions is now the largest contributor to Cloud growth, led by demand for our leading AI models. And Cloud's backlog nearly doubled sequentially, reaching $462 billion at the end of the first quarter, driven by strong demand for our enterprise AI offerings. These results are proof points of our relentless focus on ROIC, including our efficiency efforts, a robust resource allocation framework and a strategic commitment to balancing near-term returns with investments in future innovation.

As Sundar mentioned, we are investing to meet the unprecedented demand we're seeing from enterprises and consumers. For the full year 2026, we expect our CapEx to be in the range of $180 billion to $190 billion. And looking further ahead to 2027, we expect a significant increase in our CapEx compared to 2026. Turning to our balance sheet strategy. Our financial foundation is exceptionally strong. Over the last 12 months, we generated operating cash flow of $174 billion. We ended the first quarter of 2026 with $127 billion in cash and marketable securities and $81 billion in debt.

Since the end of the first quarter, we have issued approximately an additional $20 billion of debt, bringing our pro-forma debt balance to just over $100 billion across 6 major currencies and markets. We aim to maintain one of the strongest balance sheets in the world, supported by robust liquidity, access to different funding sources and prudent leverage. We view this global funding access and deep financial flexibility as a strategic component of our long-term strategy, particularly given the scale and capital intensity of the current opportunity.

We continue to execute against our disciplined capital allocation framework, which prioritizes investment in organic growth while balancing strategic M&A investments and capital return to shareholders. Our strong financial results and record customer demand reinforce our conviction to invest the capital required to continue capturing the AI opportunity in front of us.

We intend to fund these investments through a balanced approach, combining our strong operating cash flow with debt and today's equity raise. The transaction we're announcing today represent a strategic proactive move to optimize our financial flexibility and maximize long-term shareholder value creation. Thank you for your time and your interest in Alphabet.

Okay, everyone, we're going to get started. Okay, ready. This is going to -- let me start with the safe harbor, Neal, okay? So some of the comments made today by Mr. Mohan -- may make today could be considered forward-looking. These statements involve a number of risks and uncertainties that could cause actual results to differ materially. Please refer to Alphabet's Form 10-K and 10-Q, including the risk factors. Any forward-looking statements that Mr. Mohan makes are based on assumptions as of today, and Alphabet undertakes no obligations to update them. So, Neal, thank you for being here.

Thank you. Thanks for having me.

I know you guys are excited about that. Now let me tell you a little bit about Neal before we get going, CEO of YouTube, whose focus is on empowering creators, fostering community and driving the future of video. Prior to becoming CEO in 2023, he served as YouTube's Chief Product Officer. Before joining YouTube, Neal was a pioneer in digital advertising, first leading Product Development and Strategy at DoubleClick and then as Senior Vice President of Display & Video Advertising at Google. Neal holds a degree in electrical engineering from Stanford and an MBA from Stanford as well. And last year, he was named Time Magazine's CEO of the Year. So congratulations on that.

Thank you.

Okay. This is Neal's third appearance in the past 4 years, and we are honored to have him. So congrats on being named CEO of the Year by Time. In that article, you were quoted saying that the entire dynamics of the media industry are changing before our very eyes. It's incredibly disruptive. And if you don't adapt, you can be left by the wayside. So now all of us in this room are quite familiar and well versed about that disruption. But can you talk about some of the biggest adaptations that you've made in the past years since you last graced our stage?

Yes. Well, first of all, Michael, thank you for having me. It's a privilege to be here. Nice to see some familiar faces in the audience as well. I'll start by just maybe a little bit of big picture because I think that sets the context for the rest of our conversation, which is, as you point out, it is an incredibly dynamic time in the industry. Lots of aspects of it are changing, driven by all of us as consumers and viewers of media.

And so for us or at least for me, it's helpful that there's lots of consistency to our strategy. And so in terms of context, I'll say a little bit of what I shared last year because the most important thing is our strategy remains the same, which is at YouTube, we have this flywheel, and it always starts with our creators. That, I believe, is a unique proposition about YouTube. It's creators, YouTubers call YouTube their home, that's differentiated from social media.

It's differentiated from streaming and TV. And so everything starts with that. And our adaptation there is to really keep pace with what these creators want to do from a business standpoint in terms of creation tools, in terms of expanding their audiences, connecting them with new audiences all over the world. For example, eliminating in many ways, the only barrier that exists between a creator and their fans in the world, which is language through things like AI-powered multi-audio dubbing, right, multi-track audio.

So creators are the core piece, but that's what attracts the 2 billion viewers that come to YouTube every single day across all screens, across living room, across mobile, desktop, living room in the U.S. gets 200 million hours of watch time every single day. And so viewers are really important. And then ultimately, obviously, that's what attracts the monetization. We are primarily an AVOD business, but 1/3 of our revenue now is SVOD subscriptions.

And that's also an area that is very ripe for innovation, a lot of it AI-powered, which we can talk about. But -- so that's the broad flywheel, creators, viewers and then monetization. And what's accelerated it in the -- what's changed about it in the last year is it's just accelerated. And I think a lot of that is through our efforts. I'll just give you 2 examples. The first is we talk about our creators.

Many of you have heard me talk about creators, YouTubers. They are the core strength of YouTube. It's interesting to me, especially in the last 12 months, how many non-endemic creators, people who come from other parts of the media industry, whether they are athletes, right? At our Brandcast event last night, we had Dwyane Wade and Draymond Green talking about their channels on YouTube, whether they're Trevor Noah or Oprah, all of them wanting to become YouTuber.

So that's a really big sort of trend in the last few months and I think accelerates this flywheel. The other piece is going in the other direction, which is if you are a YouTuber, it opens up a whole plethora of opportunities for you. One of my favorite examples, everybody knows about MrBeast, but one of my favorite examples is a creator called Markiplier. Mark built a movie. He self-funded it. He led his millions and millions of subscribers on YouTube through that journey over the last several years and then released it in theaters. He thought it was an independent production. So maybe it would be in 2 or 3 theaters, maybe 50 theaters. Because of his YouTuber follower base, it opened to thousands of theaters and was the #1 grossing movie on the weekend that it was released. And so that trend, those trends of like creators really sort of being these businesses and other folks wanting to become creators really accelerates that flywheel, and that is a trend that we've seen in the last 12 months.

I was going to say to you, when I think about our own coverage here, MoffettNathanson, it feels to us in the past couple of years, it's striking to see the traditional media world now waking up to YouTube and the power of YouTube. And I know you've had a Brandcast for the past 7 or 8 years. What was -- you've been at YouTube for a while. What were the early bets and innovations that you thought would pay off, right? When you go back to like this crazy idea called YouTube, now it's what it is. But what attracted you to it as a business model?

Yes. I mean I think one of the nice things at YouTube and being part of the Alphabet family is we can make long-term bets. And whether it's investing in AI alongside our sister company in Google DeepMind -- but I'll just give you some examples of those. One of the longest bets that we made and people thought we were crazy at the time was investing in our Subscriptions business. Today, YouTube Music and Premium has, as of last year, over 125 million subscribers.

YouTube TV is what it is. And when we were making those bets, people are like, well, why would people pay? You already offer all of this for free. And it turns out that if you add user value, whether it's interruption-free or downloads background and building more and more features on top of it, users see value in that, and that's why we built that business. And so that was a bet that we made a really long time ago. Fast forward to today, this last quarter was -- we added the most non-trial subscribers ever in the history of premium globally, but also in the U.S. And so that was a really big bet that we invested in.

Another one, which I talk a lot about because it was also kind of one of those overnight successes that turns out was many, many years in the making is YouTube in the living room. So over 50% of the watch time of YouTube happens on TV screens in the U.S. We saw those early trends actually well before COVID, obviously accelerated in COVID and sort of kind of continued, but that was a bet even before COVID. And it required an enormous amount of technological innovation because the television market, unlike mobile is very heterogeneous, right, like all kinds of OEMs, et cetera.

We had to make the video feel cinematic, 4K, 8K. We had to invest in episodic content. Podcast is another one where it turns out that actually hosting podcasts and betting on people wanting to watch, not just listen, turned out to be a big bet, and we are now the #1 podcast platform globally. And so those are some examples of how we identify trends and then we invest in them very heavily, forward invest for many years, in some cases, before they end up actually producing returns, and they've led to the successful position we are now in living room podcast.

So you segue to my next question, which is we know that YouTube is more than $60 billion of revenues, and we know it's the biggest media company, most valuable company in the world. You talk about subscriptions. We think it's growing faster, probably twice as fast as advertising, right? So can you talk a bit about the products drive that? And any surprises and learnings? Because I don't know if you -- I know you made a bet on subscriptions, but it's growing at literally high teens rates based on our work.

Yes. And we -- as you know, Michael, we have 2 subscription businesses. We have YouTube Music and Premium. That's the 125 million-plus subscribers. And then we have YouTube TV. And I would say that the kind of the strategic insights or the product insights are similar in both of them. The first, and I think probably the most important is just product innovation. So we've invested very heavily. Hopefully, many of you are subscribers in the YouTube TV experience, right, things like Multiview, things like key plays, like really leaning into if we're fans of that media, how would we want to consume that type of content. So product innovation is one.

I already talked about how we thought about that in YouTube Music and Premium. But the second is actually giving real consumer choice. So the fact that we offer a subscription, a paid subscription that comes with a particular set of features and content, that in and of itself is a way of giving consumer choice, but we've really leaned into that. So for example, with Premium, we now have a SKU that's below that called Premium Lite. And Premium Lite is a way for people to pick the SKU that works best for their wallets, but also for the value that they're looking to get out of it.

We just launched on YouTube TV 10 different tiers, sports bundle, sports plus news, entertainment, et cetera, again, to just give broader consumer choice. And I view that as TAM expanding, opportunity expanding, delivering value for viewers. And so those have been sort of the 2 most salient insights in terms of how we've been able to grow our Subscription business.

I want to come back to the bundles and the packs. I'm very happy you did that with YouTube TV, but I want to talk to you a bit first about pricing because I think for the first time in 3 years, you increased pricing on YouTube Premium. And I can tell you that my YouTube bill for TV is higher now than it was 3 or 4 years ago. So talk to us about what's driving those price increases and what gives you confidence that there's not a consumer pushback and churn doesn't increase.

So you're right. It's the first time in 3 years that we raised price on Premium just a few weeks ago. The first and foremost is what I said, which is we look at making sure that we're delivering real user value. And there's multiple ways of doing that. Obviously, acquisition and churn is one way, sort of kind of hard metric way of doing it. We survey our users pretty regularly. We keep getting that feedback. And so if you're not delivering that value, you don't have a right to raise prices.

So that's the biggest thing. These are businesses where we also have to make sure that we are fair to our partners. It costs money to put this content in front of our users. Our SVOD businesses have a professional content provider aspect to them, whether it's the traditional media company partners, whether it's the music labels. And so we pay them for our subscribers. And so that's -- all of that goes into the consideration.

I think the most important thing that -- is that we all as consumers get to vote with our feet, so to speak. And despite the price increases over the years, our business has continued to grow. As I said, this was our strongest quarter ever in Q1. And so that, to me, at least is an indicator that our viewers who have lots and lots of choice see a lot of value in our subscription products.

What's great about having you here is that there's been a ton of discussion at this conference about AI and the dream of AI. You're actually using AI and you've been using it for a while. So can you talk a bit about how you're using AI across your creator market, your ad market, your consumer market and where you are in that journey in terms of things that -- how would you measure where you are today? And what are the things you're dreaming of in the next couple of years?

Yes. I do think that YouTube has this very sort of unique vantage point when it comes to this AI sort of revolution that we're in. On the one hand, we are working with the most cutting-edge technology, the deepest technology, inventing it at YouTube, working, as I said, very closely with DeepMind on a daily basis. And so we're exposed to that depth of technology and how fast it's moving.

But we also face the creative industry, the media industries every single day. And so the way that I think about AI and how it is transforming or really empowering the YouTube business is really empowering human creativity. And I'll go back to the flywheel that I talked about. For us, AI is, first and foremost, about making our creators' jobs easier and more effective. And so the most obvious place where you see it in the YouTube product is that when you open up the app and you hit that plus button, a lot of the creation tools that exist there are powered by Gemini, Veo, all of our investments in AI models.

And so now if you are watching a YouTube Short and you want to insert yourself in it, you can do that through AI. And that might have taken -- that might have been something you were able to do sort of kludged together, might have taken you a few days. Now it happens in less than a minute, right? And so that's -- what that does is it makes creators free to go and do other things that they otherwise wouldn't have been able to do, and it also expands the pool of creators. And so that's on the creation side.

Viewers, I think it's all about finding -- it's about 2 things. One is using AI to continually improve the power of YouTube recommendations. Hopefully, this is something that you all experience an improvement in on -- literally on a monthly basis, and that is a deep investment in Gemini and making it so that there is a much deeper understanding of all of us as viewers when you open up the app that makes the recommendations better.

And then from a monetization standpoint, it's really about making it so that we can deliver ROI for advertisers, whether it's being able to generate creative using AI that they can put in Shorts or in kind of YouTube traditional long form, whether it's about better optimizing their campaigns that run on YouTube. And so it really is every aspect of the flywheel. I could go on and on. There are so many parts of YouTube.

Of the 3 markets you serve, where do you think you're the furthest along, where you see the most tangible benefits from rolling these tools out today?

I mean I really think it's across all of these. I mean the creation tools have been in the product for, I'd say, now a couple of years. Every week, every month, we're adding new capabilities there. The one aspect that every time I talk to creators that I hear about is not just these creation tools around videos, but how do we make the whole creative journey easier.

So I was with a creator at our Brandcast event last night, and she was telling me how much she loves a product called Ask Studio. And that's when you go into YouTube Studio, which is the product that creators use to manage their videos on YouTube. And instead of going and maybe asking for a particular report or a slice of data, you can just ask Studio, and it literally will give you deep insights in terms of trends on your videos, on your channel or what have you.

And now there's infinite pieces of insights that you can get out of something that you previously would have just -- would have taken you days or weeks to actually pull that insight using all the reporting that we had. Now it happens instantaneously. And so that's -- so I'm very proud of how far we've moved the ball from a creator standpoint. That Ask, we have a feature called Ask in -- on the viewer side. So if you watch YouTube video on your phone or a desktop, you might notice that, that Ask button right below a video, that's a way for you to actually interact much more deeply with the video itself.

So if you're listening to a new Justin Bieber song or video. Well, you can ask where do those Lyrics come from? How do we think about that? And it's just a deeper connection between viewers and creators. And just in April, that got -- that had 75 million users using it on a regular basis. And so those are just some examples of how you see it in the core YouTube experience and they're enhancing it. So it's really all of these pieces are being accelerated. And every month, you sort of see new capabilities getting layered on.

All right. And are things developing faster than you thought in terms of like the...

I mean...

Are you being blown away by the things you're seeing on the creator side?

I mean I think that any time you put a new set of capabilities, in front of creators, it is really amazing how quickly they adopt them, and it's really amazing what new types of formats and creativity it produces. And given how fast the underlying AI technology is developing, especially at Google, like it's no surprise that you're seeing that in terms of how creators are using these features.

Okay. Let me ask some questions we get from covering other industries. So we cover the music industry. And a question we get a lot is, can you talk a bit about how you view the impact of AI-created content on consumer behavior when it comes to music, right? So how do you think that develops?

Well, there's 2 aspects of it. One is on the actual creation side. And music for decades, and I've been in the music industry for a very long time, artists adopt new technology, right, like the drum machine back in the '80s or new ways to actually produce new sounds, they incorporate that into their creativity, into their craft. And so that's one aspect where you're seeing that in the music industry. Again, it is a tool to empower the human creativity as opposed to a replacement in my view.

And so that's one piece, and we partner very closely with our music partners in that regard, whether they're the labels, artists directly themselves. The other piece is that my guess was that -- when it came to AI and the Media industry, music would actually be one of the places that would be impacted first and perhaps most profoundly, at least early on. And so one of the things that we did was I went to all of our label partners and tried to come up with these set of principles around how we would innovate here.

And the first principle was like the industry couldn't bury its head in the sand, like it had to be bold in terms of adopting these technologies because they were coming. But then also, we had to do it in a way that was responsible to the artists, to their fans, et cetera. And so to give you a flavor of that, one of the pieces of technology that we've worked on a lot in the AI realm is something called likeness detection. And so for those of you familiar with YouTube, you'll know that one of the foundational pieces of the entire creator economy is a technology called Content ID. And content ID is about giving rights holders the ability to actually have their content. It's actually one of the earliest uses of AI at YouTube almost 10 years ago, maybe longer, where they have control over what content comes down, how it gets monetized.

So we're taking that same principle to the AI world with likeness detection. And so if an artist's singing voice is generated, then that artist should have control over how it's used, output control over it. And so that is a unique to YouTube-type capability and principle that we're bringing to the AI realm and music is one of the places where you'll see its adoption because now if you're an artist, you get to decide, do I want to monetize that or do I want it removed?

Do you think there's a risk longer term that consumption -- consumers will start adopting more AI created content over traditional recorded music? And you see -- you probably see the data on from YouTube Music side of it?

I mean my belief is that a lot of music and why we all fall in love with music and why it's such a core part of the culture and our sort of core memories through our life is because there's human stories behind them, right? Like 2 computers can play chess, but in order for us to be interested, at least one of them has to be human, right? And so I think the same thing applies in the music realm. It doesn't mean that there won't be AI-generated music that is part of this journey. But at least in my view, I don't view it as a replacement.

Okay. I would ask you about platform health and AI slop, right? So as the technology develops, how do you prevent AI slop from really infesting platform and hurting both the user experience and your creators' ability to connect and build a community.

Look, I mean, we talked about all of these amazing tools that are -- that AI will enable. And one of them -- one of the aspects of them is a lot more people can create and cost of producing videos for YouTube can goes down dramatically. And that can lead to amazing content, but it can also lead to low-quality content. And this notion of low-quality or spammy content has been a concept that has existed on platforms, including YouTube for many, many years that we have dealt with effectively through our content policies, our trust and safety systems, but also our recommendation systems.

The way that our recommendation systems on YouTube work is looking at a concept of long-term satisfaction. So not just watch time in the near term, but are you satisfied over the long term in terms of your YouTube experience. And that is a very, very effective tool at weeding out low-quality spammy-type content in the process. And those same techniques are being applied to things like AI slop, low-quality AI-generated content. I'm pleased with what we're seeing. It doesn't mean that the challenge isn't going to be there, but it's my team's responsibility to remain on top of it so that, that doesn't become something that you associate with your experience on YouTube.

I'm confident it won't be, but that's sort of -- that's how we approach it. The other thing that I'll say very quickly on that, Michael, though, is it's also important to make sure that we don't overdo that because AI is also going to enable many new forms of creativity. And there are lots and lots of very mainstream popular genres on YouTube that exist today that might have seemed strange when they were first invented. Like the most canonical example is watching live streams of people playing Minecraft. Like who would do that? It turns out that's an enormous vertical on YouTube.

I've asked that question.

And it's created a culture that resulted in a $300 million grossing Minecraft movie last year. And so it's really, really important to make sure that we don't suppress those types of emerging forms of creativity that might have seemed strange at first. And so we're always trying to strike that balance, but AI slop is not something that we -- you're going to see prevalence of on YouTube because of some of these techniques.

So you've said it here today. You said it throughout your whole time as a leader. You're really focused on the creator world. That's something that you guys are truly supportive of. It seems like in the past couple of years, a lot of your competitors have woken up to the power of the creative universe. So what are you doing to ensure that the creators that are on YouTube continue to stay on YouTube? And can you talk a bit about the monetization opportunities that are on YouTube versus other platforms?

Yes. I mean, again, I'll just -- it's so fitting that this is happening right after our annual Brandcast upfront last night. It was a celebration of creators. We talked about so many new projects that our creators are embarking on because of YouTube show after show after show. The audience excitement, the advertiser excitement was palpable. And it goes back to the fact that when you speak with these creators, they tell you over and over that their home is on YouTube.

And when their success on YouTube creates opportunities off of YouTube, I view that as the success of those creators on YouTube but also YouTube success. Because of the audience that we have been able to build for them, the fandom that we've been able to build because of the monetization opportunities we give them on YouTube, it creates all these opportunities on YouTube, but also off of it.

And I think that, that contributes to the flywheel. And our creators tell us that. Our biggest creator did a show with -- did Beast Games, MrBeast, you go into his offices, there's a sign that says, the first rule of MrBeast is YouTube first because he knows that the font of his success, whether it's Feastables, whether it's Beast Games, derives from his strategy and commitment on YouTube, and I see that across all of our creators. They tell me that over and over.

Now it's nice -- it's amazing to see that other platforms have sort of identified YouTube creators as sort of the center of culture, and they are talking to them. But repeatedly -- the one thing that's nice is our creators recognize that they have this position of strength now, and they can dictate the terms, including remaining -- having YouTube remain their home. But some of the big things that really contribute to that are not just the audience that we build for them, but what we can do from a business standpoint.

These are entrepreneurs. In the last 4 years, up to last year, we paid out over $100 billion to the creator economy across all of our partners. That is an enormous investment in our creators. We have 3 million creators in our YouTube Partner Program that monetize every single day. And we grow new forms of monetization all the time. We talk about AVOD and SVOD, but we also have direct fan-funding models like channel memberships, like other -- like paid digital goods, gifting, et cetera, that contribute to creators' revenue on our platform, and we're going to continue to invest in that.

Okay. Let me take you to ads, something you know well from even your days pre-YouTube, DoubleClick and then post-MBA, DoubleClick again. What do you see as the biggest opportunity to drive better ad growth at YouTube?

I think for -- the big picture is the reason why advertisers are interested in YouTube starts with, again, back to the flywheel, the creators and viewers that are there. When you go to YouTube, there is every type of format, every type of creative idea from on-demand to live, from 30-second Shorts to 15-hour live streams to everything in between, to watching a highlight clip for 5 minutes to a 2-hour podcast about sports.

And so that's the essence of what actually makes it truly attractive to advertisers because of that depth of content, whether it's on the living room screen, whether it's on your mobile device, is what gives advertisers every sort of use case, meaning that from the top of the funnel to the bottom conversion, you can do all of that on YouTube. And that is something that our advertisers recognize. If you're a brand advertiser, you can build relevancy around your brand, build a story. And so just last night, we had a great example from Coach, 85-year-old brand or over 80-year-old brand. It was looking to recapture some of the relevancy around the brand, particularly around Gen Z and young users, which, of course, are on YouTube.

And so they really leaned into creative storytelling on YouTube. It was a 60% increase in awareness. 600% -- 6x increase in consideration and a substantial increase in acquisition. And so every aspect of the funnel was hit by their YouTube strategy. And I think that is a unique differentiator of YouTube from all the other players, whether it's social media on one end or kind of traditional linear TV and streaming on the other end.

The same thing applies to new opportunities like shopping. That's an area, as you know, that we're investing in heavily. So whether it's boosting creator content that already has links to -- affiliate links to your products, whether it's turning a television screen, not just as a viewing medium, but a shopping medium by having things like purchase with Google Pay or QR codes. And so our job there is to create all of these new ad products that tap into that core aspect of YouTube, which is every device, every type of format of content. And when I speak with advertisers and brands, that's what really resonates with them over the long run.

Right. So over the past 12 months, YouTube has decelerated and search is accelerated. Can you talk a bit about the factors that may be causing the kind of the change in the growth rate from where it was 12 months ago?

Yes. I mean, again, I think it's -- the way that we look at it is, ultimately, all of the ad products that we build, whether it's the AI conversation we were having, whether that empowers new creative types, new forms of optimization, it's about ROI to our advertisers, and that sort of ultimately is what grows the Google ad business and of which YouTube also benefits, not just search over the long run.

And so that's really the best way to look at it. And that's what you should expect not just from YouTube, but from Google overall in terms of how we look at it, which is ultimately, what is in the best interest of our advertisers, what are they telling us? They care about ROI on the performance side. Brand advertisers also have their success metrics that they measure in ROI terms. It's not just about views or reach, it's about how you actually hit those goals of awareness or consideration that I described in the Coach case. And that's how you should expect Google to look at that.

Okay. Can I ask you about live events? YouTube had an NFL game last year. You have the Oscars coming in 2029. What is your appetite for continuing premium events and putting them in front of your paywall? And importantly, how has the conversation changed with rights holders as you've added some of these premium events?

Yes. I think that the best way to think about it, again, just to give you sort of some kind of inside framework on this is -- it's really about live events, as you said. And YouTube is a place -- it is -- we really are the epicenter of culture, not just because of what the creators are doing every day, but also because of these big tentpole moments that exist and live as a way is one of the few mechanisms of sort of true water cooler moments and the place where that happens is on YouTube, whether it was the Artemis launch, I'm guessing most people watched it, if you watched it live on their YouTube channel, whether it's Coachella, which was this -- it was happening at 3 in the morning on the East Coast time when Justin Bieber was on stage, yet it became the world's biggest cultural moment, his vibe session in terms of his YouTube history.

And especially if you're a young person, right, like that was the culture for a week or 2. And so we see that in the data all the time. Live is a big driver of that, and that's kind of one of the thesis behind our investment, whether it's sports, as you mentioned, the Oscars because they are these cultural moments, and they get amplified when they're on YouTube because of all the fandom that happens around them that shows up in Shorts or other forms of long-form VOD content.

And so -- and we look at -- I look at it sort of 3 lens, right? Like can we -- by having it on YouTube, is there a chance to expand the opportunity? So in the context of Sunday Ticket, that was -- well, you don't need a guy to install a dish on your house, right? Like you can get it with 2 taps, that's TAM expanding, right? You don't need it to be tied to another subscription, right? Like those are -- that's one piece.

The second is can YouTube bring some real technological innovation? So Multiview, right? Multiview is a great example on the YouTube TV side on the sports. Well, guess what, one of the ways that people consume Coachella the most is Multiview because Coachella is this concert that's across multiple stages. Wouldn't it be great if you could see 4 stages at once. And so -- and then the third piece is our creators, like can they enhance that viewing experience. And so you saw that in the context of the NFL. You should expect to see that in the context of something like the Oscars.

Okay. Cool. Connected TV, we touched on it before. The growth of Connected TV has been amazing. You're now tower over the #2 company, Netflix. We get a question all the time about what type of content is driving this consumption trends and talk a bit about the demographics because in the data, older viewers like myself are growing faster than even younger viewers. So can you give us under the hood on CTV, what are you seeing?

Yes, I mean I think at the -- so there's over 1 billion hours of watch time every single day on living room devices on YouTube globally, 200 million in the U.S. There's -- there are billions of hours of YouTube -- I mean, a Shorts watch time over the course of a month in the living room. And so at that scale, from a demographic standpoint, it's really all demographics, right? Otherwise, you're not at those types of numbers.

In terms of what's driving it, I think it's back to what I said, which is can we create an experience that really allows this content to shine. And most of the content that is consumed on YouTube, on living room are from our creators, from YouTubers. That is the reality because that's what viewers, especially young viewers want to watch. So when they're turning on the TV, they're turning on YouTube, and it's their YouTube experience that they're getting. And so that's probably the most fundamental sort of profound thing that I would say about living room growth. And I think that's an area that has lots of future opportunity.

And demographically, in terms of the mix, it seems like...

I think it's across the board.

Right. So you're not seeing -- okay, got it. We have time for a couple more. Let me ask you about Shorts. It looks like at Meta, Reels is driving revenue acceleration pretty aggressively. Some of that could be ad load also on Reels. But to what extent can Shorts drive further monetization for YouTube? Last year, you broke the news that YouTube Shorts was monetizing...

Yes, right here?

Exactly. So I want to -- you bring some more news. But what are you seeing now in terms of the ability to monetize Shorts and where does it go from here?

Yes. I mean, look, first and foremost, the way that we think about Shorts is it's another creative format for our creators. It's another way for all of us as viewers to enjoy their YouTube experience. It is a mobile-first experience. Although one of the fastest-growing places for Shorts is on the living room screen, believe it or not. And so that's what you should continue to see us -- that's what you should expect us to continue to see invest in is the viewer and creator experience because in my view, and I've been in the ad business for a long time, the advertising opportunities really stem from that.

The nice thing about Shorts is that you can come up with formats that are less interruptive in the feed, right? And that is what you have hopefully seen in your own Shorts experience. You can even have formats like stickers. I was speaking about shopping as a growth opportunity on YouTube, but we have shopping stickers on Shorts that allow for product placement and driving shopping that way.

And that's seen lots of success. We have over 500,000 creators that have tagged their videos with shopping. So in terms of the RPM trends that you were talking about, we've reached parity, not just in the U.S., but in several countries. And in some countries, we've even exceeded it, including the U.S. And so that's just a continued investment really in that full flywheel. I would really encourage folks to not just think about it from an advertising standpoint in isolation. It really is about delivering creator and viewer value first.

Okay. So my last question, and it would not be a MoffettNathanson Conference if you don't ask about YouTube TV and the changes that you brought to linear-bundled television. You know I'm a big user. You announced 10 specialized plans. What has been the consumer response to those plans? We would have hoped that the bundle -- the Sports News bundle would have been cheaper versus the big bundle. Yes, I was looking for a real discount here on the Sports News. So talk to us about consumer adoption on that plan and maybe the hopes longer term to find more efficiencies.

Yes. I mean, look, it goes back to what I said earlier about giving consumer choice. Like that was our vision with YouTube TV when it first started and then these tiers that we introduced really about just giving more choice at various price points. Lots of customers of YouTube TV are sports fans. Could we give them a tier that's sort of close to that, but lots of them are also not sports fans and do we have offerings for them, too.

It's really only been launched for a couple of months now. So it's still really early, probably too early to say anything concrete about it. But it's our vision of actually giving more consumer choice. And it is -- and I don't really think about it in terms of bundling or not -- aggregation or not. For me, it's really just about consumer choice. You're describing an experience in YouTube TV. On YouTube, the main app, we also try to replicate that with things like Primetime Channels alongside the endemic YouTube content and creators that you're watching.

Do you think we'll get to a place where we can get all our content that we subscribe to at YouTube TV, right? It still bothers me to go in and out of YouTube TV to get games here and there. So...

You mean going to other apps.

Yes, I love to have a super aggregator of all my premium content in one place.

Look, I mean, I think one of the strengths of YouTube is that, like I said, you can get everything from your 15-second Shorts from your favorite creators to 15-hour live streams to 3-hour NFL games and kind of everything in between. That is the vision. That is an intentional vision. A lot of our investment goes towards that. When we see pockets of content that are missing, we innovate.

That's where YouTube TV came from, linear, live sports, live news, et cetera. That's where Primetime Channels came from. Could you get your professionally produced kind of traditional content alongside your creator content. And so that's our vision. That's our approach. We have many, many Primetime Channel partners now. We're in a handful of countries. We want to roll that out to more countries. And so that's sort of how we think about solving that particular consumer pain point.

Neal, thank you for being here. Congratulations on the success. I appreciate it.

Thank you, everybody.

Welcome, everyone. Thank you for standing by for the Alphabet First Quarter 2026 Earnings Conference Call. [Operator Instructions] I would now like to hand the conference over to your speaker today, Jim Friedland, Head of Investor Relations. Please go ahead.

Thank you. Good afternoon, everyone, and welcome to Alphabet's First Quarter 2026 Earnings Conference Call. With us today are Sundar Pichai, Philipp Schindler 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 statement.

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 located at abc.xyz/investor. Our comments will be on year-over-year comparisons unless we state otherwise.

And now I'll turn the call over to Sundar.

Thanks, Jim. Hi, everyone, and thanks for joining us today. it was a terrific quarter for Alphabet. Our momentum was on full display at Cloud next last week and the month of May brings even more with I/O, Brandcast and GML. I hope you will tune in see our progress. It's clear that our AI investments and full stack approach are driving performance across our business. In Search & Other revenue grew 19%. People love our AI experiences like AI Mode and AI overviews and they're coming back to search more.

Cloud accelerated again this quarter due to strong demand for our AI products and infrastructure. Revenue grew 63%, exceeding $20 billion for the first time and our backlog nearly doubled quarter-on-quarter to over $460 billion. Gemini Enterprise is seeing tremendous momentum with 40% growth quarter-over-quarter in paid monthly active users.

In subscriptions, this was our strongest quarter ever for our consumer AI plans, primarily driven by adoption to Gemini app. Overall, the number of paid subscriptions has now reached 350 million with YouTube and Google One being the key drivers. And our AI models have great momentum. First-party models now process more than 16 billion tokens per minute via direct API used by our customers, up from 10 billion last quarter. Today, I'll share our progress across the AI full stack then Search and Cloud, followed by YouTube and Other Bets. Starting with our AI infrastructure. It's the foundation of our full stack approach to AI, driving customer growth and product adoption.

Our custom TPUs, Axion CPUs and the latest NVDIA GPUs continue to form the industry's widest variety of compute options. NVDIA GPUs are a core part of our AI accelerator portfolio and will be among the first to offer NVDIA Vera Rubin NVL72 in addition to the Blackwell and Hopper-based instances already available. At Cloud Next, we introduced our 8 generation TPUs, individually specialized for training and serving and able to take on the most demanding agentic workloads. TPU 8t provides high-performance model training with 3x the processing par of Ironwood and 2x the performance. TPU 8i delivers cost-effective low latency inference with 80% better performance per dollar than the prior generation.

This exceptional infrastructure powers world-class AI research that includes models and tooling, which continued to progress really well. Gemini 3.1 Pro continues to push the frontier in reasoning, multimodal understanding and cost. We have quickly expanded the Gemini 3.1 Series of models to offer more choices for developers, including our cost-efficient flash models. 3.1 Flash Live, our latest audio model has improved precision and reasoning, making voice interactions more natural and intuitive. It's now powering conversational features in search in the Gemini app.

Speech to text is now available in 70 languages. And with 3.1 Pro, our deep research agent got a big upgrade, including MCP support and native visualizations. Our generative media models are incredibly popular. Lyria 3 has generated over 150 million songs since launching on the Gemini app. Nano Banana 2 reached 1 billion images in nearly half the time of Nano Banana 1. And Veo 3.1 Lite is the most cost-efficient video model today. On top of this, we launched Gemma 4, our most intelligent open model. It's been downloaded over 50 million times in just a few weeks.

In fact, our open models have now been downloaded over 500 million times. Looking ahead, we are focused on pushing the next frontiers of foundation models, including intelligence, agents and agentic coding. And we are using the latest technologies to transform how we work as a company. For example, with Antigravity, we are shifting to truly agentic workflows. Our engineers are now orchestrating fully autonomous digital task forces and building at a faster velocity, much more to come here.

Next, we are bringing healthful AI into the hands of billions of people every day through our products and platforms. Earlier this year, we introduced Personal Intelligence, which helps people get more personalized and helpful responses. It's now in the Gemini app, AI mode and Gemini in Chrome. Early traction has been good, and this month, we integrated Nano Banana 2 to make personalized image creation possible in the Gemini app. Maps recently got its most significant upgrade in over a decade with Gemini.

Users can now have a conversation with maps and get more personalized suggestions and intuitive directions and the Pixel 10A launched to positive reviews, providing the best of Google's AI features like Gemini Lite and AI-powered camera features. Turning to Search. AI continues to drive search usage and queries are at an all-time high. We continue to invest in improvements to AI overviews, which are driving overall search growth and we're also seeing strong growth in both users and usage of AI mode globally.

Personal Intelligence expanded broadly in the U.S., and we are seeing people ask more personal questions and getting responses that are uniquely relevant to them. We also shipped agentic experiences like restaurant booking to new countries and new multimodal capabilities like search live globally. We are also continuing to improve efficiency and speed.

Even as we have brought new AI features into our results page, we have reduced search latency by more than 35% over the past 5 years. And since upgrading AI overviews and AI mode to Gemini 3, we've reduced the cost of core AI responses by more than 30%, thanks to continued hardware and engineering breakthroughs. We are excited to share more about search at I/O.

Now over to Google Cloud. Google Cloud is differentiated because we are the only provider to offer first-party solutions across the entire enterprise AI stack. Our growth in revenue, operating margin and backlog highlights this differentiation. Our enterprise AI solutions have become our primary growth driver for cloud for the first time. In Q1, revenue from products built on our GenAI models grew nearly 800% year-over-year.

We are winning new customers faster with new customer acquisition doubling compared to the same period last year. We are seeing strong deal momentum, doubling the number of $100 million to $1 billion deals year-on-year and signing multiple billion dollar plus deals. And we are deepening relationships with existing customers outpaced their initial commitments by 45% accelerating over last quarter. At Cloud Next last week, we introduced hundreds of new capabilities across our vertically optimized AI stack that are designed to work together for our enterprise customers.

We introduced a new Gemini enterprise agent platform that empowers users to build, orchestrate, govern and optimize agents with the controls that enterprise customers need. Along with new capabilities in Gemini enterprise app, like projects, canvas, long-running agents and skills, every employee can build agents.

In Q1, Gemini Enterprise paid monthly active users grew 40% quarter-over-quarter. That includes major global brands like Bosch, Cityweft, Merck and Mars Inc. Our partner ecosystem plays an increasingly critical role in driving Gemini enterprise adoption. We saw 9x year-over-year growth both in seats sold with partners and in the number of partners adopting it for internal use. This momentum is leading to accelerating usage of our models.

Over the past 12 months, 330 Google Cloud customers each processed over 1 trillion tokens. 35 reached the 10 trillion token milestone. To give agents business context from enterprise data to help them reason intelligently, we introduced a new agentic data cloud. It includes across cloud Lake house, knowledge catalog and deep research agents, which combine research and analytical skills. As an example, using our Data Cloud, American Express is enabling agentic e-commerce at scale by moving an enterprise data platform along with hundreds of production applications to BigQuery.

Vodafone is proactively resolving outages, automating network planning and precisely targeting capacity. Enterprise data has become critical for agents to reason. Our strength with BigQuery and Gemini Enterprise has led Gemini-powered workflows in BigQuery to grow over 30x year-over-year.

As cybersecurity threats from the use of AI models accelerate, our expertise in AI and cybersecurity is driving strong demand for our agentic defense offerings. In March, we closed the acquisition of Wiz, a leading cloud and security AI platform, which is an incredible fit for the moment we are in. We have seen tremendous interest from customers in our unique cybersecurity and AI products and services to protect their IT estate.

The performance of Wiz so far has exceeded our expectations. Together with Google's Threat Intelligence, security operations and AI models, the business helping organizations [ deduct ], prevent and respond to threats. We introduced new Gemini-powered agents for threat detection, continuous red teaming and automated remediation to protect software code and cloud systems. Customers like Deloitte, Priceline and Shell are using our agentic defense to strengthen their security posture. All of this is powered by the AI infrastructure I mentioned earlier.

Our TPUs continue our leadership in performance, cost and power efficiency for customers like Thinking Machines Lab, Hudson River Trading and Boston Dynamics. As TPU demand grows from AI labs, capital markets firms and high-performance computing applications will begin to deliver TPUs to a select group of customers in their own data centers in the hardware configuration to expand our addressable market opportunity.

Turning to YouTube where our momentum continues. In the living room, U.S. viewers are watching over 200 million hours of YouTube content daily. And as of March, we have reached a new milestone with over 10 million channels now publishing shots each day. This level of daily activity is a testament to how people enjoy this content and how we made it easier for creators.

And in Q1, our YouTube Music and Premium offering saw its largest quarterly increase in the total number of nontrial subscribers, both globally and in the U.S. since YouTube Premium launched in June 2018. I hope you'll tune into Brandcast on May 13.

Moving to Other Bets. Waymo is on a great trajectory. It launched in Nashville a few weeks ago, that makes 6 new cities so far in 2026 and operations in 11 major U.S. cities in total. Waymo also surpassed 500,000 fully autonomous rights per week, doubling in less than a year. [indiscernible] continues to expand across the U.S. in partnership with Walmart and DoorDash and announced plans to operate in the Bay Area.

In summary, a terrific start to the year with so many great opportunities ahead. We are not slowing down. Huge thanks to all of our employees and our partners. See you at I/O on May 19. Philipp, over to you.

Thanks, Sundar, and hello, everyone. As usual, I start with the performance of Google services and then cover the progress we're delivering across search, YouTube and partnerships. Google Services revenues were $90 billion for the quarter, up 16% year-on-year, primarily driven by the continued growth of Search, adding some further color to our results. Certain Other delivered 19% growth, primarily driven by retail and finance.

YouTube advertising revenues grew 11%, driven by direct response followed by brand. Network advertising revenues were down 4% year-on-year. Starting with Search & Other revenues, which delivered $60 billion in revenue for the quarter. We are accelerating the deployment of Gemini across our entire ads infrastructure to help businesses reach more customers in more places than ever before. This is driving significant improvements across all areas of marketing and continues to fuel new performance breakthroughs across 3 areas critical for our customers' success, as quality advertiser tools and new AI user experiences.

First, ads quality. AI is boosting our ability to deeply understand user intent for a given search query and to find the most relevant ad. Even when we don't have a direct user query, we're making significant strides in improving relevance. In Discover, new AI models and Classifiers are driving higher relevance by better aligning ads with unique user interest. In Maps, we're using Gemini to ensure promoted pins are deeply relevant to user surroundings, location of interest, history and intent.

This work is improving ad's relevance by nearly 10%, leading to a significant increase in user engagement. We're pairing the strength and prediction-driven relevance with bottom of funnel precision. Over the past year, we've made over 20 improvements to certain shopping bid strategies. Smart bidding now uses Gemini to match user intent to an advertiser's product and services more accurately and further drive performance.

This level of granularity was previously impossible to achieve at scale. Second, on advertiser tools, where Gemini helps advertisers drive more efficient and effective campaigns. People no longer search in fragments, they search conversationally and share more context. We launched AI MAX to help advertisers adapt to this new way of searching. And earlier this month, it moved out of beta with improved performance quality across targeting and creative capabilities.

Take [ Hilton EMEA ]. They captured 1/3 more clicks for 1/5 of the spend, while simultaneously increasing the average booking value by 55%. And Etsy saw a 10% search volume uplift with 15% of those queries being net new to their business. We see significant opportunity as advertisers continue to make good progress on AI readiness and the adoption of AI tools. For instance, more than 30% of our customers [ search ] now uses AI-enabled campaigns, AI MAX or Performance Max. And these advertisers are seeing more conversion for the same spend. Third, how we monetize new AI user experiences in search. We aren't just bringing existing ad formats into AI experiences. We are reinventing ads for this new era.

Direct offers in AI mode are resonating with users and continue to receive positive customer feedback. GAP, L'Oreal and Chewy are just some of the latest partners who have now signed up to test this Google Ads pilot. We're also exploring new formats for retailers. AI mode already services organic product recommendations based on the users query and we're now testing a new ad format that displace retailers who sell those recommended products.

In addition, the retail industry is rapidly coalescing around the open-source universal commerce protocol, or UCP, we launched in January in partnership with the Ecosystem. Last week, we welcomed Amazon, Meta, Microsoft, Salesforce and Stripe as new members to the UCP tech Council. They joined founding members Shopify, Etsy, Target, Wayfair and Google to further accelerate the transition towards an agentic future.

Partners like Sephora and Macy's have joined companies like Ulta Beauty, who are already rolling out UCP and can now redefine consumer journeys from discovery to checkout. Ulta Beauty just last week launched agentic e-commerce within AI mode and search and the Gemini app. Shoppers can now review product recommendations, compare options and complete streamlined checkout for eligible purchases directly within AI mode and Gemini.

Turning to YouTube, which now has led streaming watch time in the U.S. for 3 consecutive years. We're in an unmatched position to connect brands with the audiences they care about in the moment they engage in. We are applying Gemini to drive better matching and discovery between brands and creators of all sizes. And Gemini now powers YouTube creator partnerships, a centralized platform integrated directly into YouTube Studio for creators and Google ads for advertisers.

We've also made it easier to buy premium ad space in top-tier podcast shows by curating the most watched podcast into popular genres. For example, Super Group partnered with YouTube creator, Liza Koshy on a multi-format shorts and long-form CTV campaign, resulting in a 93% lift for their glowscreen product and a 55% overall brand lift. Looking at monetization across YouTube, momentum continues in shorts and the living room and demand gen continues to drive momentum in direct response, in particular, with smaller advertisers. Brand who is benefiting from growth in a living room where we continue to scale greater brand deals.

YouTube subscriptions revenue continues to grow faster than ads, particularly YouTube Music and Premium. By the end of Q1, YouTube Premium Lite was fully launched in 23 countries, and we plan to launch in more than a dozen new countries in Q2. As always, I'll wrap with the progress we're seeing across partnerships. Retailers are increasingly looking to Google to support their AI transformation. This quarter, Kingfisher, Target and Wayfair closed significant multiyear cloud and ads deals. Combined with the implementation of UCP, these partnerships will help deliver personalized AI-driven agentic experiences from discovery to checkout.

In closing, I'd like to thank Googlers everywhere for their contributions to our success. And as always, our customers and partners for their continued trust. Anat, over to.

Thank you, Philipp. My comments will focus on year-over-year comparisons for the first quarter, unless I state otherwise. I will start with the results at the Alphabet level and will then cover our segment results. I'll end with some commentary on our outlook for the second quarter and full year 2026. We had an outstanding first quarter, delivering our 11th consecutive quarter of double-digit revenue growth.

Consolidated revenue reached $109.9 billion, up 22% or 19% in constant currency. Total cost of revenue was $41.3 billion, up 14%. Tech was $15.2 billion, up 11%. Other cost of revenues was $26 billion, up 15%, primarily driven by increases in depreciation, content acquisition costs largely for YouTube and compensation.

Total operating expenses were up 24% to $28.9 billion. R&D expenses increased by 26% driven by compensation due to investment in AI talent as well as depreciation. Sales and marketing expenses were up 23%, driven primarily by marketing investments to support the Gemini app and search as well as compensation. G&A expenses increased 21%, primarily due to an increase in compensation costs related to legal and other matters. Operating income increased 30% to $39.7 billion and operating margin was 36.1%.

Other income and expenses was $37.7 billion, representing a meaningful increase from the prior year, primarily due to unrealized gains in our nonmarketable equity securities portfolio. Net income increased 81% to $62.6 billion and earnings per share increased 82% to $5.11. We generated operating cash flow of $45.8 billion in the first quarter and $174.4 billion for the trailing 12 months.

CapEx was $35.7 billion in the first quarter with the overwhelming majority of the spend in technical infrastructure to support the AI opportunities we see across the company. Approximately 60% of our investment in technical infrastructure this quarter was in servers, and 40% was in data centers and networking equipment. Free cash flow was $10.1 billion in the first quarter and $64.4 billion for the trailing 12 months. We ended the quarter with $126.8 billion in cash and marketable securities and $77.5 billion in long-term debt.

And as we announced today, our Board of Directors declared a 5% increase in the quarterly dividend. Turning to segment results. Google Services revenues increased 16% to $89.6 billion reflecting strong growth in search and subscriptions. Google services revenues also benefited from a strong FX tailwind. Google Search & Other advertising revenues increased by 19% to $60.4 billion driven by growth in the retail and financial services verticals.

YouTube advertising revenues increased 11% to $9.9 billion, driven by direct response advertising as well as brand. Network advertising revenues of $7 billion were down 4%. Subscription platforms and devices revenues increased 19% this quarter to $12.4 billion due to strong growth in both YouTube subscriptions, particularly in YouTube Music and Premium and Google One subscriptions, which benefited from increased demand for AI plans.

Google Services operating income increased 24% to $40.6 billion and operating margin was 45.3%. The Google Cloud segment delivered outstanding results in the first quarter. Cloud revenues accelerate across all key areas and were up 63% to $20 billion. Revenue growth was driven by strong performance in GCP, which continued to grow at a rate that was much higher than cloud's overall revenue growth rate. The largest contributor to cloud's growth this quarter was AI solutions driven by strong demand for industry-leading models, including Gemini 3.

In addition, we had strong growth in infrastructure due to continued deployment of TPUs and GPUs and core GCP continues to be a sizable contributor driven by demand for infrastructure and other services such as cybersecurity and data analytics. Workspace again delivered strong double-digit revenue growth, driven by an increase in the number of seats and the average revenue per seat.

Cloud operating income was $6.6 billion, tripling year-over-year and operating margin increased from 17.8% in the first quarter of last year to 32.9%. And Google Cloud's backlog nearly doubled sequentially, reaching $462 billion at the end of the first quarter. The increase was driven by strong demand for enterprise AI offerings and the inclusion of TPU hardware sales that Sundar referenced earlier. The majority of the backlog is related to typical GCP contracts and we expect to recognize just over 50% of the backlog as revenue over the next 24 months.

In Other Bets, revenues were $411 million, and operating loss was $2.1 billion. For the past few years, we have been working to prioritize our efforts and investments in the Other Bets. In Q1 of this year, Verily completed an external capital raise that resulted in its deconsolidation from Alphabet. GFiber announced plans to combine with Astound Broadband, which will result in its deconsolidation from Alphabet when the deal closes, which we expect to take place in Q4, and we continue to allocate significant resources to businesses where we see meaningful opportunities to create value, such as Waymo.

Turning to our outlook. I would like to provide some commentary and factors that will impact our business performance in the second quarter and full year 2026. First, in terms of revenues, we're pleased with the overall momentum of the business. At current spot rates, we would expect to see an FX tailwind of approximately 1 percentage point to our consolidated revenue in Q2 compared to a 3 percentage point FX tailwinds in the first quarter.

In Google Cloud, as Sundar mentioned, we will begin to deliver TPU hardware to a select group of customers in their own data centers. We expect to begin recognizing a small percent of the revenues from these agreements later this year with the vast majority of revenues to be realized in 2027. It is important to keep in mind that revenues from TPU hardware sales will fluctuate from quarter to quarter depending on when TPUs are shipped to customers.

And finally, we're excited to welcome the Wiz team to Google Cloud with the closing of the acquisition in March and are very pleased with the performance to date. A couple of items to highlight related to the acquisition. First, Wiz will be reported in the Google Cloud segment. And second, we expect a low single-digit percentage point headwind to cloud's operating margin for the remainder of 2026 related to the acquisition.

Moving to investment. We are updating our full year 2026 CapEx guidance range to $180 billion to $190 billion, up from our previous estimate of $175 billion to $185 billion to now include investment related to the acquisition of Intersect, which closed in March. We are seeing unprecedented internal and external demand for AI compute resources. The investments we are making in AI is delivering strong growth as evidenced by the record revenue and backlog growth in Google Cloud and strong performance in Google services.

Looking ahead, the strong results reinforce our conviction to invest the capital required to continue to capture the AI opportunity. And as a result, we expect our 2027 CapEx to significantly increase compared to 2026. In terms of expenses, as we've discussed previously, the significant increase in our investment in technical infrastructure will continue to put pressure on the P&L in the form of higher depreciation expense and related data center operations costs such as energy.

We also expect to continue hiring in key investment areas such as AI and cloud and are investing in marketing to support our AI products. To conclude, Q1 was an outstanding quarter for Alphabet, and our teams continue to execute with a high level of discipline and velocity, delivering amazing innovation. We look forward to sharing more in the coming weeks at I/O, Google Marketing Live and Brandcast. I want to take this opportunity to thank our employees for their contributions to our performance. Sundar, Philipp and I will now take your questions.

[Operator Instructions] Your first question comes from Brian Nowak with Morgan Stanley.

I have 2. The first one, Sundar, on a recent podcast, you talked about how you were acutely constrained [ by compute ], something you focused on almost every week to sort of make sure you're deploying capacity correctly. So Let me ask you this, as you sort of look at the Search business, what are the areas that you are most excited about applying next-generation compute toward to sort of generate an ROIC on that return in search in the next 12 months. And then the second one is on the sale of the TPUs to third parties. Just can you help us philosophically understand the strategy around pricing them, given the high ROIC of using TPUs to power multiyear Google Cloud workloads a little bit?

Thanks, Brian. I'll take the Search one first. Obviously, you've seen we are taking advantage of all our investments in building the Gemini models and both obviously applying it in Search in the Gemini app, driving innovations in AI overviews in AI mode and they're all contributing to the increased usage of the product. .

I do think looking ahead across both these surfaces, there is a massive opportunity to go deeper in what we do for our users, I think, bringing agentic flows, workflows to consumers in a way that it's easy for them to do, including in the context of search, I see as a huge opportunity ahead obviously, we are in very, very early innings of all that.

But our investments in our full stack of AI approach, I think, puts us in a good position to bring those experiences to search, and I'm pretty excited about it. On the second question around TPUs, obviously, I would -- we do think about it as what are we doing through Google Cloud to help our customers. And that's the framework with which we think about it. In that context, there are situations where it makes sense. For example, you take customers like capital markets where they are running highly performing AI workloads.

They wanted TPUs in their data centers. So there are -- and those trends are true across a diverse set of industries and in certain cases, frontier AI Labs too. And so we are opportunistic about it. But I do think we step back and think about it overall as the opportunity for Google Cloud. A lot of it is providing infrastructure through cloud, at times it is direct sales of TPU hardwares to a select group of customers. But again, we do take ROIC approach. And some of it helps us get more economies of scale, scale in our overall compute environment as well. And so it helps us invest in the cutting edge, which we need to do the next generation as well. .

Your next question comes from Doug Anmuth with JPMorgan.

One for Anat and 1 for Philipp. Anat, you talked about 2027 CapEx that it will increase significantly. And I know you didn't quantify it, but how do you think about the current CapEx trajectory, the ability to service this massive backlog that you've built up in just the last quarter and what will no doubt increase going forward. And then, Philipp, can you just talk more about the drivers of search queries at an all-time high? And then how you're thinking about how much room there may be to increase coverage of search queries, just the ability to show ads against the higher percentage of queries than the 20% you've been at historically?

Thanks, Doug, for the question. Let me start with your first question on CapEx and how we think about CapEx increase going into 2027. As you've seen us over the past several years, increased CapEx every year, and we have done it very thoughtfully to meet the demand that we are seeing both from external customers as well as demands across the organization.

And you're seeing the proof point, the ROIC on that in terms of just the growth rate we're seeing, whether it's growth rate within search or certainly the cloud business, and the opportunity we have within the cloud backlog. So as we're seeing that robust demand across the business, we are looking at what can we do to support that growing demand and the opportunity ahead of us and increasing CapEx to meet that demand. We'll provide more clarity in future earnings call about what that number will be, but that's the opportunity we're seeing ahead of us.

It's quite meaningful and we want to make sure we capitalize that and we do it in a way that's responsible as we've done to date.

So the second part of your question, first of all, just [indiscernible] for a second. I mean we're very pleased with the performance of our ads business here. And as Anat shared, Google Services benefited from a strong FX tailwind-- that's important to keep in mind. The strength we saw in search was not due to a single driver, but was really the result of many parts of our business showing strength and working very well together.

If I just keep that for a second the vertical perspective, retail finance, I talked about it in health, drove the greatest contribution, although all major verticals actually contributed, we make hundreds of changes every quarter to improve the user experience, the advertiser experience. And so that's really contributing to our performance here. And we've also been able to generate very strong ad performance while significantly involving the search results page here. The queries continue to grow. And as Sundar mentioned, they are an all-time high. We see AI overviews and AI Mode continue to drive greater search usage and growth in overall queries, including in commercial queries, you specifically asked about the 20% on the coverage side, as I said before, I think with the ability of AI to better understand inten t and a lot of other vectors around it. I think there is upside in that coverage number. And overall, understanding that we have at Gemini on intent has just significantly expanded our ability to deliver ads on longer, more complex searches that were previously really difficult to monetize. And Anat shared earlier, we are deploying our Gemini models now across our ads infrastructure, and it's really driving improvements across the big 3 areas that I highlighted in my prepared remarks.

Our next question comes from Eric Sheridan with Goldman Sachs.

Maybe 2, if I could. The first one, just building on the answer so far. When you look at the backlog you disclosed today. Sundar, I would love to know if you can come back to your comments on AI infrastructure and your unique approach and how that positions you to either build capacity, scale, compute and do it in a way that is, as Anat said, sort of effective from a margin standpoint as well as a compute standpoint, just to understand where you sit competitively in your mind relative to others. That would be one. And then Philipp, to bring you into the conversation, you referenced UCP and there's been a lot of industry inertia around UCP very quickly. Talk to us a little bit about what for the services business as agenti commerce scales in the years ahead?

Thanks, Eric. Look, I do think part of -- I mean I do think we are genuinely differentiated. We're unique in the market because of our vertically optimized AI stack and the way we co-develop the components from our infrastructure and models to platforms and the tools to applications and agents. And the fact that we own frontier models, own the silicon really helps us stay ahead of the curve.

And on top of it, I'll just to put an extra point on it, the deep investment in our security layers to keep everything safe. And I think we are the only provider in the market that offers all of these vertical stack. And so overall, again, to my earlier comments to Brian, I think about it all as Google Cloud. We can -- we have many different ways to serve our customers so we can meet them in a way, suited to their needs, I think better than other players here.

And I do think looking ahead, our ability to invest in this moment and stay at the frontier I think, puts us in a strong position. And I think we are doing it based on tangible demand signals we are seeing. And it's not just on the revenue side, but I'm talking from an ROIC framework, and that's what is helping us navigate this moment responsibly.

To the second part of your question, look, I mean, we're in the early stages of the agentic era. Agnetic is more than just complete transactions. We all know this. We see agenetic experiences as additive, and it will really transform how we shop from discovery to decisions while helping obviously, brands differentiate themselves. We've been very intentional about creating an agentic experience that works for our users, our partners for the entire ecosystem. Our goal is really to remove the grunt work of shopping. So consumers can focus on the enjoyable parts. For decades, you could either shop fast or smart and I think with the agentic ecommerce, you no longer have to actually choose between speed and certainty here and the vision is to make commercial experiences across the board, assisted more personal, more fluid.

And we're carefully designing space and agenetic workflows for users to really see valuable components of their shopping journey beyond just price, such as customer service, brand loyalty and more while removing the friction of the process that I just talked about. And this is exactly where the part of your question kicks in the Universal Commerce protocol, a new open standard for agentic commerce that works actually across the entire shopping journey, from the discovery to the buying and the post-purchase support that we just talked about.

And it was really co-developed with the industry leaders, including I mentioned them Shopify, Etsy, Walmart and so on. And we received tremendous feedback so far from hundreds of top companies, payments partners, retailers really interested in integrating and it will help power a new checkout experience in AI mode in search and the Gemini app and allowing shoppers to actually check out from select merchants right as they're researching on Google and going through this journey. So we're very, very excited about it.

Our next question comes from Ross Sandler with Barclays.

Yes. Just following up on the last question on agentic shopping. So it seems like we're at the point in time where this is actually going to start happening finally. So Philipp, just to elaborate a little bit, as you look at carrying the AdWords business from kind of the old way of doing things to this new agentic frictionless shopping way. How do you see the price and volume kind of growth trends for core AdWords evolving as you start implementing more agenetic workflows in search?

Look, our #1 focus is obviously on the user experience here. And I think the most important part then this is what I mentioned before, we are carefully designing the space in the agentic workflows for the users to actually see the valuable components within that shopping journey and a second, you have the space, you obviously have the ability for interesting app advertising models. I think it's also worthwhile noting that beyond just the traditional agents, there's a lot of additional ways we can actually use AI to improve the shopping experience.

And you can think about it like our apparel try-On tools that is now available in U.S., you can think about Google Lens. So there's a lot more to do here. But I think the key part is actually what I said before. We focus on the user experience here and think -- I think all else will follow if we pay attention to the points I mentioned.

Your next question comes from Michael Nathanson with MoffettNathanson.

One for Sundar, one for Philipp. Sundar if I can connect Brian's question, Eric's question, and go a little bit higher. I wanted to understand, how are you deciding how are you allocating which divisions and projects get excess capacity given that you're constrained, right? So how do you decide between all the internal projects you have and the external projects, right? So what types of screens are you running to decide who gets incremental capacity? And then to Philipp, I have noticed you said this on the Gemini app there's more and more images that come to you in the shopping journey. Can you talk about your thoughts about adding advertising on that app? And what's guiding your decision-making here on adding ads on Gemini?

Thanks, Michael. I think a great question on an ongoing basis. I'm looking forward to Gemini helping me more and more as I'm thinking that through. Look, I do think that the foundation where we start with it is what we need from a R&D standpoint to develop models at the frontier. So what do you need for training these models. And so effectively, the compute needed for GDM because it's a foundation for everything we do.

And so that's a core principle at which we operate. And then obviously, with the ability to plan ahead, we are, we do long-range plans on our core areas, be it be it search, be it YouTube and so on as well as we see in Google Cloud. And obviously, in Google Cloud, we have -- we are providing enterprise AI solutions, which this quarter had an 800% year-on-year increase from the prior year. So we are seeing strong demand for Gemini enterprise our AI solutions there. We see strong demand for infrastructure in Google Cloud.

And as I said earlier, in some cases, we are seeing demand for TPU hardware. -- hardware and other data centers as well. So we are modeling these out and working to allocate across these areas. Obviously, we are compute constrained in the near term. And as an example, our cloud revenue would have been higher if you were able to meet the demand. So we are working through that moment, and we are investing, but we have a robust long-range planning framework, and we see extraordinary opportunities ahead, and we are allocating with that framework in mind.

And to the second part of your question, as I said in my previous answer, we are obviously focused on the user first and creating a really great user experience with all of our products, especially on newer products. And specifically on monetization in the Gemini app, our focus right now is on AI Mode. But it's fair to say that we really believe a format that works well in AI Mode would transfer successfully to Gemini app. And so today, in the Gemini app, we're focused on the free tier and subscriptions and our AI plans were a sizable contributor to our Google One revenue growth.

But let's also be clear, ads have always been a big part of scaling products to reach billions of people. And if done well, ads can be really valuable and really helpful commercial information. And at the right moment, we'll share any plans as we have said, but we're not rushing anything here.

Your next question comes from Mark Shmulik with AllianceBernstein.

Philipp, one more on search performance, if I can. You talked a few times about kind of optimizing for the consumer experience. And I guess besides higher query volume, is it fair to conclude that consumers are using these AI tools [indiscernible] or otherwise, and it's shrinking their purchasing journeys, converting at higher rates? And if so, is there a way to dimensionalize how much of the strength in search is being driven by that behavioral change against perhaps some of the newer advertiser AI tools that you'd be launching and rolling out .

I think the way to think about it is really to think about the expansionary moment we see here for search. This is the key part. AI is fundamentally changing how the world searches for and how it access information, queries are at an all-time high, Sundar said this. Traditional search really started with [indiscernible], and now we have overviews in AI Mode and they have made search more intelligent than ever and they let you ask for more complex questions. And we have Lens or Circle to search live, And search Live, Search Live now available to all countries and languages that support AI Mode again, shows you the expansionary nature of it, and we have our AI-driven search campaigns, and we have now [ SMBs ] that can reach customers at a scale that it really wasn't possible even a few years ago, and you can add in Google Translate and so on. So I feel like you've factored all of this and we're in a pretty good place and are quite excited about where this is going.

Your next question comes from Ron Josey with Citi.

Maybe this one is for Anat. We continue with margins continue to expand here. I wanted to understand maybe if you could break down the cost drivers or really the drivers of margin expansion, particularly amongst cloud, there's a thesis out that AI revenues are lower margin in general, but we are seeing margins improve. So more insights on just the cloud business and what's driving that margin expansion. Obviously, demand may be pricing, but that would be helpful.

Sure. Let me help impact the margin expansion. Obviously, we're pleased to see that there are pushes and pulls across the business, including the wiz and Cloud specifically. And I would start with the top line, when we see this robust strong revenue growth, both in cloud and Google services, it does provide leverage all the way down to the bottom line within the income statement. And you know we've been working hard to ensure we have -- we're running a productive and efficient organization, and it's not just how we operate the business, but even in areas such as our technical infrastructure, where we are investing the significant CapEx investments in our data centers and servers.

We are looking at how we drive scientific process innovation within that organization. And that is reflected both in cloud and Google services as we allocate cost based on consumption. In the past, I did talk about the depreciation associated with these investments that is hitting both Google Cloud and Google services. Google Cloud expanded margin quite significantly from a year ago, as you've seen in our numbers that we just previewed. And a lot of it is the top line growth that Google Cloud is providing or producing as well as an incredibly efficient way of running the business.

I will give Thomas and a team a lot of credit for running a very productive organization and making sure that we are supporting our customers and providing the services and products that they want and benefit from continue to drive top line growth and doing this well within the middle of the income statement, all the way from a very efficient technical infrastructure thinking through how do we leverage AI across our business. As Sundar mentioned, the use of coding internally or how Gemini helps us there optimizing our real estate footprint.

And we're going to continue to do this. This is not -- we're not going to stop here. We're going to continue to push for more efficiency, knowing that we're going to have the headwind associated with the depreciation coming with higher CapEx level.

Our next question comes from Ken Gawrelski with Wells Fargo.

Two for me, please. First, on the cloud and capacity, could you speak about how your verticalized capabilities enable you to navigate a complicated supply chain, especially when experiencing inflation and constraints. Are you factoring any supply chain price inflation into '26 and '27 CapEx commentary? And as part of that, maybe Anat, could you talk -- could you update us on the allocation of compute capacity internal versus external cloud? And then one more, please. When you think about search quality volume growth. We're clearly seeing expanding use cases. Historically, it's always been free to the consumer with and completely ad-supported do you see future use cases where certain consumer use cases are more effectively monetized via subscriptions? And maybe a different mix of the consumer "search" the new search opportunity?

All right, Ken. Maybe there are a few thoughts to it. Maybe I'll touch on it. And on overall compute. I think I spoke earlier on how we think about allocation of compute across our businesses. And I think, again, the long-range planning and the ROIC framework give us a good way to plan ahead. I do think we -- I mean, obviously, we are working through a complicated supply chain environment as you point out, and we are factoring that into any commentary we give.

But I think the scale at which we are operating and our ability to work across all layers, both -- our supply chain partners see the strength of our diversified businesses and the demand we drive and our frontier technology and the investments all through the stack, I think they help us get into deeper partnerships all across the supply chain. And I think that's -- and I mentioned earlier, the economies of scale point as well. So all of that factors in a positive way there, I think. In terms of search, look, I think we -- we are proud that we build models at all, we are at the frontier across the period of Frontier.

We do think about capability and the cost front here deeply so that we can serve users at scale. -- but at the same time, we can bring in the most powerful models for the most demanding queries. But the future, as you are right, that in a valuable as we serve more and more valuable use cases. There are going to be use cases where people will want to use the most powerful model. And there may be different ways to accomplish that. So we're going to put the user first and support them in the way they want to use the product, and we already provide various tiers of our subscription plans in which you can get access to more powerful models and that applies across your Google user experience and including in search, and you've seen the momentum we saw a very robust quarter in terms of our AI subscriptions growth, driven by interest in getting access to better Gemini models. And so I think that sets us up well to serve the breadth of use cases people would want in all places, including in search.

And our last question comes from Justin Post with Bank of America.

I expect a lot of interest in your TPU sales. So can you help us think about how you're thinking about the opportunity there? And then maybe how much break down the backlog growth a little bit between TPUs and cloud? And then second question, just thinking about the margins on these big generative AI cloud deals. How do you think about these $100 billion deals coming in and the margins associated with those? Can they be similar to your cloud business as it is?

Look, overall, I would say, look, we see tremendous interest and there's tremendous demand for both AI solutions as well as AI infrastructure, including massive interest in our GPU offerings as well as TPUs. And so we are proud that we can provide customers with a very diverse with the breadth of our offerings and let them -- they can meet them in terms of where their needs are. And maybe I'll pass it Anat to give some color on the backlog growth.

Yes. So the backlog, the TPU hardware agreements that Sundar referenced in his prepared remarks, are reflected in our cloud backlog of the $462 billion. Although the majority of the backlog is still GCP agreements. Now as you think about the total backlog, just over half of it will convert to revenue in the next 24 months. And the TPU hardware sales, more specifically, we expect a small percent of them to see coming through as revenue later this year and then the majority to be realized as revenue in 2027.

And then anything on the big AI deal margins with the generative AI companies?

Look, I think nothing to comment on any specific contracts. But overall -- earlier, there was a lot of questions about how do we allocate and remember, in a constrained environment when we are choosing to allocate across all these opportunities, we are working off a robust ROIC framework.

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.

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

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

All right. Good afternoon, everyone. We're thrilled for our next fireside chat to have Anat Ashkenazi with us from Alphabet. Thank you so much for joining us.

Well, thank you for having us here today and for everyone who's joining today to listen to the Alphabet story and your interest, yes.

It's always good to see the catch up on everything going on at Alphabet and around the world. A lot has changed in the year, which we will get to in perception at least.

But first, the disclosures. Please note that all important disclosures, including personal holdings disclosures and Morgan Stanley disclosures, appear on the Morgan Stanley public website at www.morganstanley.com/researchdisclosures. They are also available at the registration desk. Some of the statements made today by Ms. Ashkenazi may be considered forward-looking. These statements involve a number of risks and uncertainties that could cause actual results to differ materially. Please refer to Alphabet's Forms 10-K or Q including the risk factors discussed in any of these filings. Any forward-looking statements made today are based on assumptions as of today, and Alphabet undertakes no obligation to update them.

With that, there's a lot going on with the company. So you've -- let's sort of take a step back. One year ago, we were sitting here, Alphabet was about a $2 trillion company. The discussions in the hallways and just in general on Wall Street were around search disruption, long-term positioning versus new search entrants, how to think about the business' growth. Now it's almost a $4 trillion company. So it seems that investor sentiment has changed. In your -- from your perspective, what has changed internally at Alphabet with how you think about pace of productization, the long-term vision? Like how do you think about what changed internally now versus 1 year ago?

Yes. Great question, a way to start. So if you think about where we were a year ago versus where we are today or maybe where we were 10 years ago, the core strategy really hasn't changed. And it's a company that is focused on user first, consumer first, innovation-based and then looking at being an AI-first company. And that has been the strategy for many, many years. It started with the acquisition of GDM and Sundar making statements about AI being an AI-first company. And that strategy has really led us to where we are today.

So if the strategy hasn't changed and is it the execution? Or is it the pace of strategy and how we're thinking about the business. So within that strategy, we have and we have been talking about having the full stack approach, which -- and our full stack, we call it technical infrastructure sometimes in the company, which is the chips that we have, and we have our own TPUs, right? We've launched our first TPU just over a decade ago in 2015. Now we're on Generation 7, access to GPUs from NVIDIA as well as a set of data centers, which we mostly build ourselves. So we also lease data centers. Networking equipment that is, I believe, the largest, most comprehensive one in the world. That's our technical infrastructure.

And then that combined with, when I said we're an innovation-based company, world-class research capabilities, which you see through our GDM, our Google DeepMind efforts, our frontier model, the research efforts that we have across the company and then coupled with the ability to distribute and access consumers, enterprises, creators around the world with a set of products that we have. So that kind of anchors our strategy. And what we've done since deciding that we're an AI-first company is invested very strategically in building that AI infrastructure across the organization. And now it's really the foundation across the Alphabet business, whether it's in search or in YouTube or platforms and subscription business or in cloud and certainly Gemini, Google DeepMind as well as with the other bets, whether it's Waymo or Isomorphic, Intrinsic, et cetera, it is really the foundation of the research foundation that sits across the company.

What you've seen in the past year is this strategy delivering and delivering at a rapid pace of innovation. So we are now turning this flywheel, this innovation flywheel very fast and delivering innovation to consumers, enterprises, creators much faster than we had in the past, but we had the privilege of working on this for years and creating these products. And when we deliver those to consumers, and we're able to see how those are used and where we can drive continued improvement so that we can deliver the next wave of innovation. So that is what's the underlying of where we are today and what got us here and what will take us to the next decade and beyond.

Going forward. The flywheels are spinning faster, but so is the investment. So I have to ask you about -- so as a CFO and you're doing math around ROIC. So last year, CapEx was around $90 billion. This year, the guidance is for around $180 billion of CapEx. What types of analyses are you and the team doing to sort of evaluate ROIC on that CapEx just to ensure that you're getting enough revenue in a reasonable amount of time to generate return for your investors?

So as you can imagine, I spent quite a lot of time on this question and on the CapEx investment in general in terms of how much we need to invest, how much are we investing and what are we seeing as a result of that. We have a highly rigorous framework that we use to make these decisions. So the first decision is how much do you need to invest? $91 billion or this year, we said $175 billion to $185 billion. And that question we don't take lightly. And we start with aggregation of understanding the demand across the business, whether it's internal demand or external from customers like the cloud customers.

And from there, we build on what do we need from a compute perspective, what -- how do we cost it, which yields the numbers we've shared for this year. And we drive significant efficiency even within that technical infrastructure because we have the benefit of owning that full stack for the most part. We have experts who focus just on that, making sure that our data center -- we construct our data centers, we do it in the most efficient way that's tailored to our workload or to what the customers need. Our TPUs consistently looking at efficiency of the TPUs, ensuring they deliver more and more compute. So we do this across the board.

And when we get that demand, we then ask, what is that going to yield? So if I'm investing in compute for Search or compute for Cloud or at Waymo, what do I expect to get for that investment? And I look at this on a continuum. Some are more near term and more certain and some are maybe long-term earlier innovation. If you think about some of the experiments, maybe Waymo 5, 10 years ago, now it's delivering, obviously, but some of these experiments are early stages. And then underneath that, the foundational layer of Gemini and the frontier models that are supporting the entire company. So we look at, for example, if we're investing X in cloud, and I think I've shared on the call that approximately half or just over half of our ML compute this year is going to go to cloud.

What are we expecting in terms of return? Is it external? What are the -- what's the profitability numbers? So there are financial metrics, there are operational metrics as well. We do this across the business to estimate what that looks like and then we track it. And we track it on a very frequent basis just to understand, have we invested enough in an area or are we seeing more demand and we need to start reallocating or make different decisions. And the team that looks at efficiency, can we get more and more out of every chip that we have in our data center. So a lot of thought and effort and rigor goes into this, which shouldn't surprise anyone given these amounts.

Yes. The continuous nature of it, I'm sure, must play a role. Anything you could tell us what surprised you over the course of last year, you sort of adjusted CapEx sort of areas of more signal or less signal?

Yes. So last year, we gave guidance that was lower than where we ended up. We ended up at $91 billion. We gave guidance that was around $70 billion or $75 billion. So we ended the year with higher CapEx. And there are a few drivers that can cause us to be either higher or lower than the amount. One is construction of data center would be a great example. So last year, I've shared that approximately 60% of our technical infrastructure investment went to chips and about 40% to data centers and networking equipment.

If we're able to accelerate construction, we may be able to pull in some of the investments, and that's a good thing because we have such high demand for our products that the more we can bring in the more revenue we can generate. We always -- I've now said multiple quarters in a row that we exited the quarter with more demand for our cloud services than we had supply. So it's -- we certainly want to invest to be able to support these -- the customer demand. So that would be one example of something coming in or chips when we -- as we acquire chips, we can get that earlier. Now this can also be the exact opposite where construction -- you may see construction delays or something is delivered on a later schedule, so it comes in later. So those could just move the same amount over between years.

The other thing is we are seeing greater demand than what we had anticipated at the beginning of the year, and we're trying to invest more. And it's obviously challenging. There is a limit to what you can do in a year for that same year, just given the time frame. Obviously, if you need to construct data centers, it's not an overnight type of thing to order components. But those are the things that can move it up and down, and we'll be monitoring it for this year, I should say, monitor, we are actively managing it for this year as well and probably provide some updates on quarterly calls on where we are.

Great. There's a lot of sources of that demand between Search and YouTube and Cloud I want to get into. But first is on the cost discipline side. So if we think about this CapEx, the amount of D&A that's going to flow through, can you give us examples of still existing work streams you have in place to sort of manage the other costs, just to potentially put a higher floor on EPS given all the D&A coming through?

Let me start with just the approach. There's a philosophy that I have with regards to cost management. And while people may refer to a work stream or an intervention or a project, I view this differently. You always have work stream and always have projects and efforts. It's a nonstop effort. It's not that we're getting to a place where we said, now, we're the most efficient, we're not working any hard. I always say it's like an Olympic athlete. You never stop training, right? You always want to beat your own record.

And it's the same thing within a company. You always look to do a little more and drive more efficiency in the organization. And some of these things are the traditional things of just running the business more efficiently. Some are scientific and kind of innovation, technical innovation. So the technical step that I've just mentioned or our CapEx of $175 billion to $185 billion, which, by the way, is the majority of that is our technical infrastructure. There's some buildings and some Waymo, et cetera, but the vast majority is our technical infrastructure. Start with that. That's the -- when you have such a large amount is how can we ensure that every dollar we put behind a data center or components where we're putting a chip in is the most efficient.

And it's not just the acquisition cost, but it's the utilization of the data centers and our technical infrastructure across the enterprise. It's an asset. When we have a data center with chips installed, it's an asset. So we want to make sure we use that asset as much as we can, whether it's using something 24/7 or because we have -- within Alphabet, we use predominantly TPUs internally, moving that capacity to where it's needed. That fungibility is incredibly important. So we look at that as how do we drive efficiency, running a really good supply chain so that when you have the data center build-out, you have the chips to install them.

And so we don't sit in a -- we don't have inventory sitting in a warehouse waiting for months for a data center to be complete and vice versa. So you want to try to match them as closely as possible. So that's on the technical infrastructure side. And certainly, model efficiencies help as well. And then across the organization, you've seen some things we've done over the past 18, 24 months, reduction in management layer, just driving efficiency in how we run the business. We've introduced for the first time a voluntary exit program last year across the organization. We have -- we believe we have a tremendous opportunity ahead of us, and we want those that are excited about it staying and those that maybe want to do something else, they can make some choices.

We're also looking at using AI internally. So while everyone I'm sure here is using some AI tools during your day, we encourage our own employees to leverage the AI tools we have and in some cases, design AI tools for certain functional activities. So we've shared on the call the percent of code that's generated by AI and then our engineers validate or test it but we have it across the organization. I'll just give you an example from my small team of finance, where there are tremendous opportunities. We now have a treasury agent and the treasury agent goes across our balances of the cash we have on hand to maximize the return on that investment and is yielding actual real financial results. And we have the benefit of having a cloud business that allows us to codevelop these tools with cloud or leverage the tools that our cloud business has.

So having an AI-first organization as well is one of the areas that's going to drive continued efficiency. But you're right, with investments of $91 billion last year and just over $50 billion the year before, there's going to be significant depreciation that's going to run through the income statement, coupled with additional cost of just run energy. And that's -- last year, we had about $21 billion in depreciation. The year before, it was about $15 billion. That's not a small number to offset. We were able to hold some extension in our operating margins. That's hard to do. And that number is only going to grow next year just given what we've done historically.

So we need to have these efforts in place on a regular basis so that we can have more money to reinvest in the business to turn this flywheel faster. Some of it may flow to the bottom line. Some of it may be reinvested in higher or highest priority areas.

Well, the best way to trump the ramping D&A is faster revenue growth. So maybe let's talk about revenue a little bit.

Scale help.

Scale does help, and so does accelerating growth. So let's talk about Search. You've made a lot of changes to Search over the last 12 to 24 months. My team and I, we track all these AI mode, overviews, multimodal sorts, Search lens, Agentic capabilities have now started to emerge throughout it. Is there any way you can help us if you think about sort of 1 or 2 of the changes that you've made that really drove larger-than-expected benefits to engagement and monetization maybe than what you would have had 1.5 years ago?

Yes. We always start with having an estimate of what this would look like and you hope to beat those expectations. So one fun example would be Nano Banana that we launched within the Gemini app. If you are not familiar with, it's our image-generating tool, went viral, viral with -- and I want to say 2 weeks, we had 20 million new Gemini subscribers. I mean it's quite impressive if you think about just one tool and what it does so quickly. So this can happen with different launches. We were incredibly pleased with AI overview and AI mode, as you said, have changed how people search. And it's not just that you get an AI overview answer, which is great. Obviously, the summary is at the top.

But people learn they can ask questions differently or ask questions they've never been able to ask before, never thought they'd be able to ask before. And what we're seeing is we're seeing longer queries now in AI mode and AI overview. People are now digging in, they're getting -- they're interacting more. It is driving growth in number of queries as well as commercial queries. And then as you've said, different ways of searching -- so while we were used to typing something in, now you can actually point your phone or your camera at something and use Google Lens. And it's a high-growth kind of feature for us, and you see the younger users are using that more.

It's a brilliant tool for when you look to shop for something because you immediately get -- you point it, I want this flower, you point it and you immediately get the different websites that can offer it. So we have invested in innovating what was core to Google's growth for several decades now. And that's the -- as I said, it's kind of -- our strategy is focused on the consumer innovation-based organization. You have to kind of out-innovate yourself, disrupt your own business model in the sense of we have -- we had a great search product, which everyone loved and broadly used with billions of people around the world. And we brought new innovation into that model with AI overviews, AI mode, lens, different ways of searching.

And that's -- we always just be thinking about what is that next frontier, what's the next way of -- what's the next thing we're going to be introducing.

Well maybe that next frontier, it could be Agentic. We've written quite a bit about Agentic and sort of how to think about new types of search could change e-commerce, could change travel, it could change a lot of these categories. Sundar talked about Agentic. How do you think about the incremental utility for your search users, your user base as you roll out new types of Agentic capabilities? And what are the biggest technological hurdles you have to clear to really scale "Agentic"?

So Agentic Commerce, I would say, still in early days, but no doubt holds an interesting promise. If you think about how we interact with shopping today online, there are digital interactions, but they're fairly simple. And what we're looking at is taking this to a much more -- to a level where you can conduct much more complex processes all the way from searching through recommendations and then an actual purchase.

One of the complexities is the fact that there are so many sellers and so many brands out there, and you need a unified way to connect them to the agent. So we've developed what we call UCP, this universal protocol that allows them to connect in a consistent way with this agent. And we're now -- we have a few customers on this. So if you go into your web browser and you type in, I want to Silk pillow or whatever it is, it will show up the results, and you can actually -- there are a couple of them where you can immediately click on the -- there's a buy button and you put your credit card, et cetera, you can immediately purchase through that.

So early days, but there are certainly, I think, interesting opportunities if you think about truly making this seamless for a consumer that goes online and wants to shop for something and you get everything in one place. I have my own things that I really, really want to see there. I already told our team, this would be great for me personally. I'm not going to share it just yet, maybe in the future, but there are some cool opportunities there for users.

I've used the Gemini agent to search for items for me on repeat, trying to find golf pants of certain size, I can't find it e-mails me and looks for every day. Is that the type of thing you're sort of you're thinking about, just sort of real-time search, real-time price comparison, like an actual interactive personalized shopper? Or is that not how you're...

You can think about this kind of when we talk about personalization, linking it to what you're looking for and it may be a preference or it may be criteria that you've provided ahead of time so that you have that personal shopper shop for you. But again, early days, let's see what -- we share...

Stocks for TMT next year through an agent. You have all these products, you have multiple different monetization nodes. And I feel like the discussion around subscription is more pronounced now than a couple of years ago, but it's also observable that you are shipping more Gemini-based products to the free users faster than you were. So philosophically, how are you sort of making the decision on what stays behind the subscription paywall to drive that revenue stream as opposed to driving broader scaled adoption to the free user base?

So we have multiple tiers. We have a free tier and then we have several paid tiers. Each tier has slightly different offerings as you get to the higher tiers, you just have access to more tools. And different users have different needs of what they need to do. But again, if you go back to the strategy just mentioned and if the user is at the heart of our strategy, that means that we want to make sure we have the best user experience out there.

And when you launch something that's free first, you get a lot of feedback and you see how the consumer is using that product, but then also offering different tiers to users who may want to engage more and maybe they want something for a business activity as opposed to just a personal use. So we make sure that we have a variety of options. And that's something we've always done, and we'll continue to always evaluate what's free versus what's paid and what are the different levels. We also want to make sure that our tools are accessible to consumers, not just sitting here in this room, but across the globe. So we would have different pricing in different countries to ensure that consumers in those countries can access our products as well.

Understood. One of the other big sources of demand for your compute has been Google Cloud. What a difference a year makes in this business? So this is now a business growing almost 50%, a backlog of over $240 billion. Our model has Google Cloud getting to be almost 40% of EBIT in a few years. This is a big business. This is a much larger business than it was. Can you just unpack some of the drivers over the last 12 months that have really driven this revenue acceleration and the strong backlog that we were all wrong about and missing 1 year ago?

So Google Cloud has been -- has had really record results throughout the last year. And as you said, exited Q4 at $17.7 billion in revenue. So annualizing now at a very, very large business already and growing at 48% with a significant backlog. So what are the drivers of that growth? When I talked about AI, we're an AI-first company across the organization. That's true for cloud as well. And we have -- if you think about Cloud, we have the GCP products, whether it's the AI infrastructure where we have TPUs and GPUs or our AI services that we provide to customers as well as our GCP kind of our core products such as security or data analytics where people are -- cloud -- Google Cloud is known for and people come for.

And then we have workspace and some other smaller areas. And just our GCP area, which I think I've shared on the call, that growth rate was actually higher than 48%. So we're seeing AI drive the growth within cloud. So that's if you think about the vertical in terms of what drives that. Now who are the customers because I always get that question, is it one customer? Is it 2 customers within that backlog? Should we be worried? And I always look at are these new customers or existing customers and the scale, the size of these customers. And what we're seeing, which I believe is very positive is existing customers expand their work with us. So they're pleased with the results they're seeing.

They're getting the same benefits when we talk about AI-first companies, they're getting the benefits of being an AI-first company for that organization. So they're expanding their work with us and the new customer coming in and wanting to get on Google Cloud or getting access to our product services or infrastructure. So that's very encouraging to see that. And we see it across small AI labs, small companies to some of the largest enterprises. And having that diversity within our customer base is encouraging. But it's AI that's driving that growth. And Google Cloud, as you've seen in the margins, as you just mentioned, has executed incredibly well, delivering margin expansion, exiting a 30% operating margin with this 48% top line growth. is very impressive. And they've been laser-focused.

Thomas does an excellent job of making sure that we talked about efficiency not being a onetime or episodic thing, but rather a continuous way of how you think about the business. That's how he runs the business, is making sure that every dollar that's invested is the most efficient one and win more customers. We're seeing this win rate, new customer win rate going up as well. So it's exciting across the cloud organization.

Yes, the type of growth and then the 55% incremental EBIT margins is really impressive. Is there anything other than scale or anything you'd call out on that type of profitability that we should be mindful of that just sort of question the durability of that?

So scale does help a lot. And obviously, the top line has significantly expanded. And they are also faced with what you had mentioned earlier, which is the headwind associated with the depreciation, right? Because a lot of the depreciation would be -- would sit in the cloud segment. And they've worked really hard on every line item that you can think about within the P&L kind of in the middle of the income statement to ensure that it's done in the most efficient way. The same thing we do across the organization, they're doing that in cloud. But certainly, the top line expansion scale does help.

Got it. Talk about TPUs a little bit. There's a lot written, including by us about the go-to-market strategy on TPUs, how to think about where it is, how to think about where it could go. Even there's something in the media last week about potentially setting up essentially TPU Neo clouds to sort of sell those TPUs to third parties. Maybe just philosophically, can you talk to us about sort of Alphabet's strategy on how it prefers to sell TPUs, whether it's tethered to GCP workloads or on a stand-alone basis?

So our TPUs, which I said we've launched the first line over -- just over a decade ago, now in our seventh generation, we use it across the organization. So think about Gemini. Gemini was trained on TPUs. And that's a benefit that we have because they're customized to our workload. As we think about the external customer base, we offer them both GPUs and TPUs so they have the choice based on their specific needs or preferences, they can access those -- the TPUs and GPUs. And we -- the customers lead from that perspective.

So based on what the customer demand is, whether it's TPUs and how we engage on that transaction is what will decide how we're thinking about this as well as going back to your, I think, second or third question about ROIC, how do we allocate that compute? Does it go to cloud? Does it go to internal purposes? Does it go to an external customer? All those are part of that equation of making that decision.

Okay. Let's talk about YouTube a little bit. YouTube, you have -- we think about 2 billion daily active users. Our numbers show it's about 80 minutes per user per day. You have a very big corpus of users and a lot of engagement. And yet this is an ad business that, for whatever reason, we continue to get wrong, it continues to grow slower than we expected. It doesn't seem like we're seeing as much Gen AI uptick at YouTube as we are at other big scale social media platforms.

Is there anything different with the way YouTube's ad business is sort of being run than maybe other scaled engagement-based platforms?

So YouTube is different. It's not a -- probably not compared -- it's not a social media platform, right? It has much more than that. And as you think about YouTube has an ad component to it and a subscription component to it. And we've shared on the last call, the combined revenue profile and the fact that it has grown from the last time we've shared it. And that combined revenue profile is just over $60 billion. And when a consumer is on our platform, they can be an ad-supported consumer or they can be a subscription consumer, a subscription consumer, more profitable than an ad-supported one.

So we look holistically at that across ads and across subscription. And across ads, what you've seen in the last quarter, specifically, the year-over-year growth rate was impacted by the lapping of the U.S. election, which impacts, obviously, YouTube, not surprising. We had this a little bit in Q3, but mostly in Q4 of the previous year. But we've seen growth across different verticals within YouTube. But I would say, think about this in that context and AI is driving growth there for creators. So if you -- we talked about Search and how we as consumers use the Search tools. Think about the tools we're now providing for creators. The video -- compare this to video on YouTube 5 years ago versus where we are today, the AI tools they have for creation are far beyond where they were just a couple of years ago. They have video we've just launched, I'm sure you've seen a music creation tool within the Gemini app.

So we are giving them more tools that are allowing them to create differently. And when they engage more, that means the user engages more with their content. We also look at recommendations within the YouTube -- just within YouTube and that what we can do there. So there are several areas where you've seen AI impact YouTube growth.

Right. Yes, speaking to the breadth of Alphabet. So we've covered Search, we've covered Cloud. We've covered YouTube. Now we're talking about autonomous driving. With Waymo, you've made a lot of progress over the last 12 to 24 months. We have you launching another 25 potential cities over the next 2 years. It seems like there's a very impressive ramp coming. What are some of the key hurdles or the investment areas that you really need to execute on Waymo just to sort of like hit a lot of these city launches that the company has been posting about?

Yes. So we have the Waymo Driver and we call the vehicles called the Waymo Driver. It's not an actual driver. Launch was in 5 cities last year, already launched in 5 more this year. And then as you said, more to come, including potential for international cities. When we make a decision to launch in a market, we look at safety first. So the Waymo team has invested quite significantly in ensuring the Waymo Drivers are incredibly safe and the safety track record are very impressive if you look at just the number of accidents versus a human driver, orders of magnitude different. So we look at safety.

When we look at the specific market, each market. So for example, take the U.S., every state has its own laws and regulations on what it would take to operate in that market. So we need to make sure it's a market we can operate in from a set of policies and then map that market and be able to launch in that market. So we do it with a safety-first mindset to make sure we can do this well and scale up rapidly. But you're seeing the demand is so high for these Waymo Drivers that we're trying to launch in as many markets as possible, getting to these consumers across the U.S. and outside the U.S. It's one of our areas of investment.

If you look at the other bets, there are multiple things in there. And while you see it as one category, kind of if you look below the waterline, we are making choices, and that the strategy is about making choices of where do we direct investments within the other bet category, and we have shifted more to Waymo to be able to scale and get to as many consumers as possible.

That's an interesting question because on Waymo, I guess, what is the philosophy about sort of if you need to be more asset heavy to fund more cars to fund more stations in these last miles, is that an area where you're interested in investing more dollars? Or are you trying to stay more asset-light and balanced on the forward Waymo investment?

So you've seen us do both, and you're referring to partnerships, for example, right, local partnerships with other providers. We'll do both. It will depend on the market and the specific opportunity. It's not that we're one has to dominate the other. It depends on where we are.

Got it. Okay. Maybe one to wrap on a big picture. We've covered a lot about sort of how you're using GPUs and TPUs and Gen AI. Maybe you have a lot of these discussions and questions with investors. What do you think is still the most underappreciated opportunity from all of these AI-based investments for Alphabet? And what's the most overhyped opportunity?

So I wouldn't say what the -- I wouldn't say there's an underappreciated overhype, but rather -- so we talked about so many areas just in the last 30 minutes or so. And think about all the things we haven't talked about, right? So Alphabet is now such a comprehensive business with AI driving multiple other areas. So we've not talked about, for example, isomorphic and what AI can do for drug discovery and what we can bring in terms of curing diseases that currently are not curable, I come from this field, right? So I know this well, but being able to accelerate drug discovery is transformational, right, for human health across the globe.

We haven't talked about Quantum, which, by the way, has potential -- I mean, not today, but we will have potential implications in a diverse set of areas when we hope to have good use of cases within 5 years or so, and we're progressing very well there. We didn't talk about robotics and intrinsic, right? There are so many areas within the Alphabet business where AI is propelling their potential growth. Now that's where also comes the discipline of what do you invest in because you can't invest in everything, you have to make choices. But there are some tremendous opportunities we have across the organization.

And you're seeing the results, right? We talked about ROIC. I think 1.5 years ago, 2 years ago, it was a promise. Now you see it in the numbers, it's already delivering revenue growth across multiple parts of the business and will propel the future growth as well. Exciting times.

Yes. We see the growth in the ROIC ahead. Thank you so much for that.

Thank you.

Welcome, everyone. Thank you for standing by for the Alphabet Fourth Quarter 2025 Earnings Conference Call. [Operator Instructions] I would now like to hand the conference over to your speaker today, Jim Friedland, Head of Investor Relations. Please go ahead.

Thank you. Good afternoon, everyone, and welcome to Alphabet's Fourth Quarter 2025 Earnings Conference Call. With us today are Sundar Pichai, Philipp Schindler 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 statement. 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 located at abc.xyz/investor. Our comments will be on year-over-year comparisons unless we state otherwise. And now I'll turn the call over to Sundar.

Thanks, Jim. Hi, everyone. Thanks for joining us. It was a tremendous quarter for Alphabet. The launch of Gemini 3 was a major milestone, and we have great momentum. Alphabet annual revenues exceeded $400 billion for the first time. This quarter, Search continued to accelerate with revenues growing 17%. YouTube's annual revenues surpassed $60 billion across ads and subscriptions. Cloud significantly accelerated with revenues growing 48%, now on an annual run rate of over $70 billion. Backlog grew by 55% quarter-over-quarter to $240 billion, representing a wide breadth of customers driven by demand for AI products. We have over 325 million paid subscriptions across consumer services with strong adoption for Google One and YouTube Premium.

In addition, we have sold more than 8 million paid seats of Gemini Enterprise, which we launched just 4 months ago. And our Gemini app now has over 750 million monthly active users. We are also seeing significantly higher engagement per user, especially since the launch of Gemini 3 in December. Overall, we are seeing our AI investments and infrastructure drive revenue and growth across the board. To meet customer demand and capitalize on the growing opportunities ahead of us, our 2026 CapEx investments are anticipated to be in the range of $175 billion to $185 billion. Today, I'll provide an update on our AI progress and then share highlights from Search, Cloud, YouTube and Waymo. First, AI progress across the full stack. Our unrivaled infrastructure serves as the bedrock of our AI stack. We have the industry's widest variety of compute options.

That includes GPUs from our partner, NVIDIA, who announced at CES, that we'll be among the first to offer their latest Vera Rubin GPU platform, plus our own TPUs that we have been developing for a decade. In December, we announced our intent to acquire Intersect, which provides data center and energy infrastructure solutions. As we scale, we are getting dramatically more efficient. We were able to lower Gemini serving unit cost by 78% over 2025 through model optimizations, efficiency and utilization improvements. Next, world-class AI research, including models and tooling. We offer the most extensive model portfolio in the world and lead across text, vision and image to video LMArena leaderboards. Gemini 3 Pro drives the state-of-the-art in reasoning and multimodal understanding. It has seen the fastest adoption of any model in our history.

Since launch, Gemini 3 Pro has consistently processed 3x as many daily tokens on average as 2.5 Pro. Our latest model powers Google Antigravity, our new development platform where agents can autonomously plan and execute complex software tasks. It already has more than 1.5 million weekly active users after launching just over 2 months ago. Our first-party models like Gemini now process over 10 billion tokens per minute via direct API used by our customers, up from 7 billion last quarter. Third, bringing AI to our products and platforms. We are shipping innovation at scale to bring helpful AI features to people everywhere. In January alone, we have launched personal intelligence in AI Mode in search and the Gemini app, introduced new features to Gmail an updated view, reimagine Chrome as an AI-first agentic browser through features like Chrome Auto Browse; announced Project Genie, which lets users create and explore interactive worlds generated in real time using Genie 3, our general purpose world model.

And we laid the groundwork for shopping in the AI era by introducing a new open standard for agentic commerce, the universal commerce protocol built alongside many retail industry leaders. Finally, from Android to Pixel, we are getting our best AI capabilities into people's hands. At CES, a range of partners, including Samsung, showcased how they are bringing Gemini to more devices from XR to the living room and beyond. And to confirm the rumors, we'll be introducing our Pixel 10a to our best-ever-rated Pixel 10 series very soon. Turning now to key highlights from the quarter starting with Search. Search saw more usage in Q4 than ever before as AI continues to drive an expansionary moment. We have executed with incredible speed. We shipped over 250 product launches within AI Mode and AI Overviews just last quarter. We have integrated Gemini 3 directly into AI Mode in Search.

Now Search can better understand your query, dive deeper on the web and generate interactive UI experiences. And last week, we upgraded AI Overviews to Gemini 3, giving users a best-in-class AI response at the top of the search results page. We have also made the search experience more cohesive, ensuring the transition from an AI overview to a conversation in AI Mode is completely seamless. These new experiences are proving to be more helpful and are driving greater usage. A few highlights. First, once people start using these new experiences, they use them more. In the U.S., we saw daily AI Mode queries per user double since launch and AI Overviews continue to perform very well. Second, people are engaging in longer, more complex sessions.

Queries in AI Mode are 3x longer than traditional searches. We are also seeing sessions become more conversational with a significant portion of queries in AI Mode now leading to a follow-up question. Third, people are searching in new ways beyond text. Nearly 1 in 6 AI Mode queries are now nontext using voice or images. And Circle to Search is now available on over 580 million Android devices. Next, Google Cloud. Our growth in revenue, operating margin and backlog highlights the strength of our entire portfolio. One, we are winning more new customers faster. We exited the year with double the new customer velocity compared to Q1. Two, we are also signing larger customer commitments. The number of deals in 2025, over $1 billion surpassed the previous 3 years combined. And three, we continue to deepen our relationships with existing customers who are outpacing their initial commitments by over 30%.

Nearly 75% of Google Cloud customers have used our vertically optimized AI from chips to models to AI platforms and enterprise AI agents, which offer superior performance, quality, security and cost efficiency. These AI customers use 1.8x as many products as those who do not, enabling us to diversify our product portfolio, deepen customer relationships and accelerate revenue growth. Our product line has multiple monetization levers spanning infrastructure, platform and high-margin AI-powered products and services with 14 product lines each exceeding $1 billion in annual revenue. We offer leading infrastructure for AI training and inference to our cloud customers with the industry's widest variety of compute options from our own seventh-generation Ironwood TPU to the latest NVIDIA GPUs. Our 10-year track record in building our own accelerators with expertise in chips, systems, networking and software translates to leading power and performance efficiency for large-scale inference and training.

Our cloud AI accelerators serve the leading Frontier AI labs, capital markets firms like Citadel Securities, enterprises like Mercedes-Benz and governments for high-performance computing applications. We also offer our leading generative AI models, including Gemini, Imagen, Veo, Chirp and Lyria to cloud customers. In December alone, nearly 350 customers each processed more than 100 billion tokens. In Q4, revenue from products built on our generative AI models grew nearly 400% year-over-year, significantly accelerating from the prior quarter. Today, more than 120,000 enterprises use Gemini, including AI unicorns like Lovable and OpenEvidence and global enterprises like Airbus and Honeywell. 95% of the top 20 and over 80% of the top 100 SaaS companies use Gemini, including Salesforce and Shopify. Gemini is becoming the AI engine for the world's most successful software companies.

Leading enterprises are also driving strong demand for our enterprise AI agents. We have sold more than 8 million paid seats of Gemini Enterprise, our enterprise AI platform to more than 2,800 companies, including BNY and Virgin Voyages to streamline knowledge management and automate processes. Gemini Enterprise managed over 5 billion customer interactions in Q4, growing 65% year-over-year for customers, including Wendy's, Kroger and Woolworths Group. Our integration of Gemini and Google Workspace is driving wins with global brands like Schwarz Group and public sector organizations like the U.S. Department of Transportation. We are also seeing momentum with independent software vendors. Revenue from AI solutions built by our partners increased nearly 300% year-over-year and commitments from our top 15 software partners grew more than 16x year-over-year.

Before moving on, I'm pleased that we are collaborating with Apple as their preferred cloud provider and to develop the next generation of Apple Foundation Models based on Gemini technology. Up next, YouTube. I want to highlight 4 points. First, streaming. In the living room, YouTube continues to be the #1 streamer in the U.S. for nearly 3 years according to Nielsen. From the NFL to Coachella, YouTube is where people watch today's biggest popular culture moments unfold. Second, subscriptions. We continue to see strong subscription revenue growth across YouTube, particularly YouTube Music Premium. We'll soon launch new YouTube TV plans, bringing more choice and flexibility to subscribers with over 10 genre-specific packages.

And the NFL has seen strong NFL Sunday Ticket subscriber growth with YouTube, with the highest paid subscriber number ever in the history of the product. Third, podcasts. To illustrate YouTube's popularity, in October 2025, viewers watched over 700 million hours of podcasts on living room devices, up 75% from just a year prior. And fourth, AI is transforming the YouTube experience for both creators and viewers. On average, every day in December, over 1 million channels used our new AI creation tools to supercharge their creativity. During that same month, more than 20 million viewers used our new Ask tool powered by Gemini to learn more about the content they watched.

And finally, Waymo. This week, Waymo raised its largest investment round to date and is well positioned to continue its momentum with safety at the core. In December, we surpassed 20 million fully autonomous trips and are now providing more than 400,000 rides every week. Waymo continues to expand its service territory. Its sixth market, Miami, launched 2 weeks ago, and Waymo will soon expand its service to multiple cities across the U.S. and in the U.K. and Japan. The team has made incredible progress on important capabilities, including opening up public service to airports and freeways.

In closing, 2025 was a fantastic year for the company. A big thanks to our employees and partners worldwide. We are really well positioned going into 2026. Now over to Philipp.

Thanks, Sundar, and hello, everyone. I'll cover performance for Google Services for the quarter, then structure the rest of my remarks around the great progress we're delivering across Search, YouTube and partnerships. Google Services revenues were $96 billion for the quarter, up 14% year-on-year, primarily driven by accelerated growth in Search. Adding some further color to our results. The 17% increase in Search & Other was led by broad strength across all major verticals with retail particularly strong. On YouTube, the 9% growth in advertising revenues was driven by direct response. Network advertising revenues were down 2% year-on-year this quarter. Starting with Search & Other Revenues, which delivered over $63 billion in revenue for the quarter.

Sundar mentioned the expansionary moment for Search. The same is true for ads. We're investing in AI to drive significant improvements across all areas of marketing. We're expanding the entire playing field that advertisers can compete on. AI gives businesses the ability to reach more customers in more places than ever before. Gemini uniquely positions us to bring the transformational benefits of AI to ads in 3 critical areas for our customers: ads quality, advertiser tools and new AI user experiences. First, ads quality. We've been deploying Gemini models to improve query understanding at a rate of almost a launch per month for the last 2 years. These improvements drive better query matching, ranking and quality, making search ads even more effective. With Gemini across our ads quality stack, we evaluate relevance with greater accuracy than with previous generations of models.

This has significantly improved our ability to systematically deliver more helpful high-quality ads, contributing to a meaningful reduction in irrelevant ads served. Gemini's understanding of intent has increased our ability to deliver ads on longer, more complex searches that were previously challenging to monetize. Gemini models also have a significant impact on query understanding in non-English languages, expanding opportunities for businesses to scale globally. Second, we're building more agentic actions into our advertiser tools. Businesses can now leverage Gemini in conversational experiences within Ads and Analytics Advisor to identify and run recommended actions such as generating new campaigns. Advertisers use Gemini as a real-time partner to assemble creatives.

In Q4 alone, they used Gemini to create nearly 70 million creative assets via text customization in AI Max and PMax. For instance, Aritzia, Canada's premier fashion house used AI Max to find new high-value customers that traditional strategies miss, delivering an 80% incremental uplift in conversion value for Q4. L'Oreal, one of the first alpha testers used AI Max in 2025 across 800 unique campaigns in 23 countries and 30 brands. AI Max enabled the L'Oreal Group to maximize its presence across the full consumer journey, fuel its consumer growth and increase revenue for DTC brands like NYX by 23%. The third area is how we monetize new AI user experiences in Search. We have significantly increased our focus on AI Mode and are in the early stages of experimenting with AI Mode monetization like testing ads below the AI response with more underway.

For example, we announced Direct Offers, a new Google Ads pilot, which will allow advertisers to show exclusive offers for shoppers who are ready to buy directly in AI mode. This new type of sponsored content uses AI to match the right offer provided by the retailer to the right user. As Sundar mentioned, we are building the era of agentic commerce and working with our partners to introduce the universal commerce protocol in our consumer products and across the web. We've received tremendous feedback from the industry. Soon, people can use a new checkout experience to buy directly in AI Mode in Gemini from select merchants. Turning now to YouTube, which remains the #1 streamer in the U.S. for nearly 3 years according to Nielsen. YouTube creators are providing an unmatched breadth of content. Our investment in AI innovation across creativity, viewing experience and monetization continues to pay off.

We're seeing strong traction in our subscription business, are innovating to meet consumers where they are. We added a new sports tier for YouTube TV at a lower price point. YouTube Premium Lite is proving to be a popular choice. And we continue to deliver strong year-on-year growth across YouTube subscriptions, particularly YouTube Music and Premium. Looking at monetization across YouTube, momentum continues in Shorts and the Living Room. Shorts now averages over 200 billion daily views. And as we've shared before, in a number of countries, Shorts earns more revenue per watch hour than traditional in-stream on YouTube, including the U.S. The retail vertical continues to grow, fueled by smaller advertisers increasingly adopting demand channel. Likewise, direct response continues to benefit from the momentum we're seeing with small- and medium-sized advertisers.

Viewers trust product and brand recommendations from YouTube creators, and we are focused on making YouTube a premier shopping destination. Innovations like shoppable ad formats are improving advertiser return on investment. During Cyber 5, advertisers piloted shoppable mastheads, a new interactive ad format where viewers browse products and send links to their phones for an easy shopping experience. On brands, our creator partnership hub makes it easier for brands to find creators and develop campaigns. This holiday season, brands like JCPenney, Old Navy and Target worked with creators for their holiday campaigns.

Mattel partnered with 8 top YouTube creators to reach families during the peak holiday shopping season in a campaign that helped drive a 25% increase in search volume for UNO. As always, I wrap with the progress we're seeing across partnerships where customers tap into the strength and breadth of Google's products to accelerate their transformation. I would start by joining Sundar in saying how pleased I am that we are collaborating with Apple as their preferred cloud provider and to develop the next generation of Apple Foundation Models based on Gemini technology. We partnered with Reliance Jio to provide over 500 million consumers with an 18-month free trial of our Gemini suite of products and 2 terabyte of cloud storage.

Reliance Enterprise customers will also get access to Google Cloud's Gemini Enterprise and TPUs, bringing the best of Google AI to every employee and workflow. The Home Depot is applying Google AI across the board from cloud tools to AI-powered ads and YouTube creator partnerships that connect with the next generation of do'ers. Their investments in PMax and YouTube creator partnerships have resulted in double-digit increase in ad clicks and visits.

In closing, I'd like to thank Googlers everywhere for their contributions to our success and as always, to our customers and partners for their continued trust. Anat, over to you.

Thank you, Philipp. My comments will focus on year-over-year comparisons for the fourth quarter, unless I state otherwise. I will start with results at the Alphabet level and will then cover segment results. I'll end with some commentary on our outlook for the first quarter and full year 2026. 2025 was a strong year of innovation and execution for Alphabet. These efforts, combined with our investments in AI, drove meaningful results across the business. For the full year 2025, Alphabet consolidated revenues were $403 billion, up 15% on a reported and constant currency basis.

Moving to Q4 performance. We delivered strong growth in the fourth quarter. Consolidated revenues reached $113.8 billion, up 18% or 17% in constant currency and was driven by an acceleration in Search and Cloud revenues. Turning to costs and expenses. We reported a $2.1 billion stock-based compensation charge due to an increase in Waymo's valuation related to the investment round that was announced on Monday. The vast majority of the charge was reflected in R&D expenses. Total cost of revenue was $45.8 billion, up 13%. Tech was $16.6 billion, up 12%. Other cost of revenues was $29.2 billion, up 13%, with the increase primarily driven by depreciation associated with the deployment of our technical infrastructure, content acquisition costs largely for YouTube and other technical infrastructure operations costs.

Total operating expenses were up 29% to $32.1 billion. R&D expense increased by 42%, driven by compensation and depreciation. The increase in compensation was due to the Waymo charge and investment in AI talent. Sales and marketing expenses were up 12%, primarily driven by marketing investments to support the Gemini app and Search. And G&A expenses increased 21%, primarily due to a shift in timing of our charitable contributions. Operating income increased 16% to $35.9 billion, and operating margin was 31.6%. Both operating income and operating margin were negatively impacted by the $2.1 billion Waymo charge in the quarter.

Other income and expenses was $3.2 billion, primarily due to unrealized gains in our nonmarketable equity securities portfolio. Net income increased 30% to $34.5 billion and earnings per share increased 31% to $2.82. We generated record operating cash flow of $52.4 billion in the fourth quarter and $164.7 billion for the full year. This translated into $24.6 billion of free cash flow in the fourth quarter and $73.3 billion for the full year. We ended the quarter with $126.8 billion in cash and marketable securities and $46.5 billion in long-term debt.

Turning to segment results. Google Services revenues increased 14% to $95.9 billion, reflecting strong growth in Search and Subscriptions. Google Search and other advertising revenues increased by 17% to $63.1 billion, representing another strong quarter with continued growth across all major verticals with the largest contribution from retail. YouTube advertising revenues increased 9% to $11.4 billion, driven by direct response advertising. Results were negatively affected from the lapping of the strong spend on U.S. election in the fourth quarter of 2024 that we've mentioned on previous earnings calls. Network advertising revenues of $7.8 billion were down 2%. Subscription platforms and devices revenues increased 17% this quarter to $13.6 billion due to strong growth in YouTube subscriptions, particularly YouTube Music and Premium and growth in Google One, which benefited from increased demand for AI plans.

Google Services operating income increased 22% to $40.1 billion, and operating margins was 41.9%. The Google Cloud segment delivered outstanding results in the fourth quarter as the business continued to benefit from strong demand for our enterprise AI products. Cloud revenue accelerated meaningfully and were up 48% to $17.7 billion. Revenues were driven by strong performance in GCP, which continued to grow at a rate that was much higher than cloud's overall revenue growth rate. As Sundar noted, we're driving performance through strong growth in the win rate of new customers, signing larger customer commitments and increasing spend with existing customers. GCP's performance was driven by accelerating growth in enterprise AI products, which are generating billions in quarterly revenues.

We had strong growth in both enterprise AI infrastructure, driven by deployment of TPUs and GPUs and enterprise AI solutions, which benefited from demand for our industry-leading models, including Gemini 3. Core GCP was also a meaningful contributor to growth due to strong demand for infrastructure and other services such as cybersecurity and data analytics. We also had double-digit growth in Workspace, driven by an increase in average revenue per seats and the number of seats. Cloud operating income was $5.3 billion, more than doubling year-over-year, and operating margin increased from 17.5% in the fourth quarter of last year to 30.1%. Google Cloud's backlog increased 55% sequentially and more than doubled year-over-year, reaching $240 billion at the end of the fourth quarter. The increase in backlog was driven by strong demand for our cloud products, led by our enterprise AI offerings from multiple customers.

In Other Bets, revenues were $370 million, and operating loss was $3.6 billion, reflecting the $2.1 billion Waymo charge I mentioned earlier. We allocate resources in Other Bets to businesses like Waymo, where we see meaningful opportunities to create value. Alphabet funded a significant portion of the $16 billion investment round that Waymo announced on Monday, which will allow the business to accelerate its global expansion. CapEx was $27.9 billion for the fourth quarter and $91.4 billion for the full year, in line with our expectation. The vast majority of our CapEx was invested in technical infrastructure with approximately 60% of that investment in servers and 40% in data centers and networking equipment. In Q4, we returned capital to shareholders through $5.5 billion of share repurchase and $2.5 billion of dividend payments.

Turning to our outlook. I would like to provide some commentary on factors that will impact our business performance in the first quarter and full year 2026. First, in terms of revenues, we're pleased with the overall momentum of the business. At current spot rates, we would expect to see an FX tailwind to our consolidated revenues in Q1. However, the volatility in exchange rates could affect the impact of FX on Q1 revenues. In Google Services, we expect growth to be driven by ongoing innovation in the user experience as well as improved ROI for advertisers, keeping in mind the normal seasonal pattern for advertising revenue. In Google Cloud, we're seeing significant demand for our products and services, which we expect to continue to drive strong growth despite the tight supply environment we're operating in.

Moving to investments. The investment we have been making in AI are already translating into strong performance across the business, as you've seen in our financial results. Our successful execution, coupled with strong performance reinforces our conviction to make the investments required to further capitalize on the AI opportunity. For the full year 2026, we expect CapEx to be in the range of $175 billion to $185 billion with investments ramping over the course of the year. We're investing in AI compute capacity to support Frontier model development by Google DeepMind, ongoing efforts to improve the user experience and drive higher advertiser ROI in Google Services, significant cloud customer demand as well as strategic investments in Other Bets. Keep in mind that the availability of supply, pricing of components and timing of cash payments can cause some variability in the reported CapEx number.

In terms of expenses, as we've discussed on previous calls, the significant increase in our investments in technical infrastructure will continue to put pressure on the P&L in the form of higher depreciation expense and related data centers operations costs such as energy. In 2025, depreciation increased by nearly $6 billion or 38% from $15.3 billion in 2024 to $21.1 billion in 2025. Given the increase in our CapEx investments in recent years, we expect the growth rate in 2026 depreciation to accelerate in Q1 and meaningfully increase for the full year. We're also planning to continue hiring in key investment areas such as AI and cloud.

In 2025, our teams delivered amazing innovation, executing with a high level of discipline and velocity. These efforts provide great experiences for consumers and outstanding performance for creators, partners and enterprise customers, driving strong revenue growth. I want to take this opportunity to thank our employees for their contribution to this impressive performance.

Now Sundar, Philipp and I will take your questions.

[Operator Instructions] And your first question comes from Brian Nowak with Morgan Stanley.

I have 2, one on agentic, one on YouTube. The first one on agentic. Sundar, I'd be curious to hear about as you look back at 2025, where do you think you made the most progress on new types of agentic commerce products? And then looking into '26, where are you most optimistic to sort of have even more progress in utility for users and your advertisers? And the second one is on YouTube. We've seen a lot of the new content creation models like Genie, et cetera. Walk us through sort of the Alphabet long-term vision for how Genie and some of these content creation tools could be integrated into YouTube over time.

Great. Thanks. Thanks, Brian. First, maybe I'll take the agentic part first. I definitely think '25 was more about laying the foundation, getting the models to start being more robust in agentic use cases. And obviously, coding is an area where the progress was the most felt. In areas like commerce, I think we spent the year working with the ecosystem to develop the underlying protocol that's going to be needed for this agentic world. So I think the launch of Universal Commerce Protocol at NRF in January with a bunch of partners, founding partners, I think has been super well received. So I'm excited now that we have laid the foundation of interoperability on which agentic commerce can work. And now we are integrating those experiences into Gemini, AI Mode and so on. So I think this is a year where you will see consumers actually being able to use all of this, and I'm excited about the opportunity ahead.

On YouTube, look, super excited by Genie and blown away by -- spent a lot of time creating this incredible worlds. I think it's going to have a wide level of applicability. I think an area where we shine in general is multimodality and representing the real world. And I think Genie is a further step in that direction in terms of building world models. All the innovation we are doing, be it Imagen, Veo, Lyria, Genie, all that work we bring in into our products and to our cloud customers. And YouTube is going to be a natural place for creators. We are going to keep incorporating these tools. Already creators are responding by adopting these, but we do want to put creators at the center of the experience, and that's very, very important to us. And so it's for us making sure YouTube is a voice for creator expression is the foundation by which we will approach this.

Your next question comes from Eric Sheridan with Goldman Sachs.

Two, if I could. Over the last couple of earnings calls, we've talked a lot about imbalances between demand and capacity for AI, both internally and externally. With the step function change in absolute capital dollars you're projecting now in '26, can you talk about the pathway to closing the gaps or the need for compute, both internally and externally and how to think about some of the outputs of closing that gap as the year progresses. And again, the second part would be against that level of spend that you're now projecting for '26, how do you think about continuing to find operating efficiencies inside the business to fund those investment -- growth investments as well?

Thanks, Eric. You are right, and we've been supply constrained even as we've been ramping up our capacity. Obviously, our CapEx spend this year is an eye towards the future. And you have to keep in mind some of the time horizons that are increasing in the supply chain, et cetera. So we are constantly planning for the long term and working towards that. And obviously, how we close the gap this year is a function of what we have done in the prior years, right? And so there is that time delay to keep in mind. I expect the demand we are seeing across the board across our services, what we need to invest for future work for Google DeepMind as well as for cloud, I think, is exceptionally strong. And so I do expect to go through the year in a supply-constrained way. And maybe Anat can touch on the second part.

Yes. Sure. Thanks, Eric, for the question. I've mentioned on one of the previous earnings call, our approach to how we look at efficiency and productivity. And we don't view this as an episodic onetime project or effort, but rather how we run the business on a regular basis and always seek additional opportunities to drive efficiency across the business. And certainly, with the demand we're seeing, whether it's from external customers or across the organization, the more capital we can free up within the organization to invest, the better we can turn this flywheel of making investments to drive future growth.

And we're doing this across the organization, whether it's within our technical infrastructure, certainly, when we invest at these amounts, we look at how we can ensure that we are the most efficient with every dollar that goes towards our technical infrastructure. There are scientific innovation that are part of that process, technical innovation, as you know, and we've mentioned before, we primarily focus on construction of our own data centers. We do partner with some external parties on lease on occasion, but most of our data center, we can start ourselves, and we ensure that we do it in the most efficient way in a way that matches our workloads and our needs.

We look at coding productivity that Sundar mentioned in the past, our -- about 50% of our codes are written by agents, coding agents, which are then reviewed by our own engineers. But certainly, it helps our engineers do more and move faster with the current footprint. We look at how we run the business across the organization. So using AI within the business to drive daily operations. It can be all the way from the engineering team to small teams within our back office, even within my finance team, for example, we deployed agents within our treasury organization. We're deploying agents within how we run -- how we pay and reconcile invoice, et cetera. So there are opportunities across the business that we evaluate on a regular basis to ensure we can free up more of that capacity to invest in our future.

Your next question comes from Doug Anmuth with JPMorgan.

I have 2. Over the last couple of years, we've seen considerable large language model leapfrogging and many expect that to continue. What are the ways that Google can build and maintain its Gemini position around data and distribution and product integration? And then how should we think about the potential for TPUs to move outside of Google Cloud and into external data centers and develop as an incremental revenue stream?

Doug, look, I think the LLM frontier, it's been an exciting trajectory. And I think 2026 will continue to show that progress. We are obviously improving these models across many paradigms, right, on pretraining, post-training, test and compute and so on. And we are bringing multimodal models into the picture. We are bringing agentic capabilities, the coding area is showing a lot of progress. And obviously, integrating all of this together and offering a great customer experience for our -- to our products as well as through our APIs to our cloud customers. To me, it feels like there's a lot of headroom ahead. And as you've seen our trajectory over the past 2 years in terms of how we have been making progress.

I think we are in a very, very relentless innovation cadence. And I think we are confident about maintaining that momentum as we go through '26. In terms of TPUs, I would think about it as it's reflected in our overall part of what makes Google Cloud an attractive choice is the wide choice of accelerators we bring to bear here, and we meet customers in terms of what their needs are and the choice as well as other things we bring as part of Google Cloud, the end-to-end efficiencies in our data centers, all of that comes to bear. And that's what you see in the strong momentum in Google Cloud. And given the overall investment we are making, we expect to be able to drive that momentum there. So that's how I would think about it.

Your next question comes from Mark Mahaney with Evercore.

Two questions. One, could you just comment a little bit on the YouTube ad revenue, that 9% year-over-year growth? It sounded like direct response was good. And it sounded like from Search that Retail came in relatively strong. So it's a little surprising that it didn't kind of come through in the YouTube ads revenue growth. And then Sundar, can I ask you to try to get ahead of a debate in the market, which is there's kind of maybe at a DeepSeek moment again.

You talked earlier about Gemini being the AI engine for the most -- for some of the most successful software SaaS companies out there in the world. And it just seems like there's a market belief that the software companies are kind of losing seat power, losing pricing power, and it looks like it could be a really terrible customer base. I can't imagine that that's actually going to happen. But could you just talk about it? You're at the forefront of AI and the impact that that's having on software companies, why wouldn't that be or why would it be undermining the economics of your large software SaaS company base?

So Mark, so first of all, thank you for the question. For the full year 2025, our YouTube's annual revenue surpassed $60 billion across ads and subscription. In Q4, YouTube ads was driven indeed by strong growth in direct response. On the brand side, as Anat shared, the largest factor negatively impacting the year-over-year growth rate was lapping the strong spend on U.S. elections. We also saw a slight impact in some other brand-related verticals. But taking a step back, I think it's important to think about YouTube ads and subs holistically because when a user shifts from being an ad-supported user to a YouTube Music and Premium customer, it has a slightly negative impact on YouTube ads revenues, but a positive impact on our business.

And we had strong revenue growth in YouTube subscriptions this quarter, particularly in the YouTube Music and Premium category. Maybe the interesting part is what we're actually excited about our road map in brand, the opportunity on connected TVs, more innovative ad formats, for example, the shoppable mastheads I spoke about earlier that we piloted during Cyber 5. We're working really, really hard to further connect brands and creators, scaling sponsorships and enabling advertisers to showcase their products, their services during high visibility spotlight moments.

We continue to expand the functionality of the creator partnership hub, making it a lot easier for brands to actually find creators and develop campaigns. We're heavily focusing on brand deals on measurement efforts. So there's a lot of interesting work in the pipeline. And on top of that, we actually see opportunity also for upside with performance advertising. There's a lot of momentum with demand gen adoption across small and medium advertisers. We're also excited about the opportunity for continued ads innovation and direct response, like, for example, shoppable formats, including in the Living Room, which is then helping drive strength in retail, the continued momentum in Shorts and so on. So overall, we're quite excited. Yes.

Great. And Mark, on -- in terms of Gemini adoption and how -- what this moment means for SaaS, et cetera. Look, at least from my vantage point, I definitely see we have very, very good SaaS customers who are leaders in their respective categories. And what I see the successful companies doing is they are definitely incorporating Gemini deeply in critical workflows, be it on improving their product experience and driving growth or using it to drive efficiency within their organizations. And I think it is an enabling tool, just like it has been an enabling tool for us across our products and services, be it Search, YouTube, et cetera. I think the companies who are seizing the moment, I think, have the same opportunity ahead. And at least we are excited about the partnerships we have there. And the momentum, if I look at it in terms of their tokens usage, et cetera, the growth has been very robust in Q4.

Your next question comes from Mark Shmulik with AllianceBernstein.

Two, if I may. The first for Anat is, can you talk a little bit more about the relationship between investment levels and how you kind of expect core performance to trend? Is there like an operating income or a free cash flow objective that you solve towards? Or how do you think about greenlighting resources and projects? And then the second question is for all of you. A year ago, we probably could have guessed the answer to this question. But given where we are today, for each of you, what keeps you up at night here as you think about the Google story and what's next?

Thanks, Mark. Let me start with a question on the investment framework, and it's an important one. And as you can imagine, an important one for us as well. We have a highly rigorous framework that we use internally where we look at all the needs for investment, whether it's from our own organization or from external customers and have an estimate of what that investment could potentially yield, obviously, not just near term but long term as well. So we take that into consideration when we make the following decision. The first one is the total investment that we make across the company. This was, for example, in 2025, the $91 billion we invested in CapEx and our estimate for CapEx investment this year.

So what's the total envelope that we want to invest to ensure that we can drive both near-term and long-term growth for the company. And then the second way we use that framework is to just allocate these funds across the organization, determine where we should make these investments. And throughout the year, as you can imagine, we always look to understand where things are moving, whether it's external dynamics or internal dynamics, and I've mentioned some of the supply chain pressures we're seeing externally. So we look at this with a highly rigorous framework to make sure that we're making the right decision. It was exciting to see the fact that we're already monetizing and you saw it in the results that we just issued this quarter, the investments that we've made in AI.

It's already delivering results across the business. I know within cloud, it's very obvious external, but you've heard the comments on the success we're seeing in Search, the comments from Sundar and from Philipp and then the Frontier model development that really serves as the foundation for the organization. We then also look at just the cash flow, cash flow generation and the health of our financials and the balance sheet. That's important as well. So we take that into consideration when we make the decision about the overall level of investment. We want to make sure we do it in a fiscally responsible way and that we invest appropriately, but we do it in a way that maintains a very healthy financial position for the organization.

And yes, maybe I can answer on what keeps us up at night. Look, I think overall, we've been on this AI-first trajectory for over a decade now, and it's what we have been methodically thinking our way through. It's the reason why we've been working on TPUs for over a decade as an example. But I think specifically at this moment, maybe the top question is definitely around compute capacity, all the constraints, be it power, land, supply chain constraints, how do you ramp up to meet this extraordinary demand for this moment, get our investments right for the long term and do it all in a way that we are driving efficiencies and doing it in a world-class way. And so that's where I think we are meeting the moment well and -- but it's definitely an area where we're spending a lot of time on.

Your next question comes from Michael Nathanson with MoffettNathanson.

I have one for Sundar and one for Anat. Sundar, you mentioned the Universal Commerce Protocol a bunch of times. I wonder if you could spend some time talking about the rationale for developing it, the opportunity that you see it solves for and what it means for the product discovery funnel for consumers? And then for Anat, any color you can provide on the CapEx guide between longer duration assets like buildings and infrastructure and shorter cycle assets like technical equipment? That would be helpful.

Thanks, Michael. Obviously, people go through a lot of commercial journeys across many of our surfaces, Search, YouTube, Gemini app and so on. So I think there's -- as well as we support through cloud and ads, our entire retail partners as well. And the opportunity to improve the experience, I think, can be a kind of a huge foundational uplift here. But it's important to -- we are approaching it keeping in mind that our users as well as merchants here and figuring out that value. Part of what's been good in designing the Universal Commerce Protocol is it makes it much easier for users to complete transactions.

But at the same time, it allows merchants to help showcase the range of their offerings, if they want to make promotions, et cetera. So all of that is built into the protocol. And I think you have to get that value prop for the ecosystem right to make the experience better. And so it's foundational. And more importantly, we are now implementing the protocols and our Gemini models are making progress in those agentic capabilities. And I think -- so I'm excited about a future where as people are going through discovery, searching, finding new things, if they're interested in acting upon it, all of that is seamless. And so it overall creates an expansionary moment.

And the question with regards to the CapEx and the -- kind of what makes up the total that we've announced for this year and last year, approximately 60% of our investment in 2025, and it's going to be fairly similar in 2026, went towards machines, so the servers. And then 40% is what you referred to as long-duration assets, which is our data centers and network and equipment. And I think you probably are referring to the depreciation delta between them. Those long-term duration assets depreciate over -- the building could be 4 years or longer. Other components may be less than that. Another important component is how we allocate this CapEx. And we've commented in the past about the allocation of our ML compute across the business. And for 2026, just over half of our ML compute is expected to go towards the cloud business.

Your next question comes from Ross Sandler with Barclays.

Great. Just a question on the native Gemini 750 million. So we added 100 million MAUs in the fourth quarter. Could you just talk high level about usage and retention of native Gemini? And is this 750 million the right way to measure your progress against companies like ChatGPT or is there another cohort of users that aren't in that 750 million that maybe we should also consider?

Ross, I think we definitely saw, I would say, an extraordinary period of growth in Q4 for Gemini app. It's not just the growth in monthly active users, but there's definitely -- there was a sharp increase in engagement per user on the app. So all the metrics, be it active usage, the intensity of usage, retention all showed distinct progress across iOS, web, Android, et cetera, and geographically globally. So definitely, all the product experience improvements, the work we did with Nano Banana, the progress with the Gemini models all translated into strong momentum and that momentum is continuing. So we are excited about that, and we'll continue to invest. Obviously, there are many people who are getting a deeply AI-native experience in the context of AI Mode in Search as well. And we are definitely seeing strong growth and progress. And the introduction of Gemini 3 in AI Mode was a very positive driver as well. And obviously, we'll continue to evolve these experiences, and I'm excited about the opportunities there.

Your next question comes from Ken Gawrelski with Wells Fargo.

Two, if I may, both on Search. First, could you walk us through how you are evolving your views on the monetization of AI search activity? Given the more conversational nature and longer periods of engagement per session, consumer utility is increasingly driven by the on-platform results, not specifically the link-outs and referrals. In that construct, how do you think about increasing the revenue opportunity to match the consumer utility? And is this increasingly where premium subscriptions play?

And then question two, and it's related, as you think about partnerships such as the new Apple partnership on Siri, how do you think about the right way to align for success with those partners? Previously, as disclosed in the DOJ documents, et cetera, it was a revenue share relationship. But now if you think about the utility that you're driving through AI search and through Gemini on those platforms, it may be less related to the actual "search revenue." Could you just talk a little bit about how you align with partners for success there?

So first of all, it may be worthwhile to say that the acceleration we saw in Search was not due to a single driver, but was really the result of many different parts of our business showing strength and working well together. And maybe I quickly add the vertical perspective, retail, finance, health drove actually the greatest contribution to Search revenue, though nearly every major vertical actually accelerated in Q4. More specifically to your question, the ongoing innovation is, as you know, core to what we do and the enhancements to the user and the advertiser experience really continue to drive our performance, and we make hundreds of these changes every quarter.

We see AI Overviews and AI Mode continue to drive greater search usage and growth in overall queries, including important in commercial queries. Gemini-based improvements in search ads help us better match queries and craft creatives for advertisers. I talked about the understanding of intent and how this has significantly expanded our ability to deliver ads on longer and more complex searches that were, frankly, previously difficult to monetize. AI Max, for example, is already used by hundreds of thousands of advertisers and continues to unlock billions of net new queries in that sense. We see strength with SMB advertisers expanding their budgets and adopting automation tools, leading to better ROI. On the creative side, we're using Gemini to generate millions of creative assets via text customization in AI Max and PMax and so on. So we're very pleased with what we're seeing here.

And our last question comes from Justin Post with Bank of America.

Just want to follow up on the Gemini app. Obviously, great growth there. Are you seeing any cannibalization of Search as far as activity as people start using that app more? And then second, on monetization, where are you on that? And with agentic and other ads coming, could that be incremental to your growth over the next few years?

Look, right now, overall, look, I think we are giving people choice. People are obviously using Search, experiencing AI Overviews and AI Mode as part of it and Gemini app as well. And the combination of all of that, I think, creates an expansionary moment. I think it's expanding the type of queries people do with Google overall. And so overall, some of it all is what we see as a growth opportunity, and we haven't seen any evidence of cannibalization there. And maybe Philipp can comment on the monetization.

Yes. I think Sundar previously commented on agentic and how we think about it. And look, in general, as with all of our products, we really focus, first and foremost, on creating a great user experience, and we're very excited about where we are with the ads and AI Overviews and early experiments in AI Mode, including innovations like Direct Offers and our road map for the future. In terms of the Gemini app today, we are focused on our free tier and subscriptions and seeing great growth, as Sundar discussed. But ads have always been part of scaling products to reach billions of people. And if done well, ads can be really valuable and helpful commercial information. And at the right moment, we'll share any plans. But as we've said, we're not rushing anything here.

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.

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

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

Welcome, everyone. Thank you for standing by for the Alphabet Third Quarter 2025 Earnings Conference Call. [Operator Instructions] I would now like to hand the conference over to your speaker today, Jim Friedland, Head of Investor Relations. Please go ahead.

Thank you. Good afternoon, everyone, and welcome to Alphabet's Third Quarter 2025 Earnings Conference Call. With us today are Sundar Pichai, Philipp Schindler, 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 statement.

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 located at abc.xyz/investor. Our comments will be on year-over-year comparisons unless we state otherwise.

And now I'll turn the call over to Sundar.

Thank you, Jim. Good afternoon, everyone, and thanks for joining us. This was a terrific quarter for Alphabet, driven by double-digit growth across every major part of our business. We are seeing AI now driving real business results across the company. We delivered our first ever $100 billion quarter. Five years ago, our quarterly revenue was at $50 billion. Our revenue number has doubled since then, and we are firmly in the generative AI era. In parallel, we've built for the long term and diversified with successful businesses in cloud, YouTube and subscriptions. Our momentum is strong, and we are shipping at speed. As just a few examples: our first-party models, like Gemini, now process 7 billion tokens per minute via direct API used by our customers. The Gemini app now has over 650 million monthly active users, and queries increased by 3x from Q2. Cloud had another great quarter of accelerating growth with AI revenue as a key driver. Cloud backlog grew 46% quarter-over-quarter to $155 billion. And we crossed $300 million paid subscriptions led by growth in Google One and YouTube Premium.

Today, I'll discuss progress in our full stack approach to AI and then share highlights from Search, Cloud, YouTube and Waymo. As a reminder, our full stack approach spans AI infrastructure, world-class research including models and tooling, and our products and platforms that bring AI to people everywhere. First up, AI infrastructure. Our extensive and reliable infrastructure, which powers all of Google's products is the foundation of our stack and a key differentiator. We are scaling the most advanced chips in our data centers, including GPUs from our partner, NVIDIA, as well as our own purpose-built TPUs. And we are the only company providing a wide range of both.

As we announced yesterday at NVIDIA GTC, we are now shipping the new A4X Max instances powered by NVIDIA GB300 to our cloud customers. Our highly sought-after TPU portfolio is led by our 7-generation TPU, Ironwood, which will be generally available soon. We are investing in TPU capacity to meet the tremendous demand we are seeing from customers and partners, and we are excited that Anthropic recently shared plans to access up to 1 million TPUs.

Next world-class AI research, including models and tooling. Our models are world-leading. GEMINI 2.5 Pro, Veo, Genie 3 and our viral sensation Nano Banana are among the very best in class. Over 230 million videos have been generated with Veo 3, and more than 13 million developers have built with our generative models. We are looking forward to the release of Gemini 3 later this year.

Our research leadership is advancing next frontier technologies. Last week, we announced that our Willow quantum chip achieved a major breakthrough, running an algorithm 13,000x faster than one of the world's best supercomputers, and the result is verifiable, paving the way to future practical applications. Speaking of quantum, let me congratulate Michel Devoret, our Chief Scientist for quantum hardware. He received a Nobel in physics for early research he did in the 1980s. Three Nobels awarded to current Googlers in 2 years. Incredible.

And third, our products and platforms. We are bringing AI to more people and developers than anyone else. In July, we announced that we processed 980 trillion monthly tokens across all our surfaces. We are now processing over 1.3 quadrillion and monthly tokens, more than 20x growth in a year. Phenomenal.

This quarter, we took big steps to reimagine Chrome as a browser powered by AI through deep integrations with Gemini and AI Mode in Search with more agentic capabilities coming soon. In August at Made by Google, we unveiled our Pixel 10 series of devices. They are the first with our most powerful chip designed to run on Gemini Tensor G5. They're our best reviewed devices ever. And last week, we launched Android XR, our new operating system at Samsung's Galaxy XR device. It brings new ways to use headsets and glasses with Gemini at the core.

Now turning to highlights from Search. AI is driving an expansionary moment for Search. As people learn what they can do with our new AI experiences, they're increasingly coming back to Search more. Search and its AI experiences are built to highlight the web, sending billions of clicks to sites every day. During the Q2 call, we shared that overall queries and commercial queries continue to grow year-over-year. This growth rate increased in Q3, largely driven by our AI investments in Search, most notably AI Overviews and AI Mode. Let me dive into the momentum we are seeing.

As we have shared before, AI Overviews drive meaningful query growth. This effect was even stronger in Q3 as users continue to learn that Google can answer more of their questions, and it's particularly encouraging to see the effect was more pronounced with younger people. We're also seeing that AI Mode is resonating well with users. In the U.S., we have seen strong and consistent week-over-week growth in usage since launch and queries doubled over the quarter.

Over the last quarter, we rolled out AI Mode globally across 40 languages in record time. It now has over 75 million daily active users, and we shipped over 100 improvements to the product in Q3, an incredibly fast pace. Most importantly, AI Mode is already driving incremental total query growth for Search. Philipp will talk more about monetization and share how AI is helping people connect with businesses and shop on Search.

Next, Google Cloud. Our complete enterprise AI product portfolio is accelerating growth in revenue, operating margins and backlog. In Q3, customer demand strengthened in 3 ways. One, we are signing new customers faster. The number of new GCP customers increased by nearly 34% year-over-year. Two, we are signing larger deals. We have signed more deals over $1 billion through Q3 this year than we did in the previous 2 years combined. Third, we are deepening our relationships. Over 70% of existing Google Cloud customers use our AI products, including Banco BV, Best Buy and FairPrice Group.

As we scale, we are diversifying revenue. Today, 13 product lines are each at an annual run rate over $1 billion. And we are improving operating margin with highly differentiated products built with our own technology. This deep product differentiation starts with our AI infrastructure. We have a decade of experience building AI accelerators and today, offer the widest array of chips. This leadership is winning customers like HCA Healthcare, LG AI Research and Macquarie Bank, and it's why 9 of the top 10 AI labs choose Google Cloud.

We are also the only cloud provider offering our own leading generative AI models including Gemini, Imagen, Veo, Chirp and Lyria. Adoption is rapidly accelerating. In Q3, revenue from products built on our generative AI models grew more than 200% year-over-year. Over the past 12 months, nearly 150 Google Cloud customers each processed approximately 1 trillion tokens with our models for a wide range of applications. For example, WPP is creating campaigns with up to 70% efficiency gains. Swarovski has increased e-mail open rates by 17% and accelerated campaign localization by 10x.

Earlier this month, we launched Gemini Enterprise, the new front door for AI in the workplace, and we are seeing strong adoption for agents built on this platform. Our packaged enterprise agents in Gemini Enterprise are optimized for a variety of domains, are highly differentiated and offer significant out-of-box value to customers. We have already crossed 2 million subscribers across 700 companies.

Next, YouTube. In the living room, YouTube has remained #1 in streaming watch time in the U.S. for more than 2 years, according to Nielsen. Last month marked YouTube's first time as a live NFL broadcaster. This exclusive global broadcast, live from Brazil, drew more than 19 million fans and set a new record for most concurrent viewers of a live stream on YouTube.

YouTube Shorts also continues to perform well. In the U.S., Shorts now earn more revenue per watch hour than traditional in-stream on YouTube. At our Made on YouTube event, we rolled out a number of AI-powered features that are helping create a supercharged creation and build their businesses. AI is now streamlining the entire content creation workflow from generative video tools and more efficient editing to AI-powered insights that help creators optimize their channels. We are also using AI to expand monetization, automatically identifying products to make their videos more shoppable. Philipp will discuss in more detail.

And finally, Waymo, next year Waymo aims to open service in London, and they are working to bring service to Tokyo. They've also announced expansions to Dallas, Nashville, Denver and Seattle and secured permission to operate fully autonomously at San Jose and San Francisco Airports. Autonomous testing continues to scale in New York City.

The new Waymo for Business allows enterprises to offer Waymo as a work travel option. And we launched Waymo Teens accounts in Phoenix this summer. We are pleased to see usage steadily increase with positive feedback from teens and their parents alike. Waymo's growth and momentum are strong, and 2026 is shaping up to be an exciting year.

Overall, a milestone quarter, the incredible work of our teams is driving momentum across the board and our leadership in AI positions us so well for the opportunity ahead. I want to thank all of our partners and our employees for their hard work and an excellent Q3.

With that, I'll turn it over to Philipp.

Thanks, Sundar, and hello, everyone. I'll quickly cover performance for Google Services for the quarter, then structure the rest of my remarks around the great progress we're delivering across Search, Ads, YouTube and Partnerships. Google Services revenues were $87 billion for the quarter, up 14% year-on-year driven by accelerated growth in Search and YouTube, partially offset by year-on-year decline in network revenues.

Adding some further color to our results. The 15% increase in Search and other was led by growth across all major verticals with the largest contributions from retail and financial services. YouTube saw similar performance across verticals. Its 15% growth in advertising revenues was driven by direct response followed by brand.

Starting with Search and other revenues, which delivered over $56 billion in revenue for the quarter. As Sundar mentioned, AI is driving an expansionary moment and transforming how people use Google Search. Our investments in new AI experiences, such as AI Overviews and AI Mode, continued to drive growth in overall queries, including commercial queries, creating more opportunities for monetization. These AI experiences are enhancing how people connect with businesses and shop on Search. We recently added shopping capabilities in AI Mode, which now help people shop conversationally in Search, and we expanded try-on capabilities to more clothing items, now available to anyone in the U.S. Lastly, we're making it easier for consumers to benefit from deals through new loyalty offerings like personalized annotations on organic results and ads.

Looking at monetization. Businesses can now tap into our most powerful AI search experiences. Using our most advanced AI models, we can understand and predict intent like never before, unlocking entirely new commercial pathways to provide valuable new consumer connections and helping us monetize even more efficiently.

Rolled out globally in September, AI Max in Search is already used by hundreds of thousands of advertisers, currently making it the fastest-growing AI-powered search ads product. In Q3 alone, AI Max unlocked billions of net new queries. By delivering the most relevant ad across surfaces and matching advertisers against additional queries they weren't reaching before, AI Max helps advertisers discover new customers at the exact moment they need their product or service.

Kayak, for example, look to grow conversions while staying within their ROAS goals. After turning on AI Max in Search, they grew their conversion value by 12% in early tests. We continue to infuse generative AI capabilities at every step of the marketing process. We rolled out Imagen 4 in Asset Studio and Product Studio, helping businesses produce more and better creatives.

On the measurement front, we enriched the model supporting Meridian, our marketing mix model, with additional variables, and more granular reporting in PMax is making bidding more effective. Financial services company SoFi has been using PMax to meet its ambitious growth targets and help drive a 39% improvement in its conversion volume year-over-year.

Moving to YouTube, where we saw accelerated revenue growth. Our recommendation systems are driving robust watch time growth in our key monetization areas like shorts and living room. As we leverage Gemini models, we're seeing further discovery improvement. On direct response, we're excited about the growth in revenue we're seeing, especially from small and medium advertisers adopting demand gen.

We also improved performance on demand gen with over 100 launches helping to increase conversion value by more than 40% for advertisers using target-based bidding on YouTube. The retail vertical continues to lead our growth on YouTube with demand gen helping us further monetize shopping-related categories. Looking at the living room, our long-term bet, more advertisers are adopting interactive, direct response ads, leading to an annual revenue run rate exceeding $1 billion globally for this format.

For our viewers, we continue to give fans greater access across sports, while tapping into the best of YouTube's product innovation and creator-led content. Sundar mentioned that we expanded our NFL partnership with our first-ever exclusive global broadcast of an NFL game. Brands love the opportunity, and we sold all our ad inventory within a couple of weeks.

Looking at creators. A significant force behind the thriving YouTube creator economy is the collaboration between creators and brands. Tools like direct linking to deals, websites and shorts and swappable brand segments in long form will soon help creators show how they deliver great value for brands. Thanks to a collaboration with Dude Perfect, Comcast's Xfinity drove an 8% search lift, beating other Xfinity ads, recall lift on shorts by 34%. At the same time, it decreased the cost per lifted user by 50% when compared to the next most efficient ad.

We continue to invest in AI-powered features that are helping creators supercharge creation and build their businesses. With Veo 3 integration and Speech to Song, creators go from idea to iteration quicker, and new channel insights help them better understand performance.

Ending on YouTube with our subscriptions product. We're also seeing momentum with strong growth in offerings such as YouTube Music and Premium and YouTube TV. We're also applying Gemini internally to help us serve customers with increased speed, intelligence and efficiency. Our sales teams use Gemini enriched with ads knowledge to streamline customer interactions. This increased productivity by over 10% led to hundreds of millions in incremental revenue and frees up sellers to engage with more customers at a deeper, more strategic level. In our customer support division, Gemini-powered solutions have managed over 40 million customer sessions so far this year and resolved hundreds of thousands of customer inquiries, and we're just getting started.

As always, I'll wrap with the progress we're seeing across partnerships where our customers tap into the strength and breadth of Google's products to accelerate their transformation. Revolut, the global financial services company, leverages Google Cloud's Vertex AI platform and Gemini models to help power its advanced customer service chatbot, develop new hyper-personalized financial products and offer predictive insights. Revolut is also increasing its presence on YouTube adopting Veo 3 for personalized creatives, making Google a key ads partner for delivering growth and launching new markets.

In closing, I'd like to thank Googlers everywhere for their contributions to our success and as always, to our customers and partners for their continued trust. And of course, a huge thanks to all of you as we celebrate 25 years of Google Ads.

Anat, over to you.

Thank you, Philipp. My comments will focus on year-over-year comparisons for the third quarter, unless I state otherwise. I will start with results at the Alphabet level and will then cover our segment results. I'll end with some commentary on our outlook for the fourth quarter of 2025.

We had an outstanding quarter in Q3, continuing the strong momentum we've had throughout the year, delivering double-digit revenue growth across Search and YouTube advertising, subscriptions, platforms and devices, and Google Cloud. Consolidated revenue reached $102.3 billion, a 16% year-over-year increase or 15% in constant currency. Total cost of revenue was $41.4 billion, up 13%. TAC was $14.9 billion, up 8%. Other cost of revenues was $26.5 billion, up 16%, with the increase primarily driven by content acquisition costs largely for YouTube, followed by depreciation and other technical infrastructure operations costs. Total operating expenses increased 28% to $29.7 billion. R&D expenses increased by 22%, driven by compensation, depreciation expenses related to our AI efforts. Sales and marketing expenses were flat, and G&A expenses increased meaningfully, primarily due to the $3.5 billion charge related to the European Commission fine mentioned in the earnings press release.

Operating income increased 9% this quarter to $31.2 billion, and operating margin was 30.5%. Excluding the EC fine, operating income increased 22%, and operating margin was 33.9%. Operating margin benefited from strong revenue growth and continued efficiencies in our expense base, offset by the legal charge and a significant increase in depreciation expense. Other income and expenses was $12.8 billion, primarily due to unrealized gains in our nonmarketable equity securities portfolio. Net income increased 33% to $35 billion, and earnings per share increased 35% to $2.87.

We generated free cash flow of $24.5 billion in the third quarter and $73.6 billion for the trailing 12 months. Free cash flow in Q3 benefited from strong operating cash flow and recent tax changes regarding the timing of when research and development costs are expensed and assets are depreciated. This was partially offset by higher CapEx. We ended the quarter with $98.5 billion in cash and marketable securities.

Turning to segment results. Google Services revenues increased 14% to $87.1 billion, reflecting strength in Google Search, YouTube advertising and subscriptions. Google Search and other advertising revenues increased by 15% to $56.6 billion, representing another robust quarter with continued growth across all major verticals with the largest contributions from retail and financial services. YouTube advertising revenues increased 15% to $10.3 billion driven by direct response advertising, followed by brand. Network advertising revenues of $7.4 billion were down 3%. Subscriptions, platforms and devices revenues increased 21% this quarter to $12.9 billion, driven by very strong growth in both YouTube and Google One subscriptions.

Google Services operating income increased 9% to $33.5 billion. Operating margin declined year-over-year to 38.5% as healthy revenue growth and continued efficiencies in our expense base were offset by the impact of the EC fine, which was fully reflected in the Google Services segment.

Turning to the Google Cloud segment, which again delivered very strong results this quarter as Cloud continued to benefit from our enterprise AI optimized stack, including our own custom TPUs and our industry-leading AI models. Cloud revenue increased by 34% to $15.2 billion in the third quarter, driven by strong performance in GCP, which continued to grow at a rate that was much higher than Cloud's overall revenue growth rate. GCP's growth was driven by enterprise AI products, which are generating billions in quarterly revenue.

We had strong growth in enterprise AI infrastructure and enterprise AI solutions, which benefited from demand for our industry-leading models, including Gemini 2.5. Core GCP was also a meaningful contributor to growth. And we had double-digit growth in Workspace, which was driven by an increase in average revenues per seat and the number of seats.

Cloud operating income increased by 85% to $3.6 billion, and operating margin increased from 17.1% in the third quarter last year to 23.7% this quarter. The expansion in Cloud operating margin was driven by strong revenue performance and continued efficiencies in our expense base partially offset by higher technical infrastructure usage costs, which includes depreciation expense and other operations costs such as energy. Google Cloud's backlog increased 46% sequentially and 82% year-over-year, reaching $155 billion at the end of the third quarter. The increase was driven primarily by strong demand for enterprise AI. As Sundar mentioned earlier, Cloud has signed more billion-dollar deals in the first 9 months of 2025 than in the past 2 years combined.

In Other Bets, revenues were $344 million, and operating loss was $1.4 billion in the third quarter. Within Other Bets, we continue to allocate more resources to businesses like Waymo, where we see opportunities to create substantial value.

With respect to CapEx, in the third quarter, our CapEx was $24 billion. The vast majority of our CapEx was invested in technical infrastructure with approximately 60% of that investment in servers and 40% in data centers and networking equipment. In Q3, we returned capital to shareholders through repurchases of stock of $11.5 billion and dividend payments of $2.5 billion.

Turning to our outlook. I would like to provide some commentary on factors that will impact our business performance in the fourth quarter of 2025 as well as an updated outlook for CapEx for the year. First, in terms of revenues, we're pleased with the overall momentum of our business. At the current spot rates, we could see an FX tailwind to our revenues in Q4. However, the volatility in exchange rates could affect the impact of FX on Q4 revenues. As for our segments, in Google Services, year-over-year comparisons in advertising will be negatively impacted by the strong spend on U.S. elections in the fourth quarter of 2024, particularly on YouTube.

In Cloud, demand for our products remains high as evidenced by the accelerating revenue growth and the $49 billion sequential increase in Cloud backlog in Q3. In GCP, we see strong demand for enterprise AI infrastructure, including TPUs and GPUs, enterprise AI solutions driven by demand for Gemini 2.5 and our other AI models, and core GCP infrastructure and other services such as cybersecurity and data analytics. As I've mentioned on previous earnings calls, while we have been working hard to increase capacity and have improved the pace of server deployments and data center construction, we still expect to remain in a tight demand-supply environment in Q4 and 2026.

Moving to investments. We're continuing to invest aggressively due to the demand we're experiencing from Cloud customers as well as the growth opportunities we see across the company. We now expect CapEx to be in the range of $91 billion to $93 billion in 2025, up from our previous estimate of $85 billion, keeping in mind that the timing of cash payments can cause variability in the reported CapEx number. Looking out to 2026, we expect a significant increase in CapEx, and we'll provide more detail on our fourth quarter earnings call.

In terms of expenses, first, as I've mentioned on the previous earnings calls, the significant increase in our investments in technical infrastructure will continue to put pressure on the P&L in the form of higher depreciation expenses and related data center operations costs such as energy. In the third quarter, depreciation increased $1.6 billion year-over-year to $5.6 billion, reflecting a growth rate of 41%. Given the overall increase in CapEx investments, we expect the growth rate in depreciation to accelerate slightly in Q4. Second, we expect sales and marketing expenses to be more heavily weighted to the end of the year in part to support product launches and the holiday season.

Q3 was a strong quarter, and we're excited with the adoption of our AI products helped by a rapid pace of innovation and great execution by our teams. This translated into strong momentum in Search, YouTube ads, subscription, platforms and devices, and Cloud, resulting in our first $100 billion-plus quarter.

Now Sundar, Philipp and I will now take your questions.

[Operator Instructions] Our first question comes from Brian Nowak with Morgan Stanley.

The first one maybe for Philipp or Sundar. It's on agentic e-commerce and agentic travel. There's a lot of external Wall Street discussion about agentic e-commerce potentially monetizing at a lower rate than Search. So the question is what factors are you most focused on to sort of ensure a smooth transition for your search business and for your advertisers as you move over to a more agentic world?

And the second one, Sundar, is on Waymo. How far are we from an integration of Waymo into more of the core Gemini capabilities and the users on the platform taking your user data of where I'm going, what hotel I'm staying at, what airport I'm staying at and having integrated that into Waymo, so you can actually have users use their profiles to pre-schedule Waymos? How far off is that? What do we have to do?

Brian, great question. This is all early, but we see agentic experiences really as additive to the way people seek information. It helps us answer people's tough questions. It helps us -- it helps people get stuff done, and it helps businesses in the process. And we're working on multiple agentic experiences across key verticals such as travel, commerce, shopping and so on, and we're paying a lot of attention to creating a seamless user experience but also to the fact that we need to integrate different partner ecosystems in a way that it creates value for them.

And by the way, we're also working closely with a lot of our partners on the other side through our Cloud services to improve their own agentic experiences. And so maybe we go a little deeper on the shopping side where we actually use AI already very actively to improve the shopping experience. As you know, we launched a more visual experience on AI Mode. That gives people a much more intuitive conversational way to shop. You can simply describe what you're looking for now like the way you talk to a friend, and we'll show you the visual shopping results.

And then we think about building an agentic shopping future and it has to be one, again, that benefits both users and merchants here. And you know that [ AIO ], we also introduced new agentic checkout, which will let shoppers use like agentic AI to buy products from merchant sites and so on. We have a partnership with PayPal to help merchants build agentic commerce experiences. We have a new open protocols for agent-to-agent transactions and so on and so on.

And Brian, on Waymo, a great question. I was reflecting, I think, on the exact same topic. I'm scheduled to meet with the team to do a review on it in a few weeks out. Look, it is an exciting time. Waymo clearly is scaling up, particularly in 2026. And I think the possibility, as you said, of Gemini, particularly with the multimodal experience as well as services like YouTube, I think there's a real opportunity to make the in-car experience dramatically better. Definitely something we are excited about, and you'll see newer experiences in 2026 for sure.

Our next question comes from Doug Anmuth with JPMorgan.

Philipp, maybe you can just talk more about some of the drivers of the core Search strength. And I guess, in particular, when you think about AI Overviews and AI Mode, we know that query growth is accelerating. But can you help us understand from there kind of what happens in terms of clicks per query and conversion rates and pricing in these AI-driven Search formats?

And then, Anat, can you talk about where you see opportunities in the core cost space as you look to make room to absorb the rapid growth in infrastructure and depreciation going forward?

So let me give you a bit of vertical color first. In Q3, Search and other revenues again delivered growth across all major verticals, as we said, was from retail and financial services. Health care was also a contributor to the growth here. Our new AI experiences, you mentioned them, AI Overviews, AI Mode, continued to drive growth in overall queries, including commercial queries, really creating more opportunities for monetization. AI Overviews is scaling up and working for our entire user base. We're now scaled to over 2 billion users here, and we're continuing to expand ads in AI Overviews in English to more countries, across desktop, mobile and so on. And as I've shared before, for AI Overviews, even at our current baseline of ads below and within the AI's response, overall, we see the monetization at approximately the same rate.

So over time, we're excited about the opportunity of richer experiences in AI Mode and AI Overviews to basically open up then the opportunity for also much richer placements. And I think as I've said on a prior call, we manage the business to drive great outcomes for our users and an attractive ROI for advertisers. We don't really manage to paid clicks and CPC targets. But as you will see in the 10-Q, paid clicks were up 7% year-on-year and CPCs were up 7% year-on-year.

Doug, and to your question around where else can we see more opportunity for efficiency and productivity, and I think you heard me say before, this is not a onetime type of effort but rather an ongoing way in which we manage the business, and the key here is that the more we drive productivity across our business, the more we can invest in the business for growth and obviously continue to drive improvement in the P&L.

Some of the areas are things that you've heard us talk about in the past such as moderating the pace of headcount growth, optimizing real estate footprint but also as we invest more and more in our technical infrastructure, ensuring that we are optimizing that build-out and the overall technical infrastructure we have that a lot of the data centers, for example, that we build ourselves, so they're optimized and we make sure we do them in the most efficient way. Sundar mentioned on one of the previous calls the productivity associated with leveraging AI for Google. So there's the example, the percent of code, now nearly half of all code generated by AI, that's a way for us to leverage AI to drive further productivity across the business.

And obviously, we always look at making sure that when we provide services or products that we get the right economics and the right value for what we provide. So the one good example is Shorts, which has a lower revenue share than in-stream that helps to improve some of our gross margins. So this is an effort we have ongoing. I've mentioned in the past that we have a headwind with depreciation, obviously, increasing alongside our CapEx increase. So we're -- we have efforts across the organization to ensure we run the business in the most disciplined and productive way while continuing to invest for future growth.

Our next question comes from Eric Sheridan with Goldman Sachs.

Maybe 2 if I could. Sundar, when you think about your custom silicon efforts across the organization, can you reflect a little bit about the opportunity set you see with each passing generation of custom silicon both in terms of driving operating efficiencies inside the organization and potentially increased monetization efforts around those outside of the organization?

Second question would be for Philipp. Obviously, we could see the YouTube advertising revenue number in the reported results. Can you reflect little bit about the scaling of the subscription side of YouTube offerings and how the 2 parts together maybe represent an interesting framework in thinking about the monetization side of YouTube increasingly being a mix of both ads and subscription?

Eric, overall, I would say, we are seeing substantial demand for our AI infrastructure products, including TPU-based and GPU-based solutions. It is one of the key drivers of our growth over the past year. And I think on a going-forward basis, I think we continue to see very strong demand, and we are investing to meet that. I do think a big part of what differentiates Google Cloud effectively, we are the -- we have taken a full -- deep full stack approach to AI. So we are -- and that really plays out, right? We are the only hyperscaler who is really building offerings on our own models, and we are also highly differentiated on our own technology. So to your question, I think that does give us the opportunity to continue driving growth in operating margins in Cloud as we have done in the past. And also, I think from a revenue, sets the infrastructure portion of our business to be a growth driver looking ahead as well.

And to the second part of your question, look, just taking a quick step back, we often describe YouTube's business as a flywheel. Obviously, it, first of all, starts with the creators, and we have significantly invested here to be the place that YouTube creators really call their home. That's a big piece of it, the #1 piece. Viewers, of course, YouTube has billions of monthly logged-in users and every day, people watch billions of hours of video. And we talked about how our recommendation systems are driving robust watch time growth and so on and so on.

So on the monetization side, YouTube's business is really powered, I would say, let's call it a twin engine monetization strategy, combining its advertising business and its growing subscription services. Both YouTube ads and subscription saw strong growth this quarter. And so looking at YouTube Music and Premium, users are, on average, delivering more value to creators, to music, media partners and YouTube itself than even ad-supported users do.

So in other words, on average, a YouTube Music and Premium subscriber generates a meaningful higher gross profit than they were simply an ad-supported users. Fans come from all over the world. You know this and this engagement through ads and subscription generates YouTube's revenues and funds what I started with, these creators here and this then drives more viewership and engagement and so on. And that's the flywheel. And so our priority continues like this growth cycle. We're happy with this twin engine monetization strategy.

Our next question comes from Mark Shmulik with Bernstein.

Sundar, with the strong adoption of Gemini, AI Mode and Overviews across the user base, are there any meaningful differences to call out kind of around the behavior and depth of engagement for those users across the entire Google ecosystem? And then, Philipp, I know we kind of asked this most quarters, but I'm curious kind of what some of the adoption you've seen around AI Overviews and Mode, how you see the economics of Search evolving with the higher commercial and total query volume and how it kind of compares against the incremental cost to deliver these results.

Mark, look, I think obviously, AI Overviews are a natural part of the Google experience, and so engagement is very, very high. I would say AI Mode, you have varied cohorts that are people who are casual users, who are checking it out. And then -- but there's a core group, which really likes AI Mode and is passionate about it, and so you see the early adopters. The product is resonating very strongly, and they are seeking it out. So I think that's how I would highlight the difference.

With Gemini, again, a set of engaged user base who are seeking out the product and so on. But across the board, I think the trajectory has been we are definitely seeing in each of those use cases, a set of early adopters and then more people coming in and the people who are using it continue to use it more over time and report high user satisfaction. So I would say the underlying product metrics are pretty encouraging to see as well.

Look, into the second part of your question, I think we covered before -- Sundar covered the query development. And as I've just said before, for the AI Overviews, even at our current baseline of ads, right, whether above, below and within the AI response, overall, we see the monetization at approximately the same rate. And this is a great baseline for further innovation. We talked about this. We're excited about where this can go.

And on the AI Mode side, we're testing ads in AI Mode, and we'll continue to test and learn before we expand this any further. So this is in combination with what we mentioned about the commercial query overall development. I think we're in a good place here. You could also argue that on queries, that historically have not been well-monetized. We think there is a potential opportunity here where you can obviously imagine that we can build this out with smart AI integration.

Our next question comes from Michael Nathanson with MoffettNathanson.

I have 2, 1 for Philipp, 1 for Anat. Philipp, it's clear that when people use AI Mode, the query length is much longer. Could you talk about how that longer length may be impacting your ability to drive ROAS and what you're seeing in terms of some of the early -- the benefits of maybe longer query length?

And then, Anat, you came to Alphabet from a pharmaceutical company. You've been there more than a year. Can you talk a bit about how you're working to look at ROIC internally? And what early signs are you seeing that gives you confidence that the spending is really driving better returns longer term?

Look, as Sundar shared, AI Mode now has over like 75 million daily active users in the U.S. and we see a strong and consistent week-over-week growth in usage since launch, and the queries doubled over the quarter. And as I also mentioned, we're testing ads in AI Mode. We'll continue to test before we expand any further. It's really too early to tell and go into any of the details of that testing.

Yes. And the question related to ROIC and how we look at just overall our business and where do we see early signs that are encouraging. So first, I would say it's not just early signs because we're seeing returns, obviously, in the Cloud business. You've heard us talk about the fact that we already are generating billions of dollars from AI in the quarter. But then across the board, we have a rigorous framework and approach by which we evaluate these long-term investments that are meant to do 2 things. One is to ensure we have -- we built a resilient growth profile for the company, but also that we meet the demand of the customers that we have here in the more near and midterm.

So we look at it across the business. We evaluate the potential return for each one of them whether it's in Cloud, and I think that's more visible, obviously, externally given that you see the revenue generated and the fact that we're unable to meet, at this point, customer demand. We have more demand than we have supplied. In our Ads business, you see the fact that we're investing to transform Search, as you heard from Philipp and Sundar, with AIO and AI Mode. So we're excited to see what our investments are -- how our investments are helping advertisers as well. YouTube, where it's helping power recommendations.

So we're -- when we make a decision on investment in the long term, we go through a very rigorous process of assessing what the return could be and over what time frame we will see that return to give us the high level of confidence to then invest and make those investments for the long term. So it's a very rigorous approach.

Our next question comes from Ross Sandler with Barclays.

Great. About 20% of Google's search queries are commercial historically, and you've talked a bunch on this call about how AI Overviews are kind of expanding the breadth of queries. Could you talk about how new products from the monetization side, like AI Max, are potentially increasing the percent of commercial queries?

So look, AI Max, and I mentioned this in my call before, improves the ability for advertisers to target a wider range of queries. Separately, there is the question of whether queries actually increase with AI Mode, and Sundar actually talked about it and mentioned the opportunity that he sees here. So I think it's important to separate those 2 things. And I personally also see this, what I just said in my last remarks, that I think, over time, there's an opportunity to actually take, let's say, queries that are not fully commercial but could have an adjacent commercial relationship to basically expand this into more attractive ads offerings without -- while really creating a really interesting user experience at the same time.

Yes. And the only thing I would add is just stepping back broadly, I think AI Overviews and AI Mode are dramatically improving search. We can see it in user satisfaction, user quality, all our metrics, and they're universal in the nature. They apply across the universality of human needs. So I think we are seeing it in breadth. And so naturally, over time, that will apply to commercial categories as well.

Our next question comes from Ken Gawrelski with Wells Fargo.

Two questions, please. First, it appears more and more clear that all the new modes at Google with Gemini Overview -- AI Overviews, AI Mode, even ChatGPT is growing the addressable market for engagement and search-like behavior. Could you talk about what gives you confidence that it will also grow the addressable market for marketing activity and overall revenue associated with that behavior? That's question one.

And question two is just more about as you think about AI Mode, AI Overviews and traditional Google Search, how do you think -- do you see a world in 12 to 24 months, those all coexist? And does the user eventually pick what mode they want? Is -- does the algorithm pick the mode? Can you talk a little bit about how you think that will progress over the next 12 to 24 months?

Ken, thanks. Look, I think it's a dynamic moment, and I think we are meeting people in the moment with what they are trying to do. Obviously, Search is evolving, and between AI Overviews and AI Mode, I think we are able to kind of give that range of experience for people in this moment. Over time, you will expect us to -- you can expect us to make the experiences simpler in a way that, just like we did universal search many, many years ago, we may have done text search, image search, video search, et cetera, and then we kind of brought it together as universal search. So you will see evolutions like that, but I think we want to be sensitive to making sure we are meeting the users in terms of what they are looking for.

I think Gemini allows us to build a more personal, proactive, powerful AI assistant for that moment. And I think having the 2 surfaces search in Gemini allows us to really serve users across the breadth of their needs. And -- but over time, we will thoughtfully look for opportunities to make the experience better for users.

And to the first part, I would broadly say, as I do think we've been consistently saying for a while now, this is an expansionary moment, and we are seeing people engage more. And I think when they do that, naturally, a portion of that information for users, those journeys are commercial in nature. So I would expect that to play out over time as well.

Our last question comes from Justin Post with BAML.

Great. Just a couple. Sundar, I think you mentioned Gemini 3 is coming. Maybe you can comment on the pace of innovation in frontier models. Is there still just a tremendous amount of innovation? Or is it slowing at all? And then you mentioned a number of large deals signed in the last 9 months for cloud, which is great. Any changes in the economics of these deals as far as long-term profitability? Anything we should be aware of?

Thanks, Justin. The first on the pace of frontier model research and development. Look, I think 2 things are both simultaneously true. I'm incredibly impressed by the pace at which the teams are executing and the pace at which we are improving these models. But it also is true at the same time that each of the prior model you're trying to get better over is now getting more and more capable. So I think both the pace is increasing, but sometimes we are taking the time to put out a notably improved model, so I think -- and that may take slightly longer. But I do think the underlying pace is phenomenal to see. And I'm excited about our Gemini 3.0 release later this year.

On Cloud, I would point out as a sign of the momentum, I think the number of deals greater than $1 billion that we signed in the first 3 quarters of this year are greater than the 2 years prior. So we are definitely seeing strong momentum, and we are executing at pace. And in terms of long-term economics, I would say that, again, us being a full stack AI player and the fact that we are developing highly differentiated products on our own technology, I think, will help us drive a good trajectory here as you have seen over the past few years.

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.

Thanks, everyone, for joining us today. We look forward to speaking with you again on our fourth quarter 2025 call. Thank you, and have a good evening.

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

Okay. Thanks, everyone, for getting settled. Our next presentation and conversation is going to be with Alphabet, with Thomas Kurian, CEO of Google Cloud. I'm going to start with the safe harbor, give a little bit of Thomas' background, bring Thomas up, and he's going to go through some slides, and then we're going to have a conversation.

Some of the same to Mr. Kurian may make today can be considered forward-looking. These statements involve a number of risks and uncertainties that could cause actual results to differ materially. Please refer to Alphabet's Form 10-K and 10-Q, including the risk factors discussed in its Form 10-K filing. Any forward-looking statements that Mr. Kurian makes are based on assumptions as of today, and Alphabet undertakes no obligation to update them.

Thomas Kurian joins Google Cloud as CEO in November of 2018, bringing deep enterprise experience to the company. He has grown the business into one of the world's largest public clouds with more than $50 billion annual revenue run rate. Thomas is here for the third year in a row. Thomas, welcome to the conference, and thanks for coming again.

Thank you. Thank you, Eric. Thank you all for having me.

Cloud computing continues to grow as the primary vehicle through which enterprises deploy their core information technology systems. Cloud, although it has grown, it's still in its early phase because a lot of machines and applications still run on-premise and have not yet moved. So despite our growth, we see a lot of opportunity ahead.

Over the last 2 years, organizations are changing in many industries who they choose as their cloud partner. In the past, they were focused on application hosting or web hosting, increasing their looking at who can bring the technology and solutions to help them transform their business with artificial intelligence, applied in different domains in their organization.

At Google Cloud, we have deep product differentiation because of years of work in AI. As a result of that product differentiation, we're capturing new customers faster, we're deepening our relationship with existing customers, and we're growing our total addressable market.

So why are we winning? We provide deep product differentiation in performance, cost, reliability and efficiency in AI infrastructure. Second, we provide deep differentiation by offering a leading suite of best-in-class generative AI models. In order to feed these models, we use our strengths and historical strengths in data processing, analytics and security to feed models with high-quality data and keep them safe. And finally, for many years now, we've been building domain-specific AI applications and agents, and that work is now seeing a lot of interest from customers.

So starting with AI infrastructure. We've introduced chips for many years. We're 11th year now building AI systems and chips. Our AI systems are optimized for high-performance, highly reliable and scalable training as well as for inference. For example, if you're running a large-scale cluster, we have 2 times the power efficiency, meaning you get 2x the FLOPs per watt. And with power,, now the scarce resource, you get a lot more capacity. We're typically seeing about a 50% performance delta between us and other players. And if you look at the total capacity you can get on a single system, you can get 180x more throughput through our systems than you can from the next player.

In addition to that, we've integrated high-performance storage for AI-specific storage that can be used to scale out a cluster much more efficiently. And if you're doing inference, offer incredibly low latency. And our years of work in storage optimization has now seen lots of interest. We've seen a 37x increase in the volume of data being used in our AI optimized storage. We connect all of this with very high bandwidth optical networking. The value of optical networking is you can dynamically change the configuration of a cluster so that you can slice it up differently if you want it for training and inference without taking an outage, which is hugely important for labs as they see demand shifting from training workloads to inference.

And finally, Google has been at the forefront of most of the software that people are using for training, for example, compilers like JAX, XLA, Pathways and all this software expertise allows us to optimize the stack. We see demand from 4 customer segments.

First is the AI Labs, 9 of the 10 leading AI labs in the world. If you take the 10 largest, 9 of them are our customers. We see demand from traditional enterprises who are deploying AI models. We're seeing demand in capital markets. As capital markets shift from using classical computation for algorithms and are shifting to use inference, the same systems we offer can be used to provide very high frequency calculations. And we're seeing interest in high-performance computing applications.

SSI, a leading lab is a customer. LG Electronics and LG AI found both performance and cost benefits in using our infrastructure.

On this infrastructure, we offer a suite of models, not just Alphabet's, but 182 leading models from the industry. Our own models fall in 4 categories. We offer leading models for large-scale generative AI applications, Gemini. Gemini leads in many dimensions; performance, cost, quality, factuality, the ability to do very sophisticated kinds of reasoning. It's used by 9 million developers to build applications.

And just to give you a sense, compared to 1.5, which we launched in January of this year, 2.5, our latest model, reached 1 trillion tokens 20x as fast. So we're seeing large-scale adoption of Gemini by developer community.

In addition to that, we offer a leading suite of what's called diffusion models, diffusion models to create images, video, audio, speech, et cetera. We've added a third set of models around scientific computation. For example, our time series model is used by many firms in financial services to do numerical prediction of sequences. Molecular design, we offer a model to help people design molecules, which is getting a lot of interest in pharmaceutical industry. So there's a whole range of models.

And as people switch from just using a raw model to building an agent, we've introduced, based on our history in leading many open source projects, something called Agent Development Kit, which is a platform to help people build agents. It is, by far, the leading agent development platform in the industry, supported by over 120 companies.

To give you a sense of the scale, if you compare us to other hyperscalers, we are the only hyperscaler that offers our own systems and our own models, and we're not just reselling other people's stuff. The volume of tokens we process, twice other providers in half the time. So roughly 4x the volume. We have a lot of different companies using these AI models from companies creating digital products to using AI within their organization.

Canva is an example of a company using our diffusion models to create image and video content. ServiceNow is one of many SaaS companies that use our model, Gemini. And the reason they're using it is not only does it give them great performance and quality and latency, but it can also be deployed in 4 different configurations: In a cloud, in a classified environment, out on the edge, and also now on top of any NVIDIA cluster, which in the past if you wanted to run a model in your data center, you have to use an open source model because you had to give up the weights of it. And we're the only ones that offer that as well.

Now when you use models, you need to feed them with high-quality data and as you put more and more of your company's information into the model, you need to keep the model secure. So our history and expertise in large-scale data platforms has helped us as well as our focus in building security products. So we allow people to migrate data, clean it, prepare it and feed it into the models using our data cloud.

Second, we provide incredible low latency connectivity between our analytics and database platforms and models running on our cloud, allowing people to use models to process information from our data platforms.

Third, as people want to understand data and use models to reason on this data, we've introduced new data science and conversational analytical agents. Think of it as vibe coding with your data. So it's much easier for anybody to ask questions in natural language and do data analysis and also create data science models. All that is driving growth in our data platforms.

To give you a sense, we've seen a 27x increase in the volume of data processed in our data cloud, BigQuery, with Gemini. And we've seen that BigQuery, which normally, when people think of data warehouses, they think of things that handle numbers and tables. Now it's also being used to store and process unstructured data.

We have many more customers than some of the pure play providers and our strength in security is now applied to AI models. We protect new data. We have new solutions to protect models themselves so that when you load your data into a model, you don't get compromise of the model. And third, we also protect organizations with new advances that we've introduced from threats introduced using AI models to attack systems.

All that has driven growth with a lot of different customers from regulated industries to commercial enterprises to small businesses. Two quick examples of Radisson Hotels. They took all their data for, say, customer segmentation and all data from the hotels consolidate in our data cloud and use Gemini and our diffusion models to create advertising. Virgin Media is using the same combination but using it to improve the speed of decision-making and data engineering within their organization.

Lastly, we started our work to build domain-specific enterprise agents in 2021. So we've been working on it for 4 years now. We focused in 5 areas: agents to help software engineers write code. Our Gemini command line interface AI agent which we introduced in -- on June 24, has grown to close to 1 million users already. We allow people to rebuild domain-specific AI agents. For example, for marketers to create content, customer service teams to handle customer service interactions. We've seen strong growth, for example, in our customer service technology with a 10x growth in chat and voice interactions.

We're also building domain-specific agents for specific industries, for example, to help people do shopping and commerce. Today, we handle roughly 5 billion commerce transactions through our commerce agent. And we make all of these agents as well as any bespoke one that people want to build available through a single platform we call Agentspace, which provides a single panel for a company to access and use all of the AI technology within their organizations.

We're seeing growth and broadening of our addressable market by applying AI now in domains that IT departments historically didn't serve; marketing, customer service, commerce, et cetera. Mercado Libre is one of the largest e-commerce systems in Latin America. They use our shopping and commerce technology. Wells Fargo uses Google Agentspace to help their employees use AI from trade management, contract management and other domains. So our deep product differentiation has driven the growth that we're seeing in customers.

Now how are we taking all this to market? We're doing it. There are 5 important things. First of all, we monetize AI in 5 different ways. We're seeing growth from net new customers. We're seeing deeper relationship with existing customers. We're broadening our addressable market. As a result of that, we're seeing growth in revenue, our remaining performance obligations or backlog and operating margin.

The 5 ways we monetize AI. Some people pay us for some of our products by consumption. So if you use our AI infrastructure, whether it's a GPU, TPU or use our model, you pay by token, meaning you pay by what you use. Some of our products, people pay for by subscription. You pay a per user per month fee, for example, Agentspace or workspace. Some monetization comes by increased product usage. So if you use our cybersecurity agent and you run threat analysis using AI, we've seen huge growth in that. Example, we're over 1.5 billion threat hunts, we call it, using Gemini, and that drives more usage of our security platform. Similarly, we see growth in our data cloud.

We also monetize some of our products through value-based pricing. For example, some people use our customer service system, say, "I want to pay for it by deflection rates that you deliver." Some people use our creative tools to create content, say, "I want to pay based on what conversion I'm seeing in my advertising system." And then finally, we also upsell people as they use more of it from one version to another because we have higher quality models, more quota and other things in higher-priced tiers.

Because of this, we're capturing new customers faster. As I said, we've seen 28% sequential quarter-over-quarter growth in new customer wins in the first half of this year. 9 of the 10 top 10 AI labs and nearly all the AI unicorns are our customers. We're deepening our relationship with existing customers. 65% of our customers are already using our AI tools in a meaningful way. Those customers that use our AI tools typically end up using more of our products. For example, they use our data platform or our security tools. And on average, those that use our AI products use 1.5x as many products than those that are not yet using our AI tools. And that leads then customers who sign a commitment or a contract to over-attain it, meaning they spend more than they contracted for, which drives more revenue growth.

Finally, we're growing and diversifying our revenue. Our revenue does not come from a single product line. We have many different product lines, all of them growing. And as Sundar and Anat, our CFO, have both mentioned, we've made billions using AI already. We're growing revenue while bringing operating discipline and efficiency. So our remaining performance obligation or backlog is sometimes referred to, is now at $106 billion. It is growing faster than our revenue. More than 50% of it will convert to revenue over the next 2 years. So not only are we growing revenue, but we're also growing our remaining performance obligation.

We're also very focused on operating discipline to improve operating margins. The 3 big areas of focus. One is making sure we are super efficient from the point of view of using our fleet and our machines so that we get capital efficiency. There's many hundreds of projects that people have done to optimize and the larger the fleet, generally the more efficient you get because you need less buffer for any individual customer. You've also seen a study that some of our scientists published on the improvements in inferencing that we've done with a 33x efficiency in inference using some of our models over the last year. So there's a lot of focus on continuing to optimize our fleet.

We're improving, our go-to-market organization now has a large customer base to sell to. And selling to existing customers is always easier than selling to new customers so it helps us improve the cost of sales as a percentage of revenue.

And finally, we're also building on a large suite of products already. So it helps us improve our engineering productivity. You see that in our results. We're growing top line and operating income. In closing, we've spent years building advanced AI technology of our own; chips, systems, tools, agents. We made those bets very early. Much of the work that you see today has been underway for many, many years. And we're not just reselling third-party technology.

So why we're winning is because we see this deep product differentiation now being adopted by customers. That's leading us to win new customers, deepen our relationship with existing customers and broaden our addressable market. And in turn, that's leading us to grow revenue and operating income. Thank you.

Thank you, Thomas. Thanks so much for a lot of good stuff in there. So I want to come back to where you started the presentation talking about the state of the industry today. So as we exit '25, we're looking towards 2026. Where are we in terms of cloud adoption, client usage trends? And how is Google Cloud evolving in terms of that competitive landscape and those secular growth themes?

Cloud adoption is still in its early stages. You count servers depending on which analyst you read, still a vast majority of servers and apps run on-premise in people's data centers. So there's a lot of remaining opportunity ahead for people to migrate these workloads to modernize them, to transform them.

There are different adoption patterns we see in different industries. Some are moving more quickly. Some, for example, government agencies, some of them move a bit slower because of compliance and other regulation. Europe has been generally slower to move because of sovereign cloud requirements. We've now introduced them. So we are starting to see many different drivers for people to pick that up.

But in the past, people chose cloud primarily as a mechanism to get developer efficiency, meaning I can get infrastructure on demand and to host applications and to save money in hosting applications by consolidating compute and storage. And that's that continues to be important, but that's not the primary driver. The big driver now is "I really want to transform organization. Can you help me by bringing AI expertise and products to help me?"

So with that as a jumping off point, when you sit and look at the enterprise landscape today and the way enterprises are adopting AI, put a finer point on your presentation in terms of how those trends inform your strategic priorities as a company.

So we see organizations using AI in 4 domains. Some companies are using it to build digital products, Natura Cosmetics, Snap, the work we did with Warner Bros. to create The Wizard of Oz. Those are all essentially using AI to advance the digital product. Others are using it to transform customer service. And when I say transform customer service, not just in the call center as we do with Verizon, but at the point of sale as we do with Wendy's, in a vehicle, as we're showing with Mercedes today in Munich.

So there's many different places where people see that customer interface transformation. Others are using it to streamline the core of the company and the back office. And when I say the back office, Home Depot is using it to answer HR help desk. When employees ask questions regarding benefits and other things, they're using our agent to answer those questions. AES, which is a large energy company, streamline their regulatory and audit process, reducing the cycle time. Tyson Foods is using it in supply chain.

And then finally, we've seen a lot of organizations now using it in their IT departments. And their IT departments, broad brush, there's people using it to write code and not just to write code, but improve the quality of code that's being written. There are people who are using it for cyber because cyber generally, there's a bottleneck in terms of how many cyber analysts you have. And these AI tools can be used to both help you identify and prioritize what threats are occurring and then much more quickly analyze if you've been compromised. So those are the 4 big domains that we see AI being adopted for.

Okay. When you think about your full stack approach to AI, talk to us a little bit about how that might be creating competitive advantages in the marketplace? And how does it help you translate into winning deals?

It's a great question. Our stack is open. Meaning we offer our own accelerators. We have a super close working relationship with NVIDIA because people want a choice of different types of configurations of systems. Same thing with our models. We offer our own as well as third party. What it helps us do, though, is we can optimize things differently up and down.

So I'll give you an example. If you look at the work we do with capital markets, applying AI to synthesize data from information sources and then use it to actually feed algorithmic models. You need a certain set of skills to reason on it. You need a certain set of capability to choose the right tool and you need to be able to do it with ultra-low latency. So that combination of things that we bring from the enterprise, surprisingly, at the model level, it's the same thing you need to have a great coding tool. It turns out if you want to do software engineering, you have to choose the right tool for the right task. You want to be able to generate code with low latency. So when you do auto completion, it happens. And it's also the same thing that applies in certain circumstances on the search side.

So the fact that we're able to get all of these different design centers, and we're using one model series for all of Alphabet as well as our customers helps improve the model. And then because we're optimizing that model up and down and as we had Jeff Dean and our team talk about how much more efficient we've become on serving, it also helps us optimize the cost of inference and serving. So we can co-design things. We get leverage because of all of the domains we're serving, both across enterprise and the consumer side, and we can also optimize the cost structure when we deliver these things.

Okay. Building on that theme, in your presentation, you had -- you touched upon the idea of your AI infrastructure and building advantage and scale around that. Talk a little bit about where custom silicon and TPUs make sense as opposed to working with external suppliers. And talk a little bit about some of the key learnings of customers that have used TPUs and the use cases where they deploy them.

So you know we broad brush, I think when people look at models, they think there's one type of model. There are many different types of models. There's dense models, mixture of experts, sparse models, do you need a sparse core or not? So we offer a range of accelerators, where people really choose the right thing for their model based on a variety of factors. And it's often -- it comes down to the experts sitting down and actually trying it.

And we see 4 key things. First one is, are you doing a kind of hero model run? And if you're running a hero model run, it's typically on a giant cluster that you want to scale out. And they care a lot on the FLOPs per dollar, meaning how many FLOPs are you getting per dollar? How efficient are you able to load your data set into memory? So how much HBM do you have? Are all the nodes in the cluster communicating with super predictable latency, which is where the optical network comes in? And then can you then use certain things like the compiler to really optimize what at the bottom level is equivalent of an instruction set?

And so the TPU is seen as really attractive by many of the leading labs because it gets their training runs to get much more throughput through the system. It's also being used by lots of people to inference, and we've worked -- we have very close working relation with NVIDIA to allow us to -- allow customers to train on TPU, serve on GPU or vice versa. And there's a lot of things we've optimized with NVIDIA. For example, JAX is optimized not just on TPU, but GPU. So it's not just the infrastructure, but the entire software layer on top.

Got it. Understood. You laid out a lot of initiatives on the product side, the platform side, all leading to the types of growth you're seeing today. What are the biggest priorities for investments in the business in support of that growth? And how do you think about sort of getting that mix right between striking the right balance on investments and driving growth?

We look at investments in 3 big categories. Obviously, our supply chain and capital investments, which span data centers, power, long-term power contracts, what we're doing with our different geographical locations because inference now needs to be in many different countries for sovereignty reasons.

So one is our capital infrastructure, and we've had a team for years and years do that at real enormous scale, and we continue to do that. And we're very thoughtful on how we're doing it. The -- in each area, we also look at how do we get more efficient. For example, we're constantly optimizing. One example is as you get these more powerful chips, they also take a lot more power. And power is, in many cases, a short resource. We have the most efficient PUE in the world. PUE is how much power are you consuming to generate X amount of FLOPs.

And we invested very early in water cooling and water cooling gives you another lift in throughput through these systems. So that's an example of where we said, hey, there's likely to be a power issue. Let's design early a set of solutions. And that's helped us with an advantage there. We invest in products. So we are very thoughtful on how and disciplined on which domains are we solving and how much investment do we want to make in products.

And then we invest in our go-to-market organization and go-to-market organization, when I started at Google, nobody thought we'd be where we are. And in the first several years, almost all our sales were to brand-new customers and difficult to win them, but we've actually won many of them. Now we have teams that know how to sell specialization for specific products. We know how to sell to existing customers. We know -- we have a different model to sell to new customers. So all that sophistication has been built over many years.

You also talked in your slides about how the margins continue to build in the reported segment behind Google Cloud. Talk to us a little bit about not only just getting that right on the growth side, but you talked a little bit about it driving efficiencies and continue to make progress on the margin side of the business over the long term as well.

There's a lot of people working really hard to continue to improve top line and operating margin. Some of it is down to really fundamental things. Like if you look at us, we made some decisions early to say we're going to build our own chips, our own models and also products around the models. And so that gives us when you're not just distributing somebody else's stuff, you obviously can optimize cost and improve margins.

Even when we look at examples of products we built around the model. 2021, we saw a lot of companies talking to us about their call centers were shut down because of COVID. And they could not handle the volumes of calls coming in. So we said, let's build an AI-powered customer service system. That's now being used at large scale. And that's an example of something that's a very differentiated product. It's not just here's a model and access it through an API. There's a lot of capability we've built into that. Those decisions that we made early and years of continued effort, both in the past and in the future, we're very focused on that, both improving top line and operating income.

Okay. I'll try to squeeze one last one in. When you think about the deployment of AI that's happening right now in the ecosystem and you look at the infrastructure layer, the model layer, the application layer, where are you seeing the most exciting things being deployed that can be elements of driving growth in your business over the medium to long term?

I think we see a lot of interest in -- it's sort of -- we are roughly in every 6-month cycle. And what I mean by that is we find that a major model revision opens up an entire new category of capability. And that, in turn, drives us to build value-added products on top of it. And so it's roughly -- we're in a 6-month iteration.

So for instance, if you look at Veo, Veo 3 is an amazing video creation system. So we now have enormous interest from advertising companies, creative labs, media companies, movie studios, et cetera. Now that market did not exist prior to Veo reaching that level of breakthrough. And then we take that and because we're co-engineering it with DeepMind, Google DeepMind, we're able to build an entire set of assets around it as product that people can then use to apply it to specific domain. So that's roughly the cycle we're on. It may get faster. A lot of it depends on what kind of breakthroughs we're working on.

No, great example. I think we're going to have to leave it there. But Thomas, thank you so much for coming to the conference this year. Please join me in thanking Thomas and the Alphabet team for being part of the conference.