Alphabet Inc. Depositary Shares representing a 1/20th Interest in a Share of Series B Mandatory Convertible Preferred Stock Goldman Sachs Communacopia + Technology Conference 2026

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. 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 three things with them: understand the data, secure their company, and automate processes or workflows within the organization. Across our portfolio, we have strength in data management, and we connect our historical strength in data management of 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 Google 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 are growing quickly and shows you the diversification we have. We have 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.

When a customer gives us a commitment, say, for $100, typically they spend more than 50% more than that. So that is reflective of the adoption we are seeing and the growth we are seeing with our customer base. Now, let us 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 three 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 2.7 times 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 Börse, for example, is using us. 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 U.S. 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 three 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 co-design our products top to bottom.

When we launch something like Gemini 3.8 Flash and people go, "How come you guys are 2x better than anybody else in the market on inference per dollar?" It is our tokens, real intelligence per dollar. It is because we can optimize that whole stack. 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 TPU business, is more than twice that of the next largest hyperscaler. Second, our payback period on AI servers in aggregate is less than two 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 five-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 back final answer. Our platform is called Gemini Enterprise. It is used by over 90% of the Fortune 100 and thousands of small businesses. It is used in a very specific way. People want to use it as a reasoning agent. Break the plan, understand the steps that are needed, reason on it, and execute the steps. It uses a reasoning agent to understand all the information in the company to then automate that workflow process. When it does it, you want strong controls.

What kinds of controls? Companies are worried about security. They are 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. You do not 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 there is the largest insurance company in Germany. They use it for claims and underwriting analysis. We have got PepsiCo using it for supply demand planning, Macy's using it for retail commerce, and there is a whole range of companies doing it. Now on this infrastructure, we are also building domain-specific solutions that is built on this foundational platform. What do these domain-specific solutions do? They do the three 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 are at PepsiCo and running a demand plan, and you want to model the forecast. The first thing you need to know is where is all my data? 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? 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 then surface up the answer.

We do that super well. Example is our cost to run these machine learning models, 2.5 times better than the number one in the industry. Second, we do that with much greater accuracy because the definitions of your financial metrics that Pepsi 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 two 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 have actually fixed the issue.

We do the same in a number of application domains. We are not just building a base platform, we are 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 are 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? It allows us to build that frontier.