The Exchanges

Q customer, here's what Amazon might say. Google has its own models and it wants you to use them at Amazon. We have some proprietary, but our job is really to let you pick whichever model you want from anthropic, uh, on down. And you can just trust us to be, to not push our own stuff and then therefore, uh, choose us over Google. What would you say to that?

A I would say we offer 200 models in our platform. In fact, we look every quarter at what's driving popularity in the developer community. And we offer them. We offer a variety of third party models and partners, not just Anthropic, AI-Twenty-One Labs, Allen Institute. There's a variety of models there. We offer all the popular open source models. Uh, Lama, Mistral, Deep Seek, uh, a variety of them. And we base it what based on what customers want. Uh, so we track, What's on the leaderboards and what's getting developer adoption and put them in the platform. And people have been super pleased that we have an open platform. An open platform. Companies, we always feel companies want to choose the best model for their needs, and there's a range of them. We're offering a platform. You can choose the model you want. The only model we don't offer today is OpenAI, and that's not because we don't want to offer their model. It's because-

AI assessment note: “I would say we offer 200 models in our platform.”

Q they come over and let me out of the store. Um, so what do you think about this argument that generative AI is mid or, um, not, not, you know, uh, living up to all the boasts. And what type of applications have you seen in the technology? If you were going to argue the other way, which I think you are that make you believe that there's something here.

A I always say, you know, any major technology shift takes a while for adoption to happen and for people to understand it. If you look at the internet, it went through a similar thing. If you look back at 97, 98, 99, it was, there was a lot of hype that it was going to change things. In 2001, there was, you know, some of the hype fell apart, but over the long term, it has definitely shown that it's transformed the way that people find information, they buy things, They even run their businesses, so I think AI is going through a bit. Early on, there was people had maybe too rosy a view, and I think in the long term, we always say that technology is going to be really a fundamental transformation. How quickly it changes in the day-to-day, every day, time will tell, but I'll, I'll give you examples of things that we, we always say, let the customers tell the story. Let's not tell the customer story on their behalf, and We're super proud of the work we've done. I mean, Seattle Children's Hospital. They wanted their pediatricians, when they see a child, to be able to understand the guidelines for treatment. Guidelines are complicated. You need to be accurate in the information put in front of the person. We've helped them do that. At the Mayo Clinic, they wanted us to provide a system through which a doctor could find information from the electronic health record, From their clinical …

AI assessment note: “I'll give you examples of things... Seattle Children's Hospital... Mayo Clinic”

Q of the sales muscle that Google basically was used to, uh, got used to selling in an automated fashion through AdWords and didn't know how to sell to people. I think you came into Google cloud and revenue was a billion dollars a year. Now it's in the forties. It's expected to be in the fifties in 20, 25. Um, how did you guys learn how to sell to people?

A We, we, we learned how to sell by listening to customers and building a great, great, great sales team. You know, we, in order to do cloud well, I think you have to do three really basic things. You have to anticipate customer problems and solve them in different ways than other people did. Uh, so that's number one, and very proud of our ability to identify where the next customer pain point is going to be and solve it. Number two, we built a global sales team, uh, and credit to our go-to-market organization. Uh, we've done it, you know, it's a grind to build such a thing. That's why very few companies have done it successfully, and to grow from the scale we were in 2019 to where we are now, No other enterprise software company has grown that fast, and that's a credit to our sales organization. We had to bring discipline. We had to start with a certain set of countries, get critical mass there, then expand. We had to find the right mixture of sales reps, technical customer engineers, people who do customer service, customer support. We had to ensure that, for example, our contracting, legal framework, all of the other things That sit behind the sales organization world-class. Super proud of that. And third, we always have believed that cloud is a platform business, and the way that you grow is you provide a platform that lets other people grow on top of you, whether that's inde…

AI assessment note: “We, we, we learned how to sell by listening to customers and building a great”

Q When people are making decisions to buy, how much of their decisions are predicated on AI capabilities? Because what you just told me are a number of specific, I want to build an AI program, I'm coming to Google for that. Now, I imagine that's important, but when you think about the broader landscape of people making decisions to buy cloud services, how, how much does AI factor right now?

A It's a good question. It depends on the country. It depends on the industry. It depends on the segment. Let me explain what I mean. If you're an AI unicorn, meaning you're funded to build a foundation model, or you're building an application based on AI, that's really the central part of your decision. If you're in an industry that, for example, in retail, where we have a product called, ah, retail search and conversational shopping, where you can take Google-like search using text, images, video, and put it on your catalog, And you can also put conversational shopping where I can ask a question. I'd like to return this dress and have the system handle that transaction for you. It's a super important thing, for example, for people in commerce, whether that's retail or telecommunication. On the other hand, if you look at a utility, Or an industrial manufacturer. It applies to part of their organization, but it may not be the central thing. And so it really depends by industry and by customer segment. And so, but we, part of our value proposition is that we offer all of these different capabilities. And so AI is helping us. It's not the sole reason for our growth.

AI assessment note: “And so AI is helping us. It's not the sole reason for our growth.”

Q With inference. So that will be the new cost to basically taking the models and putting them into production and using them. I'm curious how big of, um, how much of the cost of that, or how much of the use of, of your services is going to be toward reasoning and what have these new reasoning capabilities allowed your customers to do that they couldn't do previously?

A It's a really good question. I mean, reasoning is something we are starting to see customers using in different parts of our enterprise customer base. For example, in financial services, we've had people say, hey, I want to understand what's happening in financial markets, summarize the information coming off, whether that's Video feeds like CNBC, financial market indexes, and other financial information, and tell me what's the, what's happening. And the model can not only build a plan for how it collects the information, but summarize it, and then reason on the summary to say, are there, you know, conclusions to be derived, right? Uh, so we are starting to see people starting to do that. Uh, how much of that will be versus Other scenarios, time will tell, but we are starting to see people doing much more sophisticated, complicated reasoning. Even in areas, we have a travel company, for example, that's working on, give me a very high level description of what you want to travel for. I want to fly to New York. I'm taking, you know, my son. We'd like to see Coney Island and the following three things. Build me a plan. And in that, it can have multiple choices, but it may say, You know, if you're traveling in June, maybe hot in the afternoon, therefore I think we should have you see Coney Island in the morning and go to the museum in the afternoon. And models are starting to be ab…

AI assessment note: “How much of that will be versus Other scenarios, time will tell”

Q Okay. So Jensen at Nvidia says reasoning costs a hundred times more to do. You also have your own compute. You're also facilitating that. Is that in the ballpark or are you seeing different numbers?

A You know, it depends on how long, right? Like, for instance, you could give it a very complicated problem, and a model can take hours to reason on an extraordinary large data set that will be more expensive. At the same time, In the example I gave you on travel, given the number of trips that are made, et cetera, that company is not going to spend millions of dollars to calculate the answer for what's the best choice of trip for me. Or in the financial markets area, given how much information is coming all the time, and how quickly you need to reason on it to present your equity traders or your private wealth managers an answer, you're also going to time-bound the reasoning computation. And so there's controls in the platform to allow you to say what is the breadth of the reasoning, meaning how large a cluster do you want to reason across, how much data and how long do you want to reason. All those factors are in the user's control and therefore drive how much they want to spend.

AI assessment note: “You know, it depends on how long, right?”