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 interests.

It's always good to see you to catch up on everything going on at Alphabet and around the world. A lot has changed in a 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 forward slash research disclosures. 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 of different material. Please refer to alphabets, forms, 10K 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 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's growth. Now it's almost a $4 trillion company, so it seems that investor sentiment has changed. From your perspective, what has changed internally at Alphabet with how you think about pace of productization, the long-term vision? How do you think about what changed internally now versus one year ago?

Yeah, 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 Sunder making statements about being an AI-first company. And that strategy has really led us to where we are today. So if the strategy hasn't changed, 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 in 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 we 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 but 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 best, whether it's Waymo or isomorphic, intrinsic, et cetera. It is really the foundation, the research foundation that sits across the company. What you've seen in the past year is the 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, then 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 the underline 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 spend quite a lot of time on this question and on the CapEx investments 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? Or, you know, this year we said 175 to 185. 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, 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, when 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 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 five, ten 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, they're 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'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

yeah the continuous nature of it I'm sure must play a role anything you can tell us about what surprised you over the course of last year Are you sort of adjusted CapEx or areas of more signal or less signal?

So last year we gave guidance that was lower than where we ended up. We ended up at 91. 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 shared that approximately 60% of our technical infrastructure investment went toward 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 investment, 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. 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 they can we can get that earlier now this can also be the exact opposite work construction may 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 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. It is 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 things or 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 and we are actively managing it for this year as well and probably provide some updates on the quarterly calls and 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, it's on the cost discipline side. So if we think about this CapEx, the amount of DNA 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 DNA coming through?

So let me start with just the approach. There's a philosophy that I have with regards to cost management. And, well, 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 and we're not working any harder. 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 stack that I've just mentioned are CapEx of $175 to $185 billion, which, by the way, is the majority of that is their 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 or we're putting a chip in is them 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 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 built out you have the chips to install them so we don't sit in a where you know 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 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 it 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 it's yielding actual real financial results. And we have the benefit of having a cloud business that allows us to co-develop 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 costs of just rent. energy. 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. When we're able to hold some extension in our operating margins, that's hard to do. That number is only going to grow next year, just given what we've done historically. 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 DNA is faster revenue growth. So maybe let's talk about revenue a little bit. 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 source, 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 one or two of the changes that you've made that really drove larger than expected and benefits to engagement and monetization maybe than what you would have had a year and a half 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 this, our image-generating tool went viral, viral. Within, I want to say, two 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 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 know, you point it, I want this flower. You point to 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 focus on the consumer innovation-based organization. You have to kind of out-innovate yourself, disrupt your own business model in the sense of 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 overview, AI mode, lens, different ways of searching. And we always just be thinking about what is that next frontier? What's the next thing we're going to be introducing?

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, could change a lot of these categories. Sundar's talked about Agentec. How do you think about the incremental utility for your search users or user base as you roll out new types of Agentec capabilities, and what are the biggest technological hurdles you have to clear to really scale, quote-unquote, Agentec?

So Agentec commerce, I would say, is still in early days, but no doubt holds an interesting promise. So 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 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 a silk pillow, 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 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 Gemini Agent to search for items for me on repeat, trying to find golf pants of certain size I can't find. It emails me and looks for it every day. Is that the type of thing 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 thinking about it?

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 shop or shop for you. But again, early days, let's see what we share here.

I'm going to take my socks for TMT next year through some agents. 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 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 you 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 this strong backlog that we were all wrong about and missing one year ago?

So Google Cloud has had really record results throughout the last year, and as you said, exited Q4 at $17.7 billion in revenue, so annualizing now 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 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 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 two 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 company. They're getting the benefits of being an AI-first company for that organization. So they're expanding the 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've just mentioned, has executed incredibly well in delivering margin expansion, exiting a 30% operating margin with this 48% top-line growth. It's very impressive. And they've been laser-focused. Thomas does an excellent job of making sure that we talked about efficiency, not being a one-time 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 when more customers, we're seeing the win rate, the new customer win rate going up as well. So that's exciting across the cloud organization.

Yeah, 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 mentioned earlier, which is the headwind associated with the depreciation, right, because a lot of the depreciation 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 the middle of the income statement, to ensure that it's done in the most efficient way. The same things we do across the organization. They're doing that in cloud. But certainly the top-line expansion skill does help.

Let's 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 was something in the media last week about potentially setting up essentially TPU neoclouds 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 standalone basis?

So our TPUs, which I said we launched the first one 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 the TPUs and GPUs. And 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 we'll 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.

Let's talk about YouTube a little bit. YouTube, we think about 2 billion daily active users. Our numbers show us 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 really 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 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 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 just the video on YouTube five 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 then what we can do there. So there are several areas where you see an AI impact YouTube growth.

Speaking of the breadth of Alphabet, 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 two years. It seems like there's a very impressive ramp coming. What are some of the key hurdles for 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? Yeah. So we have the Waymo driver and we

call the vehicles called the Waymo driver. It's not an actual driver. Launch was in five cities last year, already launched in five 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 the number of accidents versus a human driver. Orders of magnitude different. So we look at safety. Then we look at the specific market. Each market, so for example take the US, 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 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 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, if you look below the waterline, we are making choices, and the strategy is about making choices, of where do we direct investments within the other best 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-lighted 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 would depend on the market and the specific opportunity. It's not that one has to dominate the other. It depends on where we are.

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

So I wouldn't say there's an underappreciated overhyped, 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. 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've come from this field, 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 will have potential implications in a diverse set of areas when we hope to have good use of cases within five 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 a year and a half ago, two 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.

Can't wait to see the growth in the ROIC ahead. Thank you so much for that.

Thank you.