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F500 Europe 2023 TUI AG Holiday Flight Operations After Pandemic Hit

TUI ranks 210 with $17.9 billion in revenue (up 216.5%). The Germany-headquartered hotels, casinos and resorts company has 61,091 employees and made a loss of $300 million.

This year's Fortune Global 500 generated $27.7 trillion in revenues and $1.5 trillion in profits.

Fortune Global 500

This year's Fortune Global 500 generated $27.6 trillion in revenues and $1.5 trillion in profits.

The world's 500 largest companies generated $31.2 trillion in revenues and $1.7 trillion in profits in 2014. Together, this year's Fortune Global 500 employ 65 million people worldwide and are represented by 36 countries.

Global business is back. After limping through a worldwide financial crisis and economic slowdown, the 500 largest companies ranked by revenues shattered all sorts of performance records in 2013: They racked up combined revenues of $31.1 trillion, up 2.5% from 2012, and profits soared 27% to nearly $2 trillion. China’s 95 companies (up from 89 last year) posted $5.8 trillion in revenues. The U.S. has four fewer companies on the list than last year but remains (for now) the country leader, with 128 corporations on the list—including No. 1 Wal-Mart Stores—reporting $8.6 trillion in revenues. 

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TUI (TUIFF): Company Profile, Stock Price, News, Rankings

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TUI | Fortune

TUI (TUIFF): Company Profile, Stock Price, News, Rankings | Fortune

Is the traditional college degree broken? Silicon Valley seems to think so.
Andreessen Horowitz has officially launched a bold new experiment in San Francisco: a school for gifted high school graduates featuring no grades, no tests, no traditional teachers, and no accreditation. While the first cohort attends for free, future tuition could reach up to $90,000 a year.
In this episode of Fortune Daily, Gagan Biyani, CEO of the Horowitz Andreessen Academy, joins us to discuss whether this unconventional model represents the future of higher education or an exclusive bet for the well-connected tech elite. Plus, Fortune Senior AI Reporter, Bea Nolan breaks down leaked Anthropic documents and what they reveal about the company's upcoming IPO.
Additional images courtesy of Getty Images

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00:00 Gagan Biyani
17:32 Anthropic IPO


The End of College? Inside a16z’s School for Tech Titans | Fortune Daily

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Like many software companies, Intuit is rebooting its products for AI. Unlike many software companies, it’s helping Intuit steal sales from its competition. The Silicon Valley stalwart’s chief executive as well as its AI chief explain how the company’s AI ambitions have led to its fastest organic growth in a decade—and share where TurboTax, QuickBooks, Credit Karma, and Mailchimp might go next. 

CTRL-ALT-Compete

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Thanks for being here. Thank you for having us. So. I'm from Philadelphia. All right. And we're kind of big on the Revolutionary War. That's kind of our, our, the name of our game, OK? And there's an old colonial American, uh, phrase around the revolution, join or die. We're all familiar with this. Yes. Um, Sussan, I feel like for you. And your company, it has been disrupt or die. Would you agree with that? And why? Uh, so we're a 40 year old company. Yes, we were born tech. Yes, in tech. We were born in the era of shipping CDs. That's when I joined the company. Oh nice. And so first mention the CDs on this, on this stage today. There are, there, there are lights in my eyes, so I can't see this crowd, but I'm sure some of you, maybe many of you, uh, were not even born in the era of shipping CDs. And so we, you know, we've been through 4 plus massive technology shifts and, uh, and many, uh, economic cycles, and, uh, I'll, I'll tell you the DNA of the company long before me has been about what you just said, live or die, uh, and so we've had to figure out how to disrupt ourselves, yeah, yeah, and, and here you are standing today. What's your market cap today? What's Intuit worth these days? I don't know what the stock market did today, but you know we're almost 190 billion. Not too shabby after 40 years, so cheers, um. Ashoka, I want to come to you. Thank you for joining us, um. So Intuit made a big bet on machine and deep learning, as we called it a decade ago. I said earlier on the stage, stays true for you, um. And my understanding is you really have to get the data ready to do something like that. Um, tell us a little bit about that journey because we're talking about getting the data ready now, but you guys were doing that a decade ago. That's right. uh, Andrew, it's a pleasure to be here. What I'd tell you is that a decade ago, as you said, we started to invest in 3 things. Number 1, building the right team, number 2, building the right data platform, and then number 3, building the right AI platform. These three things work very, very well together. The team has been trained on AI on how to use data, how to govern it, how to manage it. We've built the clean data pipelines that bring data to our AI systems at scale, but then we Really invested in connecting the data and the AI and then we built an operating system on top of this whole thing called GenoS which allows developers now to build massive applications at scale. To give you an idea, in the last year, fiscal year 25, we had 3500 independent projects and product experiences created on Genos. Yeah, that's remarkable. I would like to know if you changed your strategy over those, again, 10 years or so, you know, if, if you could have seen what was coming, hindsight 20/20, where you, when you set out to make these changes a decade ago. Did you pivot along the way, you know, just seeing where the market was going in terms of, in terms of AI and capabilities and so forth. So, um, when Susan took over as CEO and the executive team launched, one of the things that they defined was that Intuit is an AI-driven expert platform. That is the company's strategy. That strategy has not changed. What we did do though is start to evolve the technology in order to meet that strategy. And so we've made deep investments in clean data at scale. We made deep investments in building an operating system that could allow people to access that data and build AI experiences, and we were doing this before the Gen AI revolution. When the Gen AI revolution came, we started to make investments in creating that operating system. And that has really delivered measurable return on investment for our customers as well as for Intuit itself. Yeah, Sassan, um, Intuit's founders 40 years ago said that the company's biggest competitor was paper and, and the spreadsheet as we know it today. Is that still true? Uh, it is actually, and, uh, just maybe briefly, uh, for your audience, you know, we serve consumers, uh, businesses of all sizes, and, um, and we focus on things that matter to them, right, which is money, cash flow, uh, as I hear, I hear they're concerned about the money, yeah, yeah, right, but, uh, the, the reason I start there is, you know, you're talking about cash flow, you're talking about survival, taxes, bookkeeping, and accounting. And um and there's an incredible fear factor when it comes to how do I grow my business, how do I manage my business, uh, am I gonna have money to make payroll, and, uh, and you know, 75% plus consumers live paycheck to paycheck. So this is a real, you know, we talk a lot about AI, but there's like a real customer here uh that matters and uh what, what we need to do for them, which is how do we power their prosperity. And I say that just to say that the majority to your question, the majority of both consumers and businesses fall in a bucket that we call non-consumption, and what we mean by that is, um, for instance, businesses, uh, use 10 to 25 apps to manage their business. Most of the time their data and their insights are trapped in individual apps, and so they also have to have a tax accountant, a bookkeeper to help them advise them. Uh, and we, and they use Excel spreadsheets, Google Sheets, and so it's all non-consumption, meaning they don't use a platform to run their personal life or, or their business. That's, uh, and 40 years ago, yes, it was all paper. Uh, today it's just a different form of paper. It's all in apps, but it's all still trapped. The insights are still trapped, which is why. Those that we serve rely a lot on people and we believe people will still be a huge part of the equation along with AI, which, uh, I think it's counter to what many talk about depending on the industry they serve. So if, if they're really just looking for a better piece of paper, a better spreadsheet or or whatever it is, um, but really they're looking for a solution. To their problem, right? Let's be honest, um, and we talked about this earlier on, on stage with, uh, the CEOs of FreshWorks and, and Xero earlier today, right? What do, what do they really actually care about? Just as a solution to their problem. Um, why spend all this money on AI? I mean, it's whiz bang, it's, you know, really great. We can do all this extraordinary thing. If the bar is paper low, you know, why do all this? Is it just to gain customers from. You know, the, the, the, the, the clutches of, of paper and spreadsheets. I mean, you don't have to try that hard, right? That's right, or wrong. No, I, I love the question that, uh, that you're asking. First of all, um, I, I remind ourselves at the company all the time, customers don't care about AI, right? Everybody talks about AI, but the reality is a consumer is looking to increase their cash flow. A consumer is looking to power their prosperity to make ends meet. A business is trying to get more customers. They're trying to. Manage their customers, sell them more, more services, and We bet the entire company before this topic was popular more than 70 years ago on really 3 things. One, it's what Ashok talked about, which is data. Uh, we have to make sure that our data is clean, it's customers' data, not ours. It's clean, it's usable, it's digestible by the platform. The second is in order for us to grow. We declared 7 years ago we need to deliver done for you experiences. What does that mean? From lead to cash as a business, we'll help you manage your leads, we'll help you manage your cash flow, we'll do your taxes for you, your accounting for you. We'll give you advice on whether or not you should buy inventory and done for you for consumers from credit to wealth. Help you build your credit if you don't have credit to how do you manage your money and grow your wealth. And what we said was the only way to do that was data. AI and 7 years ago we said HI, human intelligence. Uh, if you look at our addressable market, the majority of it is, is influenced by people, people that are tax experts, accountants, bookkeepers that are advising you on what to do. Although the work is completely changing and we're automating it with AI, we believe AI plus HI is essential to reshape the category, which is really what the investments are that we've made. So really it's a long answer to your very short question. That's all right. Customers don't care about AI. What they care about is help me grow my business, uh, help me prosper, and we have found the only way to do that is to combine technology, automating everything for them. With human intelligence on our platform that can actually give you the human touch and the advice. And we believe that will be the case for decades to come, but the role of the AI, the human, will change. So quick poll for the audience, how many people have gone to the store for AI? I really need to buy some AI today. Zero hands. Andrew, to your point, if I could add to that, probably no one has gone to buy AI, but probably a lot of you want work done for yourselves, right? You want to have technology that do work that you would rather do, uh, have the machine do. What we've found is that with our agents on average customers are saving 12 hours per month with routine tasks. They would have had to perform. They're getting paid 5 days faster. They're 10% more likely to be paid in full. And as a person who's run small businesses in the past, I can tell you numbers like that are very meaningful. 12 more hours means 12 more hours that I can spend building my products, understanding my customers. And we have so many more statistics like this that show that the AI and the human intelligence combined together actually help people do better in their financial life. So this is really exciting. Can I just take 30 seconds and, uh, add to what Ashok just said, make, make it sort of real in terms of what a business experience is. So as I said. Customers don't care about AI. Uh, but and what we have launched is, uh, is a virtual team of AI agents that can hand off things seamlessly to a human that will do things like help our customers manage their leads, uh, help invoice and create estimates on their behalf. They're always in control, uh, to follow up with customers that have overdue invoices, write everything for them, tell them which customers, um, are overdue and which ones typically pay on time and which ones they could actually get paid earlier. So we do all of the work based on leveraging the customer data. And AI capabilities to do the work for the customers. So the stats you're hearing from Ashok, um, we have an AI, um, accounting agent that will do a lot of the categorization. That's the 12 hours a month they're saving. Our payments AI agent, customer doesn't know what it is. It's just it helps them with estimates, invoicing, getting paid, and that's the tangible stuff that where we believe we can. Change the world to do the things for customers that has a real economic impact around the world. Totally. I just want to call attention to the fact that Ashok has the letters A and I stitched into the right sleeve of his shirt in case anyone doubted him. Well, he has one stitch on his head, but we put makeup on. You can't see. All right, all right. I was gonna say I wanna see the tattoos later, gentlemen. Um, all right, so, uh, uh, I wanna ask one more question and then, and then we should go to the audience for them, um. Look, you guys have so many customers. How many? 100 million plus. So you have, uh, an, an unbelievable view into the economy of, of a certain segment. I know you serve all segments, but you especially, uh, serve small and medium businesses in terms of quantity. I would like to know. In your opinion, how they're doing in this economy. This is a very odd economy. Um, and you know, many of them fear AI or don't care about it or whatever. So what are you seeing across the board? Yeah, it's a great question. Uh, just brief context, out of the 100 million, about 10 million are businesses of all sizes, and, uh, and the businesses that we serve aren't concentrated in any particular industry, so we will serve plumbers, landscapers, construction, real estate, wealth management. I could go on and on, um, uh, the two big things that I would say is. Listen, the job market is still strong. Uh, credit scores, um, are lower than they've ever been, um, particularly with Gen Z. Uh, Credit balances are at the highest that they've been, uh, particularly with Gen Z. Their credit balances, um, credit card balances are up 36, 37%, but they still have jobs, uh, and that's what's really keeping things together. And so therefore everybody is being watchful about what they buy, what they don't buy, at what prices. Um, for businesses what we see across our base is the word I would use is stability, um, profits, cash flows are, are stable. Um, one element of it is they're on our platform, so it's biased data, um, digitization is helping them, but what I would say is industries like manufacturing, um, insurance are actually up. Uh, industries like lending, real estate are actually down in profit, so overall it's stable, but depending on country, states, and industry, very, very sector driven. Thank you for that. All right, let's go to the audience for questions, questions for the Intuit fellows. Yes, we've got one way in the back. Your name and who you're with, sir. Thank you. Hi. Hi, uh, Samir Gulati from Zen Business. Um, one of the hardest things about small businesses is actually getting started and the first couple of years, right? A lot of the things that he talked about don't even apply in many cases. Given your position in the industry, how do you think about using AI or the combination of AI and HI to enable that and make it easier? Yeah, it's a great question. Uh, you know, first of all, I'm inspired by what many are doing to help businesses get up and running much faster than they could before, but a couple of tangible examples that I would use, one is, uh, with our platform, uh, once you give us a few pieces of information that we ask for, like what, what are your goals, what are you trying to achieve, uh, and you know, for instance, uh, depending on where you are in your cycle, if you're 30 days in, if you're 60 days in. Uh, we will, in essence, ingest your data if it's website data, if it's data that you put together a business plan on an Excel spreadsheet, whatever it may be, if it's handwritten notes where you can take a picture of, we'll ingest, because first we need to understand what are you trying to do. We'll ingest that and then based on that make recommendations, uh, like if you're make it real, if it's a, if it's a plumber. Then we'll help a plumber with what are the basic things that they need, and then a plumber, they're on the road, they're trying to get business and so being able to estimate and create an invoice with progress payments to get paid right away is the capabilities we enable them with. So really what we can do now that frankly we couldn't. Do before is based on who you are, what you're trying to do, and the millions of customers that we have on our platform that are like you, we can help you with what to get set up with right away because you don't need all the capabilities that we have. Uh, what you need is what's specific to your situation. And then based on leveraging your data because we, we use machine learning, knowledge graphs, a lot of uh data, and of course G AI to help automate tasks, workflows and functions, but we can get you up and started right away and that also teaches us, by the way, what else you may need and that we um. are in the midst of making something generally available where you can do vibe coding and just say here's what I need done and we'll go do it for you. And so a lot of that just helps reduce the time to get started and to get your first customer and to get your first sort of cash flow in your bank account. And once we do that, then you're on a roll and that's really our focus in terms of the benefit for customers to get started. Thank you for your question. All right, gentlemen, I have two quick lightning round questions before we, before we wrap it up here. The first is, um, you've recently signed an open AI deal. Um, are you concerned about controlling your customer relationship given that? Uh, we're very excited about the deal. There are 3 principles that, that, um, are really important. Uh, one is the customer experience and the relationship. When the customer, when we're engaging the customer, they know you're, they're using the Intuit platform. Uh, the second is the customer's data does not leave our four walls. So it's our data, our models that are being trained. So the data and models and the way the platform, uh, behaves matters and just the economics have to matter and the economics are the same for us before and after the deal, so. And the thing that's very exciting with OpenAI is it's, it's the values and relationship we have with them, which is we both want to drive prosperity for the end customer, and I think it's gonna be a game changer in the long term. All right, and then my last one is I'm asking on behalf of everyone here and everyone watching on the live stream, when will AI be able to do my taxes, gentlemen? Well, I don't do AI and HI does your taxes today. We, we have, uh, you know, just to make it real, right, we have, um, millions of customers where AI does a lot of the drudgery for them and then hands it off to a human and the human finishes it for them. So we'll do your taxes today for you. Wonderful. There we go, gentlemen. Thank you very much. A round of applause for them.

The enterprise AI boom is moving from proof of concept to proof of profit. The chief of Google’s fast-growing cloud computing business explains how Fortune 500 companies, startups and public sector organizationsare building foundational AI platforms that blend intelligent agents, multi-cloud ecosystems, and next-generation infrastructure. He shares what’sworking, where adoption is accelerating, and how leaders are making the leap from experimentation to measurable business transformation.

Leading the Enterprise AI Revolution

Thomas, thank you for joining us. Thank you for having me. Um, we're gonna talk about agents, but not right away. Um, this is, uh, uh, a delight for me because, um, almost a decade ago in 2016, I was able to sit down just like this with your predecessor Diane Green and talk about Google joining the cloud computing business. And the whole vibe of that conversation was we are trying to transmit to the market, communication. To the market that we are serious about business now. This is Google, right? This is the consumer company, the company that we all know that it was a formidable thing and you know, as an Oracle, a longtime Oracle veteran that you know enterprise companies are built a bit different than consumer companies. So Today, Google Cloud is, by my understanding, you'll tell me, the fastest growing major cloud service, but it's off of a slightly lower base than your competitors, 13% of market share, uh, versus Microsoft's 20% and Amazon's 30%. Does that speak your truth? Yeah, OK, um. Why today are you winning more business compared to these other companies? Both because we have great products, people are finding them very easy to use, and they're getting real value from it. And we offer a very diversified product portfolio. Um, you know, our focus on cloud has always been, take amazing products, make them super easy for people to use, take care of the complexity that companies want us to deal with. Things like security, compliance, ah, but differentiate from everybody else across the strength of our portfolio. And so that's been why. And we've been, you know, we've had amazing growth rate. Um, Over the last 16 or 18 quarters, I think we've been the fastest growing. So if, if Amazon is the, you know, the the choice of, of the little guy, and that's how they get their volume, and Microsoft's supposed to be the choice of business, what is Google Cloud? We have a very diversified base. So we've done really well with startups, uh, particularly as startups shift to doing more AI stuff. We've taken a lot of share and. We're now the number one choice of AI startups by all independent reviews. Ah, we've done really well in traditional enterprises, banks, insurance companies, telecommunications companies, hospitals, ah, pharma companies. Um, and we've done well in not just The US, but in global markets, our revenue is highly diversified globally, which also people are surprised by how much strength we have in overseas markets, and that's reflective of the we've got a very, very focused plan on how we build and take our solutions to market. We've We also got great partnerships with many independent software vendors. We've got huge practices. I think there's 700,000 consultants who implement Google Cloud globally, which is a huge number. And so we've all that's come because of the demand we see from customers. Sounds like an enterprise business. One of the other things that struck me about what you're doing, and this is very on trend last couple of years, is allowing the use of any model, right? So when you choose to, when you, when you go with Google Cloud, you don't just have to use Gemini, you really have a, a wealth of options. That is that right? Yes. OK, so how in that circumstance, which is great, how in that circumstance do you competitively differentiate, right? Because you're not the only one who, who, who makes that, that offer. Yeah, so we, we offer. Our differentiation when you look at AI is to do AI really well, you need a stack of technology. And a stack is energy, chips or systems, infrastructure, models, tools and applications. And we're the only ones who offer all of it. We also offer a choice at any layer. So for example, we have our own amazing silicon called TPU as some people call it. Ah, but we also work very closely with Nvidia because depending on the kind of model you're running, you want that choice. The same thing, if you look at how people use our platform, ah there are customers who use a combination of models. Uh, Intuit, for example, the customer, they use some of our models, Gemini and our diffusion models, Imagine and VO, but they also use other models from other people for tasks that they want to use. I would not say that because you offer a stack, it's closed. I think many other companies don't have parts of the stack, so they depend on others for silica and other people for models. We have our own, but it doesn't mean it's closed, and so you get the best of both worlds. You said closed, reminding me of the open versus closed debate, which is usually in the context of the models, but you're, you're speaking more broadly than that. Is that debate dead? We've had it here before on this stage. Is the open versus closed dead? You're a tech guy. In enterprise, you have to have, for example, if you look at our models, they can access information from any application, database, collaboration tool, because in enterprise, you have to have it work with everybody. And so we've always had an open philosophy, uh, back in 2019 when we said, Companies will want to have cloud technology from multiple providers. The incumbents said no, they would not want it. Today, 95% of large companies use cloud from many, many different organizations, and so that gives them choice. And we've always felt for enterprises, they want choice, and choice means open. So I'm gonna ask you a, a big question and we're gonna have to unpack it a little bit, so stick with me, um. When you got started, we were calling this cloud computing. AI was not quite on the tip of our tongue the way it is today, um. We were calling it machine learning and deep learning I think back then, um, solely um. How have you had to rethink this service given these massive waves that have happened since for, and this is the unpacking part, stick with me, Thomas. The AILLM arms race, the AI demand boom from your own customers who suddenly all want to be AI companies or use it, and then the actual capabilities of AI itself and how that's changed what you do. So I'll, I'll keep you on track, but Let's start with the LLM race. How has that changed what you offer and how you do what you do? So we, we've been working on AI a long time before LLMs came along. That's right. And we've always designed for the long term. So if you look foundationally, when you look at what we were trying to solve for cloud computing, it was take technology and make it accessible to everybody by making it super easy for people to use it. And with AI we've always had the same philosophy. So, at the foundational level, we said people want amazing systems to train models, but we at the same time, we also realized you can't just train forever, you actually need to do inferencing well in order to do, to make money as a company. So we built systems for both training and inference. We also knew that The the most problematic thing that was going to happen was going to be energy, because energy and data centers were going to become a bottleneck alongside chips. So we designed our machines to be super efficient in delivering the maximum number of flops per unit of energy. So that's one example. When we built flops per unit of energy, yes, it sounds like a soccer metric, but I, I digress. So, the second thing we felt was, when you look at LLMs, you can't just hand over an LLM, you need a tool set around it, uh, in order to make the LLM really work for organizations. So that toolset needed to secure the LLM, it needed to access information, it needed to help you tune it. Uh, it helped needs to remember what you're asking it and handle long-running things. So we built an entire tool chain around it, and we gave it to developers. And to give you a sense, when we launched Gemini 2.5, uh, we went from 0 to 1 trillion tokens in like 7 hours. Because it's so easy now to get it and scale it. Uh, so that's the 2nd thing. Third thing, we always felt that along with those who want to roll their own as developers, there's always going to be parts of the organization that don't have the capability to roll their own. For example, a legal department that wants to use our models to understand uh contracts and which contracts have indemnification and some warranties. Uh, marketing people who want to create advertising and really tailor the advertising to the segments, and they want to create the advertising content using a model. Uh, for all of them, we built a portfolio of applications. Uh, we took Gemini, which is our app. And made it available for companies by integrating it with all the enterprise systems they have and we built some domain specific ones that have gotten great, great traction. For example, we built one to help people find products online, uh, because today when you search, I have never found any product online. Never. No, I'm kidding. I'm kidding because you're not used to it yet. I'm kidding. The internet is an addictive thing. Well, look, if I may distill, um. A lot of iterative building for to to to get what your your business ready for these successive waves. I wanna, I wanna go back to something you said earlier about power so. We are all well aware of the finite amount of power that we can generate on this planet. I'll go so far as to just let leave country, you know, international boundaries behind. Um, are you concerned about that? Yes, you've been building for efficiency, but the adoption curves have been. Massive and fast. I mean, we've never seen anything pick up like this before. Are you concerned about your ability to power your business, your ability to keep, you know, pace with the, the, the, the amount of demand that you and your competitors are seeing? Take us into your, uh, uh, your brain as far as the, uh, power side of thing is concerned. We are working on a variety of new technologies that would you like to share any here? Well, I. Tell you that there are 3 parts to what we're working on. One is to be as diversified as possible in the kinds of energy you can use to power computation for AI. Uh, it's, most people say, can you use any form of energy to power AI. That's actually not true, because if you're running a cluster for training and you bring it up and you start running a training job. Uh, the spike that you have with that computation draws so much energy that you can't handle that from, uh, some forms of energy production. So one is diversifying those. Second, being super efficient in how we reuse energy within the data center itself, so that you're not just bringing energy once in the data center, but the thermodynamic exchange can be super efficiently done and used. And third, some new fundamental technologies to actually create energy in new forms, and we're working on all three of them. Since you often ask, where are we applying AI, we're using applying AI, the control systems that monitor the thermodynamics in our data centers are all governed by our AI platform. The dog fooding, as we like to call it, right? Um, let's talk about the, the TPUs, the custom silicon that that Google is working on. Again, another trend, all the big tech providers, the hyper scales are very into the custom silicon thing. What role does that play for your business forward? You're already using it. You have been for a while. Um, are you trying to, you know, eliminate all of the chips from other vendors or is it in certain circumstances, where does the, the TPU roadmap go vis a vis your business? We've, I mean, we've worked on TPUs since 2014. That's right. So a long time before AI was fashionable, we were working on, on the silicate. And we kept investing in it because we fundamentally believed that you can change the design of an accelerator, which is what a TPU system is in fundamental ways. Uh, we offered that to our customers through our cloud. Ah, many companies are using it. Many of the top labs use it, many of the unicorns use it. And we are now broadening it to support new forms of computation. So for example, capital markets firms are switching from numerical computing to inference, and they're using it. There's people in high-performance computing, there's people in molecular simulation, energy simulation. So there's a variety of different domains. And at the same time we worked very closely with a number of companies on adopting and making our technology work amazingly well on GPUs, most importantly Nvidia. We've optimized Gemini and our models on it, and so that's been a long term partnership as well. Um, I wanna ask you one more question, but I, I, I do want to come to the audience as well. So, uh, just give me one more and then I'll come to you, um. I was going to ask you about that very, that very company in fact. So, uh, Jensen, Jensen Wong, the CEO of Nvidia, um. If he were sitting here right now, would he say that he was Excited and thrilled about the TPU roadmap that Google has, or is he concerned that it's going to harm his business? You should ask him. Oh, wow. No, but truly, I mean the, the big push for custom silicon. The big push for custom silicon threatens the business of, of these, of, of the, of the other chip makers. Obviously you need a chip maker to make the TPUs, a contract chip maker. Jensen is a great partner of ours. We've done work for a very long time. You know, I'll, I'll give you an example of the kind of work we've done. For a long time, if you wanted a, uh, a model that was uh an LLM that had a closed weights and didn't share the weights. But you had information in your data center that you wanted to hook up to it, and you wanted to bring the model into your data center, you could not do it. He asked, he gave us the idea and we worked for 3 or 4 years now to really optimize Gemini. And you know, earlier this year, we announced that you can take Gemini and run it on an Nvidia GP cluster anywhere. And we have to do it while protecting our model weights and our intellectual property, and that's an example of a joint project. We work with them on compilers, workload management, there's many, many things. Because I think there's there's way too much in the press on. It's a zero-sum game, when in fact, for those of us who have been working on AI infrastructure, there's many different kinds of chips and systems that are optimized for many different kinds of models. And as the market grows, we're creating opportunity for everybody. Yeah, yeah, understood. All right, let's go to the audience for questions. Forgive me, it is rather bright. Yes, right up here in the front, wait for a mic, and then your name and who you're with. Thank you. Hello. There we go. Hi, I'm Ash from Adeline. Um, Google works with a lot of customers through solutions engineers and getting them to implement LLMs within their organizations, customers, enterprises. And some of these customers, I'm sure they go through the journey of trying to build something but don't come to the other side, meaning it doesn't work for some reason or last minute it gets pulled back. What are the reasons from your perspective that enterprises today still struggle to push something out, either as an internal tool or to their customers, and what will we need to do as a market to kind of get them over the hump? Thank you for that briefly, Thomas. Typically, it's one of 3 things. They didn't design the architecture right. Second, they didn't clean up the data. Third, they didn't test the model and think about things like security, and if the model gets compromised, ah, how to handle it. Uh, and lastly, we've seen often, they didn't, because they didn't think of the problem the right way, they didn't think about how to measure the return on investment on it. There you go. Check yourself before you wreck yourself. One more, please. Your name and who you're with. Hi, Julie Morris, um, thought leadership for cybersecurity, etc. Um, thank you so much for this. I'm curious, so Google Cloud acquired or Google acquired Wiz, um, biggest acquisition, um, but there's still this big gap around model security and Uh, I'm curious, is Google Cloud going to try to step into any of that to do more? We, I think we are working on two different dimensions on, uh, with AI. One is how to protect against threats that are created using AI models, and second, how to protect AI models themselves. Uh, because a lot of company-specific data is now inside these models, and people are always worried about somebody hijacking them and taking access to the data. We have a lot of deep science work going on at Google on both dimensions, and you'll see us present more and more of it as we go forward. There you go. Thank you very much, Thomas. Thank you so much for coming back to Brainstorm. A round of applause for him. Thank you. Thank you so very much.

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