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Andrew Nusca

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Revolut doubles its annual profit as the fintech’s global customers cross 52 million

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Ben Stupples

Mark Bergen

Revolut billionaire Nik Storonsky embraces AI by launching ‘truly systematic’ $200 million VC firm

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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

Conferences

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.

The rush to get in on the ground floor of a revolution has led AI and SaaS startups to list monumental revenue and valuation numbers. How real are they, really? What here is actually part of the AI bubble? What are the red flags VCs are looking for? What metrics can we trust when we're looking at AI startups? And when the AI bubble bursts, what will be washed away, and what will survive?

Don't Burst my Bubble

Hello, hello. Can you all hear me now? Hi. Thank you. Hi Cathy. Hi Steve and hello to all of you. Um, I have a question for everybody. Show of hands, who believes there's an AI bubble? Come on, don't be shy. Wow, fewer than I was gonna say fewer than I expected. You're surprised, Cathy. Why? Well, I think the headlines are everywhere. We're in an AI bubble. I actually tend to agree with some of the people who kept their hands down. I think. It's hard to say that AI is in a bubble across the board. Certainly for some companies and some um pockets of AI valuations have far outstripped any sort of fundamental, but we have to remember. We don't know what the demand for these AI tools look like. There's two big differences. The growth curves that we're seeing now far outstrip the growth curves of companies we've ever seen before, and I'd love to hear Steve's perspective on this. But number 2 is the t, the total addressable market. I don't think we have a good sense around how big some of these companies can ultimately become. And in hindsight it's so easy to say, oh, you know, XYZ paid so much for that company, but then when you're right, when when they're right, people tend to forget about that, that it was spicy at the time. Steve, were you surprised? Uh, no, I mean, I, I actually agree. I think it is a bubble, but it bubbles are good for innovation. Um, I think bubbles popping are bad, right? And so bubbles bursting and all of that. So, um, you need a bubble, you need a bubble and, uh, technology and startups and venture capital to not only attract the world's best talent to work on a certain set of problems, but you also need the capital to fund them. And you don't have that that that focused concentration of talent and capital without what we would call a bubble. So bubble is a finance term that finance people use, but what we would say it's a new technology wave, and every 567 years there's a new technology wave that captures everyone's attention, and you'll see it. All the big tech companies lose people to start that they're starting companies and as an early stage investor we love that. We love to see the top talent. That's a good signal, right? Uh, that's not a counter signal. That's a good signal. That's a signal that says this is a real technology wave when the best and brightest PhDs, engineers, founders leave cushy jobs at Google and Meta and Uber, and they want to go start companies and and so you know uh uh the AI wave or the AI bubble depending upon where you come from in tech or finance is a good thing, you know. I think bubbles bursting are obviously bad because that means that you know the the the the blast radius on that includes everyone exactly the thing that I'm wondering about, but we actually, so by the way, first if we keep markets our perceptions, right, markets are a set of perceptions that are dynamic. So as long as we keep talking about a bubble and fortune. Other magazines and publications keep talking about the bubble. It's good. Markets will release pressure and take air out of that bubble. They'll take money off the table, and this ebb and flow is actually a healthy thing. That's what we want to see, right? Just like Bill Gurley doesn't like IPOs that pop, we don't like bubbles that pop, right? And so, so the idea here is that. Well I think what you're trying to get at is whether or not there is foundationally core economic value being created for all of this capital and all of this talent movement, and what I would say today as an early stage investor is that absolutely there is a ton of commercialization now happening with AI. So where, so where do you get that? Um, you see that in enterprise, you're starting to see it in consumer, you're starting to see it, um, already, I would say actually most, most pronounced way would be in, uh, developer tools. So look at codegen, look at how people create apps, everything from. Uh, uh, text-based code generators, uh, looking at generative media, looking people creating advertising and videos through generative video models and generative image models, uh, look at, um, we have a company Play AI that got acquired by Meta superintelligence. Their their speech models are already inside of Instagram being used by Meta. And so this is, you know, they acquired them 6 months ago. So that's a real case with an incumbent. Cathy, where are you seeing value definitely be created beyond a shadow of a doubt. The key things that I look for across any type of company, not just AI company, is number one. Is this a feature or is this actually a workflow? Let's get real. AI is no longer a differentiator. Actually I think we're entering into the era where it's no longer AI for X, AI for sales, AI for customer support. In the future it's just going to be a customer support workflow tool and every company will be powered by AI. The key difference is companies that are able to actually embed. And fully overtake an existing workflow while having significant and building significant switching costs. The second thing I look for is really is distribution built in, right? What does that mean? So does the tool Is a tool integrated in usability? Is it integrated in all the other solutions that it needs to be in order for the workflow to be fully functional? People don't want to learn yet another tool. They want to work where they're already working and have almost super powers to let them work faster. And the third thing that I look for is. Do these companies get stronger over time, right? People call this the compounding durability. So with every new user, does the solution get better? Does it get cheaper? Does it get faster? And when you have all three, that's something that I'm really looking for. And where I'm seeing that today, by the way, I like to answer your question directly, is at the application layer and particularly around vertical AI. I was going to say we get the application layer gets a lot of love. Who makes it in the end? Application layer companies, model companies, both? How does this all shake out? Well, you know, with any sort of new a true technology way that's very differentiated, the whole stack changes, right, not just 11 layer horizontal layer in it. And so right now you're seeing it all the way from chips all the way up through application layer, um, but we're still in the early phase where we're still building out we're designing and building out that entire new stack. So what that means is, is that we have chips now and we have more chip companies, right? So it's starting to flush out. Then we have neo clouds competing with hyper scales, which is really interesting and it creates, you know, you'd think that there would be massive amounts of price pressure right if there's that many new entrants and AI compute. But guess what, you know, the neo clouds, they have 60-70% gross margins. The hyper scalers have 50 to 70% gross margins depending upon which one. So look, if the margins are still high, that means that in aggregate they have pricing power on all of the application layer companies. And so I think what you're going to continue to see is that we're we're innovating and investing largely from the bottom up because it's a new wave and AI compute is so critical to this wave. I mean, it's a super wave, right? It's, I don't want to call it a tsunami because it has negative connotation, but it's a super wave because it really affects every industry, every global market. Um, the application layer as an early stage investor, we're looking probably about half at specialized frontier models and AI infra, specialized specifically, not generalist. We can talk about that. And the application layer we're also investing, but we know that anything in the application layer, it's art and science, and it's a lot of hard work and luck. So the risk is much higher, I think, on the application layer. If you get it right, it's incredible. Everyone will know your brand, everyone will be using your products, right, every industry, but there's a lot of risk there because the substrate. The underlying layers are still being worked out, so we're like in robotics, we're at sort of the GPT 3.5 phase of robotics where everyone's using these basic VLMs and VLAs and motion planning. And that's all going to get 10, 100 times better. So a whole bunch of robotics startups that we're building on top of these primitive models are going to have a lot of like, you know, heartbreak when the models improve and they built for something sort of in the past. And so, uh, and this happened to Application layer and consumer device startups two years ago in the LLM based era, right? And so I think that like if you look at what's happening right now, even at an early stage fund. We are looking at how do we invest in companies accelerating and modernizing the AI infrastack so that GPU is a service, GPU marketplaces, chips, frontier models that are specialized that perform a very deterministic high quality outcome, no hallucinations. You don't have to worry about your regulated. industry product getting in trouble with bad information output which makes a big healthcare fintech. Think about those industries where it makes a big difference, Cathy. I would love to chime in. You know, I, I'm spending most of my time on the application layer right now. I would agree with Steve that it's tricky, right? Things are changing so quickly, but I do think there's a pocket. Within the application layer where it's very, very interesting right now. I, I tend to think about it as a 2x2, right? Um, on one axis you have either PLG or kind of core enterprise. And the other axis is is it a feature or a workflow when you go to the quadrant that is enterprise it's a workflow that's where we see real stickiness happening, right, because data has a center of gravity. Think about selling into a large company, Fortune 500, being able to collect all their data, being able to understand their business processes and convert that into your product. That is something that is very, very difficult to rip out once you get there. And when you think about prior technological waves, people always say, oh yeah, the first wave of companies never make it, right? It's like the MySpace fallacy. But if you look at the enterprise, OK, what was the first cloud HR company? It was Workday. What was the first cloud CRM? It was Salesforce first mover advantage is actually real, yeah, and they're still around. The, um, we're going, we have to go to the audience for questions. We have mic runners. If you could give me your name and company. Any questions for Kathy and Steve? I have more. Can I, can I please go ahead. So, um, I would actually say, uh, I agree with Cathy on this that it's a mixed bag, right? There's first movers, second movers, right, um, that win. uh, I don't know if there's a rule or, um, on, on one or the other. I think it's, it's mixed, but I think when I look at enterprise, I look at that not as application layer only, but my framework on that is that. Whether it's enterprise for, you know, productivity software of some sort of business productivity. Some function or generative media, it's actually a vertical alignment, right? So the models that you use, you may post train your own models, you may use third party models, you may pre-train your own models, who knows, but there's a mix of that happening and then you have to build your own developer tooling and your infra in a particular way and then your application that faces the customer and the customer uses. Um, is also customized. So I look at things like enterprise, generative media, robotics as vertical columns within this, this technical stack. Now you have quite a spicy prediction since we're getting towards the last 90 seconds here. You have quite a spicy prediction about the future of robotics, Steve, don't you? Well, I think I said it earlier, which is, um, robotics is robotics is difficult. So what's the um Who, who here has ridden a Waymo or a cruise or anything like that? OK, so, so good. Majority of the crowd, good crowd. So that is the number one modern state of the art robot that everyone is using today, right? It's a Tesla FSD. It's a Waymo. It's a Zoos or a cruise cruise, rest in peace. Zukes is out there. Zeu is out there. Neuro is out there, uh, um, uh, and they'll be powering Uber's, uh, Uber's own branded self-drive. Fleet later next year. So we're all going to be using robots on a daily basis. Our kids are going to be riding in robots on a daily basis. That area is super exciting, but think about all of the startups building humanoids. Think about all the armed robotics for industrial. You're either faced with a huge enterprise sales cycle that's within industrial. I can't think of anything more painful for a startup than that. Maybe second to that is to try to prove that a humanoid won't fall down, it won't screw up, won't have bugs, and it's going to move around in your office or household or on the street, even. So there are long development cycles to get society ready, governments ready, your home ready, your family ready, your friends ready, so. Think about that, and that is a deep tech problem that is going to, you're going to see a lot of consumer robotics companies fall by the wayside, get sold and shut down because of one, what we've talked about before, the underlying models need to improve to help them, and then the deployment and adoption cycle is going to be really long. We're at time, Cathy, last word, one sentence prediction for 2026. AI it's gonna be even more difficult to go into the enterprise. We think that model is getting better, better accuracy. People want to use more AI, but I think for the reasons Steve mentioned, it's actually gonna be harder because people are gonna be more focused on trust and visibility, and we haven't really solved that problem yet. We'll leave it there. Thank you so much, Kathy Steve.

Rapidly changing technology is transforming corporate culture. What is the designer’s role in helping a company adopt change? How can designers look ahead to establish a direction and identify opportunities for growth and innovation? Hear from two experts, one a veteran educator, bilingual in both business and design, and the other, who revitalized a design program at IBM and revolutionized how almost 400,000 people worked.

Designing Corporate Cultural Change

GLP-1 drugs, once developed for diabetes, have rapidly become cultural and economic disruptors, transforming bodies, behaviors, and billion-dollar industries. In the U.S. alone, millions are already on these treatments, curbing appetites not just for calories, but for snacks, alcohol, and fast food. Snack sales are slumping, restaurant chains are rethinking menus, and some of the world’s largest food and beverage companies are under pressure to adapt. The health upsides—weight loss, reduced cardiovascular risk, and lower diabetes incidence—are striking. Yet the long-term risks, costs, and cultural ripple effects remain uncertain. As medicine rewires metabolism itself, are we ready for the economic, ethical, and societal aftershocks of an appetite-suppressed future?

The Appetite Revolution: How GLP-1 Drugs are Reshaping Food, Health - and the Future of Consumption

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