President & Chief Executive Officer (Board Member), Nutanix, Inc.
Rajiv Ramaswami, Nutanix ｜ RAISE Summit 2025
About Rajiv Ramaswami
Rajiv Ramaswami, CEO of Nutanix, delivered the opening keynote at a company event in Chicago on July 7, 2026, where he discussed the company's platform roadmap and the impact of agentic AI. During the keynote, he described hypothetical AI agents for personal tasks such as regulating metabolism, expediting airport security, and managing email. In a conversation with a customer from the casino industry, Ramaswami noted that the customer planned to open a new resort using Nutanix's hypervisor and management tools, and that the customer's CEO was driving AI adoption across guest-facing experiences, personalization, and operational efficiency. Ramaswami also criticized VMware and Broadcom, stating that Broadcom is "forcing you to buy the full stack" and "capturing the maximum amount of profits," and urged customers to consider whether that is where they want to invest. He added that Nutanix aims to help customers "reduce your dependence" on such vendors and "meet your current and future needs with a modern platform." On the company's Q3 2026 earnings call on May 26, 2026, Ramaswami said Nutanix sees demand driven by businesses modernizing IT, adopting hybrid cloud models, and deploying cloud-native applications including AI. He noted that AMD recently invested up to $250 million in Nutanix and that the companies are working on joint solutions with AMD GPUs. Regarding hardware supply, Ramaswami stated that lead times for appliance vendors range from a few weeks to six months depending on configuration and vendor, and that Nutanix expects hardware prices to remain elevated into fiscal year 2027. He also attributed a higher average contract duration in Q3 to a mix of larger, longer-duration transactions across land, expand, and renewals.
Transcript (42 segments)
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John Furry0:06
Welcome back everyone to the cube's live coverage here in Paris, France. I'm John Furry, your host of the cube. We're here for two days of wall-to-wall coverage of the Raise Summit 2025. We got a distinguished cube alumni, Rajiv Ramaswami, CEO of Nutanix. Rajiv, I feel like we just saw each other a few months ago at Nutanix Next in D.C., but we're in Paris, France. Welcome back to the cube.
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Rajiv Ramaswami0:28
Glad to be here. Good to see you. Great city. Paris.
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John Furry0:31
Great city. You just had a keynote interviewed by a leader at Nvidia on the main stage. Thanks for coming into the postgame review keynote. What was the topic?
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Rajiv Ramaswami0:43
Yeah, the topic was around enterprise AI. How can we make it easy for companies to go build all these generative applications, consume these models and run them? And what we talked about is how Nutanix combined with Nvidia is enabling that full stack, the infrastructure stack as well as all the AI stack on top of it to provide inference endpoints for companies to build these applications.
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John Furry1:04
Nvidia's got their hands in a lot of action. Obviously, they've been dominating on the supply chain. Dave and I were just reporting last week Intel's market share dropped from like 73% in x86. Now it's like 13, 15%. It's in the teens basically in one year which is incredible that that could happen. It just goes to show you that the GPU side of the market is booming. This event here in Paris is speaking to entrepreneurs, developers, builders, neoclouds, infrastructure leaders like you and Nutanix where you have that enablement on that supply chain enabling the AI infrastructure and then the next layers of the stack are tooling up and scaling up and then that's enabling a wave of applications and builders. You're on that layer of the infrastructure layer. So great partnership. What does it mean to customers? Because at the end of the day, is it do I have to do anything differently? I must just add it on top of Nutanix. I love Nutanix. What do I do with it? I want to go to Nutanix. So as customers make these generational decisions because we are talking about right now large enterprises, large cloud companies are making generational calls on the system that they're building. So it's kind of a one-way door. Yes, but they're doing their homework. What is your answer to that?
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Rajiv Ramaswami2:23
The vast majority of enterprises, customers are going to be consuming models and building inferencing applications. They are not in the business of creating these models or training these models. That's a handful of companies that have the capital and investment to do that. And we know who they are. But here we have this broad universe and that's really where value actually gets created. If you think about it, this is where real applications get built that can change the nature of how you run your business. And so the vast majority of them don't really have the wherewithal to build or the sophistication to really build all of this stuff. They want a turnkey solution.
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John Furry2:58
Yeah.
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Rajiv Ramaswami2:58
And so that's what we are doing at Nutanix, right? We always started out by making the infrastructure stack really simple.
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John Furry3:03
Yeah.
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Rajiv Ramaswami3:04
Right. Everything is hidden under the covers. We hide a lot of the underlying complexity, make it really simple for companies. We used to call it invisible infrastructure back in the day, but it really is about simplicity. And then now the new layer is an AI stack that sits on top of it to create these inference endpoints and that is where we partnered together with Nvidia and others to say okay now we have an infrastructure platform on top of which we have a Kubernetes platform and then we have the AI stack to really bring it all together into a turnkey solution where a company now who wants to build an AI application can simply say I got everything else covered, here's a shared infrastructure that I can use just like I used to run my regular applications, I can now program to an API that this provides and build my applications and run them.
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John Furry3:47
Yeah. And that's an innovation strategy that flips the IT blocking and tackling to a value creation extraction formula.
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Rajiv Ramaswami3:56
Yeah, indeed. And by the way, we've seen this over multiple generations. Whenever things start getting mature, right, it needs to be able to run it and operate it at scale to be able to deliver value to that business. And that's really what this is about, right? We are in the early stages of enterprise AI adoption and it's getting to a point where now again IT teams have to embrace this, figure out how to stand up this infrastructure and be able to deliver shared services to their internal clients that can then build these applications easily.
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John Furry4:11
And you know, you mentioned the rich. My words, you didn't say rich. The rich companies, the banks, and we know who they are. JP Morgan Chase got a 17 billion dollar IT budget. Not a lot of other people have that. So, let's call them the 1%ers. The enterprise, but the rest of the enterprise, they're doing cool things. So, they have it. They bought you over the years, many generations of servers and storage fabrics, storage systems, probably network attached storage. They've always been kind of foreclosed and left out of the mix. Yeah, they probably got some homegrown apps. They probably got some Salesforce here. They got some off-the-shelf applications. They've been kind of in this lull. This is our research pointing to where yeah, there's some examples. They got to do with some cloud native stuff, but not actually writing transformative apps.
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Rajiv Ramaswami5:11
I think that's what you're referring to, is there's a sweet spot of market that's untapped. Is that what you're kind of referring to?
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John Furry5:18
Well, I think first of all, we include the 1% as well. So, the big companies, the most sophisticated ones which have huge investments, big software developer teams, of course, they're going to be the ones that are going to be building thousands of these types of applications themselves. Now, the others can still actually very quickly make use of simple use cases, right? So, one of the most simple use cases is around customer support. And it's very easy on a relatively small infrastructure, a four node cluster to be able to build a customer support use case, a chatbot using standard open source models and put these things together. It doesn't require a lot of effort. So the simple use cases, document summarization, translation, chatbots, those I think can be easily put together and even content creation. You're going to have third party providers, for example, for software development. You got a bunch of companies out there, so there'll be prepackaged providers as well for AI, AI software that companies can use. Now what they need though is to be able to run these wherever their data is located, and the data can be anywhere. They need to run it securely. They need to make sure that their IP is protected. And so some of this is going to be run in the data center. Some of it is going to be run at the edges. Others will be run in the public cloud. And so they just want to be able to consume and build these apps and run them simply. So there's a huge sweet spot for broad scale adoption.
I didn't mean to pigeonhole Nutanix as going after the underserved. What I was trying to get at, and good call out on that. Yeah, you're a public company. You got to make your number. You're doing well. You got those companies already, but I think it's a growth strategy. Would you say that that's a growth opportunity for Nutanix as a business? So it's not necessarily you're not doing any pivots. You already have a great customer base.
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Rajiv Ramaswami6:59
Today, we have 28,000 customers. That includes thousands of the top 2,000 globals in the world. So we have the big customers, we have the small customers. We are across every vertical. And all these customers are on a journey. Right? They all have these bulk of their applications today as virtual machine applications. They are slowly but surely moving into a world where more of their modern applications are containerized and Kubernetes is a platform for doing that, which we provide. The next phase in this journey is almost all the applications they run are going to have some form of AI embedded, and that's the next 5 to 10 years, and that's just at the beginning. So for us, it's just about enabling all the applications that our customers want to run on our platform.
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John Furry7:36
Yeah. And I think that's the point to take away is that the gen AI apps sit on top of what you do. You make it easier for them. You mentioned Nvidia. I also know you have a really strategic relationship with AWS. I talked to Tark and Maynard about that the other day. That's going to stream live tomorrow on the cube. On the cloud, you got cloud, multicloud or distributed computing, hybrid cloud. You got all the customers, you got the gen AI wave. What's the NVIDIA piece? What are you working with them on? Is that enterprise go-to-market? Is that enterprise functionality? What's specifically around NVIDIA?
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Rajiv Ramaswami8:13
So, there's three components of our relationship with Nvidia. And we've had a long history with them dating back to days before AI where we were virtualizing GPUs for virtual desktops. But today the first component is Nvidia has an Nvidia AI Enterprise stack that also includes their microservices engines for inferencing. So we include all of that right in our platform in the AI stack that we can provide to the enterprise, and customers can put that together, have automated workflows to download models from the repository and do that. Second, Nvidia has a reference architecture for compute that's based on top of Kubernetes. We are part of that reference architecture. Third, they have a similar thing for storage. It's called AIDP, I believe, for data protection storage. So we are part of that as well. That includes all the storage elements, vector databases, etc. So we are a fully validated design partner of Nvidia and we take elements of their stack and use it. Customers can use those elements on top of our infrastructure to get a turnkey stack.
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John Furry9:14
So you're basically taking platform engineering concepts and infrastructure concepts that you guys have been doing well and bringing Nvidia into the table to bring in all the AI goodness that they're coming in with so that those enterprises can scale up.
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Rajiv Ramaswami9:28
Think about it as building on top of our Kubernetes stack. Right? We already have a Kubernetes stack now. All of these AI stack elements are being built on top of that. The different kinds of models that people can download now, the security and governance elements of that that we provide, the automation of the workflows. So that's the value that we add on top of this to be able to provide that turnkey kind of solution for enterprises.
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John Furry9:49
You know what's interesting about what you guys are doing. I mean, I think it's first of all great strategy. I have to give you my thumbs up on it. But because you have done the hard work, it's still not going to go away. It's just going to be abstracted away. But now gen AI is going to enable the agent layer which is going to create more services. So you got the data checked off. You got the Kubernetes which is cloud native, that's scale out and scale up with the gen AI. As you look at customers, what are they doing? Can you share some examples of where they're innovating and where they're extracting the value? I love the Nutanix AI stack because in the enterprise, that's been a real problem. Kubernetes has actually done great, it's kind of de facto become the orchestration layer for cloud native services. But the enterprise has kind of been holding, like there's a blockage. It's a blockage of PCs trying to get into production.
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Rajiv Ramaswami10:42
That's exactly right.
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John Furry10:43
This is a problem that's getting solved. Sounds like you're solving it.
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Rajiv Ramaswami10:46
It is. I mean, we've seen some simple use cases go into production. I will put them into three categories. The first category is simply around summarization of content, automated translation of languages, etc. That's a very simple category. That's something that simple LLMs can do really well. Second use case is around customer support in chatbots. Another simple use case. By the way, these can be done with very small clusters. It doesn't require massive investment in farms. Small clusters. The third use case is content creation, whether it be creating code, documents, video content, whatever it may be. These are three relatively simple use cases for enterprise AI. Now, in all these cases, the data may be on-prem, the data may be in secure locations. You need to provide governance, security, role-based access control, and protect the IP. So those are all critical things that you need to simplify. So these are three simple use cases today. Now this stuff is becoming more sophisticated. You can see a world where you're going to have much more sophisticated multi-agent workflows that get enabled on top of a platform like this. And that's coming. Not quite here yet, but it's coming.
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John Furry11:57
You know, I have to ask you because I love interviewing startups because they like to throw out the haymakers. I interviewed a startup earlier. I won't say their name. People could go look at the videotape, video disc, or stream. He said in three years everything that we do is going to be automated. Now, you live in the automation world. So I have to ask you the reality. I mean, he's mainly talking about tasks with agents, but it's been hard to automate end-to-end workflows to get it 100% accurate because you really don't want a failure. Forget the hallucination. That's more on the top of the stack. But like take me through your mindset on how you see automation accelerating or not accelerating. What's your view? You can comment on the three-year mark if you believe that or not, but like what he said, that's a startup. What's your view on automation?
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Rajiv Ramaswami12:47
Okay, first of all, it's a continuous process. It never ends. It never ends. So this notion that we're all going to be done in three years? No, it'll continuously be going on. I'll just give you an example. This is off the cuff. You know, as a software development company, whenever we develop new software, of course, we write test cases for it, right? The first time you do it, you have to write it yourself, but now we can potentially use AI to start generating those test cases, but after that, it's automated. But you're going to create new content all the time. Some of that stuff may be manual but over time more and more will get automated. So it's a never-ending thing on that front. But I'll say one more thing: it's not just about automating existing workflows, it's about how you think about reimagining the workflow itself. Because the workflows don't have to be the same. I'll give you an example, this is not original, I heard this from a McKinsey guy some time ago. Think about it: if you're interacting, for example, between three different people, maybe there's a designer who mocks it up, and then afterwards writes a set of requirements, and that process could take weeks to months. Now all of that you can actually specify what you want from a workstream perspective, from a flow perspective. All of that can be autogenerated and done very quickly. So you don't need these three people doing these things. You can actually do them all in a much simpler way.
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John Furry14:10
So that's just one example of how a workflow could be. Automation really is about efficiency of task.
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Rajiv Ramaswami14:15
Absolutely.
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John Furry14:16
So that's what you're getting. It's not so much more like robots.
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Rajiv Ramaswami14:18
You know how these tasks will change. You don't have to do them the same way that you're doing before.
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John Furry14:22
Rajiv, it's always great to have you on the cube and congratulations on a great keynote and your business success at Nutanix. You guys have got a lot of customers. I have to ask you a personal question. I've interviewed you many times. You've seen many waves of innovation.
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Rajiv Ramaswami14:38
Yes.
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John Furry14:38
You have a great pedigree, technical pedigree. You've written books on the topics that we've talked in the past. Given all your internal knowledge and instincts, scope the opportunity that's in front of us because we're seeing such a major shift. I mean, you could look at any dimension of it: the data side, the automation, how that's going to have to be delegated and deployed, the operating system side. There's so much tech and computer science now. There's so much business transformation, technology transformation, societal transformation. What is, how do you see the future? I guess from your personal perspective, because if we were in college again and said hey Rajiv, let's do a startup, what would we do? There's so much to go after.
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Rajiv Ramaswami15:24
I mean, that's what's exciting about tech. I've been in tech now for 35 plus years and it's never still. It's always evolving. There's always a new thing that comes in and these things take time to shape. There'll be ideas and then there'll be investments. There'll be some wrong starts. There'll be some losers and then eventually winners and then once it's established then it starts taking root over a period of time. I've seen this with the internet a long time ago, 20 plus years ago, and there was a big boom and there was a bust and then there was a steady uptick and now it's such that we don't even think twice about it. It's everywhere and we don't even think twice about it. It's the same kind of thing with AI. I think we are in that same phase now where it's huge potential, long-term to change how we think about things in every way. But we're still, we're past the early stages now and it's evolving at a very rapid clip. You know, we talked about LLMs and inferencing and now we're talking about agents and this thing changes every year. And that's exciting for a tech guy like me to keep up with it, learn about it, and then figure out how to eventually make this stuff be available in production. Right? As an industry, we have to figure out it's not enough to think about cool ideas. It's about how do we actually enable it and enable people to become users of this stuff.
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John Furry16:37
Yeah. What jumps out at you technically? I know you've done a lot of tech. You're a deep techie at heart. Are you coding still? Are you vibe coding?
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Rajiv Ramaswami16:44
It's been a long time. I don't think I should ever code at this point. And in fact, frankly, most people are not going to do actual coding, right? They're going to figure out how to use these models and do...
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John Furry16:54
I actually vibe coded two weekends ago. Didn't write a single line of code. And it was actually fun.
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Rajiv Ramaswami16:59
Yeah, it was entertaining and the app was built. Something I really wanted to build but actually did to the low level. That I do. I play around with chatbots or other tools all the time and tell it what I want to do and it does it.
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John Furry17:10
As a techie, what's jumping out at you? I know you have a deep tech background. Are there any things that, it doesn't have to be Nutanix related, is there anything that's popping up that really gets your attention that if I had free time I would dive down that rabbit hole? Is there anything you see that gets you personally energized?
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Rajiv Ramaswami17:30
I would just say I think the potential to see broad-scale adoption. I mean, it's not one piece of technology. To me, it's actually about how do all these different pieces of technologies come together to create something that people can use. Because you see a lot of concepts being thrown around, but how do people actually get to use them and simplify them? It's more about that for me at this point. And that's really what Nutanix's philosophy is about: trying to make these things simple, hide the underlying complexity, and make it useful for people.
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John Furry17:58
I can tell you're an operating systems guy. That's what an operating systems thinker, systems thinker thinks about. Rajiv, great to have you on the cube. Thanks for closing out our day one. Rajiv here, the CEO of Nutanix, cube friend, alumni, and deep tech leader. Closing out day one of the cube here. Our stream is wrapped up. We'll see you tomorrow on thecube.net. I'm John Furry, your host of the cube. Thanks for watching.