All right. Welcome, everybody. Delighted to have Nutanix here with us today. Before that, I'm going to read a quick disclosure. For important disclosures, please see the Morgan Stanley Research Disclosure website at morganstanley.com/researchdisclosures. If you have any questions, please reach out to your Morgan Stanley sales representative
For those who don't know me by now, at the end of this week, I'm Meta Marshall. We're delighted again to have Nutanix here with us today, Rajiv Ramaswami, CEO, and Rukmini Sivaraman, CFO. Rajiv, Rukmini, thanks so much for being here with us today. Nutanix often gets pitched as a VMware beneficiary or kind of beneficiary story, but there's a much stronger kind of hybrid IT story beyond that. Can you just explain that value proposition and how it's grown as enterprises have kind of cemented themselves to be hybrid?
Thank you, Meta, and thank you for having us here. So if you look at what we've been doing for the last many years, we started out with the HCI hyper-converged value proposition, which is really about consolidating compute and storage infrastructures together on commodity hardware with a software layer to make it much simpler for companies to use and also give them significant TCO benefits.
That still very much applies, but that was, I would call that, the first innings of HCI. From there, the platform evolved to being what we call a hybrid multi-cloud platform, where now companies can use this platform to run their applications either on-prem or on the edge, but also in the public cloud of their choice, including AWS and Azure and, over time, other cloud providers.
So that was, I would say, the second innings, and we're well into that innings at this point. But then, beyond that, we are already in our third and fourth innings with the platform, which is now we also have a complete cloud-native platform that can run modern applications with Kubernetes, a nd it's a full-stack Kubernetes offering, and everything, again, can be run either on-premises or in the public cloud.
So now companies can use us to run both their existing virtual machine-based applications, but also all the new applications that they're building, which are largely on Kubernetes, a nd they can mix and match, and they have the complete flexibility to do that on their platform, a nd then the final piece of this is that we are also a great platform for AI inferencing and fine-tuning.
So we have done a lot of work on the platform to add what we call Nutanix Enterprise AI, which provides a layer that makes an easy out-of-the-box experience for companies to build generative AI applications using the models of their choice and run them on an Open AI-compatible API that's provided by our platform. Today, this platform can consolidate and modernize your infrastructure. It can help you run modern applications both on-prem and in the public cloud, and it can help you run your AI applications.
That's the evolution, and therefore the market opportunity and where we play. Now companies, our customers, look at us as a platform that they can grow with, evolve with. Of course, yes, we are the easiest option to replace VMware, and there's a lot of interest in that topic, and we do expect that to be a multi-year journey for us in terms of contributing to our business. But beyond that, it's also a platform for the future, and our goal is to become this de facto platform for apps and managing data.
Great. You laid out there that this kind of combined TAM is about $76 billion. Outside of core HCI, just which one of the kind of multiple product lines do you expect to kind of be the next biggest contributor to?
Yeah, and I think if you look at the platform, there is a core HCI platform, as you said, and beyond that, I mean, the simplest attach, if you look at it, is if you think about the platform, there is building the cloud, which is really our core platform, right? It allows you to build a cloud infrastructure. Then the easiest attach for us is what we call Nutanix Cloud Management, and that is everything that it takes to operate the cloud.
That includes things like operations, management, cost, governance, all of those, and that's an add-on, right? And we call that one of our portfolio products, and that's the easiest attach to the core platform, right? Then we have Unified Storage, which is really the ability to store blocks, files, objects. So that's an easy attach as well to our platform, a nd then we have the newer elements of the portfolio. We've got a PaaS solution for running and managing databases, a nd in fact, we've got very big customers like Wells Fargo that are using that solution to manage all their mission-critical databases, right?
So that's a layer up in the stack, and that's an add-on portfolio product as well, a nd then beyond that, we talked about our cloud-native and AI solutions. That are still early days for us, right? That's relatively new for us, and those are still, at this point in time, small contributors, but with a lot of room for growth.
Okay, perfect. Most of the time, investors tend to think that applications moving to the cloud is a one-way street. You've talked about seeing some repatriation of applications, particularly just given kind of cloud optimization initiatives over the past couple of years. Can you outline why customers have done this and just why Nutanix is kind of so important to be able to kind of have that flexibility?
Yeah, I think repatriation is one side of it, and the other side of it is just what do you put in the cloud, so I think that the nature of that has changed quite a bit over the last few years. It used to be much more, many companies say, "I can put everything in the public cloud," and there was this journey that was truly one-way, kind of as you pointed out, Meta, but if you look at it today, it's much more nuanced. People are not saying, "I'm going to go put everything in the public cloud anymore." Public cloud is still a great vehicle, and we embrace it, but it's great for net new applications. It's not great for existing applications.
It's great when you want to get an easy on button to get something going, but it's much more expensive when you're running something at scale, so people are realizing that if they have steady-state workloads that consume a lot and are running at scale, they can actually run it much more cost-effectively in their own environment, and then there's concerns around data locality, data sovereignty, and where you want to put your data, privacy, all that, and then with the latest trends in AI, for example, where is the data being generated?
You want to do your AI inferencing where the data is being generated, not necessarily have to generate, for example, data from the edge and then ship it all the way to the cloud to go get inferencing, right? All those are now driving this notion of what we call hybrid, which is people are looking at it and saying, "Well, there's going to be a bunch of applications that it makes more sense for me to run on-prem or at the edge rather than be in the public cloud." Not everything will go to the public cloud anymore.
Now on your question on repatriation, we see that anecdotally we've seen some examples of that, and again, driven by the same factors, right? Which is I can run these things cheaper on-prem. I've realized it's easy for me to go start in the public cloud, but then once I get going, it's cheaper for me to go run it on-prem. We, as a platform, provide that flexibility to move back and forth and run and scale wherever you'd like to.
Yeah, okay, perfect. Stepping to the opportunity with VMware customers, you've obviously been competing against VMware for years. Where did you have success previously, and then just how do you see that kind of opportunity developing over time?
Yeah, again, that's been quite an evolution over the last few years. It used to be back in the day, the way customers would adopt us is they would adopt the storage stack. I mean, if you look at our platform, we started out by actually creating this hyper-converged, and that was a storage stack, a nd then later on in time, we added our own hypervisor, which is called AHV. And initially, the way we were deployed was most customers were very happy with the VMware hypervisor.
They would use their hypervisor, but they would deploy our storage stack instead of buying three-tier storage, a nd in fact, that was the bulk of our deployment as we got to market initially, and that kept us going for a while. But that's changed. We introduced our own hypervisor. Now it's 11 years ago, a nd since then, we've seen a steady uptick in terms of customers adopting our hypervisor because our philosophy is a hypervisor is a commodity. Ours is open-source based, a nd the days where, I mean, yes, VMware essentially invented it, and it was a great concept, and they still have a great hypervisor, but it's really largely open-source based commodity.
So we included it with our platform, and so more and more customers started using our hypervisor along with our storage. But that's now turned into huge momentum. If you look at the last four quarters of what we've sold, I mean, 78% of what we sold was on our own hypervisor, a nd if you look at this last quarter where we added like about 700 new customers, I think the vast majority, probably 90% of them, started their journey with Nutanix using our hypervisor.
That, I think, is a function of two things. One is our hypervisor. It's now 10 years plus. It's very mature. It's got all the ecosystem pieces. It's got all the capabilities that people care about. That's number one. And number two, there's more of a desire to exit VMware than there was before.
Okay. Is there a type of customer that it makes the most sense for them to transition, or just how is your view of kind of what makes that ideal kind of customer that you can take away from VMware change?
Yeah, and we see that, by the way, across the entire stack, all the way from, say, Fortune 10 at the very top to much smaller, right? And I think what gauges this is, I think, very simply a few items. When is their VMware renewal coming up? When is their hardware refresh coming up? And what is their mindset?
Do they have a lot of inertia around saying, "Well, I'll just stick with what I've got," or is there a proclivity to say, "Well, I need to go look at a longer-term partner who can actually, I can trust. I can trust to innovate. I can trust to take care of me." And that determines whether the customer is really going to do a migration or not, or I mean, I think either do a full-scale migration or bring us in as a second vendor in their environments.
But it can be both large customers and small customers, and it runs across the entire spectrum. What we see is in the larger customers, we typically land with a small portion of their estate and then grow over time. The smaller the customer, the more wholesale the selection is.
Okay. You talked about seeing more success kind of in these transitions and fiscal Q2 earnings last week. Just how much of that is we're now a year and a half into this acquisition, people's contracts are coming up versus just better targeting, better channel incentives? Just how do you think about kind of the increasing success you're seeing?
Yeah, I think, Meta, it's a combination of all the above, right? So first, certainly, the pipeline has matured, right? Now people have had soak time. They've looked at what's going on. They've looked at the price increases they're seeing. And so there's a maturing of the pipeline for sure. That's one factor.
The second factor is I would say we have gotten much better as a team at qualifying these customers and trying to understand because who's actually ready to make the move and who's actually why now kind of thing, right? Which is there's a lot of interest and a lot of customers exploring, but who's actually going to go make a move? Are they ready? Are they qualified? For example, when is their license coming up for renewal? When is their hardware coming up for refresh?
So, these are basic qualifying questions that we know how to ask and get a sense of, is it here or now? That's the second part, a nd then we've had now in the field a number of programs around broadening our partners, around our strategic partners. Like Cisco, for example, contributed quite well to our new logos last quarter and for the last few quarters, a nd maturing of those programs, right, with our partners, with our customers themselves in terms of some of the incentives and so forth, and again, with our own sellers in terms of getting better at targeting and understanding what it takes to close a deal. So it's that combination coming together.
Okay, perfect. Rukmini , I want to bring you into the conversation. What are the pricing dynamics you're seeing kind of in these large enterprise deals? Particularly, Broadcom can kind of respond aggressively sometimes. Just how are you thinking about kind of how to kind of appropriately price and get these deals?
Thank you, Meta. So first, I'll start by saying that when we talk to customers, we begin with all the things that Rajiv talked about. Why are we the best platform, not just for today, but for the future? Talk about our innovation, our investments in R&D, and about the fact that our customer focus is absolute in the sense that we have a Net Promoter Score from our customers that's 90, kind of unheard of. It's been that way for 10 years now, even as we've scaled.
So that's where we start the conversation, a nd then to your question on pricing, it does matter, of course, and Broadcom has certainly chosen to make some changes with how they've approached their customers. So it is dynamic out there, Meta, and what I would say is we think about it more as what value can we bring to the customer.
So we have a very strong value proposition from a total cost of ownership standpoint, and that can be total cost of ownership reduction of 40%, sometimes more depending on the configuration or thereabouts, which is quite compelling to customers. So we tend to focus on that and look, of course, we're going to be competitive to make sure that we win our fair share of the market out there, a nd so we closely watch what competition is doing, but that's where we try to orient the discussion.
Okay. I mean, you had talked about maybe kind of getting a little bit more flexible or kind of evaluating kind of what potential incentives are. How do you balance what clearly has been kind of an increasingly profitable company with kind of the desire to kind of take advantage of the opportunities you see?
It is a balance, and I think we have to. That's what I meant when I said we are carefully watching things. Some of the larger customers, and part of your question is around larger customers, they are very bespoke situations. So we go in there.
We have a team of folks called cloud economists, and their role is to be there with the customer and with the sales team to analyze what the customer's environment, what they might be dealing with from a when is their renewal with VMware, what is their hardware investment, when does that depreciate, working with them on effectively a financial model that works for them and works for us. They're all actually, many of them are former finance people, so they really understand what the customer is going through, and we try to be customized and bespoke in those, especially in those larger situations, so we can create a win-win.
Okay, perfect. Rajiv, maybe turning back to you for a second, can you just provide more details on kind of the GPT-in-a-Box and kind of the adoption patterns you're seeing, or just kind of reiterating kind of a little bit of what that value proposition is?
Yeah, early days for us, but what we see here is this, right? If you look at the deployment of AI in the enterprise or in organizations, so there's a relatively small number of people who are building and training these models, whether they be the LLMs or whether they be the more recent ones like these reasoning models that we heard about, right, DeepSeek and others, so there's going to be a small number of people, and those are going to be trained on generic data.
Now, when companies or organizations look to use AI, they're looking to build these applications and essentially use one of these models, fine-tune them, or do a RAG on them with their own data sets, and then run them in a secure manner, right, in an easy way, and they might be having many use cases, right? Simple use case might be customer service chatbot or document search and analysis o r we've seen more interesting ones like federal agencies using us to detect money laundering patterns, or banks using us to build homegrown applications that are actually summarizing the calls that their salespeople make and look for patterns of non-compliance.
So these are all types of enterprise applications that are being built. Now, when they build these things, they need a simple platform to run this on, a nd what they don't want to have to worry about is the infrastructure, a nd the realization also is that these need to be very cost-effective. A lot of these real-life use cases can be done on small clusters, not on massive ones, four-node clusters, eight-node clusters. At Nutanix, for example, we have our own internal solution for our customer service engineers where it's a natural search interface.
Support engineer gets a call, they type this in, and then our homegrown application searches through all our documentation and tries to come up with what the potential root cause might be. And we use that as an assistive tool to help drive our productivity up for our support engineers. That thing runs on a four-node cluster. So it can be very cost-effective. So what we do with GPT-in-a-Box is it's the same Nutanix Cloud Platform running on standard hardware with NVIDIA GPUs of your choice.
You pick any vendor of choice, a nd then on top of that, we overlay what we call Nutanix Enterprise AI layer, a nd that layer takes care of all the routine day-to-day handling of what we call machine learning operations needed. It takes care of making sure that the GPU is virtualized, the language model is tied to it, and then we expose an OpenAI-compliant API. So what the developer gets out of the box is an OpenAI-compliant API, and they can build their application, generate an application, and call it, right?
So the rest of it is automated, simplified, securely run, and that's what we do with GPT- in- a-B ox. So it's a great platform for people looking to simply deploy these AI inferencing applications, whether it be in the private cloud, whether it be in the edge. This platform, by the way, and AI can also be deployed on top of any native Kubernetes substrate in the public cloud. Early days on that front, though, but most of our initial use cases, and it's still early days, mind you, are with these types of use cases that are applications that are being run securely in their data centers.
All right, perfect. Another area that you've expanded a lot over the past year is channel, forging deeper relationships with Cisco and Dell. Just how have they contributed thus far, and just kind of how do you see them developing over the years?
Yeah, I think we have been working hard to build these relationships, and they take time. So today, if you look at some of the strategic, and I'll sort of break it up into channel partners, strategic OEMs, and public cloud partners. So if you look at the channel partners, we've been growing our channel base. We've refined our channel program over the past couple of years, and we've gotten onboarded a number of new channel partners, and they're now contributing across the full stack.
So for smaller customers, we now have a channel that segment where they are the frontline. They are the ones prosecuting the deal. We don't have sellers calling on those customers. We are there to support the channel entirely. At the top end of the pyramid, the channel is playing more of an assistive role for us.
They work hand in hand with our direct sellers as we go into these customers. But what I see is now much more effective engagement across all tiers of our customers with the channel partners, and that's helping us grow. The second on the OEM partners, if you look at Cisco, Cisco is now reselling our solution, has been for the last year, and they no longer have their own solution in the market, a nd they've been a good, healthy contributor of new logos for the last few quarters as their business has started ramping there, a nd we look forward to doing more with Cisco, very well aligned.
They're, of course, a big player in the market with a big footprint overall in this networking space, not as big in the compute and hyper-converged space, but we have full alignment. Dell, again, early days of Nutanix, we had a great relationship, but then after they bought EMC, that went away, EMC and VMware, and now we've rekindled it. So today we have two things going on with Dell. First, they do have a solution. They are reselling our HCI solution in the market. On that front, though, keep in mind that Dell has many different solutions.
They are a big player in this market. They are a big storage player. So they have their own storage solutions. They have our solution. They have a bunch of other third-party solutions. So we count ourselves as one among several, but Dell, of course, is nonetheless a huge footprint. The second part of the Dell relationship is we are starting to support Dell's storage arrays, starting with their PowerFlex storage array.
We expect that to be generally available the second calendar quarter, so not far from now, a nd there's very tight alignment because those customers using Dell storage are looking for an alternate hypervisor, and that's what we are providing, a nd so there's 100% alignment on that front. On the core HCI piece, it continues to be one among a set of portfolio products for Dell, but we look forward to growing that relationship, a nd then the public cloud players, most recently AWS, we've also had partnership with Azure.
They are interested in using us to help migrate customer workloads to the public cloud faster and run them on their infrastructure. Not just for migration, but they are happy to have this run on our platform because these applications don't have to be changed. The existing applications can be run as is on a Nutanix platform, whereas for them to run natively requires a lot of surgery, and so that's their motivation.
So with AWS, for example, they're very motivated to get VMware workloads onto AWS, and we help doing that in a very easy way, right? And AWS is therefore offering migration credits to customers who are using Nutanix on AWS to bring home these VMware workloads. And we've seen now that resulting in some examples of some customers who were using VMware in the public cloud starting their journey with Nutanix in the public cloud, not on-prem. So the first deployment of Nutanix was actually in the public cloud, and then later on they adopt us for on-prem.
Okay, interesting. Rukmini, turning back to you, you've noted that you're seeing some moderately elongated sales cycles and continued scrutiny on spend, while simultaneously kind of achieving very strong results in fiscal Q2. Could you just provide a little bit more color on deal structure and approval processes and just how they've evolved kind of in the spending environment?
We have been talking about those effects both on the modestly elongated sales cycles relative to historical levels and the increased scrutiny on spend for a few quarters now, Meta, and we think that's because of the uncertainty in the world right now, and we think that is likely to continue to be the norm and that it's not going to be dramatically different in a switch to flip the switch kind of way because it's more gradual.
There continues to be some uncertainty in the market. You're right, we did have really good results in Q2. We beat across all metrics and so on, while we think that it's going to be somewhat dynamic in the environment out there, we did see more of these larger deals in our pipeline start to close in Q2. So we've also talked about how the mix of our pipeline is evolving with us seeing a greater mix of larger deals in the pipeline, and we're happy to see more of those start to close in Q2.
Okay. You saw some improvement in the federal business in Q2 from Q1, but just how are you thinking about kind of that federal business and kind of what is assumed in the guide?
So, first to level set, our U.S. federal business has been historically 10% or less of our revenue in each of the last three fiscal years just to give folks a sense of the relative exposure there, a nd as you pointed out, in our October quarter, which is our fiscal Q1, we did see some challenges on the U.S. federal front.
We also had a tough compare to the year ago quarter, a nd in the most recent quarter, for the January quarter, we did see improvements. It was a more normal seasonal quarter, I would say, for January quarter. Not a catch-up by any means, but still a good quarter. So that was January quarter. Now, to the last part of your question on how should we think about this going forward, I think it's too early to say.
I think we're all reading in the news what the federal government is thinking about and starting to put into place. Certainly, puts and takes for us. On the one hand, we have a very strong value proposition from an efficiency perspective. We've talked about total cost of ownership benefits and so on by maintaining high performance. So that could be appealing.
At the same time, it's unclear how they're thinking about in terms of overall spend and what's going to happen with the U.S. federal government. So puts and takes, too early to see how that'll play out, and we have factored in some of that uncertainty into our updated guide for the full fiscal year, which we were happy to take up on the last call.
Perfect. You've planned to increase some kind of sales and marketing and R&D investments in the second half of the fiscal year. Just what metrics are you using to kind of measure the ROI on these investments and just kind of the trajectory of how you're thinking of OpEx, particularly in light of kind of all of the opportunities that Rukmini is laying out?
It's a great question and one we spend a lot of time internally as well. So in terms of what metrics we use to assess the ROI on those, there's the macro and then there's just sort of the operational. So on the macro front, look, we have been on a journey of improving our overall OpEx as a % of revenue, both on the sales and marketing side especially because we think there are some efficiencies there.
So that's kind of at a company level from what we report out. And then at a more operational level, we think about where are we investing and how do we get those returns. So for example, we look at sales productivity, how much ACV is any given rep bringing. We've talked about at some of our previous events, the fact that we've been trying to improve productivity.
We've seen some really nice improvements. We do think there's still room for us to get to what we would consider sort of benchmark levels. So we're continuing to drive those up. Same on the channel. So as you've talked about our channel approach and some of the investments we've made there, we look at how do we think about how much of the business is being brought in autonomously from the channel, for example.
That is small today. It should continue to grow over time. Another way to look at it is how many new logos are these, whether it's the channel partners or the OEMs, how many are they bringing into us where we did not have an opportunity before with those new logos? So what doors are they opening for us? So we look at metrics like that.
On the R&D side, those are longer lead times, of course. There are investments that we make today where it's very much an opportunity that we know is there today. It might be large customers that we're looking to service. It might be security upgrades that we know we need to make, things like that here and now, which is where the bulk of our investment is.
There are also investments that we're making on that are more future-focused, like all of the cloud native or the GenAI investments. Not as much contribution here and now, but really important for the future of the company and for the industry as we think about innovation. The R&D framework is a bit different. It's divided into kind of here and now opportunity and then more longer-term horizon.
Okay, great. I have a bunch more questions, but are there any questions from the audience? All right, perfect. I get to stick to my questions. All right. With the recent convertible note issuance and kind of new revolving credit facility, you also generate a fair amount of cash as well. Just how are you thinking about kind of capital allocation priorities between organic, M&A, and potential share repurchases?
I'll start with sort of why do the financing, which I think is implied in your question, Meta, so to remind folks, a few things. Last summer, we had the holders of our 2026 convertible notes, Bain Capital, chose to convert those notes, which meant as part of the contract, we paid them over $800 million in cash as part of the contract. That cash went out the door last summer.
If you look at our cash as of our October 31st quarter end on the balance sheet, we had a little over $1 billion in cash. About half of that is overseas. That meant that we didn't readily have access to that without some friction involved in bringing them to the US. As we thought about that, we also have our 2027 notes, convertible notes, so we have some time to address those.
But we looked at all of these factors, the fact that we had a sizable amount of cash leave the balance sheet in our payment to the 2026 notes conversion, how much of our cash is overseas, and then the upcoming 2027 notes, and decided that December actually was a good time to access the capital markets. Convertible markets were in a good place then as well, which of course we watch closely.
So we decided to access the markets and we ended up raising $862.5 million of convertible notes at 50 basis points. So we got pretty good rates overall. What we did with that is we used a portion of that to pay off a portion of our 2027 notes. So we've started to work on that maturity. We did a $200 million share buyback with the net proceeds back then, and the rest went to our balance sheet, and in terms of, well, what do you think of our capital allocation with that going forward?
We'll continue to look at addressing the 2027 maturities as it makes sense over time. We will continue to also look at tuck-in acquisitions, so we've made some recent acquisitions. One we did recently was a company called D2iQ about a little over a year ago, and they have really allowed us to strengthen our Kubernetes offering, so that's our NKP Nutanix Kubernetes Platform solution, is what that was integrated into, and it's been a great addition to our portfolio as Rukmini talked about, so we'll continue to look for opportunities like that, and then, of course, we'll continue our share buyback.
So we have just under $200 million left in our existing share repurchase authorization, and we'll continue to do those over time. So no meaningful change in our capital allocation in terms of how we think about it, but we feel that our balance sheet is now in a better position to allow us to address all of these priorities, a nd then on the revolving credit facility, we closed a $500 million facility in February, a nd that, I would say, is we're not drawing that right now. It's undrawn. It gives us flexibility to the extent we need it a t some point, it's there if we need it.
All right, perfect. Last question that we've been asking to a lot of companies at the conference, a nd , Rajiv you aleady kind of noted an example, or I heard you mention an example, but just how are you using AI internally within Nutanix or kind of looking to use it over the next couple of years?
Yeah, for me, I look at internally, I mean, for us, I look at AI as an answer of productivity and efficiency inside the company. I'll give you one of the early use cases for that support example that I mentioned, and what I'd like to see there is like a 25% at least minimum improvement in productivity of a support engineer, so what that means is we should be able to resolve tickets much faster, for example, than ever, and what that allows us is, even as we scale our business, we want to be, again, be able to maintain our customer experience and the NPS that we talked about, but in a more efficient way.
Similarly, the other second use case for us internally is in software development. We are a software company. That's a big pool of people for us, and we want to make them more productive. So we have started using tools to automate unit testing, for example, not as much on the developer code generation because our code is very much system-level code. It's not user code for the most part. It's really deep kernel system-level code.
But certainly for all the unit testing use cases, all the surrounding stuff to our mainline code, we are using tools today. But then there's also every functional organization is starting to use AI. Legal is using AI now, starting to use AI to do auto-generation of contracts, for example, RFI responses, these types of things.
So I do think this is going to be, and we've now gotten to a point where we have a central governance team headed by our CIO to systematically look at what every functional group is doing. Can we have more centralized data? We have a data science team that's also centralized that's looking at all of these and saying, should we be consolidating on a few platforms to be able to do this? Of course, for us, we want to be running these things on a Nutanix platform, being a Nutanix that we provide an AI platform, a nd so we have a more systematic approach towards evaluating the ones that will give us better ROI and focusing on investing in those to drive productivity increases.
All right, perfect. Well, Rajiv, Rukmini, thanks so much for being here today and telling us more about Nutanix.
Thank you for hosting us.
Thank you very much, Meta. Thank you. Thank you all.