We are excited about today's discussion and the future of Everpure. Let me remind you that we will be making forward-looking statements today that are subject to assumptions, risks, and uncertainties. Actual results could differ materially from those anticipated due to a number of factors, including those referenced in the detailed disclaimer at the beginning of our presentation slide deck and in our public filings with the SEC, which we encourage you to review. The presentation slides discussed today will be available on our investor relations website at investor.everpuredata.com. Today's discussion will include non-GAAP financial measures. Reconciliations of these non-GAAP measures to the most directly comparable GAAP measures are available on our investor relations website at investor.everpuredata.com.
Welcome back, everyone. Good afternoon. Good afternoon to everyone that is on the webcast, and I hope you enjoyed the lunch. I know it was a bit early, but for those that were joining from the East Coast, perhaps a welcome time to have lunch. We are looking forward to speaking to you this afternoon. I am just going to briefly go into the agenda again for this afternoon. One is I am going to present to you a very high-level overview of our overall strategy, reminding you where we have come from, but also where we are going and how that fits into context of Pure Storage and our overall business strategy.
You will see Rob again coming on stage talking about how these different areas in which we are competing, why we have a right to win there, what underpins our technology, and he will be going into a little bit more detail on the hyperscale side of the business, and reason why we are as compelling a solution as we are in the hyperscale. Then, after the market closes, we will have Tarek come on stage and tell you what that means in terms of our financial profile for the next several years. I am sure that will be something that you are very much looking forward to, but we are going to wait until after the market closes to get into that. With no further ado, the story is actually quite straightforward. Have you ever seen a line that straight in any financial environment before? Look at that line.
It is about as straight as it can be. This is our increase in market share year after year. In all kinds of markets. This is well over 12 years now of share gains each and every year. We outpace the market, and we outpace our competitors. It is not just year after year, it is quarter after quarter. It is a very remarkable and consistent share gain. So when we have said on earnings calls that we have two quarters of visibility, that is actually quite true from the standpoint of pipeline in Salesforce and from our sales team. But we have 12 years of visibility of continuous market share gains, and that gives us a lot of confidence that we are going to continue to see market share gains as we go forward. In fact, we believe that is going to accelerate.
If you want to know what is the most fundamental element of our strategic competitive advantage in this space, it is this. We treat data as high technology, not as a commodity. Why is that a differentiator? Well, if you were to ask Joan Magretta, who is up there with Michael Porter in terms of one of the foremost business strategy analysts, they will tell you that if you structure your business, which is then displayed in a P&L, if you structure your business around a different and compelling value proposition, that that can be sustainable because it is very difficult for your competitors to mimic that business model. We have a technology forward, R&D-led business model, high margin, but also high investment in R&D. We are a technology first company, and most of our competitors are commodity players.
They cannot overnight, or even over many years, become a technology player. Think of what that would do to their P&L in the medium term. This is a fundamental structural advantage that we have as we go forward, and we continue to invest in innovation. In fact, if you look at the last 12 months, on a GAAP basis, it is now over $1 billion a year, and this is, of course, increasing, and that puts us at the number one investor in R&D in our segment. It has been true for many years that we spent more on a percentage of revenue, but now we actually spend more dollars. I would argue that we spend it very efficiently and effectively in our business, and that is allowing us to gain market share. What does it buy us?
Well, at least third parties, whether it is Forrester or Gartner, have consistently put us as the innovator in the space, always in the top quadrant, and at least for the last many years, the furthest north and the furthest to the right. We are being recognized increasingly, not just by analysts, but more importantly by customers as being the innovation champion in this space. We are not just the innovation champion, but if you look at our Net Promoter Score, nobody even comes close. We are very customer focused as well, and now we have scale to back this up. It is not just that we invest more, but we are more efficient in that investment. You heard all morning about our unified data plane, about a unified control plane, about Evergreen. What are the advantages of this?
Well, having one software environment that covers many different markets is the very definition of scale. Because we invest once, and we can serve many different markets with the same investment. We do not have to invest in a half dozen different operating systems to service different parts of the data storage market. This one investment we make in Purity and in DirectFlash, which is part of Purity, allows us to satisfy Core, as you heard this morning, as well as scale AI for the NeoClouds, and as well as hyperscale solutions. These are not just benefits to us, they are benefits to the customer in terms of they get a more consistent solution overall. They get higher quality because the great thing about having a single software environment is what I phrase as bug consistency. You fix a bug once, it is fixed everywhere.
You do not have to fix it multiple times. Simplicity, it is simply easier to be able to manage an environment that is consistent rather than different. It benefits the company, as we said, because it is about scale and efficiency of investment. It improves our overall quality, of course, and the focus of the team overall. Whether it is the sales team or the engineering team. What are the shareholder benefits? Really, we get efficiencies in scale and growth. Over time, that should pay off in terms of enhanced profitability overall. So we spend more, and we are more efficient in the way we spend it. Over the years, we have also put a lot of investment in our go-to-market team. If you look at it now, we have over 2,300 professionals in our go-to-market organization.
They are part of that Net Promoter Score that I mentioned. It is not just about product, it is about the way we engage with our customers. When we speak about Net Promoter Score, it really is a measurement of every aspect of our company, whether it is the way we engage with our customers, the way our channels engage with customers, they are part of this measurement, the way we interact with our customers from a financial standpoint, because that is part of the measurement, and of course, the quality of the support that we provide those customers. In the enterprise, we are in 64% of the Fortune 500, 44% of the Global 2000. We have a lot of global strategic partners. By this we mean technology. Both technology partners at 50, global strategic partners in terms of partners we go to market with. We have 4,000 channels worldwide in the commercial market.
We are widely respected by our channel as being a company that works with the channel well, a company that is trusted by the channel, that provides the channel what they need to be successful. Consistently ranked number one by CRN and many of our channel partners. We have a direct presence in 32 countries. We sell in well over 60 countries on an international basis, 15,000 customers overall, and increasing our presence in governments on a worldwide basis. That had been a weakness of ours in the past, and now that is an area of growth for us. That has allowed us to reach an inflection point. If we look at it solely from a reported revenue basis, now up 38% year-over-year. I will call your attention to the fact that we have eight quarters of increasing revenue growth.
It is not just about the most recent price increases. We have been increasing our revenue growth consistently over these eight quarters, gaining share. This is why we now believe it is durable. This is not just a spike. We are growing the margin at the same time. A 50% increase in the overall margin of the business over that same period of time. Again, this is we think proof that we drive growth through innovation. It is innovation-led growth. I am going to very briefly speak to you now about the four areas that we believe we are engaged in as we go forward. Of course, we have been in Core and now Core AI for almost two decades. That seems to be working better. Almost two decades. You might want to get me another mic starting to-
It's on.
Okay. That area we believe is accelerating. Our growth in that area is accelerating. Second, we're going into, as you heard this morning, a number of different areas of software in the enterprise environment in order to enhance the data estate in that enterprise. Everything from Data Intelligence, which is characterizing the data, to Data Stream, which is vectorizing the data, to modern virtualization, which is allowing new areas of virtualization to operate at greater efficiency inside the organization. Third, scale AI, so expanding our product area into the largest of the large AI cloud environments, 10 to 100 times higher speed, performance, and capacity than we had been selling into the enterprise. Then finally, last and certainly not least, the hyperscale solutions.
I'm going to give you a brief overview of these, and then we're going to follow on with Rob, who will go into much greater detail here. All right, so starting with the core, this is another look at that 38% revenue growth. But the difference here is I'm looking specifically at the product growth. All right, so sales of product over the last seven or eight quarters, and then tied into that, the growth of our subscription business. This is Evergreen//One, right? When we don't sell an array, but rather provide a storage service based on our arrays, right? It's a little bit difficult to be able to identify specifically when we sell an Evergreen//One contract, exactly what would that have been had we sold an array rather than just a service contract.
Because obviously that revenue is spread out typically over about a 3.5 year period. There's a variety of different term lengths that we engage with our customers, but it's in the three to 3.5 year range. So it's just a rubric. You cannot square this circle of converting from one to the other. But very loosely speaking, if we took 70% of our TCV sales of Evergreen//One and add that to our product sales, this is the growth rates that you will see over the last seven quarters. Okay? This is why we believe we are really accelerating in this core AI market. It is accelerating product growth at a significant level. We are outpacing both the market as well as our competitors, and we believe that this momentum is really something that is going to continue.
At the same time now, we are seeing acceleration of sales into AI environments. I want to describe a couple of things. One is you will notice that this is not revenue, this is GPU attached systems. We have completely pulled out any association with price rises in this, right? This is the number of systems that we are selling into direct attached GPU environments, not what systems might be used by AI somewhere in the enterprise. These are arrays that are attached directly to GPUs. Why the sudden gain, really at the beginning of this year? Well, it had to do with the fact that the models just got so much better. It really was not specifically anything we had done.
Of course, we had kept developing our product, but unless the models got to the point where enterprises were starting to build their own AI factories, then we would not sell into it. We would not have a GPU connected device if the customers did not have GPUs. The beginning of this year really marked the period when customers said these open weight models largely are good enough, where we can actually get good work done with positive ROI by bringing it in-house. Once they started bringing it in-house, of course, they needed storage systems to attach it to. That is what we started to see at the beginning of this year, and it is just getting started. We think that this is an area that is very much going to improve. So it is accelerating on-prem GPU deployments.
The second thing, and this was really critical, is the combination of S and EXA proved to be a very compelling story for our customers. There was a time where customers were being told that, "Well, you may not have the requirements today, but at some point you are going to want to scale. When you want to scale, do you want to scale on a product in an environment where you may not be able to scale and then you would have to buy something entirely different? Or do you want to start with something that is too big for you now, but you could scale into?" We are not forcing our customers to make that choice. The S and the EXA are the same technology. They are fundamentally the same product, just structured in a different way.
With our track record of non-disruptive upgrades, with our track record of taking our customers along the journey of scaling their environment with as little disruption as humanly possible, they trust us in this area. The strategy of having both S and EXA in the same environment, very, very powerful. What we are also seeing is pilots now starting to go into production. There had been pilots before, but it was largely with very large customers. They wanted to try a GPU environment, but it was not going into production because it just was not proving out the ROI. Now it is going into production. What is the difference between a pilot and a production? Pilot can be down for several weeks. You could buy a pilot, and it is kind of fun to start putting it together for the first several weeks.
If it takes that long to build, okay, fine. Production, it is like you expect to buy it and put it into production right away, and you expect it to work, and you expect it to work with quality and reliability. Those are all the things that Rob and others spoke about this morning. This is what we have designed for. It is what customers expect from us. This area has been accelerating quite dramatically over the last, I would say, six months. The thing that you heard a lot about this morning, the thing that we are most excited about, though, is a fundamental change, we believe, in the architecture of IT environments that as Rob and Prakash and Ashish spoke about this morning, we think is a 10-year journey for customers.
That is this inversion of the relationship between data and applications to where applications have been the way we have designed our IT environments for 30 years, completely application dominated to the point where organizations are now fragmented in all of their applications. It is hard to put together any one element now from a single application. It always requires data from multiple applications to get this going. The problem is not so much that there is no single source of truth. The problem is there are so many sources of truth and they just do not agree. Every one of the databases, an SAP database, a ServiceNow database, a Salesforce database, your systems of operation that have databases, they all have their version of truth. Yet try to figure out what is the full relationship with any individual customer, and you have a very hard time.
It was already starting to break with so many applications. Now with AI, that requires access to data. If you get different data from different applications, how does an AI know what is true or what is right? You could spend a lot of tokens to figure that out every single time. Or you can start building technology that allows for a single source of truth or that provides context to all these different databases that explains the source of truth. So we have put together a paper, those of you in the room have it in front of you. Those of you online, this is a QR code that will allow you to get access to it, that really lays this out. We think this is a seminal paper on IT architecture.
Not specifically on AI, but what AI now has exposed as the flaws in current IT architecture and where we think that it needs to go. It is going from data processing through the full client server multi-app environment, to what we believe is data primacy that will really allow organizations to make the best use of the future application environment as well as the ability of allowing AIs, AI agents to run more of their business. There is a big difference between AI in a general environment and AI in an enterprise environment. I can explain that very easily. I think each one of us have used AI in our personal lives, right? I know that my wife, this occurred when ChatGPT 3.0 first came out, and she took about 20 hours of work to plan out a family holiday, right?
She had all these things planned out. She asked me about it. I said, "Let me try something." I was on my iPad. I went to ChatGPT. Within about five minutes, with about five prompts, I had recreated her 20 hours of work with an output of ChatGPT. Of course, as you might imagine, it wasn't exactly what we wanted, but 95% correct is a really good answer for something like that. 95% correct. How good is that on an invoice? How good is that on financial reporting? I think we'd go to jail if we had 95% financial reporting. Enterprises require 100% accurate information 99.9% of the time. That's the difference between AI being useful as a personal productivity tool and AI being useful in an enterprise environment.
Enterprises require repeatability, consistency, diagnosability, because they need to know why did I get this answer if it was not the correct answer, and accuracy in their results. That's a very different problem if your data is fragmented. If you have different sources of truth that have different information, how is an AI to know? Very difficult. So organizations have to get control of their data. Data primacy, we think the first step is Data Intelligence, which is, first of all, being able to catalog all your data from every source, any source, in the organization, characterize that data, understand the semantics, be able to provide context around it, and then understand the similarities and differences between those data points in different datasets across your organization. Then be able to express that, whether it's to an AI or even to just another application environment. Right?
Have the same context, have the same information, for the same purpose inside the enterprise. We believe this is the first step to help enterprises on their way towards data primacy, or much more simply, just to get better answers, whether it's from their existing applications or from AI agents as they go forward. So discover, classify, contextualize it, and have it be explicit, not buried in an application structure somewhere. All right. Then we're going from AI in the enterprise to AI in the large scale, neo cloud environment. Rob went through this with a lot of detail this morning. We're seeing growing sales for EXA. As we go from an environment of tinkerers and science experiments to a world of production and reliability, and efficiency, and profitability.
We're seeing that our existing bona fides, if you will, around those elements are becoming much more compelling to our customers. Rob mentioned this morning that we had a meeting with a customer on a Friday afternoon, did a POC and a demo on a Thursday afternoon, did a POC and a demo on Friday, had it up and running on Monday. This just doesn't occur with anybody else. It just doesn't. We design products that have finished quality associated with them, not science experiments. Again, it's the same Purity engine. It's the same architecture, same management capability. By the way, as he also mentioned, NeoC louds have lots of different storage requirements. In everything from the world's highest performance data engines all the way down to archive and to backup, we can provide all of those capabilities to those customers.
The other thing we pride ourselves on, and you really need to look into competitive offerings to really understand this, is we scale without surprises. When we publish a number, that number works under all scenarios. It is not under carefully selected environments where you have to tweak all the parameters and have it be structured in just such a way. Our numbers, as published, we guarantee to work under all conditions. So we scale without any surprises, without any tweaks, without nerd knobs, without a lot of futzing around. Our hyperscale solutions. I want to make this very clear, and Rob will be going into much greater detail, but our strategy is very simple. We want to be able to replace all of their SSD and hard disk architectures with DirectFlash. That is our goal. Very simple.
With that one solution, be able to cover their full breadth of requirements from AI levels of performance, all the way down to archive levels of cost with one architecture and one structure. Why would a customer want this? Our reliability is 10x . In the case of a hyperscaler, probably more like 5x better. Our performance is much higher and much more consistent. We have much greater density, and depending on whether you are measuring against SSDs or hard disks, significantly greater density. It is from an ongoing engagement standpoint with the hyperscalers. It is a much simpler solution because it operates above the operating system, operates in user space, does not require them to constantly tweak for every different type of storage, or for every new generation that comes on board. We have double the lifetime of any other solution they have out there.
It lowers their overall operating cost. It is a less complex integration over time. The way we are able to do this is we provide an abstraction layer between the NAND, which is the underlying media, and their operating environment. The opportunity here is huge. It is well over 1,000 exabytes on an annual basis. You pick your number. There is a lot of forecasts on that currently. We are seeing, and we have contracts going forward. So we now have reasonably good visibility into this, and this is something that Tarek will be going into in much greater detail. But we are seeing the uplift start. Of course, we have the opportunity not only with more hyperscalers, but just as importantly, if not more importantly, the expansion within existing hyperscale customers. All right. I am just going to sum up now before handing over to Rob.
We believe we are transitioning the company to durable and accelerated growth. That is, we are going to be growing faster, and we think that that is durable. Eight quarters have shown that we have seen higher revenue growth and higher share gains across the board. That investment that we make, we think is more efficient than other players out there because we can spread it across more markets and more customers. The new markets we are adding, so this is the modern data, scale AI, and hyperscaler, are non-overlapping with our core market. This is truly additional new sources of revenue and income for us. I would say that whereas where we were two or three years ago, where we were predicting that we would sell into the hyperscaler but did not have one to show for it, we are now in two, we believe we will be in others as well.
And we have been actually tested in both SSD and HDD environments. Now, of course, with the price rise, perhaps HDD replacement is going to wait until another day. But again, it is the same solution. It is just a matter of when prices come back down. In hyperscalers, if the latest price increases in NAND have proven anything, is that we are resilient, whether it is about HDD replacement or SSD replacement. This is one solution that covers all of their storage needs outside of tape. So with that, as you can see, we are very proud of the fact that we have one architecture now, but many, many paths to growth. We have our core, we have our new markets, and that is what we are going to be spending the rest of the afternoon on. So Rob, welcome to Sage.
All right. Well, welcome back from lunch. I am going to pick right up where Charlie left off, unpacking these four high-growth market areas that we are focused on as we look forward. Before I do, perhaps I should say that Charlie had a slide up there that said we are at an inflection point. I have been at the company, this is my 13th year, and I would say I have never been in this position where we are standing on the precipice looking forward at growth, not just in the thing that we do today, but three really exciting new areas really just starting to take off in the industry. So I am going to dive into each of these in my discussion today. Before I do, I think I want to put a little bit more definition and structure to how we are thinking about how we are defining these new market areas.
I will start with the core. So we look at this as a lot of what we have been doing since day one, serving the enterprise, serving enterprise storage systems. We have added to that, we have included in that what we are describing as core AI. So effectively, this is what we spoke about, Chadd spoke about this morning, Charlie spoke about a little bit right before me, the enterprise deployment of AI systems within their environments. We are including this within the core business because it is the same customer segment, it is largely the same solution set, but also because it is different and apart, it is separate and apart from a new opportunity we have created, which is our scale AI opportunity, which I will get to. Now, as we look beyond the core, I would say extended by core AI, we see three new markets, high-growth areas that we are really turning our focus to.
Number one, what we are calling modern data software. You have heard elements of this this morning and in Charlie's intro. And this is really, I would say, stepping back from it, looking at where we sit as in an industry at the beginning of, I think, what is going to be a 10, 15, 20 year long secular transition in how software is built, in how application environments are assembled, and as a result, how the infrastructure that has to serve them comes into place. What are the drivers of this? Well, you have got a number of things. You have got AI, you have got new approaches to analyzing data. You have got things like open source. We will get into all of that.
One of the things I want to highlight here is that while we're serving a lot of the same customers that we would serve in the core business, the enterprise, the way that we're going about this delivering these solutions as software, we're delivering the software value over and above our own storage footprint in arrays. Our goal here is to add value to customers' data footprint no matter where that data sits, whether it's on our arrays today, hopefully our arrays tomorrow, or competitor environments or SaaS or cloud environments. The future for us in this category is really to start adding value over and above that storage infrastructure tier.
Coming back to storage infrastructure, two exciting new markets, again, very high growth that we're focused on, I would say extending our core IP and packaging and delivering in new ways to meet the needs of these newer markets. One, scale AI, what we're terming the NeoC louds, the frontier model builders, the tech titans, essentially think the folks pushing the boundaries of what's possible with AI. So extending our IP to go meet the needs of this new market, and then certainly the hyperscale solutions, which we've spoken quite a bit about, and I'll dive deeper into. So if we step back from it, four high-growth areas that we're going to focus on. The last thing I'd emphasize on this slide is that we're approaching these areas not as disparate efforts within the company. We're approaching each of these markets based on a single solid foundation.
That solid foundation is built on a shared set of IP that we've developed over 17 years, perfected in the enterprise, are now finding ways to go repurpose, repackage that IP to meet the new market needs, and we're doing this on the basis of a significant customer base and a significant understanding of what the enterprise needs. All right. Let's start with the core business, core plus core AI. Equation here is fairly simple. We're going to keep gaining share, and we're doing it at a point in time when the market's growing. That market is growing for a number of reasons, but I would point to AI. I think you've got two effects driving core enterprise storage market growth from AI. You've got an indirect effect and a direct effect. The indirect effect, fairly simple. AI makes data more valuable. How much data does an enterprise store?
Depends on how much value they get. If they get more value, they can store more data. The direct effect, this is the deployment of new AI-attached use cases, things like as inference comes back on-prem within enterprise's digital walls, agentic deployment in real-time systems. We're starting to see these projects go from pilot to production, and so we see that growing the enterprise market as well. But really two legs to this. We're going to gain share, we're going to keep gaining share, and the market's growing. So what are the drivers for our continued share growth? Charlie showed the straightest financial chart in history. We're going to keep driving that. Actually, if you look at that chart, at the very end, there's a little uptick up. We're going to keep driving that same level of growth, if not even faster.
This is really based on a couple key advantages that we've assembled over the last 15 to 17 years. Number one is we now are able to approach this market with an entirely complete platform and what I describe as an enterprise-wide value. If you go back six, seven years in the company's history and you look at a lot of our enterprise sales approaches, it might look something like this. You go to the customer say, "Hey Mr. Customer, you've got a database, I've got the best thing for you. I've got the fastest array. It's going to save you so much time. Your DBAs are going to be so happy." We nail it out of the park. The problem with that is you only get so far. As a point solution provider, you're not seen as a strategic vendor, you're not seen as a strategic partner.
Well, we're now in a completely different position, having completed the portfolio, having been able to articulate and deliver that portfolio, not just with a unified data plan, but now with the intelligent control plane, we can go after these enterprise accounts with an enterprise-wide value, a franchise value, right? We talked about, we quantified this in the 2Q call. There was a point in time in the company's history where if there was a $1 million, $5 million deal, I would probably know about it. We're doing eight, nine-figure deals these days, and I see them as they go by, right? This is just a testament to the fact that we're now in a position to go compete and land very large deals in the largest enterprises. Not just land, but expand.
We're in a much better position to expand, and I would call out two things here. One is we have the stickiest product in the market, and the second is network effect. When I think about stickiness, Evergreen comes front and center. Chadd did a great job talking about the difference in mindset when it comes to the traditional rip and replace motion that customers go through versus an Evergreen modernization. No customer goes through a non-disruptive Evergreen upgrade and says, "Wow, that was great. I want to go back to the old crappy way of doing things." Nobody does that, right? When we think about the stickiness, when we think about the customer retention that Evergreen affords us, it's amazing. If you think about this just from a purely transactional point of view, we don't have refresh cycles. I love refresh cycles.
A competitive refresh cycle is a bid opportunity for us. It's a sales opportunity, right? With Evergreen, we don't afford our competition the same favor. Now you couple that stickiness with network effect. You couple that stickiness with what we've done with the intelligent control plan with Fusion. We've now introduced a set of capabilities such that having one array is great, having five arrays is great. We're actually now at a point in time where as you have the fifth array, as you have the 50th array, buying that 51st array is actually incrementally better than it was to buy the first one because it's easier to manage, it adds to your pool of resources. It just fits very nicely into that cloud operating model. Number 3, we're continuing to expand our technology differentiation across the board.
I'm going to go deeper into a couple of these areas in the next couple of slides. Charlie talked about what sets us apart as a unique, sustainable advantage is that we are a high technology company. This drives differentiation, and we do this intelligently, right? We're now operating in a size and a scale across multiple markets that we're able to take those technology investments, the outputs of those technology investments, and leverage and use those in multiple different ways, which affords us a tremendous amount of leverage and scale. We spend a lot of money on R&D. One of the things that I want to highlight here is most of our R&D spend, most of our resources and our efforts are actually directed towards software, right? Well over 95% of our R&D spend actually goes to software, and that's quite intentional.
It's intentional because most of the value, most of the differentiation we deliver is actually software-driven. It also means that because it's software-driven, we can go and deliver that on multiple hardware platforms across the entire portfolio. That results in an expanding set of differentiation. We only have a certain amount of time. I'm not going to go through the entire list. Charlie flashed some industry accolades. You look at the Gartner results, the Forrester results. These are all third-party validation of that differentiation that keeps growing in time, right? Usually, you see these things clumped towards the middle. You see us moving further and further apart. We do this in a number of areas. Flash management, DirectFlash. We'll talk about that with the hyperscale solutions is one of the key areas.
The other thing I'd call out, though, is that no matter what we're talking about, whether it's filling out the portfolio, whether it's particular features, we're not chasing competitors. We're not just trying to build a better version of somebody else's feature. We're trying to solve those problems better. We can point to numerous examples, whether it's in terms of data protection, what we've introduced with SafeMode. We can look at storage management, what we've introduced with Pure Fusion. We're investing heavily to find better ways for customers to do things, and we're getting rewarded for it. We're getting rewarded for it because customers are seeing the outcomes. So customers are seeing the outcomes in increased efficiency, better reliability, overall significant TCO savings. Massive, right? This is a virtual cycle. We invest in R&D, that grows differentiation. We deliver better outcomes, we drive growth, we reinvest in R&D.
Simple story. One more thing to point out here. We have a secret weapon, which is Evergreen. With Evergreen and Evergreen NDU, we make it a lot easier and more appealing for customers to take software upgrades. We can accelerate this cycle in ways that nobody else can, right? Think about it this way. Your typical enterprise vendor builds a feature, they ship it, customer says, "Okay, that's great. I'm going to wait for the second major version until all the bugs and kinks are worked out." Two years pass, they finally deploy it. Think about that time to value realization from the R&D investment to when the customer gets value to the rebuy. It's a really long cycle. We shrink that thing. We're gaining share. Market's also growing.
I mentioned before, I would say two major effects that ultimately combine to accelerate the growth of the enterprise storage market. Historically, this market's grown, call it mid, maybe high single digits. We see the market as accelerating growth over the next couple of years to about 12% CAGR. I'd pull out two drivers for this, both relating to AI. One, AI is just making data more valuable. We produce a ton of data as an industry. Most of it today doesn't get stored. Because it's not valuable enough to store. Your customers aren't getting results from it. As AI becomes more prevalent, data becomes more valuable, it's got to be accessible. So we see that as constructive for the market growth. But number two, new use cases.
As AI matures, as enterprises focus more on data sovereignty and control and bringing AI in-house, these new GPU-connected arrays, what we're calling core AI use cases, are on the rise and are going to add to what we've historically looked at as the enterprise storage market. As an example of this, I'll highlight one of the examples that we have seen with a major multinational bank, and this has been an effort that started in their labs, has gone through pilot initial stages of production, and is now going to full scale across the entire bank. This is a large bank that effectively has said, "Hey, for all the work that other people do, going to Claude or OpenAI to build agents or tools, we don't want any of that. We want to do that all in-house.
We want to build a system for our employees, the bank's employees, to build agents, tools, the whole nine yards, and we want to run it all in-house." They chose Pure to back this at multiple levels, both the data going in, the agent memory, the user memory, the KV cache, as well as the agent development environments. Now you might ask, what was it that drove them to choose Pure? Well, one, we were deployed as part of an NVIDIA reference stack, and so they went to NVIDIA, they looked at the entire ecosystem. But what made us stand out apart from the pack is that and they had looked at competitors.
What made us stand out from the pack is that we could deliver not just the performance well above the performance that they anticipate needing, but we could do that in a way that no other vendor could, coupled with all of the enterprise capabilities. Because these are mission-critical environments, right? Security, reliability, availability, all the things that we're known for, nobody else on the market could bring both of those things together. All right. So if we step back for a minute and we look at the core business, core plus core AI, growth equation's fairly simple. We're going to keep gaining share. We're going to keep reinvesting to gain share and accelerate the pace of that. The market's growing. Those two effects compound to drive significant growth in our core markets. All right, so let's talk about some new stuff.
As we look beyond the core, three distinct new high-growth market opportunities in areas that we're turning our focus increasingly to. Number one, modern data software. We'll talk about scale AI, and then last but not least, I'm sure we'll go deep into hyperscale solutions as well. With modern data software, I would say this is, in many ways, we're in the early stages of this journey. In fact, our first foray down this path, I would say was actually Portworx. With Portworx, our thesis was and still is that application environments are fundamentally going to change. They're going to change over time, and we want to be on the forefront of driving that change. When we acquired Portworx 5 and a half years ago, that change was largely driven by containerization, open source, Kubernetes, all of which are still true.
You now have other secular forces coming to play, coming together to accelerate what we believe will be a five, 10, 15-year journey, significantly upending the application environment in the enterprise. Talked about this a little bit in the morning, but AI plays a big part. AI forces a rethink of how traditional enterprise architectures are built, how software is built. The focus shifts to data. The focus shifts to making sure that AI and AI-based systems are getting fed the right data, that it's organized and governed properly. These applications are built entirely differently than monolithic traditional enterprise applications. They're built on containers, Kubernetes, making use of open source. Also there's an importance now, as we talked about this morning, our goal, where we see the future heading is a customer being able to work with their data no matter where it sits.
Data sits all over the place. It sits on-prem, it sits across multiple prem environments. It sits in the cloud, across clouds, so we need to be able to help serve customers where that data sits across these different environments. If we unpack this, Prakash talked a bit about this this morning, and Charlie hit on this as well. The first leg of this is meeting the needs of helping customers identify what the right data is. Most of the time when I go talk to a client in the enterprise, they'll say, "Hey, I want to do all this great stuff with AI." I'll say, "Hey, that sounds awesome. Where are all your data sources today?" 9 times out of 10, customer will probably chuckle at me and say, "I have no idea. It's 16 different places. Every department has their own database.
We have multiple copies." Problem number one is figuring out what is it you have, where does it sit, and what does it mean? This is where OneTouch comes in. This is where Data Intelligence, where we're headed with that. Ultimately it's about helping customers in a very automated way, AI assisted, find, catalog, and understand what the right data is, what it means, and understand what are the right ways to use it and feed it into AI systems. Garbage in, garbage out. You got to feed the right data in. We're going to go solve that. Number two, once you find and understand what the right data sets are, it behooves you to organize it in a way to make it easy to work with. Easy, efficient, fast.
With Data Stream built on NVIDIA's AI data platform, we brought all of the pieces of data organization for AI together, automatic ingestion, vectorization, and in organizing and providing it in a way to RAG and inference systems that both optimizes the performance as well as preserves all of the data security needed across the board from the source data all the way to the vectors. We have a couple capabilities. We are going to deliver this software above and beyond and outside of our arrays across multiple environments. If we look at the broader application stack, this application stack looks very different than traditional enterprise apps. They are built on containers, built on Kubernetes, and at the same time, a lot of the traditional apps are moving to containers.
With Portworx, we are now seeing increased demand for Portworx, both in terms of virtualization modernization, as well as supporting data pipelines, data analysis, and inference systems. Last but not least, there is a greater desire and a greater need to be able to connect data sources and work across clouds, across prem and cloud. This is where Everpure Cloud, the evolution of Cloud Block Store into a managed service, and Portworx come together to be able to provide that multi-cloud capability across both traditional applications as well as modern applications. Shifting gears, we will chat a bit more about the scale AI opportunity. This is a focus now on the NeoClouds, the largest AI model builders, the frontier builders, and the tech titans.
Simply put, this is where the needs of AI environments we see in the enterprise are well-served with our existing solutions. If you scale that up by 10 or 100x, and you look at the NeoClouds, that is a whole other level of scale and performance and demands that traditional solutions are not well-placed to serve. At the same time, NeoClouds are clouds. They have reliability constraints. They have SLAs to meet. The balance of getting the right performance levels and the right manageability and reliability becomes of utmost importance, and we are the only ones that can go provide the balance of those two.
The last point I will make here is with the scale AI offerings led by FlashBlade//E, and Charlie hit this a little bit upfront, coupled with FlashBlade//S, we are now the only vendor that is able to cover the entire spectrum of AI needs from the smallest labs to the mid to large enterprise, all the way to the NeoClouds. In my next section, we will talk a little bit about the hyperscale. Let us put some numbers on this. We talk about core AI, we talk about what is happening in the enterprise. What we typically see in the enterprise, and in the example I gave before, is an enterprise might deploy an environment serving tens to hundreds of GPUs, maybe a petabyte to tens of petabytes of data, supporting hundreds, maybe up to 1,000 users or so. FlashBlade//S uniquely fits this spot well.
We provide that entire range of performance, scale, capacity, and the flexibility to grow and move within that space to serve RAG or inference environments, agent deployments. We do this with an all-in-one package including the storage, the software, as well as the networking. As you look at the NeoClouds and you look at the concentration of demands that are happening in the NeoClouds, think about scaling this up by two orders of magnitude, 10X, 100X, right? You are not serving tens to hundreds of GPUs. You are serving thousands to hundreds of thousands of GPUs. You are serving hundreds of petabytes to exabytes of data. You are serving tens of thousands, maybe hundreds of thousands of users. Now, you might say these are completely opposite ends of the spectrum, right? They are completely different demands. What they are not is completely different products, right?
What we have been able to do with FlashBlade Exa is extend the same technology we use to serve the enterprise with FlashBlade//S, the software that drives that, pair that with a more open hardware platform to meet the needs of this new level of scale. So complete opposite ends of the spectrum, same technology that allows us to address both. What does that look like? Charlie introduced this a little bit upfront. Really three things that we are leveraging, using, extending from the core FlashBlade and Purity software. Number one is the utmost performance we can deliver. Two is the scale and flexibility, and three is that mission-critical bulletproof reliability. On a performance front, everybody tends to focus on bandwidth and how many bytes per second, gigabytes per second, terabytes a second.
I apologize for those in the room who were with us through the morning session, but I will draw another analogy. It turns out in AI workloads, both data performance and metadata performance are important. What do I mean by that? Think of it this way. If you ask a computer to open a file, the computer has to do some work to find the file, figure out where the data sits, and then it has to do the reading. The reading part is the data performance. The finding the file is the metadata performance. Okay, it does not sound too bad. Let me draw a more human analogy. If I handed you a book, turned to a page, and asked you to read the page, it might take you a minute or two. Not too bad.
I hand it to a speed reader, might take him 20, 30 seconds. Wow, that is a whole heck of a lot faster. If, on the other hand, I point you to a shelf of books and I say, "Hey, go pick the third book off the first shelf, turn to page 17, read two sentences, pick another book off the fourth shelf, go to a random page, read a couple sentences." The more work I ask you to do to find the data versus reading the data, right? The more metadata work I am asking you to do versus data work. What happens in AI workloads is you need a balance of both these things, right?
If I ask you to go pick a whole bunch of random books and read a sentence or two from each, we can agree that it does not really matter how fast you read, you are going to spend all your time finding where that data sits. We do a really good job of both of those things. We do a really good job of both of those things because of the software. We have built FlashBlade, and with FlashBlade//S, we have been able to extend that and decouple that to create independent scale, the ability to supercharge the performance for both metadata and data. We have been able to also extend the scale in terms of capacity. So really now with FlashBlade//S covering the entire gamut of tens of terabytes to tens of exabytes, tens of gigabytes a second to tens of terabytes a second, and everything in between.
Most importantly, I mentioned this before, as we think about NeoClouds, GPU utilization, reliability, usability, operational simplicity are of utmost concern. We have been able to do this on open standards without a whole bunch of tinkering, without a whole bunch of nerd knobs for customers. We have been able to do this so that NeoClouds can operate these environments on thin staff in a way that gives them mission-critical reliability that we are known for. Some results. MLPerf is the industry's leading benchmark when it comes to AI systems and particular storage. These are results we recently published with the MLPerf, the latest MLPerf 3.0 benchmark. These are standardized tests. What our submission has shown is that we are number one across all categories and all configurations. Somebody asked me over lunch, "Hey, your peak throughput for checkpoints went up last year. You did 10 nodes.
You now do 30 nodes. It is up 3x. Why is it only 3x?" I said, "Well, it is entirely linear." It is entirely bounded by how much hardware we decided to buy. If we were to deploy twice as many nodes, that number would go up 2x. This is exactly the power of taking the software scalability and the underlying platform knowhow that we have with FlashBlade//S and taking it to the next level of scale. Giving our customers in the NeoClouds the same predictability that the enterprises had for the last 15 years. All right. If I zoom back out and look at the NeoClouds, a couple of things. One is these large-scale AI environments, three, four years ago, the focus was entirely on performance. Now, yes, they need the performance, but two things are happening. They need performance across an increasingly varied set of workloads.
It is not just large model training. It is training, it is also inference, it is also agent deployment. It is all of those things happening at the same time. It is all of those things happening at the same time across multiple tenants. They need all of those things with the same reliability, same tenant isolation, same security. A lot of the enterprise capabilities start creeping into these environments. This is an area where we are uniquely positioned to be able to meet both ends of those needs in a way that nobody else on the market is able to do. All right. What everyone has been waiting for. Let us talk a little bit about hyperscale. As we look at the hyperscale solutions, three takeaway points here. Number one, we deliver significant technical and structural advantages to the hyperscalers. Two, it is not just the point technology.
We deliver them a compelling solution that gives them a consistent, unified, single architecture that gives them consistency across performance tiers, as well as flexibility to adapt to new workloads. There is a race to deploy flash, and we present the most compelling option to get there fastest. Why is that? If we look at what has been happening over the last five, 10 years in hyperscale storage, and I simplify this to big animal pictures, a couple of things. One, performance demands keep going up. It is a thing called AI. Two, hard disk drives, relatively speaking, keep getting slower and slower. Yes, they might get larger, but they are not getting any faster, so performance per terabyte is actually going down. That gap to flash increases. There is an increasing rush to deploy QLC flash.
Fairly simple equation. What does this result in? It means that every hyperscaler out there is doing a couple of things. One, they are racing to secure SSD supply, QLC in particular, as fast as they can. They are doing this across multiple vendors, which then puts the onus on them to do multiple qualification cycles with different SSDs from multiple vendors, and they are doing this at a point in time when their workloads are changing rapidly. They have to make this whole thing efficient. They are at the same time trying to figure out how to get power, how to get space, how to build new data centers. They have to drive efficiency out of these investments at the same time. There is a lot going on. What are their choices? How do they get there? There are a couple paths.
Option one, option A, is what is called the status quo. They can continue to build out based on conventional SSDs. This is what they do today. They are going to keep doing some of this. It is getting increasingly inefficient. The SSD architecture has inherent limitations. As they deploy more, as the workloads get more performance demanding and more varied, those limitations get stretched even further as they have to qualify multiple SSDs. There is more work on the front end. This thing is getting harder, but they are going to keep doing some of that. They could try to change the approach. Instead of working with conventional SSDs, they could try to build substantially similar technology to work directly with NAND, and they try to build that in-house. Could they do it? Yes. Will they do it? I do not think so. Why? It is a lot of R&D.
We have been at this for 15 years. We have some of the most sophisticated knowledge in the industry, even including the NAND manufacturers themselves, in how flash works, and we have an enterprise business that pays for it. When you think about the R&D to develop the technology, when you think about the ongoing maintenance and qualification of each new NAND part from a vendor, much less each new vendor that gets onboarded, that is a lot of R&D to get started. It is a lot of R&D to continue investing. You can do it, but you have to consider opportunity cost. They have option three. You get the best of both worlds.
They can get the technology benefits working directly with NAND, don't have to take on the onerous R&D, work with Pure, who now has a proven technology integrated with multiple hyperscalers, and this is really how we see the opportunity playing out. All right. We've talked about this in the past, a little bit of a rehash. Not going to go deep into hard drives. They're terrible. They're still terrible. They're getting worse. SSDs versus DirectFlash. At the end of the day, the structural inefficiencies that are inherent in SSDs come from the fact that SSDs are effectively a technology coping mechanism. They're a coping mechanism to expose semiconductor NAND flash to software, computer software, in a way that makes it look like a hard disk drive, right? In order to do that, you actually have to do a bunch of work inside the SSD. There's complex firmware.
There's circuitry to make that firmware work. There's a little controller chip. There's DRAM to make the whole thing. It's basically a little computer in a box. That thing keeps getting more and more complex every generation of NAND. As the memory manufacturers make denser NANDs, stack more dies, it gets harder. That thing is bursting at the seams. What does that mean? It means compromises in reliability. It means compromises in efficiency. It means compromises in performance and performance predictability. We bypass all of that. We bypass all of that with our software, which we run at the host level, essentially treating flash the way it's meant to be treated, as a semiconductor. That's what we're delivering with DirectFlash. But it's much more than just the technology. It's much more than just DirectFlash. It's how we've packaged the solution to fit within the hyperscaler architecture.
I'm going to go deep into this a little bit. We've talked on the earnings calls a little bit before about how the hyperscalers design storage in a horizontal tiered fashion. We now have conversations with multiple hyperscalers that all substantially look very similar to this. At some layer, they have their own distributed storage software that runs across multiple storage nodes. Underneath this storage software layer, they design specific hardware storage node combinations depending on price, performance, capacity needs, that typically have multiple tiers, a hot tier, high performance, a warm tier, pretty good performance, and a cold and archive tier, best economics possible. Typically, you'll find SSDs at the top, hard disk drives at the bottom, and then maybe a combination of both in between. This is kind of the starting point. This is how hyperscalers have traditionally designed their storage environments.
What we've done is two things. One is we've packaged our DirectFlash technology, which I just explained, has all these benefits over SSDs. We've packaged that technology in a way that fits very cleanly, integrates very easily into their distributed storage software. It's not hardly any software change at all on their distributed storage software. We slot in just like their existing storage nodes. We just do it much better. We do it much better because of the attributes I just described. We also do it much better because we give them a consistent architecture to be able to serve the different tiers, whether it's hot, warm, or cold, with one architecture dialed up and down based on software configuration and media configuration, right? When you think about that, economies of scale, right?
Chadd talked this morning about how the enterprise benefits from operating the same way across multiple environments, huge benefits. Think about what that means at the hyperscale. Massive. The other thing that is happening is it is not just a set of clean tiers anymore. With AI, with the advance of AI-driven workloads, even the hottest tiers are now getting stressed for performance. As new workloads appear, new applications appear above the hyperscaler storage software, it is pushing performance boundaries beyond what a single SSD tier can provide. The hyperscalers are now being forced. They are being pushed in a direction of designing multiple different hardware configurations for each of these, what used to be one tier. They can do that, but again, it is more work, it is more inefficiency, it is stranded capacity, it is one more thing to operate.
Here is again where our single consistent architecture can now give them flexibility, software tuned, media configured, but entirely consistent architecture and flexibility to meet not just all the performance tiers, but adapt to new workloads very, very quickly. Ultimately, the two value propositions we are delivering to the hyperscalers is the core technology, which gives them all these benefits, performance, reliability, efficiency, but then the packaging, how we are delivering it in a way that gives them operational consistency across multiple environments, whether it is SSDs now, in time, as prices drop, all the way down to disk drives, as well as adapting to the needs of new workloads. It is not just the technology. There is a lot behind this. There is a lot of history. There is also a lot of vendor relationships and know-how about Flash. We have been at this a long time.
We work with multiple NAND providers, key partners like Kioxia, that we have worked with over a decade to help co-engineer and co-design future roadmaps. We work with multiple NAND generations across multiple suppliers. We do the qualification. We do the reliability engineering. We have got an enterprise business to go pay for that. We can go and offer the benefits of that to hyperscalers. I mentioned qualitatively of some benefits. Not going to unpack this slide, but just to put some numbers on it. When you think about twice the lifetime, when you think about 5X reduction in footprint, when you think about 2x to 5x improvement in reliability, these are serious numbers. These are serious numbers no matter who you are. These are serious numbers to an enterprise. Think about operating at hyperscale, right?
The operational savings, the headcount reduction, the reduction in tickets, the improvements in SLAs, these are huge benefits. At the end of the day, if we simplify this, the three things that hyperscalers are looking to us, looking to our solutions to provide, number one is the efficiency, whether it is, again, equipment efficiency, utilization, power, reliability. It is the uniformity. It is how we have packaged that solution, that technology to give them one approach to deal with multiple tiers, multiple uses, multiple needs. It is our supplier relationships and diversity, our ability to give them access to the bulk majority of the world's NAND supply without having to do and bifurcate their qualification work. It is because of these things that have driven our first couple of top hyperscaler wins, as well as now an increasing set of interest beyond the top five hyperscalers for this set of solutions.
All right. I'm about at time. I'm going to try to wrap this thing up. I've hit a lot, but I think the takeaway message here is simple. We're now at a point of inflection in the company where we're pursuing not one, not two, but four different high-growth market areas that are expanding into new areas of software, extensions of our hardware technology, and everything in between. It's taking us into significantly new and larger market segments. We're doing this not as a bifurcated set of efforts. We're doing this based on one foundation, same technology, 15,000 customers, validation at the largest scales, driven by one product organization, one architectural foundation set of IP, and one sales organization. Thank you. All right, now for the session you all have been waiting for.
I have the pleasure of welcoming my friend Tarek to the stage, who will take you through how all of this comes together to influence our views on our longer-term financial model.
Thank you, Rob. You ran the clock like a true football coach. Amazing. Thank you very much. The market is closed, so now I can freely speak, and I'm delighted to be here with all of you. Good afternoon, and welcome everyone. For those who don't know me, I'm Tarek Robbiati. I'm the CFO of Everpure, and I just been in this seat for about a year, and I have to say, I'm having a wonderful time. Someone asked me at the break, how do I feel given my past career? I reiterate, I'm having a wonderful time, and most importantly, I remain deeply impressed by our massive market opportunity and the team executing against it. As Charlie noted, we treat data storage as an essential technology, enabling customers to manage their most critical assets in this day and age, and that is data.
Supported by a collaborative culture driving infrastructure modernization, we are literally redefining the industry, and I think you are seeing that come through today. Today, I will outline the foundation we've built for durable growth, detailing our long-term financial framework for growth, profitability, and capital allocation. We will begin with our current business performance. Then I'll discuss how strategic investments are expanding our addressable market to deliver sustainable, profitable growth and long-term shareholder value. You heard it from Charlie. We are at a major inflection point. Our business is accelerating. It is built on a strong multi-year foundation of differentiated software, a recurring customer base, and a culture of innovation. Today, multiple opportunities are converging to drive a new growth phase for our company. We are redefining data infrastructure and expanding into high-growth markets like scale AI, modern data software, and hyperscale solutions.
What is very important to me and crucial to me as CFO is I see us pursuing these opportunities while maintaining very strict focus on profitability and financial discipline. What I am going to tell you is going to be broken down in three chapters I would like to cover today. First, we will deep dive into the current business foundation. Then we will evaluate our ability to expand our core and capture share in new high-growth adjacent markets. I will tell you the potential of each one of those markets. Finally, and most importantly, I will share with you how our financial profile is resetting, and resetting durably. This is not an incremental change. It is a reset, and we believe this reset is durable. I have got to start where we finished last time we met. That is our Q2 result.
Our Q2 results, any way you want to look at it, was nothing short than outstanding. We grew revenue 38% and operating income 77% year-on-year, beating guidance and consensus. For FY 2027, as you already know from the guidance we provided and reiterate today, we target at the midpoint $5.05 billion of revenue. That is implying a 38% year-on-year growth and $950 million in operating profit that is implying a 50% year-over-year growth. According to Jefferies, Everpure is one in seven companies in the tech software space expected to grow revenue more than 30% in the next 12 months. Everpure is firmly in that club. Plus, we have the operating leverage inherent in our model to drive even faster growth in our bottom line. No, this is not a flash in the pan. Just make sure you are clear.
You know, and you probably heard that Everpure's addition earlier this week into the S&P 500 index is a testament to delivering strong growth with margin expansion consistently, quarter after quarter, over many years. This is what the profile of a high-performing company is. That brings us back to the inflection point. This top-line acceleration reflects strong demand, deep customer engagement as they rethink their infrastructure requirements. You know this already, but it is important we put some perspective. Let us frame our performance in a longer-term context, showing our revenue trajectory from fiscal year 2022 to fiscal year 2027. Over this five-year period, we expect an 18% revenue CAGR. That is really premium growth in a historically cyclical market. Looking to the right of the chart, the expected growth for fiscal year 2027 accelerates to 38% year-on-year. There are two key takeaways.
First, this is not a new growth story. You heard it from Charlie. We delivered durable growth for years. Second, and this is news maybe, our growth trajectory is now accelerating. This inflection is the result of a very strong foundation and continuous investment in our software platform, our technology, customer relationships, and product velocity. We believe this faster growth will continue across economic cycles because it is driven by structural tailwinds in how customers build and manage data infrastructure, which we will address shortly. Also, when you look at growth, not every growth is created equal. Quality growth is really important. Cyclical growth is not quality growth. Examining our performance in detail reveals increasing revenue quality driven by our SaaS offerings, particularly Evergreen//One. That is the market's only true SLA-based storage-as-a-service solution.
Unlike traditional product sales, which can be more directly affected by component cost fluctuations, Evergreen//One is built on long-term customer commitments with lower upfront capital requirements. With Evergreen//One, customers can ramp into growth and are billed on a consumption basis, which allows them to better match expense outlays to the growth of their solutions. Two points worth noting. First, Evergreen//One has gained tremendous traction in the current environment as we are able to contain prices. Second, because we control the configurations of the solutions that underpin the Evergreen//One SLA-based contracts, we are able to fully manage the margin of our Evergreen//One offering.
The key takeaway is that as of the second quarter of fiscal year 2027, Evergreen//One's TCV is on track to surpass an annual run rate of $1 billion, and that is driving sustained revenue growth and earnings quality for the foreseeable future. As you know, we have to look at the bottom line, and when you look at the bottom line, you will probably realize that we prioritize long-term profitable growth rather than growth at any cost. From fiscal year 2022 to fiscal year 2027, operating income is projected to grow at a 32% CAGR. That is outpacing our 18% revenue CAGR we spoke about a moment ago. For the current fiscal year, operating income is expected to increase by about 50% to $950 million at the guidance midpoint, demonstrating that we are growing operating income significantly faster than revenue and therefore creating operating leverage.
As we scale, we will continue leveraging past platform investments and allocating R&D towards high ROI growth opportunities. This is the crux of the business model that Charlie spoke about. Reinvesting to accelerate growth remains central to our financial philosophy. In so doing, we ensure that those investments translate into durable revenue growth and increasing profitability. Ultimately, our long-term builds a virtuous cycle where growth and profitability reinforce each other, with profits fueling the continued enrichment of the Everpure software platform. I want to hammer this point for the third time. This is an R&D-driven model. Hopefully, you will realize that. It is very, very important. It brings me to the most important differentiation factor of Everpure, and that is innovation. You heard it from Charlie and Rob already. On a non-GAAP basis, we invest an industry-leading 19% of revenue into R&D annually.
With all engineering talent focused on a single platform, that is key. If you have to spread your dollars over multiple platforms, you are wasting time and resources, and dollars. Here, all the engineering talent is focused on a single platform for faster time to market and better ROI. Our software-centric approach, backed by skilled hardware engineers designing simplified products, delivers superior software intelligence that extracts maximum performance, efficiency, and value from the underlying NAND. We have used this approach to enhance the customer experience across many areas. You heard that. We pushed innovation in data management, in power and space efficiency, automation, and also enabling new business models like Evergreen//One. The key point to take away is that our R&D investments fuel future growth, creating product velocity that penetrates new markets and drives durable revenue and profits for years to come.
Let me summarize what we discussed so far in the first chapter. First, our strong core business has delivered durable multi-year growth, which is accelerating to 38% revenue growth in FY 2027, compared to an 18% CAGR since FY 2022. Second, we are driving significant operating leverage, with operating income projected to grow about 50% this year and at a 32% CAGR from FY 2022 to FY 2027. Third, our continuous innovation strengthens our modern tech stack to better serve our customers and sustain revenue growth and profits. In short, the Everpure value proposition combines a differentiated technology platform, durable core growth, and resources to fuel acceleration. Moving forward, we plan to capture share in high-growth adjacent markets with large untapped potential, translating our R&D leadership into long-term financial performance.
Before we talk about the expansion of our addressable market, I want to spend some time on a very important topic that I am sure is top of mind. It is a very important dynamic that is impacting our industry right now, and this is related to the significant increase in component costs. This chart illustrates the magnitude of the NAND and system cost increases since the past 12 months from the third quarter of FY 2026 to the third quarter of FY 2027. The increases we are seeing across both QLC and TLC NAND, as well as overall blended system COGS, are truly unprecedented. While price adjustments to offset these costs have aided reported revenue growth, we are not and never have been a price-driven growth story. We have not and never have been a price-driven growth story.
Our momentum is primarily fueled by strong underlying demand, market share gains, and expanding customer footprints. As you look at our growth trajectory, it is important to distinguish between the acute benefit of pricing and these far more sustainable growth drivers. The next slide provides some historical perspective on that. You have seen this chart already in Charlie's presentation, this fantastic line with the uptick, as Rob noted. We have added a new lens for you to identify the periods where NAND costs were inflationary and deflationary. The vertical bands on the chart identify each period. As you can see, putting today's environment into the historical context, we have demonstrated since 2013 that we reliably gain share through every NAND cycle, regardless of whether input costs are inflationary or deflationary. Here is why. In inflationary environments, as total investment costs rise due to trends like AI, what do customers do?
They prioritize quality and upgradable products to protect their investment ROI. Conversely, in deflationary environments, our differentiated architecture leverages falling component costs to drive more efficiency, while customers also optimize for inflationary TCO factors such as labor, power, and space. Ultimately, we do not view NAND pricing as a determinant to our growth. Let me repeat that. Ultimately, we do not view NAND pricing as a determinant of our growth. Our gains in share are driven by the enduring value we provide to customers. With this growth proven durable across multiple cycles, you may wonder, "So what happens to your gross margin?" Let us get into that. The simple punchline here with the same analysis at the gross margin level is that we are gaining market share without compromising our economics. We are maintaining a substantial product gross margin lead over our nearest public competitor across all market cycles.
We control our gross margins. You hear me? We control our gross margin, and we do so through disciplined, deliberate pricing and balanced near-term performance with long-term growth by delivering customer value while adapting to fluctuating cost structures. Ultimately, our financial model creates substantial operating leverage capacity, one that builds a durable, high-quality business that grows share, leads in margins, and increases profitability at scale. You may say, "Why is that?" Why do customers choose Everpure? I want to bring you back to the beginning. Our differentiation starts with software, and as Paul always says, "It's all about the software." Look, there's nothing special about having access to the underlying NAND itself. SSDs do that all the time. The secret sauce is providing customers with the best mix of software and architecture to extract the most value out of that NAND.
As Rob and Chadd noted earlier in the morning, Everpure cuts power and space by up to 5x , delivers over 10 times greater reliability, requires up to 10 times less labor, and features constant upgrades with our Evergreen//Forever program. That is driving a 50% lower total cost of ownership or TCO. Now think about the sheer economic value that this delivers for customers running large data centers. Those customers' buying decisions are ultimately based on the total cost of operating the infrastructure over its useful life. Most customers are not asking, "Who has the lowest price per terabyte?" Most of them ask, "What is the most efficient way to run my data center infrastructure?" By drastically reducing power footprint, labor, and complexity, our solution delivers compelling long-term economics despite a higher initial purchase price. Here's what you can conclude.
In other words, if you look at our gross margins, our customers reward us with a premium for our solutions because we lower their total cost of ownership. This drives industry-leading NPS, durable market share gains, and margin leadership. Moving forward, this is news. We will track our progress in the core and new markets that were identified by Charlie and Rob. We have a solid foundation with our core and have multiple paths for future growth. While continuing to report revenue and subscription revenue as we do, we are organizing our revenue into two main categories to help investors track our progress with respect to the expansion of our business model. The first category, core and core AI, encompasses established platforms that you know already, FlashArray, FlashBlade, and Evergreen//One, supporting both CPUs and GPUs, representing our ongoing growth and market share opportunities in the core.
The second category, which we're going to call new revenue, introduces three growth vectors. Scale AI, you heard from Rob, it includes our excess solutions for neo clouds, AI titans, et cetera. Modern data software, which includes Portworx, Pure Protect, Everpure Cloud, Data Intelligence, also known as OneTouch, and Data Stream. Hyperscale solutions, which leverages our DirectFlash technology for major hyperscale customers. I would like to reiterate that these are interrelated businesses that are natural extensions of our R&D capabilities. I know you hate changing your models. You're not going to change your models. That's the good news. Moving forward, we'll keep the same reporting segmentation, but in addition, we intend to provide you at the end of each year with annual updates about the growth in new revenue, which will track the expansion of our business beyond the core.
So how do we view new revenue within our overall market opportunity? Historically, every publication around the storage market was referring to a $50 billion market growing about 9% per year. However, AI and data proliferations have dramatically expanded our addressable market, ushering the age of data primacy, driven by two structural tailwinds, exponential data growth and massive AI-related data infrastructure deployment. To put things in perspective, in 2025, the world generated 173 ZB of data. I do not know how many of you know what a zettabyte is. It does not matter, but it is a one followed by many, many, many, many, many, many zeros. Anyway, it is a 90-fold increase since 2010, when the world was generating only 2 zettabytes of data. This has been driven by IoT, AI workloads, video surveillance, cloud adoption, real-time processing, you name it.
In parallel, concurrently, you know this, you research the market better than we do, cumulative CapEx for hyperscalers related to AI infrastructure is estimated at about $2 trillion-$3 trillion over the next three years. That is 1.5x to 2x the U.S. Defense budget. This helps paint the picture as to why we are so bullish about Everpure growth opportunities in the core. Now, let us examine also the projected addressable market sizes Everpure can capture over the next three years beyond the core. We said already that the core is accelerating at a 12% annual growth rate, reaching $79 billion by fiscal year 2030. Here we compete with traditional enterprise storage providers like Dell, NetApp, HPE and IBM.
In hyperscale solutions, which I am showing you on the chart, based on our estimates, we estimate the TAM will grow at a 22% CAGR to $97 billion from what it is today at $44 billion, $97 billion by 2030. Here we are competing against internal cloud solutions, SSDs, and hard disk drive providers. Our modern data software TAM, spanning app modernization and data intelligence, is projected to grow at a 26% CAGR to $22 billion by fiscal year 2030. Here we face different types of competitors. NetApp, Dell, Veeam, Cohesity, IBM, and specialized providers like Databricks. Finally, scale AI is projected to nearly double to $9 billion by 2030, competing against traditional providers, high-performance storage, and AI specialists such as VAST Data, DDN and WEKA. We compete in four high-growth markets against different players in the industry.
These estimates, I would like to underscore this, it is really important. It is very important to understand that these estimates are based on our current solution set, with potential for further TAM growth as we expand our current solution set and customer adoption and use cases continue to increase. Let me summarize the key points to remember here. Our core and core AI business opportunity is now growing at an accelerated double-digit pace as a result of the explosive growth in data and AI deployments. Number two, Everpure modern tech stack and rapid product velocity has us very well positioned to compete in several new, faster-growing markets, hyperscale solutions, modern data software, and scale AI. Our total addressable market opportunity over the next four years is increasing by a whopping almost $100 billion, from $108 billion to $207 billion.
Now, of course, the TAM is only useful if we have a credible right to participate in that market. That is why I also want to show you our serviceable addressable market, or SAM. Our serviceable addressable market, SAM, focused strictly on all-flash technology because that is all we do, is doubling from $34 billion in 2026 to $69 billion in 2030. That is driven by moderating commodity pricing at some point in the next four years, and AI-created workloads. Anchored in our all-flash DNA, differentiated technology platform, and deep customer relationships, our strategy extends our core advantages into faster-growing data infrastructure and data management markets. With this strength, our ambition is clear, and is to be the number one provider of all-flash solutions, uniquely positioning Everpure to compete and win across these four markets. All right.
I think I need to relax a little bit because now we are getting into the meat of the presentation. I am not sure, Paul, my ears are ringing. I am hearing everybody saying, "It is a Jerry Maguire moment. Show me the money." All right. That is what I am hearing you say. All right. I am going to show you the money. We spent the last several slides explaining why we believe the opportunity is expanding. Now I want to give you a preliminary outlook view for fiscal year 2028. Okay? The quick punchline is that Everpure is entering fiscal year 2028 with significant momentum and an expanding set of growth opportunities. Building on our fiscal year 2027 revenue outlook of 37%-38% growth per year, our preliminary fiscal year 2028 outlook is here. Is $7 billion-$7.3 billion, implying 39%-45% growth year-on-year. That is right.
Someone said, "Wow." This indicates accelerated top-line growth, delivering a two-year CAGR of approximately 38%-41% for fiscal year 2028 to fiscal year 2028. This is well above historical levels and current consensus. I want to make sure you are level set on this. This premium growth is driven by the diversified portfolio we spoke about with Charlie and Rob, continued core strength, emerging hyperscale solutions, expanding into modern data software, and scale AI. Here, I am asking you to cut me a little bit of slack because it is unusual for me to provide you guidance for the next year at the middle of the current year. This is not guidance, but it is preliminary outlook, and it is shared to reflect with you our strong visibility and confidence in this trajectory. It is an outlook.
We stand by that, but we will provide further details about it and formal guidance consistent with our usual timing as we wrap up fiscal year 2027 and enter fiscal year 2028. The range is wide, and that is because we are in the middle of our planning cycle. When we complete our planning cycle, we will come back to you with an hour range. That make sense? Okay. Now the question everybody wants to talk about: What about your operating margin? All right. The good news is not limited to accelerating our revenue growth outlook. It extends to a step change, higher in expected profitability as well. Okay? Let me bridge that for you. You have seen in our guidance what we intend to grow for between fiscal year 2026 and fiscal year 2027.
For fiscal year 2028, we expect operating income of approximately $1.7 billion to $1.9 billion. That is representing growth of approximately, excuse me, 80%-100% from fiscal year 2027, and accelerating from the 50% growth year-on-year that we guided for from fiscal year 2026 to fiscal year 2027. This equates to an operating margin range of 24%-26% as a result of further operating leverage in core and core AI, combining with new revenue beginning to scale. As we've demonstrated historically, our model has the ability to convert incremental revenue into incremental operating profit at an attractive margin rate. Our overall financial framework seeks to optimize growth and profitability over the long term, compounding the overall value we're able to deliver to shareholders.
I'll let you think through these numbers for a minute, because even when I look at them, I cannot have a neutral reaction. Let me put things in perspective for you because this is the same sort of mental model I had to go through myself. Fiscal year 2027 is expected to grow from $3.7 billion in 2026 to $5.05 billion in 2027 at the midpoint of our guidance. We are skipping the fours. We're skipping the fours. Fiscal year 2028 revenue outlook shows that we will be growing to $7.15 billion at the midpoint of the outlook from $5.05 billion. So we're skipping the sixes.
Are we skipping eights?
Nah. Thank you, Steve. In terms of operating profit, we would be almost doubling operating profits in fiscal year 2028 relative to fiscal year 2027. That's where we stand today, and we will refine our outlook and turn it into proper guidance as we lap fiscal year 2027. All right, so let me summarize chapter two, the key takeaways of chapter two before we discuss the long-term financial profile. First, again, please understand, this is really important. Irrespective of the NAND cycle, we continue to gain share and deliver durable growth across all cycles, despite component inflation or deflation. Second, our customers reward us with a value premium that translates into high gross margins because of our differentiated software, which delivers them superior TCO. Third, our market opportunity is expanding significantly.
AI is driving a TAM well beyond our historic $50 billion figure, with a serviceable all-flash market expected to double from 2026 to 2030, from $34 billion in 2026 to $69 billion in 2030. Fourth, our preliminary FY 2028 outlook reflects this momentum, driven by accelerating revenue growth, operating leverage, and scale benefits. Ultimately, our confidence in the next phase of growth rests on a strong core, consistent share gains, cycle in, cycle out, and expansion into much larger growth markets. This is why we believe that our profile is resetting durably for the long term. Let me bring that all together for you and what it means. The key takeaway is precisely that. Our financial profile is resetting to a higher and more durable level, characterized by sustained growth, increased revenue streams, and expanding profitability.
If you look at Rule of 40 scores, roughly for 2026, we had a score of 33. For those of you who follow our earning transcripts, we did say that for the past three quarters, we hit Rule of 40 already, and we also guided for the end of this year to a point where we're implying a score of 50 to 57. That's where the 50 comes from. For FY 2028, when you take that into account and everything I gave you on our preliminary outlook, we'd be in the 60 to 70 range. For the long run, beyond FY 2028, I can go beyond three years, you can expect that we would be in a range between 50 and 70.
The core assumptions in here is that our core and core AI continues to gain share, and that our new revenue from this new revenue category I articulated for you is going to represent roughly 20% of total revenue by 2030. In so doing, we want to make sure that we continue to grow the quality of our revenue and have more durable, more recurrent revenue with Evergreen//One. That's a very important part of our strategy. We expect our ARR CAGR for the next three years to be greater than 20%, and we expect our remaining performance obligation growth to be greater than 25%. As a result of our push in hyperscalers and Evergreen//One, one has to assume CapEx as percentage of revenues to be in the mid-single digits.
In terms of tax rates, at this stage, and there's a lot of things we can discuss about tax, which is a topic that I absolutely love to discuss. You must expect an annual non-GAAP ETR, effective tax rate, in the vicinity of 20%. Just a bit of advice, do not model tax on a quarterly basis. You get the equation wrong because you don't know the mix of our profits by geography. So what you have to assume for the tax rate when you model it in your models, 20%, more or less, on an annual basis. Then we expect free cash flow as percentage of revenue to track operating profit margin. No change.
Now you're going to say, "How do you intend to deploy the capital you're going to be generating?" Our capital allocation framework focuses on three key priorities to drive long-term per share value creation for our shareholders. First, we got to prioritize investing in our core and strengthening the balance sheet. That is absolutely a must, and we'll continue to do so. We will maintain discipline, forward-looking R&D-led investments to sustain our technology leadership and expand into high-growth markets. Second, we approach strategic M&A with discipline. We are targeting tuck-in opportunities that offer differentiated technology, exceptional talent, or expanded go-to-market capabilities. Many opportunities will be evaluated strictly on strategic fit, risk-adjusted returns, integration complexity, and a fair price and a fair valuation.
Third, after funding the business, strengthening the balance sheet, and pursuing strategic M&A, we return excess capital to shareholders through share buybacks and withhold-to-cover purchases to offset stock-based compensation dilution. Also, over and above that, we may select to execute additional buybacks with excess capital. Hopefully, this makes sense. I'm sure you will have questions. This brings me to the end of my presentation, and you've been very patient throughout the day. I want to thank you for listening and flying in from afar. Andrew flew from all the way from Montreal, some of you from the East Coast, some of you on the webcast have been listening patiently. Many, many thanks to all of you. I want to summarize this before we get into Q&A by saying Everpure stands at a pivotal moment, a pivotal inflection point. No doubt Charlie will reinforce that.
We're backed by this strong core business, industry-leading R&D, and premium margins. AI is significantly accelerating our addressable markets, driving accelerated growth, increasing operating leverage, and reinforcing a durable financial profile above Rule of 40 throughout fiscal year 2028 and beyond. As our cash generation increases, we are committed to deploying that capital thoughtfully for the benefits of our shareholders. We believe we have the technology, the market opportunity, the financial business model, and most importantly, the people and the culture to build a substantially larger and more valuable company over the long run. Thank you very much for listening. With that, it's now time for Q&A.
I would like to invite Charlie, Rob, Bill, who is our General Manager of Hyperscale Solution, Prakash, who you already met, and of course, we couldn't hold a Q&A without him, our founder and Chief Visionary Officer, Coz, to join me on stage for the Q&A.
Thank you. Wamsi Mohan, Bank of America. Thanks for doing all the presentation today. Very helpful, both from a technology standpoint and, Tarek, some real impressive numbers here. My question is firstly, you obviously articulated a very bullish sort of outlook over here, well above consensus both for revenue and profitability. To execute this from a go-to-market standpoint, can you share what you might be doing differently, particularly on the hyperscale side? Within your guidance, if I could, are you anticipating incremental hyperscale wins or is this sort of based on what you currently have announced and maybe some color around that?
Sure. The go-to-market is quite different as one might imagine, between everything outside of hyperscaler. Hyperscaler is a very different go-to-market. It is engineering led, right? Bill and his team really lead that engagement. It is engineering to engineering. Really there is, while we do have salespeople and they are very good, but honestly when it comes to getting the win and closing the deal and all the complexity associated with the supply chain and contractual agreements, it is really led by the hyperscale team overall. On the core side, the go-to-market has substantially changed. We have made a huge investment in that area. It is far more sophisticated today than it was just a few years ago. It is not just product, and it is not just selling skills, it is really financial engineering that is working with the companies that we engage with, so it fits into their model.
Most importantly, Rob sort of alluded to this, it is the difference between selling into a particular use case and now selling at the enterprise level as a partner, where we are either one or one of two primary suppliers here, and we call that a franchise win. In other words, it is not a win against a specific workload or use case. They are choosing us to be their partner in data storage, right? I would say that has matured over the last couple of years to a really remarkable level, right? As we gain experience in that, as we gain more and more wins with franchise wins, we expect to see even more franchise wins.
As I said, while we really only get about two quarters of visibility in the core business from a pure pipeline level, on the other hand, we have years of experience in terms of gaining market share, and we see that gives us the confidence to say we are actually entering a new growth phase. Now, you might ask why now? It is largely because we filled out the product line only about a couple of years ago. We couldn't aspire to really be a franchise winner inside a customer. We didn't supply enough. Now we do, and with advanced value add. I would say. Yeah, please.
Oh, I was just going to jump in and say, Wamsi, to the second part of your question, I think, which is, hey, does the FY 2028 or beyond outlook contemplate or rely on needing a second or, sorry, third or further hyperscaler win? I just go back to how we've looked at the multiple high-growth areas that we're now pursuing. Each of which, alone has, as Tarek has shown you, drives a tremendous TAM and SAM expansion. So in some ways, as we go and pursue and prosecute those markets, yes, hyperscale is one of them. We have multiple paths to drive that growth. We see multiple paths to get there. We have a diversified, essentially, approach to go do that without having to bifurcate whether it's our R&D or sales engine to go
But I would say, look, 2028 is based on today's business, and the visibility we have into 2028 doesn't require anything fundamentally new associated with 2028. Of course, our expectations are that we will, in fact, get new hyperscalers. Our expectations are also that we will penetrate a greater proportion of their storage estate as well.
Yep. Thank you.
Jason.
Yeah. Hey, guys. Jason Ader from William Blair. Just on the FY 2028 guidance, Tarek, can you give us a sense of how much of that 39%-45% growth might be coming from pricing? And then also, it was great to get the 20% FY 2030 from new markets. Could you give us a rough estimate of FY 2028 from new markets? And then, completely separate for Coz, can you just talk about what this journey has been like? You're a founder, you're sitting here today, whatever it is, 15, 16 years later. Maybe just talk through how you're feeling.
Snuck in two questions. You are first, or-
Well, I-
You want me to go first? Okay.
I cannot do that. I have to give you the honors.
Yeah. So actually, next week will be our 17th birthday, on Wednesday of next week. It has been a great journey. Obviously, at this point, I am having a lot of fun. S&P 500 is cool. What we are doing with the hyperscaler is really cool. The team. I did not have to get up here and present. They got to do it for me. That is really good. Our goal from day one has been to build revolutionary technology and really change this industry. You have seen it from the start with all Flash and Evergreen, and you see what we are now looking to do in the future, which is go beyond just the storage, and really revolutionize the way people can control their data and understand their data. Everything we are doing around data primacy, data intelligence, helping people with the AI.
We continue to want to do extremely revolutionary technology that will totally drive something different. It is a lot of fun to do. It is a lot more fun than trying to squeeze another dollar out of boring old tech, and things like that. We are going to keep doing that, and I think you are starting to see the results, and that is also very gratifying because I am sorry, I feel we have got the best product, so I do not know why anybody buys anything else.
I would agree with that. That is for sure. Just to answer your question on the financials. The first thing to look at and to help you with, in terms of your modeling. You saw about 6 points of operating margin expansion between the end of this fiscal year and the preliminary outlook, Jason. I want to use the word outlook, not guidance, right? Take this 6 points. If you assume about 65%-70% of that coming in from new revenue to three categories that Rob has spoken about. Modern data software, scale AI, and hyperscalers. Then if you assume a reasonable amount of OpEx, going against this new revenue, you will see that our core is growing. We are not assuming any particular pricing benefit associated with that growth. We continue to grow irrespective of where prices go.
I am never, and no one in here, Charlie, no one in here will tell you that this pricing environment will sustain itself ad vitam aeternam. We do not believe it is going to be the case, but our preliminary outlook factors in any scenario that you want on that front.
Eddy Orabi from TD Cowen. Thanks for hosting us, and amazing numbers. Tarek, if I look at the slide you showed, FY 2028, it shows white bar and orange bars. Orange bar is from new revenue and the white one from core. Just visually looking at it looks like the new revenue, which include hyperscale and scale AI, is approximately going to be anywhere from 10%-20%. I would just like to-
I tried to blur it so that you don't take a ruler and go and do the geometrical arithmetic to come up with that number. No, seriously, I'm not going to break that out. One thing I want to leave you assured of, and here's how new revenue is going to work. At the end of fiscal year 2027, we'll give you a baseline of where we landed on new revenue. During the course of the year, we execute. Some of this revenue is lumpy in nature, so don't measure it on a quarterly basis. At the end of fiscal year 2028, we'll tell you where we landed. And that's how we intend to update you about penetrating these new markets. I think it's the simplest way, and you will also see through the expansion of total revenue and operating profit margin where we get to.
Got it. Just a follow-up. You guys have two hyperscalers already. Can you give us a sense of how much DFMs within the SSD capacity or non-HDD exabytes your share is by the end of next year? Just to get a sense of how much the revenues from these customers can grow in the future. Thank you.
I can't give an exact percentage because I don't think we have full access to that type of information. What I can say, it's still a very small number relative to their overall purchases. Yeah.
Excluding HDDs.
Well, excluding HDDs. Although we think as prices revert to the mean, which we are not going to predict right now because we would inevitably get that wrong, as prices start to revert to the mean, then HDDs are open season for us as well.
Erik.
Erik Woodring, Morgan Stanley. Thank you again for everything, and congrats on the outlook you provided today. Just a quick question. As we think about going from fiscal 2028 outlook to that fiscal 2029, fiscal 2030 kind of Rule of 40 framing, the low end of that 60% to 70% comes down to 50%. I assume you are giving yourself some leeway there, but I would just love to understand, when we think about that, is giving yourself some leeway on growth? Is that margins? Just to maybe understand
Erik
The trajectory a bit.
Erik.
We are not predicting a slowdown.
Erik.
That is what we are looking for.
Okay. Listen. Rule of 40 at the 50% mark. Slice it the way you want to, my friend. I will be happy. Okay?
And maybe just one clarification, Tarek, just on the 20% tax rate.
Yeah.
Annually, when do we expect that to start? Is that fiscal 2028 or?
So great question. This is a very important point. For those of you who have read our 10-K, you will see that we have a pretty big valuation allowance. It was about $838 million, from memory. As our profitability becomes more durable, more certain, we will have to release that deferred tax asset. Our current thinking is that we will have to release it in the next few quarters. That is why I cannot be very precise today on the non-GAAP ETR. We will be, and I think for the moment, the assumptions we gave you, roughly 20%, is what you can assume.
Thank you.
Hi. Howard Ma with Guggenheim Securities. Truly incredible numbers. I think we are all ready for celebratory drinks at this point.
Wait a minute. Hang on. We have six months of results to deliver and another full year to deliver numbers in accordance with that outlook, Howard.
Tarek, that is partly why the initial FY 2028 guidance is so impressive, just given how far out it is. My question is, what would you identify as the two main, or the two biggest areas of conservatism in the long-term framework? I will share my thoughts, okay? Because I cannot imagine you guys are assuming much recovery in volumes, because those are limited by IT budgets. If you look at top line, pricing will still be a tailwind next year, but volume likely still a headwind. The implied core revenue growth cannot be too high, so you are implying a lot of it is already coming from new product. If you look at it from a margin perspective too, you guys share that total COGS slide. You are clearly not raising prices, and it is still going to hurt your product gross margin. Right?
You are not going to get that much margin expansion from the core. It kind of backs into likely a very strong, very robust new revenue contribution already. But I cannot reconcile that with the 20% number three years out. It almost would imply that number does not grow much.
Well, let me start with that. So elasticity, and we've learned this, and Tarek showed the slide where we saw the volume and price elasticity in both positive inflationary markets and deflationary markets. In each case, supply-demand pricing economics work the way you would expect, which is when prices go up, on a relative basis, demand either doesn't go up as much or can go flat. But it doesn't make up for the fact that overall the dollars are higher. On the reverse side, if prices come down, elasticity goes up. In other words, volumes go up, but it doesn't quite make up for the price decline from a total dollar standpoint. So they go in opposite directions. Price dominates versus volume, typically. That's been our experience over 15 years. So I think that's pretty well established. By the way, that's supply-demand economics. That's Economics 101, right?
That being said, we expect to continue to pick up market share on a significant basis. If prices stay where they are now, we expect to pick up market share. That means both the volume and dollars. So we expect to continue to grow. If prices start to deflate, we expect to be able to manage that. Of course, it depends on whether they deflate or crash, but again, that's beyond our ability to be able to predict. So current predictions are based on current pricing. We're not expecting any dramatic change one way or the other, if that makes sense. But that being said, no, we are expecting the growth going forward to also come from core. But you'd be correct in saying that the majority of the growth is going to come from those new markets.
Thank you. Asiya here. Just to follow up on the prior question. If you think about supply, I think that's been a topic. I think you addressed that in the last earnings call, that you have good visibility, good supply, and therefore, you provided an upside to your fiscal 2027 guide. Just help us understand, this outlook that you've provided, how much is supply a concern through fiscal 2030?
I would say that on the new revenue side, which is primarily software. But to the extent that if a hyperscaler can't source, or their suppliers, I should say, can't source, then of course, that would get us in trouble. We don't have concern in that area right now. On the core side, yeah, the concerns around availability of supply have gone way down. It doesn't mean we don't scramble from time to time to deal with shortages here and there, but for the most part, that's not, I would say, a major concern now. Now, if you were to talk to our head of supply chain, they're being kept very busy. But from an enterprise risk standpoint, I'd say it's far less than it was a few quarters ago.
NeoCloud, is there a supply constraint for NeoCloud?
I would say that tends to be memory constrained right now. But our volume relative to the overall memory constraint's relatively small, so I wouldn't say that's a big area of concern.
Yeah, I would say when we think about the NeoCloud solution with EXA, very similar to the hyperscale solution where the NeoClouds themselves, through their supply chains, are procuring the hardware. We're providing mostly a software solution. And then, relative to how we're thinking about the core business, no particular supply constraints contemplated in the numbers we put out.
Yep.
Yes. It's Mehdi Hosseini here from Susquehanna. Two follow-ups for Tarek. First, you said we don't need to change the model, and you provide an annualized number at the end of each fiscal year. Should we assume that the incremental or new revenues would be lumped into product revenue? If you could help us with the distribution of incremental revenue into the buckets of revenue that you provided. Then number two, you have a CapEx to support the hyperscaler. If you could just provide what that CapEx actually is and for what application.
Yeah. I'll take the first part of the question, and I'll ask Rob to opine on the CapEx, which he will elaborate on more. So yes, the vast majority of the new revenue today is software term licenses, effectively, if you want to simplify it. No change to what we said around hyperscale revenue. It's license plus some non-NAND related componentry. It all goes into product revenue. You may want to ask, and I'll take the opportunity to answer the question, what would be the assumable gross margin for new revenue in total? I think what we gave you for hyperscale revenue, 75%-85%, is what you guys should assume.
On the CapEx side, I would say very similar to what we've done in the past in terms of developing our core technology around DirectFlash, but now think of it as qualifying multiple different vendors of Flash, multiple different sizes and chips. As we think about serving multiple customers and going to larger drives, you should assume a slightly larger spend commensurate with what Tarek outlined.
Thanks for asking the question. It gives me an opportunity to provide more color.
[Steve Canman], Morgan Stanley. I figured I could ask a strategy question. It is basically the same one I asked before, which is, storage is not the highest influence, highest power part of the enterprise data group, right? It is not the best horse to be riding. How do you get consideration? I think you answered the question very well technically, but strategically, how do you get in there and say, "We should be part of your data stack. We are a software company, we are not a storage company." How do you do that from that position of having your strongest relationships in storage? What is the go-to-market challenge there? What do you have to do in terms of hiring bodies? How do you change your go to market? Is that dialogue happening naturally?
Well, it is happening, I would say, naturally, but it is also in transition. It is transitioning from. When I say it is transitioning, we have a set of value adds today that will continue. Now we are transitioning to a set of even higher value adds that appeal beyond the traditional storage purchaser. Today, an amazing amount of work and effort and labor and thoughtfulness has to go into managing a data storage estate today. One asks why. As you point out, shouldn't it be a bit more mundane than that, right? What we are doing with, first of all, just making the product simpler to begin with, but then going into what we are calling Pure Fusion and the Enterprise Data Cloud simplifies their data storage estate, and allows them to invest their time into higher level concerns. We truly want to make it automatic.
You do not go down into your basement and futz with different plumbing in order to take a shower in the morning. You just turn a knob. That is what we want to get to with respect to data storage as well, right? They just turn a knob, they get what they want. Where it is going is your point. While the data storage itself doesn't have power, what we are really predicting is data is going to have the power going forward, even more than the application environment. Because without clean data, you are not going to get clean results, right? Applications are going to be much more focused on the workflow and not on controlling the data itself.
That's where the conversation is going, and that's where we're putting in a lot of our efforts around Data Intelligence, around being able to better manage that once you have added context to the data, once you've made the data more valuable and more accurate, being able to manage it according to the customer's policies rather than, again, futzing with the plumbing down in the basement. We believe we are getting into much higher value conversations with higher levels in the IT world in this area.
Steve, if I could add onto that. I think from my lens, there's no one single answer, right? I think there's a number of things that we have to activate across the entire go-to-market engine, well, frankly, the entire firm, some of which are well underway, some of which we're, I would say, in the early stages of. As Charlie mentioned, our historically typical buyer has been a storage administrator. That person, that individual may not resonate, may not care too much about the value we're creating. As I alluded to in my discussion, back when we were competing for workload by workload, $500K PO, $1 million PO, that individual could sign off on it. When we are now going and competing at a franchise level, putting eight-figure, nine-figure deals in front of customers, you can bet that's getting us conversations way higher up in the stack.
When you think about pursuing and articulating the technology differentiation strategy that Charlie just mentioned, that requires new DNA in our sellers. That requires new DNA in our SEs, our partners, the enablement, the training to go do that, so we're well underway with that. I think just even most visibly, it starts with not getting boxed out of the front door, right? One of the things that really led to rebranding the company and changing the company name is making it very visible, very optically obvious that we are doing much more than just data storage. So I think it's a number of these things that have to fall into place, some of which are well underway, all of which we're putting muscle into. But it's all across the entire go-to-market engine, as well as the product strategy and bringing that all together.
Yeah. I'll comment.
Go ahead.
What largely gives us confidence at this point, because as Rob said, it is early innings. But when we looked at our customer install base of Data Intelligence versus Pure return, we saw in the franchise wins, the large enterprise-regulated environments, about an 80% logo overlap initially. So we saw in the Fortune 500 strong synergies, and we have gotten, at least in the early days, I think Ashish covered this, the personas are these franchise wins that are doing these big fleet engagements with us are favorable and do like us. Our Net Promoter Score is high, and they are bringing the chief data officer or CISO to the conversation. So I think part of the leverage we have is just our customers like us. We have treated them well, our Net Promoter Score is strong, and they want to do more business with us.
Many years ago, we used to say that when we were a block only before we really got into too much file, the number of sales reps at Pure and customers that told us, "If you only had file, we really want to work with you," right. And we are finding, five to seven years later now, that same reception in this within our installed base across the multiple personas. So, the initial interest and demand is fairly strong across the access to the people we wanted to get to.
A couple of more questions here in the front, maybe MP, and then YC, or Oh, sorry.
Matt Calitri at Needham. Thank you guys for having us and doing this. It has been a great day. Rob, I think it was in your section that you noted that hyperscaler interest is expanding beyond the top five. What exactly does that mean, and how big is that overall pool of organizations before you start bumping up against the neo cloud opportunity, I guess?
Yeah, absolutely. I will start, and I will tag Bill in as well, keep him on his toes. I think what I was hoping to articulate is the core advantages we are delivering to the top hyperscalers with our solution set today, how we are packaging and delivering that, how that fits in the architecture. Yes, a lot of the dialogue we have had with the financial community over the last couple of years has been focused at the top hyperscalers. But it turns out, if you look at, call it clouds five through 100, five through 1,000, many of these firms, many of these environments design their environments, their architecture in substantially similar ways. It would stand to reason that they would benefit from a lot of the same attributes. But Bill, I do not know if you want to add to that.
Yeah. Not only are they transforming their architecture to look more like a hyperscaler, they are also looking for supply chains that look like hyperscalers. They are buying at such high scale, they want to see this supply chain that they can rely on multiple vendors through one solution. It is very attractive to them. By the way, Neo Clouds are not distinctly different from that. They are acting that way too. The build-outs there and with the frontier model companies, they also want to look like a hyperscaler. We are seeing that there is a lot of opportunity for the hyperscale solution outside of the top five hyperscalers.
Just to put it in perspective, though, I want to make it really clear. There is a very clear set of requirements on the, whatever we call these companies, tech titans, hyperscalers, et cetera, for them to even qualify for our hyperscale solution. That is, they have to have their own storage software stacks.
That's right.
If they don't have their own storage software stacks, then we're very happy to sell them our standard product, because our standard product has those software stacks in it. It's only to the entities, that up until relatively recently, were only the hyperscalers that had their own storage software stacks. There is a clear distinction.
Thank you. MP on behalf of Joe from JPMorgan. Just wanted to ask, is there any meaningful difference in OpEx intensity for the core versus the new revenue opportunities?
What I would say is you can expect the different OpEx lines, obviously as percentage of revenue, the percentages to go down. We will continue to invest in R&D. We have to invest in sales and marketing, less so in G&A, but we have to scale up G&A as well. You can expect absolute dollars to go up for R&D and sales and marketing. To get to deliver the kind of growth we articulated, we need to scale up across the organization. There's no doubt some absolute dollar increases have to be factored into account into your models. But overall, it's top-line growth and significant OP margin expansion as a durable, resetting financial profile.
Right. If I might, on a blended basis, we showed you what it looks like. You have new product economics, whenever you introduce new products into new markets, and those new product economics are always generally lower gross and operating margins to begin with, and with market share comes margin at every level. So as we gain market share in those products, we should expect them to accrete to company average and then preferably go beyond company average and start being accretive to the overall business.
Correct. Thank you. A quick follow-up. On the new revenue opportunities, I think in the chart, it looked like there was some contribution in FY 2026 as well. Was that meaningful enough, or are you willing to quantify that?
It was. The answer is no, I'm not willing to quantify that. It doesn't really matter. What we said, if you look back at our earnings transcripts, is that we said we had tens of millions of dollars from one particular revenue stream back then, it was hyperscale. But now you have to look at it as a combined category. The three are really where we're going to put our attention to. There's a tremendous opportunity in modern data software, a tremendous opportunity in scale AI, and also an equally tremendous opportunity in hyperscale solutions. So we'll give you a view. The 2026 number is, to a large extent, not very important. What's more important for you is the fiscal year 2027 baseline and how we're going to progress to the 20% by fiscal year 2030.
Got it. Thank you.
Would you please pass the mic to Wamsi, who has been waiting patiently? Thank you.
Thank you. Thanks for the follow-up. I guess, just at a high level, would you say that adoption of AI is changing procurement cycles for you guys? In terms of visibility, particularly on the hyperscale side, can you talk about if you have extended visibility and commitments in terms of exabytes or revenue, or how are these commitments shaping out over a multi-year period?
I will take the first one. I think if we look at the core business, I would not identify any specific change in procurement cycles due to deployment of AI, other than the industry CapEx deployment against AI has obviously created a supply chain pricing dynamic, which that has certainly, and we have talked about it ad nauseam, certainly factored into enterprise procurement. But in terms of enterprise AI deployment, have not seen really any direct effects of that in the core business in terms of procurement cycles.
I will say in our discussions with hyperscalers, working with their procurement teams, they do share their long-term spend and long-term outlook versus AI. That gives us good insight and ability to plan.
Yeah.
2028 is, we have firm commitments through 2028. On the hyperscale side, the other portion of 2028 is based on continued market share gains and no fundamental, as we talked about. Costs will change somewhat, we know this, but we are assuming pricing stays reasonably near where it is today.
Well, we tired everybody out.
I think everybody is ready for a drink, I suppose.
Well, we could certainly break for a drink early. Yeah. It is 5:00 in New York, right?
Yeah. Absolutely right.
Yeah. There we go. Well, look, again, we really want to thank you all for traveling to see us today, spending so much time with us, being very patient as we got through the trading day to get to the meat. But also, really, your interest and curiosity about how our business is built and what the fundamentals are really was quite. We were very impressed with the. Tarek, Rob, and I were going back and forth about how insightful many of the questions were and how knowledgeable about the market. So really want to thank you all for your time today and look forward to spending more time with you this evening. So thank you.