Everpure, Inc. (P) Earnings Call Transcript

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.everpureata.com.

Welcome back, everyone. Good afternoon, good afternoon to everyone that's 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 welcome time to have lunch. So we're looking forward to speaking to you this afternoon. I'm just going to briefly go into the agenda again for this afternoon. One is I'm going to present to you a very high-level overview of our overall strategy, reminding you where we've come from, but also how this fits, where we're going and how that fits into the context of Pure Storage and our overall business strategy. Rob, you'll see Rob again coming on stage talking about how these different areas in which we're competing, why we have a right to win there, what underpins our technology, and I'll 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. And then after the market closes, we'll have Tarek come on stage and tell you what that means in terms of our financial profile for the next several years. And I'm sure that will be something that you're very much looking forward to, but we're going to wait until after the market closes to get into that. So with no further ado, I mean, the story is actually quite straightforward. Have you ever seen a line that straight in any financial environment before? Look at that line. It's about as straight as it can be. This is our increase in market share year after year, okay, in all kinds of markets, right? This is well over 12 years now of share gains each and every year. right? We outpaced the market and we outpace our competitors. And this -- it's not just year after year, it's quarter after quarter. It's a very remarkable and consistent share gain. So when we have said on earnings calls that we have 2 quarters of visibility, that's 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're going to continue to see market share gains as we go forward. In fact, we believe that's going to accelerate. If you want to know what is the most fundamental element of our strategic competitive advantage in this space, it is [Audio Gap] he 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's 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 change -- 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. And in fact, if you look at the last 12 months, on a GAAP basis, it's now over $1 billion a year, and this is, of course, increasing and that puts us at the #1 investor in R&D in our segment, okay? So it's been true for many years that we spent more on a percentage of revenue, but now we actually spend more dollars. And I would argue that we spend it very efficiently and effectively in our business. And that's allowing us to gain market share. What does it buy us? Well, at least third parties, whether it's 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, and so we are being recognized increasingly not just by analysts, but more importantly, by customers as being the innovation champion in this space. And we're not just the innovation champion, but if you look at our Net Promoter Score, nobody even comes close, right? So we're very, very customer focused as well. And now we have scale to back this up. And it's not just that we invest more, but we're more efficient in that investment. You heard all morning about our unified data plane, about a unified control plane about Evergreen. So what are the advantages of this? Well, having 1 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 don't have to invest in a 1,000 different operating systems to service different parts of the data storage market, right? This one investment we make in purity and in direct flash, 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. Now these are not just benefits to us, they're 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 -- what I phrase is bug consistency. If you fix a bug once, it's fixed everywhere. You don't have to fix it multiple times. And simplicity. It is simply easier to be able to be able to manage an environment that's consistent rather than different. It benefits the company as we said, because it is about scale and efficiency [indiscernible] investment. It improves our overall quality, of course, and the focus of the team overall, right, whether it's the sales team or the engineering team. And then what are the shareholder benefits? Well, really, we get efficiencies in scale and growth, right? And 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've also put a lot of investment in our go-to-market team, right? And 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's not just about product. It's about the way we engage with our customers, right? When we speak about Net Promoter Score, it really is a measurement of every aspect of our company whether it's 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's part of the measurement. And of course, the quality of the products, the quality of the support that we provide those customers. In the enterprise, we're now -- we're in 64% of the Fortune 500, 44% of the Global 2000. We have a lot of global strategic partners. By this, we mean technology -- well, both technology partners at 50 global strategic partners in terms of customers we go to market with -- sorry, partners we go to market with. We have 4,000 channels worldwide in the commercial market. We're widely respected by our channel -- 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 #1 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's an area of growth for us. All right. And that's allowed us to reach an inflection point. So if we look at it solely from a reported revenue basis, now up 38% year-over-year. I'll call your attention to the fact that we have 8 year -- 8 quarters of increasing revenue growth. okay? So it's not just about the most recent price increases, okay? We've been increasing our revenue growth consistently over these 8 quarters, gaining share. And this is why [ we all ] believe it's durable. This is not just a spike. And we're growing the margin at the same time. All right, a 50% increase in the over margin of the business over that same period of time. And again, this is, we think, proof that we drive growth through innovation. It's innovation-led growth. So we're going to -- I'm going to very briefly speak to you now about the 4 areas that we believe we are engaged in as we go forward. Of course, we've been in core and now core AI for almost 2 decades. Let's see if -- okay, that seems to be working better. Almost 2 decades. And that area, you might want to get me another mic. Certainty. In 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 characterized in 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. And then finally, last and certainly not least, the hyperscale solutions. So 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 those -- over the last 7 or 8 quarters. And then tied into that, the growth of our subscription business. This is Evergreen 1, right, when we don't sell in array, but rather provide a storage service based on our arrays, right? And it's a little bit difficult to be able to identify specifically when we sell an Evergreen 1 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 links that we engage with our customers. But it's in the year 3 to 3.5 year range. So it's just a rubric. It's not -- you cannot square the 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 7 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're seeing an acceleration of sales into AI environments. And I want to describe a couple of things. One is you'll notice that this is not revenue. This is GPU attached systems. So we've 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 things that are -- these are arrays that are attached directly to GPUs and 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. I mean it really wasn't 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 our own AI factories, then we wouldn't sell into it. We wouldn't have a GPU connected device if the customer didn't 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. And once they started bringing it in-house, of course, they needed storage systems to attach it to. And so that's what we -- that's what we started to see at the beginning of this year, and it's just getting started. But I think it's -- we think that this is an area that's very much going to improve. So it's accelerating on-prem GPU deployments. And the second thing, and this is what's 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're going to want to scale. And 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'd have to buy something entirely different? Or do you want to start with something that's too big for you now, but you could scale into? We're not forcing our customers to make that choice. The S and the EXA are the same technology. They're the same -- they are fundamentally the same product just structured in a different way. And with our track record of nondisruptive 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. And so the strategy of having both S and EXA in the same environment, very, very powerful. And what we're also seeing is pilots now starting to go into production because there had been pilots before, but it was largely for -- with very large customers. They wanted to try a GPU environment but it wasn't going into production because it just wasn't proving out the ROI. Now it's going into production. And what is the difference between a pilot and production? Pilot can be down for several weeks. So you could buy a pilot and it's kind of fun to start putting it together for the first several weeks. And if it takes that long to build, okay, fine. Production 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. And those are all the things that Rob and others spoke about this morning. And again, this is what we've designed for. It is what customers expect from us. And so this area has been accelerating quite dramatically over the last, I'd say, 6 months. All right. So 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. And that is this inversion of the relationship between data and applications to where data -- 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. And it's hard to put together any 1 element now from a single application. It always requires data from multiple applications to get this going. And the problem is not so much that there's no single source of truth. The problem is there are so many sources of truth and they just don't agree. Every one of the databases, and SAP database or ServiceNow database, a Salesforce database, your systems of operation that have databases. They all have their version of truth. And you try to figure out what is the full relationship with any individual customer and you have a very hard time. And it was already starting to break with so many applications. But now with AI that requires access to data Well, if you get different data from different applications, how does an AI know what's true or what's right? Well, 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. And it's going from data processing through the full client server multi-app environment and to what we believe is data primacy, that will really allow organizations to make the best use of their -- of the future application environment as well as the ability of allowing AIs, AI agents to run more of their business. Because there's a big difference between AI in a general environment, and AI in an enterprise environment. And 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 -- actually, this occurred when ChatGPT 3.0 first came out, and she took about 20 hours of work to plan out a family holiday, right? And she had all these things planned out and she asked me about it. I said, let me try something so I got I was on my iPad, and I went to ChatGPT and [ within ] about 5 minutes with about 5 prompts, I had recreated her 20 hours of work with an output of ChatGPT. And 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 report? 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 and consistency, diagnosability, because we need to know why did I get this answer if it was not the correct answer and accuracy in their results. And 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, very difficult. So organizations have to get control of their data. And data primacy, we can 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 data sets across your organization, and 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. And so we believe this is the first step to help enterprises on their way towards data privacy 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. And then we're going from AI in the enterprise to AI in the large-scale neocloud environment. Rob went through this with quite -- with a lot of detail this morning, but we're seeing growing sales, for example. And as we go from an environment of [indiscernible] 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 in a demo on Friday, had it up and running on Monday. This just doesn't occur with anybody else. Just doesn't. We design products that have finished quality associated with them, not science experiments. And again, it's the same purity engine. It's the same architecture, same management capability. And by the way, as he also mentioned, neoclouds have lots of different storage requirements. Everything from the world's highest performance data engines all the way down to archive and to [ backup ] and we can provide all of those capabilities to those customers. And 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's 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 when we -- as published, we guarantee to work under all conditions. So we scale without any surprises with our new tweaks without nerd knobs, without a lot of fusing around. And then our hyperscale solutions. So I want to make this very clear, a very and we'll -- 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. And with that 1 solution, be able to cover their full breadth of requirements from AI levels of performance all the way down to archived levels of cost with 1 architecture and 1 structure. Why would a customer want this? Well, our reliability is 10x -- well, 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're measuring against SSDs or hard disks, significantly greater density. It's from an ongoing engagement standpoint with the hyperscalers. It's 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's a less complex integration over time. And the way we're able to do this is we provide an abstraction layer between the NAND, which is the underlying media and their operating environment. And we are -- the opportunity here is huge. It's well over 1,000 exabytes on an annual basis. You pick your number. There's a lot of forecasts on that currently. We're 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 a much greater detail. But we're seeing the uplift start. And 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. So I'm 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're going to be growing faster, and we think that, that is durable. [indiscernible] quarter have shown that we've seen higher revenue growth and higher share gains across the board. We have -- 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're adding. So this is the modern data, scale AI and hyperscaler are not overlapping with our core market, right? So this is truly additional new sources of revenue and income for us. And I would say that whereas where we were 2 or 3 years ago, where we were predicting that we'd sell into the hyperscaler but didn't have one to show for it. We're now in 2, we believe, we'll be in others as well. And we've 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's the same solution. It's just a matter of when prices come back down. And we can operate in hyperscalers, if the latest price increases in NAND have proven anything is that we're resilient, whether it's about HDD replacement or SDD replacement, right? This is 1 solution that covers all of their storage needs outside of tape. So with that, as you can see, we're very proud of the fact that we have 1 architecture now, but many, many paths to growth. We have our core, we have our new markets, and that's what we're going to be spending the rest of the afternoon on. So Rob? Welcome to stage.

All right. Well, welcome back from lunch. I'm going to pick right up where Charlie left off, unpacking these 4 high-growth market areas that we're focused on as we look forward. As I -- before I do, perhaps I should say that Charlie had a slide up there that said we're at an inflection point. I've been at the company, this is my 13th year. And I would say it's -- I've never been in this position where we're standing on the precipice looking forward at growth, not just in the things that we do today. But 3 really exciting new areas really just starting to take off in the industry. So I'm 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're thinking about how we're defining these new market areas. I'll start with the core. So we look at this as a lot of what we've been doing since day 1, serving the enterprise, serving enterprise storage systems. We've added to that. We've included in that, what we're describing as Core AI, right? So effectively, this is what we spoke about. Chad spoke about this morning, Charlie spoke about a little bit right before me. The enterprise deployment of AI systems within their environments. We're including this within the core business because it's the same customer segment. It's largely the same solution set. But also because it's different [ in a part ]. It's separate and apart from a new opportunity we've created, which is our scale opportunity, which I'll get to. Now as we look beyond the core, I would say, extended by core AI. We see 3 new markets, high-growth areas that we're really turning our focus to. Number one, what we're calling modern data software. You've heard elements of this morning and Charlie's intro. And this is really, I would say, stepping back from it, looking at where we sit in the industry at the beginning of, I think, was going to be a 10-, 15-, 20-year long secular transition in how software is built and 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've got a number of things, you've got AI, you've got new approaches to analyzing data, you've got things like open source. We'll 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 doing -- we're delivering the software value above -- 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, right? 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 to exciting new markets, again, very high growth that we're focused on. I would say, extending our [ core IP ] and packaging and delivering new ways to meet the needs of these newer markets. One, scale AI, what we're terming the neoclouds, 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, 4 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 this -- 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. So let's start with the core, business core plus Core AI. Equation here is fairly simple, right? We're going to keep gaining share. And we're doing it at a point in time when the market is growing. That market is growing for a number of reasons, but I would point to AI. I think you've got 2 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. It depends on how much value they get if they get more value, they got some more data. The direct effect, this is the deployment of new AI attached use cases, things like as inference comes back on-prem, within enterprises digital walls, agent 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 2 legs to this. We're going to gain share, we're going to keep gaining share and the market is 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. And this is really based on a couple of key advantages that we've assembled over the last 15 to 17 years. Number 1 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 6, 7 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 DBA is going to be so happy, and 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 the 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. When we think about large -- there was a point in time in the company's history where if there was $1 million, $5 million deal, like I would probably know about it. We're doing 8-, 9-figure deals these days, and I see them as they go by, right? And so this is just a testament to the fact that we're now in a position to go compete and land very, 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 2 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. Chad 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 nondisruptive 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? And so when we think about the stickiness, when we think about the customer retention, that Evergreen affords us is amazing. If you think about this just from a purely transactional point of view, we don't have refresh cycles, right? We -- I love refresh cycles. A competitive refresh cycle is a big 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 1 array is great, having 5 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 is -- it just fits very, very nicely into that cloud operating model. Number three, we're continuing to expand our technology differentiation across the board. I'm going to go deeper into a couple of the areas. 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. All right. So we spend a lot of money in 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? So 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, but 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. And 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 clump 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 a -- better version of somebody else's feature. We're trying to solve those problems better. And 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 to 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? And this is a virtuous 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 the cycle in ways that nobody else can. 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 2 years past, 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. All right. So we're gaining share. market is also growing. I mentioned before, I would say 2 major effects that ultimately combine to accelerate the growth of the enterprise storage market. Historically, this market has grown, call it, mid high single digits. We see the market is accelerating growth over the next couple of years to about 12% CAGR. I'd pull out 2 drivers for this, both relating to AI. One, AI is just making data more valuable, right? 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. 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 I becomes or 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 the historical -- 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 have 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 9 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. The 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 meeting, 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 [indiscernible] with Core AI, growth equation is fairly simple. We're going to keep gaining share, going to keep reinvesting to gain share and accelerate the pace of that. The market is growing. Those 2 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, 3 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, right? In fact, we were -- our first [ foray ] on this path, I would say, was actually Port Works, right? With Port Works our thesis was and still is that applications 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 Port Works 5 years ago, 5.5 years ago, that change was largely driven by containerization, open source, Kubernetes all of the -- 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 5-, 10-, 15-year journey, significantly up ending the application environment in the enterprise. I talked about this a little bit in the morning, but AI plays a big part, right? AI forces a rethink about -- 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. And also, there's an importance now, right, as we talked about this morning, our goal, where we see the future heading is being able to work with -- a customer being able to work with their data, no matter where it sits. Data sits all over the place. It sits on-prem, sits across multiple prem environments, sits in the cloud, across clouds. And so we need to be able to help serve customers where that data sits across these different environments. So, if we unpack this, and Prakash talked a bit about 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 you go talk to a customer, when I go talk to a client in the enterprise, they'll say, "Hey, I want to do all this great stuff with AI. They'll say, "Hey, that sounds awesome. Where are all of your data sources today?" And 9 times out of 10, the customer will probably chuckle at me and say, I have no idea. 16 different places. Every department has their own database. We have multiple copies. So problem #1 is figuring out what is it you have, where does it sit? And what does it mean? This is where [ One Touch ] comes in. This is where data intelligence, where we're headed with that. And 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 how best to use -- 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, well, it behooves you to organize it in a way to make it easy to work with, easy, efficient, fast, so with data stream built on NVIDIA's AI data platform, we've 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. All right. So we have a couple of capabilities. We're going to deliver the software above and beyond and outside of our arrays across multiple environments. But if we look at the broader application stack, application stack looks very different than traditional enterprise apps. They're built on containers, built on Kubernetes. And at the same time, a lot of the traditional apps are moving to containers. And so with Port Works, we're now seeing increased demand for Port Works, both in terms of virtualization modernization as well as supporting data pipelines, data analysis and inference systems. And then 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. And so this is where Everpure Cloud, the evolution of Cloud Block store into a managed service and Port Works come together to be able to provide that multi-cloud capability across both traditional applications as well as modern applications. All right. So shifting gears, we'll 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. And 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's 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. So the balance of getting the right performance levels and the right manageability and reliability becomes of utmost importance, and we're the only ones that can go provide the balance of those 2. The last point I'll make here is with the scale offerings led by FlashBlade EXA and Charlie hit this a little bit upfront. Coupled with FlashBlade S, we're now the only vendor that is able to cover the entire spectrum of [indiscernible] from the smallest labs to the mid- to large enterprise all the way to the neoclouds. And then in my next section, we'll talk a little bit about the hyperscale. So let's put some numbers on this. We talk about core AI. We talk about what's happening in the enterprise. What we typically see in the enterprise in the example I gave before is an enterprise might deploy an environment serving tens to hundreds of GPUs, maybe a petabyte to 10s of petabytes of data, supporting hundreds, maybe up to 1,000 users or so. FlashBlade uniquely fits the 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. And 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 2 orders of magnitude, 10x, 100x, right? You're not serving tens of hundreds of GPUs. You're serving thousands to hundreds of thousands of GPUs. You're serving hundreds of petabytes to exabytes of data. You're serving tens of thousands, maybe hundreds of thousands of users. Now you might say these are completely opposite ends of the spectrum, right? They're completely different demands. What they're not is completely different products, right? What we've 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? Well, Charlie introduced this a little bit upfront. Really, 3 things that we're leveraging, using extending from the core FlashBlade and Purity software. Number 1 is the utmost performance we can deliver; two is, the scale and flexibility; and three, is that mission-critical bulletproof reliability. On the performance front, -- everybody tends to focus on bandwidth and how many bits per second gigabytes per second terabytes a second. And I apologize for those in the room who are with us in the morning session, but I'll draw an other analogy. It turns out in AI workloads, both data performance and metadata performance are important. What do I mean by that? Well, think of it this way. If I ask you to -- if you ask a computer to open a file, the computer has to do some work to 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 doesn't sound too bad. Let me draw a more human analogy. If I handed you a book and go to a page and ask you to read the page, it might take you a minute or 2, and not too bad. I hand it to a speed reader, might take them 20, 30 seconds. Wow, that's a whole heck of a lot faster. If, on the other hand, I'd 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 2 sentences, pick another book off the fourth shelf, go to random page, read a couple of sentences. The more work I ask you to do to find the data versus reading the data, right? The more metadata work I'm asking you to do versus data work. And so 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 2 from each we can agree that it doesn't really matter how fast you read, you're 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've built FlashBlade. And with FlashBlade//EXA, we've been able to extend that and decouple that to create independent scale of the ability to supercharge the performance for both legs of both metadata and data. We've been able to also extend the scale in terms of capacity, right? So really now with FlashBlade//S and EXA 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. And then most importantly, and I mentioned this before, as we think about neoclouds, GPU utilization, reliability, usability, operational simplicity are utmost concern. We've been able to do this on open standards without a whole bunch of tinkering without a whole bunch of nerd knobs for customers. We've been able to do this so that neoclouds is going to operate these environments on [ thin ] staff in a way that gives them mission-critical reliability that we're known for. Some results. So MLPerf is the industry's leading benchmark when it comes to AI systems, in particular, storage. These are results we recently eateries, the latest MLPerf 3.0 benchmark. These are standardized tests. What our submission has shown is that we, 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's up 3x. Why is it only 3x?" I said, "Well, it's entirely linear, right? It's 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. And so this is exactly the power of taking the software scalability and the underlying platform know-how that we have with FlashBlade//S and taking it to the next level of scale. And giving our customers the neoclouds the same predictability that the enterprise has had for the last 15 years. All right. So if I zoom back out and look at the neoclouds, a couple of things. One is these large-scale AI environments. 3, 4 years ago, the focus was entirely on performance. Now yes, they need the performance, but 2 things are happening. They need performance across an increasingly varied set of workloads. It's not just large model training. It is training. It's also inference, it's also agent deployment. It's all of those things happening at multiple -- at the same time, it's all of those things happening at the same time across multiple tenants. And so they need all of those things with the same reliability, same tenant isolation, same security all the enterprise capabilities start creeping into these environments. And this is an area where we're uniquely positioned to be able to meet both ends of those needs in a way that nobody else in the market is able to do. All right. All right. So what everyone's been waiting for. Let's talk a little bit about hyperscale. So as we look at the hyperscale solutions, 3 takeaway points here. Number one, we deliver significant technical and structural advantages to the hyperscalers. Two, it's not just the point technology. We deliver them a compelling solution that gives them a consistent, unified single architecture that gives them a consistency across performance tiers as well as flexibility to adapt to new workloads. And then number three, there's a race to deploy flash and we present the most compelling option to get their fastest. So why is that? Well, if we look at what's been happening over the last 5, 10 years in hyperscale storage, and I simplify this to big animal picture is a couple of things. One, performance demands keep going up, thing called AI. Two, hard disk drives, relatively speaking, keep getting a lower. Yes, they might get faster -- sorry, yes, they might get larger, but they're not getting any faster. So performance per terabyte is actually going down, that gap to flash increases. And so natural to see that there's been a rush, there is an increasing rush to deploy QLC flash, right? Fairly simple equation. So what does this result in? Well, it means that every hyperscaler out there is doing a couple of things. One, they're racing to secure SSD supply QLC in particular, as fast as they can. They're doing this across multiple vendors, which then puts the onus on them to do multiple qualification cycles with different SSDs, for multiple vendors, and they're doing this at a point in time when their workloads are changing rapidly. And then number three, they got to make this whole thing efficient, right? They're at the same time trying to figure out how to get power, how to get space, how to build new data centers, they've got to drive efficiency out of these investments at the same time. So there's a lot going on. So what are their choices? How do they get there? Well, there's a couple of pads, right? Option 1, Option A is what's called the status quo, right? They can continue to build out based on conventional SSDs. This is what they do today. They're going to keep doing some of this, but it's getting increasingly inefficient, right? 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's more work on the front end. So this thing is getting harder, but they're going to keep doing some of that. Well, 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 tried to build that in-house. Could they do it? Yes. Will they do it? I don't think so. Why? Well, it's a lot of R&D. We've been at this for 15 years. We have some of the most sophisticated knowledge in the industry even including the NAND manufacturers themselves and [indiscernible] works. And we have an enterprise business that pays for it, right? 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's a lot of R&D to get started. It's a lot of R&D to continue investing. You can do it, but you have to consider opportunity cost, right? They have option 3. Get the boost 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 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. So 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 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. Well, what does that mean? It means compromises and reliability, 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. And that's what we're delivering with DirectFlash. But it's much more than just the technology. It's much more than just direct flash. It's how we've packaged the solution to fit within the hyperscaler architecture. So 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. And 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. And underneath this storage software layer, they design specific hardware storage node combinations, depending on price, performance, capacity needs. They typically have multiple tiers, hot tier, high performance, a warm tier, pretty good performance and a cold and archived 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 2 things. One is we packaged our DirectFlash technology, which I just explained, has all these benefits over SSDs, we package that technology in a way that fits very cleanly, integrates very easily into their distributed storage software. It's not a big -- it's not hardly any software change at all on their distributed store software. We slot in just like their existing storage. 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 1 architecture dialed up and down based on software configuration and media configuration, right? When you think about that, economies of scale, right? Chad talked this morning about how the enterprise benefits from operating in the same way across multiple environments, huge benefits. Think about what that means at the hyperscale, massive. But wait, there's more. The other thing that's happening is it's 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, pushing performance bounders beyond what a single SSD tier can provide. So the hyperscalers are now being forced, they're being pushed in a direction of designing multiple different hardware configurations for each of these, what used to be 1 tier. They can do that. But again, it's more work. It's more inefficiency, it's stranded capacity, it's one more thing to operate. And here's 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. And so ultimately, the 2 value propositions we're delivering to the hyperscalers is the core technology which gives them all these benefits, performance, reliability, efficiency but then the packaging, how we're delivering it in a way that gives them operational consistency across multiple environments, whether it's 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's not just the technology. There's a lot behind this. There's a lot of history. There's also a lot of vendor relationships and well, how about flash. We've been at this a long time. We work with multiple NAND providers, key partners that like [ Kioxia ] that we've worked with over a decade to help co-engineer and co-design future road maps. We were with multiple NAND generations across multiple suppliers. We do the qualification. We do the reliability engineering. We've got an enterprise business to go pay for that. We can go and offer the benefits of that hyperscalers. I mentioned qualitatively some benefits, not going to unpack this slide, but just to put some numbers on it, right? When you think about twice the lifetime, we think about 5x reduction in footprint, when you think about 2 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. And so at the end of the day, right, if we simplify this, the 3 kind of things that hyperscalers are looking to us, looking to our solutions to provide, number 1 is the efficiency, whether it's, again, equipment efficiency, utilization, power, reliability, it's the uniformity. It's how we've packaged that solution, that technology to give them 1 approach to deal with multiple tiers, multiple uses, multiple needs. And it's 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. And it's 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 5 hyperscalers for this set of solutions. All right. So I'm about a 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 1, not 2, but 4 different high-growth market areas that are expanding into new areas of software, extensions of our hardware technology and everything in between and it's taking us into significantly new and larger market segments. And we're doing this not as a bifurcated set of efforts. We're doing this based on 1 foundation, same technology, 15,000 customers, validation at the largest scales driven by 1 product organization, 1 architectural foundation set of IP and 1 sales organization. Thank you. All right. So 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. Market is closed. So now I can freely speak, and I'm delighted to be here with all of you. So -- so good afternoon. and welcome, everyone. For those who don't know me, I'm Tarek Robbiati, I'm the CFO of Everpure. And I've just been in the 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 multiyear 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's 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'm going to tell you is going to be broken down in 3 chapters I'd like to cover today. First, we'll 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'll tell you the potential of each one of those markets. And 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's a reset, and we believe this reset is durable. So I've got to start where we finished last time we met. That is our Q2 results. 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 '27, and as you already know from the guidance we provided and reiterate today, we target at the midpoint, $5.05 billion of revenue 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 1 in 7 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 enhancing our model to drive even faster growth in our bottom line. And no, this is not a flash in the pan, okay? Just to make sure you're clear. You know, you probably heard that Everpure 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. And that brings us back to the inflection point. This top line acceleration reflects strong demand, deep customer engagement as they rethink their instructure requirements. So you know this already, but it's important to put some perspectives. So let's frame our performance in a longer-term context showing our revenue trajectory from fiscal year '22 to fiscal year '27. Over this 5-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 '27 accelerates to 38% year-on-year. There are 2 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's 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 built 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. Two, and 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 2017, 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, if we 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 '22 to fiscal year '27, 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. And 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 or the third time. This is an R&D-driven model. Hopefully, you will realize that, but it's 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's key if you have to spread your dollars over multiple platforms, you're 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, deliver superior software intelligence that extracts maximum performance efficiency and value from the underlying NAND. We've 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 markets and drives durable revenue and profits for years to come. So let me summarize what we discussed so far in the first chapter. First, our strong core business has delivered durable multiyear growth, which is accelerating to 38% revenue growth in fiscal year '27 compared to an 18% CAGR since fiscal year '22. Second, we are driving significant operating leverage with operating income projected to grow about 50% this year, and at a 32% CAGR from fiscal year '22 to fiscal year '27. 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. So before we talk about the expansion of our addressable market, I want to spend some time on a very important topic in the [indiscernible] top of mind. It's a very important dynamic that is impacting our industry right now, and this is related to the significant increase to component costs. This chart illustrates the magnitude of the NAND and system cost increases since the past 12 months from the third quarter of fiscal year '26 to the third quarter of fiscal year '27. 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. So as you look at our growth trajectory, it's important to distinguish between the acute benefit of pricing and these far more sustainable growth drivers. And the next slide provides some historical perspective on that. You've seen this chart already in Charlie's presentation, this fantastic line with the uptick, as Rob noted. We've 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. And as you can see, putting today's environment into the historical context, we've demonstrated since 2013 that we reliably gain share through every NAND cycle, regardless of whether input costs are inflationary or deflationary. And here's why. In inflationary environment, 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 environment, 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 what happens to your gross margin. So let's getting to that. The simple punch line here with the same analysis at the gross margin level is that we are gaining market share without compromising our economics. We're maintaining a substantial product 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 balance 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? So 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 net. As Rob and Chad noted earlier in the morning, Everpure cut power and space by up to 5 times delivers over 10x greater reliability requires up to 10x less labor features constant upgrade with our Everpure Forever program. And that is driving a 50% lower total cost of ownership or TCO. So 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 their 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. And by drastically reducing power footprint, labor and complexity, our solution delivers compelling long-term economics despite a higher initial purchase price. And 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. And this drives industry-leading NPS durable market share gains and margin leadership. So 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 2 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 3 growth vectors. Scale AI, you heard from Rob, it includes our excess solutions for neoclouds, AI Titans, et cetera. Modern data software, which includes Port Works, Pure Protect, Everpure Cloud, by intelligence, also known as OneTouch and Data Stream and 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. This is the good news. Moving forward, we'll keep the same reporting segment. 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 do 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, offering the age of data primacy driven by 2 structural tailwinds. We exponential data growth and massive AI-related data infrastructure deployment. So to put things in perspective, in 2025, the world generated 173 zetabytes of data. I don't know how many of you know what a zetabyte is, it doesn't matter, but it's a 1 followed by many, many, many, many, many zeros. Anyway, it's a 90-fold increase since 2010, where in the world was generating only 2 zetabytes of data. And this has been driven by IoT, AI workloads, video surveillance, cloud adoption, real-time processing, you name it. And 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 to $3 trillion over the next 3 years. that is 1.5x to 2x the U.S. defense budget. So this helps paint the picture as to why we're so bullish about Everpure growth opportunities in the core. Now let's examine also the projected addressable market sizes Everpure capture over the next 3 years beyond the core. So 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, HP and IBM. In hyperscale solutions, which I'm showing you on the chart, based on our estimates, we estimate that TAM will grow at a 22% CAGR to $97 billion [ from one ]. It is today at $44 billion, $97 billion by 2030. And 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. And here, we face different types of competitors. NetApp, Dell, [ Veeam ] , Cohesity, IBM and specialized providers like [ DataBricks ]. And then 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 [ BAST, DDN and WECA ]. So we compete in 4 high-growth markets against different players in the industry. And these estimates, I'd like to underscore this is really important. It's very important to understand that these estimates are based on our current solution set with potential for further time growth as we expand our current solution set and customer adoption and uses continue to increase. So let me summarize the key points to remember here okay? Our core and core AI business opportunity is now growing at an accelerated double-digit 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. And our total addressable market opportunity over the next 4 years is increasing by a whopping almost $100 billion from $108 billion to $207 million. Now of course, the TAM is only useful if we have a credible right to participate in that market. That's 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's all we do is doubling from $34 billion in '26 to $69 billion in 2030. And that is driven by moderate commodity pricing at some point in [indiscernible] 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 the strength, our ambition is clear. and is to be the #1 provider of all-flash solutions uniquely positioning Everpure to compete and win across these 4 markets. All right. So I think I need to relax a little bit because now we're getting into the meat of the presentation. I'm not sure, Paul, my ears are ringing. I'm hearing everybody saying Jerry Maguire moment, "show me the money." All right? That's what I'm hearing you say. All right. So I'm 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 our preliminary outlook view for fiscal year '28, okay? The quick punch line is that Everpure is entering fiscal year '28 with significant momentum and an expanding set of growth opportunities. Building on our fiscal year '27 revenue outlook of 37% to 38% growth per year, our preliminary fiscal year '28 outlook is here. Is $7 billion to $7.3 billion, implying 39% to 45% growth year-on-year. That's right. Someone said, wow. This indicates accelerated top line growth, delivering a 2-year CAGR of approximately 38% to 41% and for fiscal year '28 -- to fiscal year '28. This is well above historical levels and current consensus. I want to make sure you're 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. And then here, I'm asking you to cut me a little bit of slack. Because it's unusual for me to provide you guidance for the next year at the middle of the current year. So this is not guidance, but it's preliminary outlook. And its share 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 '27 and enter fiscal year '28. The range is wide and that is because we are in the middle of our planning cycle. And when we complete our planning cycle, we will come back to you with a narrow range. That makes sense? Okay. So now the question everybody wants to talk about. What about your operating margin? All right. But good news is not limited to accelerating our revenue growth outlook. It extends to a step change higher in expected profitability as well. Okay. So let me bridge that for you. You've seen in our guidance, what we intend to grow for between fiscal year '26 and fiscal year '27. For fiscal year '28, we expect operating income of approximately $1.7 billion to $1.9 billion. And that is representing growth of approximately -- excuse me, 80% to 100% from fiscal year '27. Accelerating from the 50% growth year-on-year that we guided for from fiscal year '26 to fiscal year '27. And this equates to an operating margin range of 24% to 26% and 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 and 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. So 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 '27 is expected to grow from $3.7 billion in '26 to $5.05 billion in '27 at the midpoint of our guidance. So we are skipping the 4s, yes. We're skipping the 4s. Fiscal year '28 revenue outlook shows that we would be growing to $7.15 billion at the midpoint of the outlook from $5.05 billion. So we're skipping the 6s. Thank you, Steve. In terms of operating profit, we would be almost doubling operating profits in fiscal year '28, relative to fiscal year '27, right? That's where we stand today. And we will refine our outlook and turn it into proper guidance as we lap fiscal year '27. All right. So -- let me summarize Chapter 2, the key takeaways of Chapter 2, that 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, our value premium, excuse me, 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 '26 to 2030 from $34 billion in '26 to $69 billion in 2030. Fourth, -- our preliminary -- excuse me, fiscal year '28 outlook reflects this momentum driven by accelerating revenue growth, operating leverage and scale benefits. Ultimately, our confidence in the next phase of growth [indiscernible] 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. So let me bring that all together for you on 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. So if you look at rule of 40 scores roughly for 26, we had a score of 33. And for those of you who follow our earnings transcripts, we did say that for the past 3 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, okay? That's where the 50 comes from. And for fiscal year '28, when you take that into account and everything I gave you on our preliminary outlook would be in the 60 to 70 range. okay? For the long run, beyond fiscal year '28 I can [indiscernible] would be on 3 years. You can expect that we would be in the 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 articulate 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 3 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 a percentage of revenues to be in the mid-single digits. And 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%. And do not model 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, okay? And then we expect free cash flow as a 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 3 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 base. that is absolutely a must and will continue to do so. We will maintain disciplined 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 expand go-to-market capabilities. M&A 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. So this brings me to the end of my presentation. You've been very patient throughout the day. I want to thank you for listening and flying in from far someone, Andrew flew all the way from [indiscernible], some of you from the East Coast, some of you on the webcast have been listening patiently. So 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. And we're backed by the 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 2018 and beyond. As our cash generation increases, we are committed to be 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 before Q&A, I would like to invite Charlie, Rob, Bill, who is our General Manager of Hyperscale Solutions, Prakash, who you already met. And of course, we couldn't hold the Q&A without him, our Founder and Chief Visionary Officer, Coz, to join me on stage for the Q&A.

Wamsi Mohan, Bank of America. Thanks for doing all the presentation today, very helpful both from a technology standpoint and Tarek, yes, some really impressive numbers here. My question is, firstly, you obviously articulated a very bullish sort of outlook over here, well about 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? And within your guidance, if I could, are you anticipating incremental hyperscale wins? Or is this sort of like 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 the -- everything outside of the hyperscale or hyperscaler is a very different go-to-market. It's really it's engineering lead, right? And Bill and his team really lead that engagement. It's engineering to engineering. Really, there is -- while we do have salespeople and they're 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's really led by the hyperscale team overall. On the core side, the go-to-market is -- has substantially changed. We've made a huge investment in that area. It is far more sophisticated today than it was just a few years ago, everything from -- it's not just product and it's not just selling skills. It's really financial engineering that is working with the companies that we engage with, so it fits into their model. Most importantly, it's -- and Rob sort of alluded to this. It's a difference between selling into a particular use case and now selling at the enterprise level as a partner where we are either 1 or 1 of 2 primary suppliers here. And we call that a franchise win. In other words, it's not a win against a specific workload or use case. They're choosing us to be their partner in data storage, right? And that is -- I would say that has matured over the last couple of years to a really remarkable level, right? That gives us -- and as we gain experience in that as we gain more and more wins with franchise wins, we expect to see even more franchise wins. So -- as I said, while we really only get about 2 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're actually entering a new growth phase. Now you might ask why now? It's 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. So I would -- yes, please.

I was just going to jump in and say once you do the second part of your question, I think, which is hey, does the FY '28 or beyond outlook contemplate or rely on needing a second or -- sorry, third or further hyperscaler win? I'd 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, it's good.

But I would say, look, '28 is based on today's business and the visibility we have into '28 doesn't require anything fundamentally new associated with '28. Of course, our expectations are that we will, in fact, at new hyperscalers. Our expectations are also that we will penetrate a greater proportion of their storage estate as well. Yes.

Jason Ader from William Blair. Just on the FY '28 guidance, Tarek, can you give us a sense of how much of that 39% to 45% growth might be coming from pricing? And then also, it was great to get that 20% FY '30 from new markets. Could you give us kind of a rough estimate of FY '28 from new markets and then [indiscernible] let separate for Coz. Can you just talk about like what this journey has been like? I mean, you're a founder you're sitting here today, whatever it is, 15, 16 years later. Maybe just talk through how you're feeling

So actually, next week, we'll be our 17th birthday on Wednesday of next week. So I mean, it's been a great journey. Obviously, at this point, I'm having a lot of fun. I mean, S&P 500 is cool. What we're doing with the hyperscaler is really cool. The team, I didn't have to get up here and present. They got to do it for me. That's really good. I mean, our goal from day 1 has been to build revolutionary technology and really change this industry. And you've seen it from the start with all-flash and Evergreen. And you see what we're 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 and everything we're doing around data privacy, data intelligence, helping people with the AI, we continue to want to do extremely revolutionary technology that will totally drive something different. And it's a lot of fun to do. It's a lot more fun than trying to squeeze another dollar out of a boring old tech and things like that. And so we're going to keep doing that. And I think you're starting to see the results, and that's also very gratifying because all right, I feel we've got the best product. So I don't know why anybody buys anything else.

I would agree with that. That's for sure. So 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? So take this 6 points. If you assume about 70% of that -- 65% to 70% of that coming in from new revenue to 3 big categories that Rob has spoken about. Modern data software, scale AI and hyperscalers. Then you will -- if you assume a reasonable amount of OpEx going against these new revenue, you will see that our core is growing. And we are not assuming any particular pricing benefit associated with that growth. We continue to grow irrespective of where prices go. And I am never -- and no one in here, Charlie, no one in here will tell you that this pricing environment will sustain itself at [ Vita Materne ]. We don't believe it's going to be the case. But our preliminary outlook factors in any scenario that you want on that front.

Hadi Orabi from TD Cowen amazing numbers. Tarek, if I look at the slide you showed FY '28, it shows like white bar and orange bars. Orange bars [indiscernible] revenue and white bars from core. Just visually looking at it, it looks like the new revenue, which includes hyperscale and scale AI is approximately can be anywhere from 10% to 20%. And just like...

I tried to blur it. So 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, okay? 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 '27 will 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 '28, we'll tell you where we landed, okay? And that's how we intend to update you about penetrating these new markets. I think it's a simplest way, and you will also see through the expansion of total revenue and operating profit margin, where we get to.

Got it. And just a follow-up. You guys have 2 hyperscales already. Can you give us a sense of how much the FMs within the SSD capacity or [indiscernible] not HDD exabytes you guys have -- 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.

It's -- we can't give you an exact percentage because I don't think we have full access to that type of information. But I can say it's still a very small number relative to their overall purchases. Yes.

Excluding HDDs.

Well, excluding HDDs. Although we think on a longer -- as prices revert to the mean, which we're not going to predict right now because we inevitably get that wrong, when -- as prices start to revert to the mean, then HDDs are open season for us as well.

Eric 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 '28 outlook to that kind of fiscal '29 fiscal '30 kind of rule of 40 framing, right? The low end of that 60 to 70 comes down to 50. I assume you're giving yourself some leeway there, but I'd just love to understand like when we think about that is giving yourselves some leeway on growth, is that margins just to maybe understand.

Hey, listen, a rule of 40 at the 50% mark. So I said the way you want to, my friend. I'll be happy. Okay.

And maybe just 1 clarification, Tarek. Just on the 20% tax rate annually, when do we expect that to start? Is that fiscal '28 or...

So a great question. 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. And our current thinking is that we'll have to release it in the next few quarters. And so -- that's why I cannot be very precise today on the non-GAAP ETR. But we will be. And I think for the moment, the assumptions we gave you roughly 20% is what you can assume.

Howard Ma with Guggenheim Securities. Truly incredible numbers. I think we're all ready for celebratory [ trainings ] at this point.

Wait a minute. Hang on. We got 6 months of results to deliver and another full year to deliver numbers in accordance with that outlook, Howard.

So Tarek, that's partly why the initial FY '20 guidance is so impressive just given how far out it is. And so my question is, -- what would you identify as the 2 main -- or the 2 biggest areas of conservatism in the long-term framework. And I'll share my [indiscernible] thoughts because I can't imagine you guys are assuming much recovery in volumes because those are limited by IT budgets. So if you look at top line pricing will still be a tailwind next year, but volume likely still a headwind. So the implied core -- the core revenue growth can't be too high. So you're kind of implying a lot of it is already coming from new product. And if you look at it from a margin perspective, too, you guys shared that COGS, that total COGS [indiscernible]. And you're clearly not raising prices. And I still going to hurt your product gross margin, right? So you're not going to get that much margin expansion from the core. So it kind of backs into likely a very strong -- a very robust new revenue contribution already and that -- but I can't reconcile that with the 20% number 3 years out. It almost would imply like that number doesn't grow much.

Well, yes, let me start with that. We are -- 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. And 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's not -- it doesn't make up for the fact that overall, the dollars are higher. And 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 that's -- I think that's pretty well established. By the way, that's supply-demand economics. That's the 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.

Asiya here. Just to follow up on the prior question. Just 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 '27 guide. Just help us understand like these outlook that you've provided? Like how much is supply concern through fiscal '30?

I would say that on the -- 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 spot in that area right now. On the core side, yes, the concerns around availability of supply have gone way down. Doesn't mean we don't scramble from time to deal with shortages here and there. But for the most part, that's not a, I would say, a major concern now. Now you talk to our head of supply chain, they might have -- 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.