Delighted to have this Charlie Giancarlo CEO of Everpure formerly Pure Storage. I guess before we get started I'm just gonna read a few statements on behalf of Everpure. Statements made in these discussions which are not statements of historical facts are forward-looking statements based on current expectations. Actual results could differ materially from those projected due to a number of factors including those referenced in Everpure's most recent SEC filings on forms 10Q, 10K, and 8K. Thank you very much. Well, Charlie, thanks a lot for being here. I really appreciate your time. Maybe just even before we get kicked off, start off with all the questions, you folks reported earnings a couple of weeks ago, very strong numbers in Q1, there's a lot of questions around the guide and the back half expectations, maybe just level set kind of some of the key points on the earnings call, how you folks thought about the back half, and then we'll dig into the questions from there.
Well, I mean, the key points about the first quarter is it was another billion dollar quarter and the first time ever a billion dollar quarter in our first quarter, building on a billion dollar quarter in our fourth quarter of last year. And just as a reminder, our quarters end the end of roughly the end of January each year. We reported 35% overall revenue growth, which is certainly a significant pickup in overall revenue growth, but I think even more importantly, 55% increase in our product revenue growth, which by all accounts is a proof point of a significant gain in market share overall as a company. You know, we also raised our guide for Q2 and for the year as a whole. So to give you a sense, last year, at the beginning of the year, we had guided to 11 percent. We delivered over 15, close to 16 percent for the year. This year, thank you so much. This year we guided at the beginning of the year to 19 percent. The guide at the end of Q1 for the year is 22 percent, so already a 3 percent increase. Now, what we did do is that 22, that extra 3% came about because of the beat we had in Q1, as well as we increased for the full year by the amount that we're projecting for Q2, which is, again, a significant increase in terms of our guide as well as year over year. What we didn't do was raise the second half of the year. There's a lot going on in this market. Prices are increasing, which is creating all sorts of effects. uh supply chains are tightening which creates other other types of effects we didn't think it was really worthwhile or that we were adding anything to the by trying to define the macro in the second half of the year also we have a history of not uh changing guidance for a second half of the year after a first quarter uh because we just don't believe that uh there's enough information Generally, if you will, we guide on the basis of our pipeline and, to some extent, our backlog, which is only one to two quarters in advance. So guiding towards the end of the year, we generally don't do that after a first quarter. So there was some disappointment, I think, by some people in the stock that we didn't guide even higher for the second half of the year. But on the other hand, we're not trying to be the best crystal ball viewers. We're trying to run a company by improving and gaining market share each and every quarter, which we are doing. Perfect.
It certainly seems a lot more prudent of a guy that's perhaps more dearest, I would say, than maybe some of the other ones that are out there. I'm sure we'll come back and talk about memory a bit. But maybe at a very high level, I think about it, right? There's massive investments in AI infrastructure, more from hyperscalers. Heavily on compute, but certainly on memory storage as well. As enterprises start to get AI ready, right? And some signs this is happening. Can you just talk about what role does storage play for enterprises as they get AI ready, as they look to build out the AI infrastructure, and where does Everpeer really play into that?
Sure. Well, for enterprises to get ready for AI, it's very interesting. A lot of the focus on the media is getting ready for AI on the compute side, right? Building out compute farms, super pods, adding components for that, whether it's networking or storage. What the media doesn't pay a lot of attention on is the fact that getting ready for AI is much more in the enterprise about about getting their data ready for AI. So what do I mean by that? Well, for decades, we've had an application-centric, application-focused model for how we build infrastructure. Across the board, it doesn't matter. Enterprises, hyperscalers, it's all been application-focused. What that's done is it's fragmented the data across the enterprise. You know, we are, you know, we hope to be soon, actually, part of the Fortune 500, but we're not yet. But despite that, we've cut our application use in half just to be more efficient. We're still well over 500 applications, okay, and that's the ones we know about. Most of the enterprises we sell to are thousands of applications. Every one of those applications has their own data set, okay? Everyone has their own data set, which is slightly different than another application. and to do anything in an enterprise such as issue a quote or an invoice you have to pull information from many different applications well this is what's happened is the data is not consistent across these applications and when you have inconsistent data such as you know a customer means one thing the customer name means one thing in one application a different thing in a second a different thing in the third then coming up with the correct answers to an AI inquiry is it's a data problem it is not specifically a compute problem and so there's a lot of work going on if you would ask enterprises what are your biggest challenges for AI they'll say they will say it's their data and so there's a lot of that we are doing and helping customers get their data ready It's part of the reason why we acquired OneTouch. It allows customers to create a complete catalog of their data. It allows them to rationalize the different data that exists in these different data sets that they have. So enabling customers to get their data ready for AI, in my opinion, in the enterprise, is a huge market opportunity that is not being, it's still very nascent in its infrastructure. That being said, let's go to what the popular media talks about. Our FlashBlade product has been in place for six to seven years in AI environments, can easily handle up to several hundred GPUs, which is what most enterprises, to the extent they build their own infrastructure, will be using. Our EXA, which now goes up to several hundred thousand GPUs, is available for sovereign clouds, for GPU clouds, for tech titans who scale to that level. And then finally, Portworx is in tremendous use right now in data pipelines around ETL. So we have a lot of irons in the fire when it comes to AI.
And maybe in the spirit of the irons of the fire with AI, you folks have this engagement with hyperscaler, I mean, Meta is the one you folks have talked about a fair amount. Clearly, I think investors have a lot of focus on this hyperscaler engagement. But maybe just stick with Meta because you have publicly discussed this. You know, what are you helping Meta with? What is Everpeer offering them that they can't do organically, perhaps?
I'm going to genericize this a little bit because it's the same set of values that we can offer to any of the hyperscalers. So it's not specific to one. And we're developing this hyperscale solution to be the same, roughly the same solution. I mean, each of them will have their little differences here or there, but it's the same solution for any of the hyperscalers. And that's one of the things that makes it attractive to them. So what are the advantages? Well, first of all, you know, we're the only company literally in the world that has what we call direct flash technology. So what is that? Most people think, as we would as consumers, that an SSD is flash. And that's what everybody uses today, with the exception of our customers. They use SSDs. But what is an SSD? Well, the truth is that an SSD is flash masquerading as a hard drive. In order to get flash into things like laptops and everything else, instead of forcing all of the different operating system vendors to have to change their operating system, the Flash manufacturers came up with a brilliant idea. Let's just make it look like a hard drive, and then anybody can use it. No change in software necessary. And that's great, except that hard drives and Flash work entirely differently. So by developing software that works directly with Flash, we automatically have about a 30% to 40% price performance improvement. We're not going to spend time on that now, but we have lots of patents and papers in this area. People can go reference that. Well, that's valuable to a hyperscaler as well, right, because storage is their second largest spend in infrastructure after servers. And as more and more of their business goes to Flash, we can not only save them a lot of money, but we have a number of other advantages as well. So one is we make better use of the Flash, so better space, power, and cooling. our lifetimes are at least twice as long as an SSD if not longer we are our our failure rate is one-tenth of SSDs one-tenth so you know that there are those benefits and then finally if that weren't enough every SSD every every hard disk every different size every different vendor when if there is a flash change every new flash changes requires tweaks to the operating system because that's where disks are managed out of the operating system our software goes into what's called user space in an operating system so no changes to the operating system and the hyperscaler doesn't have to worry about modifying the operating system for the next generation or just for a different scale our software takes care of all of that so we're just a much easier infrastructure to be able to manage. So a lot of advantages to the hyperscaler. Of course it's a big change from what they do today and hyperscalers only modify their environment on roughly a three-year development cycle so and there's a lot obviously of qualification so it takes time and it's an engineering to
engineering interface so it takes time. If I just think of a lot of the way you described the way of understood pure right it's a lot about we can help you will do this in a more efficient manner, right? It could be that raw NAND, raw memory, right? And maybe hyperskillers could ignore it historically because it's like the price of NAND just kept going down. So your problem was getting smaller, not bigger in a way? If you're in a place where memory just keeps going up at this point for several years, isn't in a way like, I mean, it's not time, but the problem you solve
is getting bigger. The dollar, even if you keep the percentage the same of our advantage, the dollar value of that percentage goes up. And therefore, it's more attractive.
And so he's talking maybe two things on that, right? Like, A, why wouldn't the hyperscalers accelerate qualification processes? Because you're going to save them a lot of money, I'd rather do it now than later. And then, secondly, maybe you can just talk about how does this engagement work from a financial basis for you? Is it yours installed, use how much data we manage, and use how we get paid for it? Or is it a la carte? Just talk about those two vectors a bit.
Yeah. Well, what the current environment has done is open the aperture, widen the funnel, so we're getting more interest. But I'd also say that the hyperscalers are more busy now and have got more to do, so it hasn't shortened the qualification cycle. Secondly, we're only one part of their design, we're a relatively small part of their design process. Their design process is governed by the next generation processor, the next generation of their own software, the next generation of the switching systems that go in there. So we're not, the qualification could be speed up but not their design element. That doesn't get sped up at all, right? So, you know, unfortunately, while we're getting more interest, it hasn't slowed, it hasn't sped up the design cycle. So, all right, so now on the economics. It's a different set of economics than our traditional economics in that we're not selling a system per se. We're providing technology. A large portion of it is not our supply chain. So we allow, in a sense, the hyperscalers to, they're responsible for the NAND. They're also responsible for, and they buy a completed product from their supply chain. Now, our involvement is, it's our design. We work with their supply chain so that they can supply it but the NAND is sourced by the customer, in this case the hyperscaler. So what we get is a royalty on every what we call direct flash module that the hyperscaler buys. We have some cogs because while the hyperscaler contracts, if you will, for the NAND directly there are other components that they don't want to be responsible for, so we're responsible for that. So our gross margins, you know, and there are mix issues that will vary the gross margin, but generally in the 75 to 85 percent range given the mix.
You know, a lot of companies right now are talking about, hey, we're seeing this uplift and general purpose x86 servers in a very profound manner actually. Historically, I think there's been a decent relationship between, hey, if you want to spin up these new servers and create new workloads, you've got to store your data somewhere, i.e., storage will fall through at some point. Just talk about, like, historically, how's that lag been between compute versus storage and does that happen as you go into an AI world as well?
Well, they have somewhat different cycles, sales cycles, but on the other hand, you know, storage in a traditional data center, so let's leave AI out of this for the moment. In a traditional data center, you know, compute will generally be on the order of 60% to 70% of spend, storage 20% to 25%, and what's left over is networking at 5% to 10%. That's, broadly speaking, about correct. So storage is larger than a lot of people think in terms of percentage of spend on infrastructure in a data center. I'm not counting power supplies and air conditioning and stuff like that. that. In an AI world, because of the cost of both HBM and GPUs, storage is much smaller. And in fact, because AI uses the fastest, highest performance networking, networking is actually number two, storage is number three in an AI environment. I'm blanking on it's not so much that yeah I would say that the purchase cycle around processors and the purchase cycle around storage tends to they're not tightly connected because a lot of times you can buy processors and you have spare capacity on your existing storage sometimes you start running out of storage because the data increases so it may not be tied to a directly so directly to to a compute purchase cycle.
You know, memory's been another topic that everyone's been focused on. I'm sure you spent a lot of time on it from an input cost basis. But if I think about what's happening with memory, maybe in a bit more of a strategic basis versus just the prices. Historically, when you've had these periods of component tightness, it's reshaped a lot of competitive dynamics in the industry as well at the same time. Can you just maybe talk about how doing the current memory environment influences your competitive positioning and you know how do customers value proposition change with us right so when we talk about memory
I want to broaden it a little bit it you know there's the memory such as DRAM you know and now HPM but there's also storage so you know flash is storage memory right both of them are going through the same you know significant price rises the way it affects us is you know quite interesting We, as you know, up until recently, we can talk about that, you know, our gross margin on the product side has been in the upper 60s, right? Even past 70s, whereas our competitive set, the storage industry as a whole, generally has about 50% gross margins on product. So right away, the equivalent increase of cost in our bomb results in a lower requirement for us to raise prices. So we've been able to raise prices less and later than our competition because we don't use as much memory or storage for us to be able to deliver the same capacity of storage. Secondly, we've chosen to share some of the pain with our customers who are operating at the lower end of our guidance while these price rises are taking place for a variety of reasons. But at the end of the day, I can't go into a customer and say, I'm so sorry that you have to pay more and we're going to take more profit, you know, at the same time. So, you know, our first value as a as a company is customer first. And we live that. It's not just a you know, it's not just a saying. So, you know, in the short term, we have chosen to operate at the lower end of our 65 to 70 percent guidance. But at the same time, that also makes us more competitive. I think, you know, I think we're going to see enhanced growth from a from a competitive standpoint, a market share standpoint, because we don't have to raise as much. And we're choosing to be in league with our customers.
And maybe just on the price increase, I think there's been three price increases that I can track in the last, since the start of this year at least, right? Since the start of the year. And it is the best we can track, you know, your price increases being less benign. They're all crazy numbers, but they're less benign than what others are doing. You actually had a letter out to your customers, which I thought was very well- You mean more benign, less increase. Less increases, more benign to your customers. You touched on this a bit already, but just talk about why are you able to provide or have a lower price increases than your customers, And is it resulting in you winning more business over time, actually?
So one of it is just the physics of the financials, which is, you know, if traditionally our cogs were 30 percent of our product sales versus 50 percent, you know, a 15 percent, you know, let's say a 50 percent rise in your cogs would take you to 45 for the same price. You only have to raise prices by 15 percent, roughly speaking, to make it up. Versus if it's 50%, you have to raise it higher to keep the same margin, right? So just the physics of the financials make it possible to raise it less. Secondly, we are choosing to share some of the pain with our customers, so that's even less. We bring it down to roughly 65. And if that doesn't raise market share, then I don't know what will. What I will say is that Q1 still, you know, because of the lag between pricing, shipments, and backlog, Q1 was still mostly at Q4 pricing, right? Q2 will be much more in Q1 pricing, which, as you said, you know, has increased. So I think what you're going to see as we go forward, or what I hope to see, I should say, going forward is increasing market share. Got it. Now, I will say, let me just go further. When we announced last quarter, I mentioned, you asked me about it right up front, 35% growth, right? Rough estimate, we thought about a third of that was on the basis of price increases in pull-ins. That would be about 10% to 12%. We grew 35%, which means 20% growth. If you look at our competitors, they all grew less than 12%. I don't think we're special in terms of how many pull-ins are coming in, no matter what anybody says. So if their pull-ins are roughly the same because everybody's responding to price increases by trying to get in a bit early to avoid future price increases, then we're growing quite substantially.
It's totally fair. You know, beyond pull-ins, which, you know, I think everyone's going to debate, is it happening and how much is it happening? but there's only this element of it. When you talk to your customers, right, I mean, IT budgets at the end of the day are relatively finite. Correct. How are they navigating this surge in storage pricing very specifically? Are they saying, I'll take a lower-care storage, I'll take maybe some HDDs, I've heard from people, for example? Just how are customers
dealing with this right now? It is, in fact, from a numbers standpoint, too early to say, because the prices are just starting to take place and customers are attempting to get ahead of future price increases by ordering early so right now demand is you know very very high which is why we're trying to parse out what is true demand what is market share gains versus what is pull-ins right because what we care about is market share gains those will last market share gains will last you know short-term activities by customers associated with with price rises or anticipated price rises you know that's that's temporary right so I think it's a bit early to say but historically you know our experience is that with price rises or price declines which is tradition the traditional you know environment that one lives in is that demand of bits right does vary does change with price rises or decreases but not so much as to overcome the price itself. So price trumps capacity but capacity does respond to price changes in both directions.
What about a desire to shift to slower storage or even HDD or hybrid arrays? Are you seeing customers actively make the decision? I mean historically it's been a one-way train, everything goes more
flash. Right, well yes that that has changed because the ratio and it's still it's still in transition. It's still changing now because both flash and hard disks on the source side are sold out through 2028. Okay. So, you know, there's no more supply of flash. There is no more that hasn't been accounted for of hard disk until 2028. So prices are still, you know, demand, sorry, supply is not kept up with demand or looked upon another way. Demand has not declined enough to match supply and that's why we're seeing continuing price increases. What we can say right now is that the market that we were very active in about a year ago of flash being competitive on a total cost of ownership basis with hard disk, that doesn't exist right now. That's gone into stasis. Now I'm a big believer in reversion to the mean, it'll come back, it's just not with us right now.
Got it. And then from your vantage point, right, you have to buy a lot of memory to meet your customer's demand as well. Just talk about, like, you know, what sort of visibility, what sort of conviction do you have with your memory as an input cost?
You're talking about both flash and RAM? More flash because it's a bigger input cost fee, I feel, than DRAM.
But even broadly, just talk about your LTAs, the visibility on bits and pricing or just bits.
So we have very good visibility on both, and part of that is because of our hyperscale commitments that we currently have in place. So we have plenty of flash. What has changed is that there is a separation now between being able to get long-term commitments on volume and getting long-term commitments on price. There are no long-term commitments on price right now. It's an allocation game and you know pricing is basically every 30 days, right? So we have allocation on capacity. We price was a 30 which is part of the reason why you know prices are Escalating is because you you can no longer say yeah for six months and I can buy a head for six months. You can't You can get capacity, but not price. Does that change how you quote
business your customers at a 30-day increment as well or yes so we very you
know for our entire life as a company we we've been quoting with a 90-day quote expiration we're down to 30-day quote expiration now by the way that's longer than most of our competitors yeah yeah I've heard stories about you know right
for cancellation right to change pricing up on the shipments it's yeah until shipment it's all kind of fun clauses are getting implemented and see how well they stick over time, but, you know, one of the dynamics that's emerged is like this NeoClouds as a category, right, and CoreWeaves and others of the world, like, they have a lot of spending dollars, they're doing a lot of stuff. How do you think of that as an emerging customer base for EverPure? And you, I think, have a relationship with CoreWeaves on a couple
of different levels. Just talking about, like, NeoClouds, what do they want? How are you tackling that kind of customer base? So the NeoClouds or GPU Clouds, or for that matter, the Sovereign Clouds, was a market that we did not have a product to engage with up until the last year or so. And to put that in perspective, most of the AI environment, the large-scale, sorry, LLM, large-scale AI environment grew out of the HPC market, the high-performance computing market. that for storage was typically somewhat of a of a cul-de-sac it was a very specialized market very scientific in nature very specs oriented versus reliability oriented very tuned you were selling to scientists and they like to tune things I like a lot of nerd what we call nerd knobs in the business and and And it wasn't a, think of it sort of like, oh, what was the name of the company that HP bought? What's that? No, the supercomputer company. Thank you so much. Think of it like Cray. Cray didn't expand to do other things in the standard commercial market, right? And that was a lot like the companies that satisfied the HPC market. Well, you know, AI grew out of that. And so it turns out that it was the people that were in HPC that now people all of the large-scale GPU environments, whether it's sovereign clouds or the tech titans or the GPU clouds. And so, you know, we didn't have a product in the race. We didn't speak the same language. We didn't go to the same schools. We didn't have the secret Dakota ring. And we certainly have certain beliefs as a company, in particular that all of our systems should operate on the same software and have the same abilities, if you will. Reliability, upgradability, the evergreen capabilities that we have. And so it took us a while to modify or add a new set of capabilities to our FlashBlade product. But now, as I said, we introduced FlashBlade EXA about a year ago, same purity operating system, different configuration, where we are now publishing the best performance specs in the business, bar none. And while we're still a little bit behind in features, we're catching up and we're starting to, not only are we replacing some of the traditional competitors in that market, but we're starting to play a bigger role in these GPU environments. So it's still early days, but we're very – and we feel that as these GPU clouds start to have to perform in a more – you have to be able to provide a higher quality, more reliable, consistent service, they're going to appreciate a lot of our abilities that come with our product versus some of the ones that were designed specifically for the scientific community.
I was wondering where you would stack up, I mean, one of these I hear is like, hey, these new clouds with GPUs need a parallel file system, and that's not what you need to deploy, but then that, to your point, doesn't come with all the redundancies and services that you want. Is that kind of the delta that, hey, as they get more mature, purity will work, or?
Purity will work. I mean, these are all just features. You know, I personally, having been in the business a long time, things like reliability, sustainability, diagnostics, that's really hard stuff that you only get over a lot of experience over a lot of period of time. Things like individual features like a parallel file system or, you know, KV cash or, you know, quality of service guarantees. These are all just features that, you know, one of the other things that distinguishes us that, you know, is at a much more foundational business level is that we are a dyed-in-the-wool R&D company. You know, one of the things that 70% gross margins allows us to do is we spend 20% of our revenue on R&D. By the way, in storage, that makes us the largest R&D spender in storage bar none. Okay, we invest more than anybody else. Now, I think that accrues to our growth. it accrues to the quality of the product that we can put out but it also allows us to expand in other areas and you know unless you believe that we're not terribly efficient at that but it's something we work on a lot to make sure we are efficient in the way we we invest our R&D and I think our growth shows that you know these I don't worry about a feature gap if we're focused on the
market segment. Perfect. If you shift gears a bit, Evergreen One, right? It's sort of your subscription services offering. I would imagine in a time where memory is inflationary, it would resonate more with customers versus not. I'm sure it is. But just talk about a bit more of the Evergreen One offering and are you seeing a better uptake of it in an environment like this?
Yeah. So just to put some numbers behind that, Evergreen One grew, I think it was 50% year every year in Q4 and roughly 70% in Q1. That indicates that it is growing faster than our CapEx product. Our historical experience with this is that as prices go up it becomes more competitive. And by the way, our price increases on Evergreen One is far less than our price increases on the CapEx. We can go into why, but there are reasons why it is not as directly affected by price increases. or input cost increases and so what that would say as we go forward and we only have one quarter of price increases really so far to measure this off of but i'd be surprised if it didn't mean that that our pers that the relative amount of bookings that come in via evergreen one will be higher than uh
than the capex and maybe just two things on evergreen one what are the gating factors like why wouldn't why aren't why wouldn't customers operate faster than already have like what limits them in that well first of all it's a different way for the you know
even though they're used to it's it's so funny even though they're used to buying on subscription you know in the cloud right more for SAS or for the hyperscalers they're very unused to doing it inside their own data center it's a capex model inside their data center and honestly sometimes we spend two to three months you know with the the finance organization in the customer trying to train them on how they do it. And obviously it's growing faster than our CapEx product, but it's not unusual for them to finally say, you know, it's just going to be easier if we do CapEx, so we're just going to do that. Now, pricing does make a difference, so we'll see. But, you know, that is, customer financial models sometimes are more difficult to change than their technical model. But why isn't it just automatic? It's generally for those reasons.
And then why is the investment, from your side, lower for Evergreen One versus the traditional products?
Yeah, well, first of all, obviously our terms are longer, but it's more than that. Our Evergreen One is a true subscription, so it's not a lease, it is not a financing. And what do we mean by this? What we sell to the customer is purely a service-level agreement. It's a service-level agreement on performance, it's a service-level agreement on capacity, it's a service-level agreement on reliability, right? That's it. There's no serial number involved. We own the equipment. We get to choose. There's no guarantee of new equipment. I mean, if you go to Amazon, do they tell you we're delivering new equipment to you? No. You're just getting a service. We provide a service. Our Evergreen Forever subscription, which is for customers that do purchase, they are guaranteed hardware upgrades every few years. That hardware comes back has more life associated with it. We're able to use that in our Evergreen One service. And what does that mean? That means we're blending costs over a long period of time. We refurbish it, of course. All the data is erased, but we're using refurbished equipment in addition to new equipment. And we get to sell it at prices that are competitive with as-a-service pricing. So our costs don't go up automatically and we have to be in a great cycle because we just went through a refresh on our CapEx product which means we have a lot of equipment coming back for refresh that was priced at three years ago. Perfect, that is helpful. You folks
recently changed your name from PureSearch to EverPure. You've certainly done a few deals like OneTouch, Portworx a couple of years back, you are trying to become more than just, hey, here's the storage layer, right? Just talk about like, maybe this should be my question to you at the start, not the end. What is the vision with this? Is it more like we're going to be a storage bottleneck solution provider? Because it's more than just traditional storage medium.
No, we fundamentally believe, and I alluded to this early on, that the nature of applications and data is fundamentally changing. And it's fundamentally changing because the app world has fragmented the customer's data environment. And AI, if left to the current way of doing things, is going to fragment it even further. Everything wants another copy of your data. Every SaaS company wants, every SaaS company's strategy right now is give me all your data and I'll give you the AI answers. You know, the Databricks of the world and the snowflakes of the world, they all say give us all your data. and we will provide you all the AI answers." And how many different organizations is a customer going to give all their data to? I don't think it's scalable. I don't think it's a viable solution. Our belief is that if these applications are no longer systems of record, which they can't be because you know in order to do anything you have to have information from all of these different systems and if it's if that information is not consistent the world's in big trouble so what can be consistent is no longer the application but the data has to be consistent so if we're gonna if the world's gonna migrate from app centric to data centric data primacy and what we're calling data primacy then we're in a very good position to help customers get there so what does one touch do one touch for example allows customers to get a catalog of all their data, all of it, not just what's on our stuff, what's on competitive stuff, what's in SAS, what's in the cloud, what's, you know, what is with partners, and allows them, first of all, to get not only get a catalog, but get a map of how this data set relates to that data set, you know, that has the same name of the customer in two different places, but it has different semantics for what that customer is doing. It has different information about that customer and one touch like the name sounds brings that all together so you can have a common semantic understanding of an entity like like a customer or like an asset uh or like a um like a like a quote all right uh or an order and so uh what where we're going is we're going from pure data storage, pun intended, to data management where we can help customers manage their data and have fewer copies, have more comprehensive understanding of their data as we go into this world. But the world is in, it's not just about AI. There is a data crisis going on. If you ask any customer, what is your biggest challenge to implementing AI? They're going to say right away that it's their data their data is a mess and and that's the that's so you know perhaps we're taking a what is not a well-known or untraditional approach to helping customers in this world of AI but we believe it's fundamentally a data problem and that's what we're going after.
I certainly have an affinity to the name Everpure but maybe it's because I work at Evercore there's a link there but I certainly think it's more than just a data storage problems, it does make a lot of sense. I think the time's almost up, so maybe I'll pause my question there, Charlie, I'll turn it back to you. Anything we did not touch on that we did not cover that you think folks should be aware
Well, you were very kind and not spending half the time on the hyperscale challenge, so I appreciate that. But that, you know, we are very pleased with the progress we're making there overall. But outside of that, no, it was a great conversation. Thank you very much.
Appreciate your time.
Thank you.