Sandisk Corp (SNDK) August 2026 Capital Markets Day Transcript

Thank you very much for joining us today. Before we begin, please note that today's presentation will contain forward-looking statements based on management's current assumptions and expectations, which are subject to various risks and uncertainties. These forward-looking statements include expectations regarding our technology and product roadmaps, our new business models and multi-year customer partnerships, our business plans and performance, market trends and opportunities, and our future financial results. Please refer to our most recent annual report on Form 10-K, our quarterly reports on Form 10-Q, and our other filings with the SEC for more information on the risks and uncertainties that could cause actual results to differ materially from expectations. We will also make references to non-GAAP financial measures today. Reconciliations between these non-GAAP measures and the most directly comparable GAAP measures are included in the earnings releases for the relevant periods and in the appendix to the presentation materials, which are posted in the Investor Relations section of our website. Please welcome Vice President Investor Relations at Sandisk, Ivan Donaldson.

Thank you very much. I just want to say thank you to everyone for being here today. I've been in this industry for 22 years, and the journey with Sandisk has just been astounding. So it's been an amazing ride. We have an amazing management team and board of directors, amazing employees across the globe. And we're just really excited to be here today. I'm going to talk a little bit about the agenda just really quick. So we'll go over the, obviously, company overview, strategic vision, future for the company. Then we'll go through the technology roadmap, or really the way I think of it as our innovation engine in the company, which is just astounding. We'll also take a step and look at the industry transformation, what's happened, how we got here, essentially, which is pretty phenomenal. And then we'll go into a deep dive of the AI infrastructure. Essentially, why? Why is this happening? Why are we seeing such a step change in demand and where we see that going forward? What are some of the key variables and dynamics for that? And then followed by the financial model from Luis and really trying to deliver that we have a tremendous opportunity to drive shareholder value well into the future, and we hope that's going to be the takeaway from today. And then at the end, Alper will come back up and talk about sort of future innovation roadmap and where we see that going to conclude, you know, again, where we see some future technologies and emerging memory opportunities, followed by Q&A. We tried to take into account a lot of your questions of the last year of what you guys are most interested in. So hopefully we'll get to all of those. So again, I appreciate everyone being here. Thanks for your support.

Sandisk, the flash storage vendor, was added to the S&P 500. With a look at Sandisk.

We're going to be talking about Sandisk today.

Sandisk is more than doubled. Sandisk, look at that chart on Sandisk.

Sandisk, the best stock in the S&P this quarter. Many on that conference call for Sandisk specifically, what do you want to ask?

I want to better understand how inferencing is going to help or impact SanDisk's demand into next year. I want to know how SanDisk is going to be able to preserve margins.

Please welcome Chairman and Chief Executive Officer at SanDisk, David Geckler.

All right. Welcome. It's great to be back here in the room where we launched this company 18 months ago. A lot has changed in that time, and today we're going to talk about the company going forward. I can tell you, I was, you know, as I was talking to some of you as we were preparing or just this morning and gathering, we were talking through a lot of stories of stuff that has happened over the six and a half years, whether it's roadshows or conversations we had about this franchise. I joined Western Digital, of course, in March of 2020, actually the same week that COVID started. That's like probably people know about that a lot more than they know about me starting at Western Digital. But we started on this conversation, I think, that's been going on for almost six and a half years now about where we were going to take this franchise. I thought from the beginning this was just an unbelievable franchise that we could really unlock the value, but it took some time. It was going to take... There were big markets. It was going to take a lot of moves about how we got there. And I can tell you as I stand here today, I feel like I've finally gotten to the starting line of where the real value creation is going to happen. Now, that may seem like a pretty big statement based on what's happened since the launch. But, you know, I think when you walk away from here today, you'll see kind of the conviction we have in what is really the earnings power of this franchise and why going forward, we've finally got things structured in a way we can really start to reveal that on an ongoing basis. All right. I'm going to set some context here on kind of how I think about the business, kind of the big picture of how all the different things we're thinking about, how we integrate it. This is a business where you can't just think about one thing. There's like two, three, four variables that are in motion at all times. And it's about getting them balanced. And how are we going to make changes, not only to the technology, we're always changing the technology. Like that's something that goes on and on all the time. We're world-class at that. And we are going to hear from the people today that are really driving that. And that's an incredible story all by itself. But then also, how are we thinking about the business model around that? How are we structuring the business? How are we changing the relationships with our customers? How are we allocating capital in the business? All these kinds of issues to create, just on an ongoing basis, relentlessly create intrinsic value in the franchise that will be revealed as we continue to execute the business. All right. So that's really kind of, there's kind of three big categories to that. The first one, look, I was going to, when I was putting to get this talk together and I was thinking about this, I was going to spend some time going over what we committed to back the last time I was on this exact stage. So what was that? February of 2025. We made a bunch of commitments of what we were going to do with the company. I think it's just fair to say it went pretty well. I think most of the things we said we've delivered on. We talked about we wanted to win in data center. We needed to establish data center as a major growth pillar of the business. That had been a long, I think, an issue with the company for quite some time. I think we're getting there. I think we just delivered significant outsized growth in the last fiscal year, and I think you're going to see that continue as we go through FY27. We talked about we're going to focus on the consumer business and hopefully you stopped by outside and saw the products. They all look very different. It's the same products, different branding, showing up a different way. This is just an incredible brand that I think is a bit of an unpolished gem and we're continuing to work on it. We've gained two points of global share in that business. because we'll talk a little bit about that business today, about why it's so important to the business model as well. You know, we made some statements about, we made some controversial statements at the time, quite frankly. We got up here and we said, hey, pricing is going to inflect positive in the second half of the year. And that became a big, big talking point. No, it's not. Yes, it is. No, it's not. Yes, it is. It turns out when we got to the end of the year, things were going in the right direction. And if anything, we significantly undercalled it. And so anyway, I think things went well. Those of you that believed in the company back then and, you know, invested in us, we really sincerely appreciate that. We take that very, very seriously to be good stewards of your capital. And we worked very hard and you got a good return and we're very happy about that. But that's the past, right? We're not going to talk about that. We can't go back to that point in time. That was a special point in time. It's not coming back. But what we can do is talk about going forward and how we're going to create value from here going forward. And I can tell you, everybody you're going to see up here on stage just has unbelievable conviction that this franchise, we're finally starting to reveal the true earnings power of it. And that earnings power is going to go on for a very, very long time. So we'll talk about that. All right. So let's start with this first. When we say, you know, we have this capital allocation strategy. Number one, we're going to invest in the business. That's always the most important thing, invest in the business. So what are we talking about? And, you know, I'll preface this by saying there are a lot of things we committed to doing back when we launched the company. And I think we were, like I said, I think we were largely successful in making progress on those. But there's a lot of things over the last 18 months, and especially the last nine months, where we were presented with some opportunities to really change the business. a lot of momentum and we took advantage of those and you know i think we have fundamentally restructured the business and we've we kind of put a pin in the map on this day early this year realizing you know we could see what was happening in the business we could see we were restructuring the business and we're going to need to stand up and explain it all to you because it is very different what it was back in february so anyway with all that said let's talk about all the things we did and kind of how we thought about investing in the businesses we want. So the first thing, the most important thing, you know, we're a technology company. Like if you don't have great technology, you could probably like not, you shouldn't be doing what we do. You always have to have unbelievable technology. So this is always the most important thing we're going to invest in. And I think we're in the best position we've been in, in a very, very, very long time. As you look across the portfolio, something I've been doing my whole career is investing in technology and thinking about this multi, what I think of as a multi-horizon innovation investment plan. You can't just think about what's going to happen next or what's going to happen this year or next year. And you can't just think about what's going to happen 10 years from now. You got to think about all of it and how do we invest across this entire horizon to make sure we have the right technology today, tomorrow, five years from now, 10 years from now. And this is where I think we've, you know, if just when you look at our business, you know, it starts with what Ivan said, kind of the engine of the company is the Bix roadmap, is the fundamental NAND roadmap. If you don't get that right, it's kind of hard to make up for it at the system level. It's kind of hard to like hide that. You have to have really good NAND technology. And we're constantly investing in many, many generations of NAND technology. We don't talk about all of them all the time. We just announced Bix 10, I think, a couple weeks ago. By the way, we announced Bix 9 yesterday, and I'm sure everybody is confused. Like, why did you announce Bix 10, like, three weeks ago, and then you announced Bix 9 yesterday? Because 9 is before 10, and we thought you would have announced that first. So Alper will explain that. I think it's a very important point to understand. Like, the technology strategy is changing based on the fundamental Bix architecture, and he'll explain to you how we now have multiple ways we can move this technology. So we're investing in Bix 9, Bix 10. There's actually people working on Bix 13 right now. So one thing you should have confidence in, and this is with our partner, Kyoxia, we have a long roadmap of really strong fundamental NAND technology. You know, one of the big advantages of the JV, the big benefits of the JV, is we invest together on R&D. And together, we're a third of the market. So that means we can invest as much or more than anybody else in the market in making sure we have the best technology. So that's going to be there for a very long time. Alper will go through that. We also build the systems capabilities. You know, the VIX investment gets you through the wafer through the fab. The wafer comes out of the fab. I have to do something with it. You know, you could basically, we could just go sell all the wafers. But we actually turn them into systems ourselves. So we have people working on all of the controllers, how to build SSDs, how to build all the different products in the markets we operate in across consumer, across edge, and now across data center. So you're going to see Kuram up here today. He's going to be talking about AI in the data center. And it's really his team that builds all this stuff. So just enormous systems expertise across all of these markets. I think this is one of the big, if you look at the foundation of the company as a technology, one of the positions that we now are in, and one of the reasons I have so much conviction about the future, is we now have optionality across the entire market. We have a very unique consumer franchise, edge, we've always been very, very strong, PC, smartphone, IoT, all of the things on the edge, and now we're very strong in the data center. So we've got all this optionality, and we're going to keep all that optionality. One of the big things of the strategy of the business is make sure that all of this technology remains very relevant, very on point, to continue to drive this innovation across all these different markets. So that's always going to get invested in. Now we pick our head up a little bit, look a little further down the field. build, one of the things we talked about 18 months ago as we announced this product or this strategy around high bandwidth flash. If you would ask me, a lot of stuff has happened in the last 18 months, this may be one of the things that is actually I'm the happiest about. We basically stood up here and said, we're going to build this thing called high bandwidth flash, and I think the reaction was pretty much, what are you guys talking about? Nobody has any idea what you're talking about. What is high bandwidth flash? We only new high bandwidth memory, but we were kind of targeting this idea that, hey, when we get to inference, like AI is a massive opportunity. At that point, all the focus was on model training, and appropriately so. But I think we were looking down the field, our team, especially this wasn't me, this Alper and his team, a tremendous amount of insight to see that, hey, you know, at some point we're going to move to this inference phase, you're going to have to scale this, and we're going to have to come up with a different memory architecture or storage architecture for inference to really scale. And we have an incredibly important technology that we can bring to the party. And so we announced it, and we said we're going to form an ecosystem, and we are going to start building this product. And, you know, Alper will be here later. That's the last talk that we're going to have today. He'll be here later and give you an update where it's at. But I think if anybody was at FMS last week, they probably saw there was a lot of activity around high-bandwidth flash. And even last year at FMS, high-bandwidth flash was awarded like the most innovative technology in the industry. So, you know, that's a horizon. And I know a big question is going to be when is it going to ship and all this kind of stuff. And we'll get to that. We're not going to get to that today, so it's a little bit of a spoiler alert. But we're getting there. I think the the ecosystem is being developed, people are coming to the table, a lot of very good discussion. And so, well, you know, that is on the horizon. Then we look even further down the field, we had this idea of 3D matrix memory, you know, little longer term project, continue to make progress. So we're basically, you know, this is like the first priority, invest across all of these things and make sure they're all healthy and bring them to market to the extent that we're getting feedback that they resonate with our customers. All right. The second thing we talked about was get to this idea of a cash positive balance sheet, right? It's not a, it's not exactly a novel idea, but it's like, we need to get the debt out of the company. And I think this is something we're, you know, very happy about it last, it's happened faster than we probably thought market. We got a, we got a receptive market and we got to this position where we don't have any debt. We have significant cash reserves. And one of the things Luis is going to talk about later today is, you know, this franchise, I think one of the things you guys are all seeing, when you start to scale this franchise, it really is good at generating free cash flow. That's good, right? That's what we think our job is, to generate free cash flow for all of you. So we're in a position where we can generate a lot of free cash flow. What are we going to do with it? Luis will talk about that uh when he gets up here but we feel like we're we're we're in a very good very good spot there all right now there was that was kind of like things we need to do every day but you know once the business started turning we started thinking about okay what else can we do to invest in this business that's what we want to do first first thing we want to invest in the business so the way i think about this is we we constantly go through a process of how do we systematically de-risk the business? How do we systematically make investments? We're basically de-risking the future. And there are a couple of things that were a big part of that, right, that we were able to get done. First one was extend the joint venture. It's one of the first things you saw us do. We invested over a billion dollars with our partner Kyokshia, which was a recognition of the scale of this joint venture. But, you know, we, one of the first things we spent our money on was making sure we had production of NAND from 2030 to 2034. That was very important to us because we have a tremendous amount of conviction in the future of this franchise. And if you want to be in the NAND business, you need to have a NAND fab. That wasn't always the case, by the way. Like part of the issue with the industry in the past is you could procure NAND very inexpensively because the people that own NAND fab seem to be selling it at prices that were not basically the marginal cost. I think that world is gone, quite frankly. My personal view is it's not coming back, and it's going to be very difficult to be in the NAND business unless you have access to a NAND fab, which we do. We have it at scale, and we have You know, all the R&D benefits of that and all the manufacturing benefits of that, you know, the other thing I hear sometimes is, oh, NAN's not that hard, NAN's a commodity, anybody could do it, right? Well, if you actually believe that, I would encourage you to go to Yokaichi and take a look at the fab that's there, right? And if you want to duplicate that yourself, find a lot of money and I'll see you in about 10 years, right? It is extraordinarily difficult to be in this business, and this JV is a huge part, a huge strategic asset for us, and we took the opportunity to extend it when we could. Now, one other thing we did in this kind of de-risking, we knew that, look, we want to play in the data center market. Why do we want to play in the data center market? Well, you guys know all that. It's a very attractive market, but also it was the market that's going to help us change this dynamic with our customers. We want to go from this kind of negotiate price every quarter, this kind of highly transactional, highly volatile business, and we want to turn this into a business where we kind of dampen that cyclicality. We have more long-term relationships. We have more long-term visibility into what demand is going to be, and the customers that are most likely to do that are the data center customers, right? There's a lot of reasons for that. We can go into that later if we want in the Q&A. But we have a willing partner that wants to go down that path with us. So if you're going to be big in the data center business, and you're going to grow that business, you need access to DRAM, right? And we're no different than anybody else, right? The DRAM market is very tight, as they say. And so we needed to make sure if we're going to go to our customers and say, hey, we want to strike a five-year agreement on selling enterprise SSDs, and we're going to put this huge contract together that's worth tens of billions of dollars, we need to make sure we have access to all the pieces to actually fulfill that contract. So this became extraordinarily important to us. It maybe wasn't as clear to all of you at the time that we were putting all these building blocks in place that was leading to this different contractual relationship with our customers, but that's kind of what we were doing. So we had the opportunity, We took a 4% equity stake in the company. It's worked out okay. I think we just took, Luis is smiling down there, our CFO. I think this quarter may be one of the few times where our gap earnings are higher than our non-gap earnings because we recognize like an $800 million gain on that investment. So that's been going well. But we didn't invest in it for the return. It's great that we get that. We invested in it because we need access to the technology. So look, I think the net of all this is we've taken the opportunity over the last 18 months to really make sure the foundation of the business is just incredibly solid. We have the right technology, we have the right roadmap, we have the right innovation, we have the right relationships, we have access to all the things we need for years and years into the future. And then on top of that, we're going to think about how do we change the business model? How do we get this franchise where it's sustained value creation over the long term? And there's a lot of questions about that. I remember when I took this job, we went through the separation, some of you were actually pulling me aside, saying, Dave, what are you doing? Why are you taking this job? Do you realize this industry has never made any money? And I'm like, yeah, I got it. Like, we'll figure it out, right? We're going to get there, right? We can change things and we can get a better outcome. That's one thing we really believe in and change things and get a better outcome. So how do we think about that? What we're going to change? This is kind of how I think about it. The three imperatives for sustained value creation, right? So there's not, I've talked about these before, but I'm going to just go through them a little bit. Number one, we have to increase profitability. I think if you go back to that time of February of 25, when we launched the company, this was probably the big debate. I actually love all you guys, because there's always a debate. When we have a conversation, there's always a debate. And there's always a debate about something. And as soon as you get past that debate, there's a new debate. So there's always a debate. But the debate, I think, a year and a half ago was, could you get the profitability where you need it to be? I think the debate today is the second issue. How do we reduce cyclicality? Now it's like, oh, okay, Dave, we get it. We understand profitability is at a level it's never been before, but it's just a matter of time. Just a matter of time. Faster things go up, the faster they come down, all these kinds of things. So it's like, now it's all about cyclicality. And we're going to talk about that, right? I think we're doing some things. Again, over the last nine months, we've been extremely intentional about the way we run the business and the way we structure our relationship with our customers to try and reduce cyclicality. We're not saying cyclicality is going away, right? The whole world is cyclical. There's a business. The whole business is cyclical. What we're trying to do is get this wild cyclicality of this business, just dampen that down, get more sustained value creation. And if you do those two things, in every business, you got to grow. You got to get consistent revenue growth, right? And, you know, this is hard. In most businesses, this is hard. It's hard to grow. Like, usually you run out of TAM. And so you have to go acquire and do all kinds of things. And we'll talk about in this business, it's actually quite different. All right, so let's dive into these just real quickly. Oh, last point, what I said earlier. You have to do this across all time horizons. It's not about maximizing value for the next two weeks, right? What's pricing going to do the next two weeks? That's important. You got to do that. You also have to do it across the midterm, the longterm. So we're constantly thinking, how do we balance these things across all time horizons to get to this point of sustainable value creation. So it's important. So you've got to think in two dimensions. And a lot of the questions I get, a lot of the questions we get, tend to be about one of these things, independent of the other two, and about a certain time horizon. And just so you know, what we're always, when you ask us questions, what we're always doing is trying to translate your question into, okay, how do I think about that question across all of these variables in all time horizons and give you an answer that makes sense. Because answering point questions doesn't really help advance your understanding of the whole franchise of what we're trying to do. All right, let's just go into a little bit. Profitability. Like I said, honestly, I think profitability was always the one of these three that was just like hiding in plain sight. Like when I was managing the franchise when they were together, and we went to separate the company, it was this issue, will this business really ever create value over the long term? And to me, that seemed like, I just didn't have a question about that, right? Because I thought the intrinsic value was always there. The question was, could you get the business model right? The way the business worked, the way you engage with your customers, Like, could you change that to actually let this intrinsic value come out? And it really kind of starts with this kind of observation. It's kind of a very simple observation, but I think some people forget it sometimes. We own the whole stack. We do everything, right? This is not a fabless semiconductor company. Like, that's impossible. And so we own the NAND IP. So the fundamental IP it takes to build NAMM. I said, you know, one thing you should take away from the fact that I just said we have people working on Bix 13, which is, like, going to be launched sometime. Don't come back and ask me 100 questions about I just said when Bix 13 is going to be launched. But you're talking, like, well beyond 2030. People are working on technology a decade into the future. So this is, like, just on its face, extraordinarily difficult to do. And there are hundreds of engineers that have dedicated their lives to building some of the most sophisticated semiconductor technology, and that's just one part of this chain. So we have all of that, and we have it at the largest scale, again, because of our collaboration with our partner, we have it at the largest scale of anybody in the world. And as somebody that's managed technology franchises for decades, very large technology franchises, your market share makes a big difference in how much you can invest in R&D. You can basically invest R&D commensurate with your market share. So what that says is when you get bigger, you should get better if you're doing your job right. And I think you're seeing that show up. You see that show up in our roadmap, and Alper will go through that. Once you have the NAND IP, we don't call somebody else up to build the wafers. We have our own fabs with Keokshia. Like I said, they're quite spectacular. They're some of the largest fabs of the world. The scale we operate at is just incredible. So we do all the front-end manufacturing. Wafers come out of the fab, what I said earlier. We have another engineering team. Hundreds of people that are working on all the systems expertise. How do I take this wafer, cut it up into dye, put it in an SSD, put it in an enterprise SSD, a client SSD, put it in something that can go into a car, whatever it happens to be, we have all those people too. We're doing all that work, and as I said, across all markets, consumer, edge, and data center. And then we have the back-end manufacturing as well. You can go to Malaysia, we have a big factory there, we have a factory again with a partner in China. We do the back-end manufacturing, too. And then, of course, we do the whole go-to-market piece. So, you know, there's a lot of debate. There I say it. I said it. I'm adopting all of your language. There's another debate, right? So there's this thing, like, oh, my gosh, your margins are so high. Your margins are higher than the fabless guys. Well, we do a lot more than the fabless people, right? Nothing against them. They're great companies, incredible companies. But when you look at this, there's just minimal profitability leakage across this whole model. So that's why I say this thing has been kind of hiding in plain sight the whole time. The issue was the business practice was wrong, and you never could see it. And not only was it there, but we've been doing this for like 25 years. It's like crazy. Like we've got like 25 years of paying engineers, like hundreds of millions of dollars a year. These engineers, they're not cheap. You know, they're like expensive. They're a little bit temperamental sometimes. They can be hard to manage. They're wonderful people. I'm one of them. But it's like not something you just roll out of bed and do. So, you know, we've been investing that for years, decades, decades, decades. And, you know, quite frankly, one of the things I see today when people say, oh, you know, like this is just memory. It's a commodity. I'm like, yeah, you try it like you try to do this stuff. It's like incredible what people are doing. And so we've been investing in this for like decades. In fact, you know, Alper will be out here later. He leads his team. Most of the people, a lot of the people in that team have been at Sandisk, like, way before the Western Digital phase, right? That was just, like, a little phase of the company. So we've got, like, this huge amount of expertise there. And then on the manufacturing side, we've been investing billions of dollars. Again, for 25 years, we've just been investing billions and billions of dollars of building out this incredible scale manufacturing capacity. So, you know, in many ways, like I said, I think this profitability thing was just hiding in plain sight. This thing is like a coiled spring that's been fed for like 25 years, and it really hasn't produced, and now it's producing. And now it's about how do we sustain that over long periods of time. And again, I think hopefully one of the things you take away from today is that the way we're engaging with our customers, the level of strategic engagement has fundamentally changed that allows us to completely reveal the profitability of this franchise. Now, a little bit, what did it take to get here? How did this get unlocked? Again, if I go back two years ago, it was like, oh, Dave, can NAND ever be 50% gross margin again? I'm like, yeah, of course it can. So I think we've kind of put that to bed. But the issue was we just have to proactively manage this business from the supply side. I think 23 showed us, like the great meltdown of the industry showed us, that if you manage this from like, I have new technology, I should release it, that's going to lead to a bad economic outcome for us. And I'll go through a little bit of that later about why that's the case. So a little more proactive supply management, and pretty soon things come into balance. And this next statement may be a little bit controversial. I actually think the market is adjusting to these dynamics quite quickly. And I think that's one of the things we're here to talk about, right? The market is changing actually quite quickly in the way we engage with our customers around securing future supply. It's moving rapidly from this quarter to quarter to multi-year timeframes. All right, let's talk about cyclicality, the current debate, all right? So how do we think about cyclicality? So you may be surprised, I said earlier, the first place I think about this is the consumer business. It's one of the reasons why the consumer business is so important, right? Why is the consumer business important? 351,000 points of sale around the globe, right? This global brand equity. You can almost go, it's fun, you know, it's fun in this job because you can almost go anywhere in the country, anywhere in the world, and you're like, people know our brand. There's, you know, last time I was in China, I like, there's always a photographer. I pulled him over and said, hey, open your camera. He opens his camera. He's got a SanDisk card in it. It was great, right? So people know us all over the place. Billions of products sold in the last decade. You know, the dynamic range of this company is incredible. We sell like a single product to billions of people, and we sell billions of dollars worth of products to single customers. That's kind of what the estate is across the whole thing. But let's look at the financial dynamics of this. I don't think we've ever showed this chart before. But this is industry gross margin going back to the first quarter of 2017. So there's the cyclicality for you. And you can see the great 2023 washout that kind of impacted how we changed the business in a major way. But if we plot consumer gross margin on top of this, you see that it generally tracks the business over almost all time periods, but especially when you start to see the down cycles, it insulates you from those, right? Because it's just a broad-based market. So it's like this shock absorber on the business that allows us to generate a consistent level of profit on the base of the business over long periods of time. And you can see, I don't expect 23 to come back, by the way. Like, I know some of you are waiting for that to come back. I don't expect that to come back. But you can even see, even in the worst, the darkest, darkest days, maybe in the history of the industry, this business was still producing profit, right? So, very important business will always be an important business, and it's a great asset to the company. but it's not enough right the issue is it's not big enough not big enough to insulate the whole business it's great it gives you some protection it's we need to make it bigger by the way that's the key of why we're taking a brand first approach to this business it's about growing it right it's financially it's a great business and there's actually some little tricks behind the scenes that make it very profitable because we can use more of the wafer in this market than we can in other markets. There's lots of little things like that that goes on. But it's about growing this business now. And like I said, since we launched the company, we've gained a couple points of share globally. It's hard to do. We've rebranded the whole portfolio again. Janet is here. She leads the business. Stop by outside and see all the great new products. So it's not enough. So this is where we really went into this whole concept of new business models. We've talked a lot about this last couple quarters, we've tried to be very transparent on what we're doing, right? As transparent as we can be, given these are like confidential customer relationships. I'm not going to go through these in great detail, because Luis is going to go through these in great detail. And he's the best person to go through them in great detail, because he's actually the guy that's negotiating them. So that should tell you one thing about how important they are. It's like literally the CFO of the company. And now there's a lot of people around, there's a big team, but And he's the one that's got his hands on the steering wheel of what all the terms and conditions are and what we'll agree to in engaging at this most strategic level with our customers. So I'm not going to go through all this detail. Luis will go through it. The one thing I'll tell you, I've just heard a lot of stuff about these agreements. Just incredible. There seem to be people that are very informed about the way these work that have never read the contracts. It's incredible. I watch TV, and people say, oh, there's more holes in these things, and you can imagine. I'm like, really? Like, how would you possibly know that? Like, there's only, there's like the number of people that actually read these, you know, you don't really have to take your shoes off to count the number of people that have read these contracts. Like, it's like just both hands, right? It's, they're just like incredibly detailed, incredibly strategic, very, very consequential, and they're different than what has been done before. We have a lot of confidence in that. There may be a lot of reasons you choose to invest in this company or not invest in this company, but don't choose because you believe something about this just based on the history of the way the industry has always worked. Take the time to understand how these things really work, and I think you'll get some of the conviction that we have about why I can make a statement, I feel like I'm finally at the starting line of where the real value creation is going to happen on a sustained basis. Now, I will say this long-term customer visibility, demand visibility, and pricing stability is replacing this quarter-to-quarter world. It's a crazy industry. Everything is negotiated constantly. You know, I only have to go back like three, four quarters. Everything is negotiated. We come into a quarter. We're still negotiating with our customers on what they're going to take and what the price is going to be. We have, like, three months of visibility. And we're making investments that are 10 or 15 years long. We have three months of visibility. Within two quarters, we've gone from three months of visibility to over four years of visibility. So what I said earlier, I think the market is adapting very quickly to these changes. I think that's 100% true. Two quarters is very quick to have customers of this scale, and we can say things like, 50% to two-thirds of our supply is under agreements, and we have, like, average visibility of four years. That happened in two quarters. It's kind of amazing. And I can tell you, even since our earnings call last week, the number of customers that are coming to us, and now they're proposing the agreement, not us. We started with we would go to them, and we would propose, like, hey, how do we put an agreement together to get more visibility? You know, we got some people that wanted to work with us, and we got it done. Now it's turning, not everybody, don't get me wrong, not everybody, but now I'm starting to see customers are coming in and proposing, hey, how about we do a three-year deal? How about we do a five-year deal? Here's what we'll offer on pricing. Here's kind of how we'll do the financial guarantee. So this market is changing very, very rapidly. All right. The third part of this is there are still high-value businesses where customers are not going to sign multi-year agreements. We're going to stay engaged in these businesses. We want to stay engaged in the whole market. I think one of the very brilliant things about the NAND business, it's kind of an evergreen business. There's always something new. There's always something new. There's always a new device, like this is the magic of innovation. Somebody's always thinking of something brilliant. We don't know what they are, but when they think of those things, if they have to store data, they're going to use NAND. So we want to make sure we have some of our supply available to make sure we can play in those markets. There's very important customers and very important businesses that maybe don't have the scale, so there'll always be a way to play in this other part of the market. And quite frankly, there's also going to be some customers who just want to do business the way it was done a year ago or two years ago. And we'll continue to engage in those markets to the extent we have the supply to make it available. So we're going to meet our customers on their terms. And we want to stay, like I said before, we want to stay engaged in the whole market. And, you know, one of the things about this business, again, one of the things that encouraged me a lot about this business a couple of years ago when we were talking about coming to work here full time was we have spectacular sort of customers. I mean, we have like the who's who of technology companies in the world as our customer base. So that's amazing. We want to stay engaged with all those customers to the extent we can. All right, so that's cyclicality. I know it's the current debate. I'm sure we'll continue to debate it, but that's kind of our view of why we do think this market is changing very, very quickly. And this is a very intentional strategy to try and change the relationships and kind of bridge this gap of, like, on the supply side, having three months of visibility, right? But we have to make 10-year investments in fabs, and we end up in this vast middle ground where it seems like nobody is happy, right? Either the supply side is, like, scrambling because we're not making enough money to invest, or the demand side is upset because they can't get everything they want, it's hard for me to see how that strategy works for anyone. And I think we're now rapidly converging to something that is a little more sane and we can all get enough visibility to make sure everybody can get what they want. Again, what I tell customers, you want to buy NAND? You're in luck. We sell NAND. And not only do we sell NAND, we have the whole stack, right? We have all the IP, all the production from beginning to end. So I think we're getting to a very different spot. All right, I'm going to cover one more thing. And I think as an investor, this is important. This is important for you to understand. And we've talked about it before, and maybe you already understand it. But I just want to bring it out. We talked about growth. And again, my experience in running a lot of technology franchises, this is kind of the hard one. Especially when you get to very good economics. Because when you get to good economics, you kind of start running out of TAM, right? And so you have to then figure out, how do I expand my TAM? How do I develop new products? How do I move into new markets? How do I go acquire different companies? All that stuff, I've done all that stuff. It's all expensive, and it's very difficult. We have kind of the opposite issue. Like we said, we're going to commit to grow, we're going to grow supply, we're going to growth volume, mid to high teens, and then we debate, is that too big or too low, right? Oh, it could be higher. Well, it could be lower, right? It's like some, you know, last year, if we went to February 25, people would probably say, oh, that's too high, right? Pricing is going to go down. We got to January, and it's like, oh, that's way too low. We can go higher. But we're committed to this kind of mid to high teens volume growth. When we look at the whole market, we think this is sustainable over long periods of time. And when we talk about mid-to-high teens growth, I think one thing that's important for all of you to understand, it's become clear to me over the last couple of weeks, when we talk about mid-to-high teens volume growth, we talk about that as an input to a process of developing like a whole fab strategy. And we were talking yesterday, and I asked somebody on our team a question, which I'm not going to tell you the answer to, but the question was like, what's going to be our BICS-10 mix at the end of the decade? They pull up a spreadsheet and they tell me, we have a plan already. Like we have a fab plan like years and years and years in advance. So this investment is the input into that plan. That plan over long periods of time grows volume mid to high teens. Now the output on a quarter to quarter basis or on a year-to-year basis is going to have some variability in it because things change in the quarter you're in. If you just pick two endpoints and you say, oh, you're growing faster, you're growing slower, it depends what endpoints you pick, right? Kegers are very sensitive to endpoints. But again, one thing, we're committed to this grow volume mid to high teens. Some quarters it's going to be less, some quarters it's going to be lower, some years it's going to be higher, some years it's going to be lower, but over long periods of time, this is what we're going to grow. And that's an amazing place. If you can get the economics right, you get the cyclicality dampened, and then you grow, that's an unbelievable franchise, right? And you'll see it in the business model that Luis talks about. But this is important. How do we grow? How do we grow? This is really important as an investment point of view. How do we grow? So you go back to BICS BICS roadmap. BIC cost scalable roadmap. We say BICS all the time. Sometimes people don't know what it means. It's right there in the title. Cost scalable. How do we build this scalable technology? So I was sitting at my desk a couple of weeks ago and I just pulled out some material and I said, let me do some calculations. Let me look at this. And I looked at about a 10-year period. About a 10-year period from calendar year 20 to calendar year 30. And I looked at kind of the plan of launching nodes across that ten-year period, or nine, let's call it nine years. Five nodes over nine years, right? Bix 5, 6, 8, 10, 11. Now, again, there's a Bix 9 in there. Alfred will explain that. But these are the big major nodes. The average, the Average generation to generation, bit growth per wafer was 54%, right? It's a pretty impressive number. Every time we put a new node in the market, or every time we turn the crank on that innovation engine I talked about, we get 54% more output per wafer. It's kind of amazing. Now, the issue is we don't do a node every year. So five nodes over nine years, you know, you can do the math on what that is per year. When I was a young executive, they sent me to PR training. And one of the things they told me in PR training is never make the statement, you can do the math, right? Because nobody does the math or nobody can do the math. So now all of you can do math, I'm pretty sure, but I'm going to do the math for you. And so when you back that out to a yearly kegger of productivity growth, it's 27%. So through the application of innovation, we can grow output per wafer at a rate of 27% a year. Which, by the way, tells you, you can't just release nodes whenever they're available. Otherwise, you're going to flood the market with supply and that you're going to have another 23 situation. But that's in the past. But what this tells you from an investment point of view, what it tells me from an investment point of view, is growth is primarily driven in this business by the application of intellectual capital, not financial capital. It's the paying of those engineers and those NAND designers to continue to drive that roadmap forward is what is going to drive the growth. Now, there is more CapEx. Each node is more steps. More steps is more tools. More tools is slightly more clean room space. But you can see from this equation why we're constantly like, you have to reduce wafers on an ongoing basis because the technology you have is so productive. So it's like getting the business model around that really, really high-powered engine, getting that right. And it's an incredible business. And what this means is, again, from an investment perspective, the ability to take bits, turn them into revenue, and then revenue to free cash flow is quite high. And I think at scale, you have a franchise that has an extraordinary ability to generate free cash flow margins, right? And you'll see a little bit of that later on. All right. That's where we are. That's the big picture. I'm going to turn it over now to the people that are really driving all of this fundamental technology and financial greatness. So Alper, as Ivan said, Alper is going to come up. He's going to talk about the NAN roadmap. As I said, like you're not going anywhere in this business if you don't have the right roadmap. And he is expert on this. We have a lot of people working on this. He'll tell you about BICS 8, BICS 10, where that's going. We made this statement on our earnings call last week. Earnings calls are always fascinating events, because something always happens you don't expect. I love earnings calls. Everybody, like, I talk to my peers, and they're like, oh, I have never heard somebody say they love an earnings call. Like, we love earnings call. We get to talk about our business. But we made this statement that said we think the market's going to be 300 billion this year and 500 billion the year after that, right? And everybody's like, oh, my gosh. Like, we never heard that number before. And they started backing into thinking that was like a revenue forecast for 27. It wasn't, right? It wasn't. One thing you need to understand about that number, it includes China. So when you include China, it skews the numbers if you're trying to back into everything else in the world. And that's an exercise left to the reader to figure out what that is. But, you know, Eric Churchstrom, we have a team on market intelligence. This is why we were able to stand up here last year. And we said with conviction, we thought pricing was going to inflect in the second half of the year. And we, and that was very, again, another debate, very debatable. And the reason we had conviction in saying that is because of work Eric does in his team. So we thought we'd give you some visibility into how he thinks about this and kind of how he the big picture of how this market has kind of resettled over the last two, three, four years. Very important to understand this. You know, not only is Sandus changing, the playing field we're on is changing dramatically. And I think when you understand the dynamics of that, it starts to unlock some of this value creation as well. All right, Kerem's going to come up. He's going to try. He's going to talk about AI inference. You know, there's obviously a massive tailwind to the business right now there's a lot of questions a lot of conversations about kvcash how is nan used in inference he's going to try and demystify all that a little bit how our products fit into the data center how do we think kvcash is going to grow in the future and then luis is going to come he's going to wrap it all up into the business model he's going to go through the nbms a little more detail. And just like last time, we're going to put HBF. We'll talk a little bit about 3D matrix memory, too. We're going to put it at the end. And the reason we put it in, it's not in the model yet.

Right?

When it's in the model, we'll tell you. But until then, what Luis is talking about is the model for all the core business. And you should see these as like future innovations that we'll continue to update you on. But I think Alper will give you a very good view of all the progress that's happened in the whole world around HBF in the last year and a half. It's really been really quite exciting. All right. Thanks for your time. Thanks for being here again. And I'm going to turn it over to Alper to get into the technology. Thank you.

Please welcome Chief Technology Officer at Sandisk, Alper Ilkbihar.

Thank you. Good morning. Good morning. Great to see all of you. Welcome. Welcome. I'm really excited to be with all of you here and talk about the memory technologies our teams at SanDisk are driving. So let me start off highlighting the main pillars of our technology strategy. Our number one priority is to keep our exponential scaling engine running. In semiconductors, especially in memories, Scalability is the most critical factor we're looking for. And you're going to hear us talk about scalability, the importance and the role it plays in our business over and over today. And we're going to talk about how our 19-generation strong scaling engine keeps going and how our roadmap extends well into the next decade and beyond. Next, we are laser focused on what our customers are looking for, which is performance, power efficiency and density. You're going to hear from us how we are leading in every single one of these metrics and how we're delivering the most capital efficiency amongst our peers in the industry to deliver superior financial results. And finally, we strive to innovate to emaze. We are innovators, we are engineers, technologists, and we are really looking for ways of improving all the applications and products every single day, but not only the existing ones, but we're also innovating in creating new applications and new markets every single day. With that, let me start jumping into the next slide here. This is a slide that I shared with you last year in February here on the stage. And at the time, I shared with you how since 2001, our teams have delivered 17 generations of NAND technology. So today, only 18 months later, we have added two new generations of NAND technology, BIX9 and BIX10, to our roster. This data tells me two important things. The first one is the pace of our innovation is accelerating to match the demands of the markets. And number two, NAND flash, being the most scalable semiconductor technology, is actually the only technology that can match the exponential growth of AI. And that's why you're seeing, and will continue to see, the increased adoption of NAND in AI architectures. Last year, I also showed you this slide to explain the vectors that are driving and fueling our scaling engine. At the time, I had talked about how we are prioritizing the more technically difficult, but significantly more capital-efficient ways of scaling, which are lateral scaling, logical scaling, and architectural scaling. These are our priorities over the easier, yet financially more challenging and costly vertical scaling, which is adding layers. Our strategy hasn't changed. And we are really pleased with the results we're getting through this strategy. And it's exemplified in this slide. I shared a similar data with you last year. At the time, the data ran through 2024. This year, we added 2025 as well. And what we're showing here is capital intensity of SanDisk and our JV partner Kyokshia and compare it against the capital intensity of the industry. And we are defining capital intensity as how much capex we have to put there to get an incremental petabyte of bid output. Of course, the higher this is, the worse you're off. So you're trying to minimize your capital intensity. And the white line here is showing the average of the industry divided by our numbers, so the ratio to that. So now this data, what it's telling you, is in every data point, by the way, here is backward looking for three years and averaging that. In 2025, the industry spent, on average, 2.66 times more capital than we did to generate the same output. 2.66 times more. So this is the capital efficiency our strategy is delivering. Looking at the same data through a different lens, here I'm showing you the percentage output, bit output of each of the peers in the industry versus the percentage of the capex they spend every year. Actually, we're looking here in a period of 21 to 25, so this is a five-year period we're looking at. So on the left, you're going to see that between us and our joint venture partners over that five-year period, we produced 29% of the industry's bets while spending only 13% of the capital. 29 versus 30. So when we look at these ratios for each player one by one, I captured that data on the right side. what you're going to see is that our capital efficiency is just about 2x that of our nearest competitor. And this is possible through our technology strategy that I just highlighted, through our execution of that incredible technology roadmap, our scale, our operational capabilities and excellence, as well as the intense focus we have on tool reuse, all of which make this possible, and delivering superior financial results for all of our investors and shareholders. Now, as we're pushing our technology forward and pushing the limits of scaling in every single generation, we actually advance the technology across two dimensions. The first dimension is what I'm capturing in the x-axis here. Every generation gives us more bits. This is happening through pursuing those four vectors of scaling. And you have seen earlier, every generation we get 50% to 60% more bits, and that's happening through scaling. The other frontier we pursue is what I'm capturing in the y-axis here, which is performance and power efficiency. And we get those improvements in every generation as well through device and design innovation. Now, you have seen this roadmap before. I'm mostly going to talk about Biggs 8 and 10 today on that roadmap. But as you had earlier, our teams are already working on Bix 11 and beyond. And the scaling engine continues running, and we see actually no end to the scaling limits in the foreseeable future. So we're going to run this for a decade and longer, of course. Now, we have talked earlier about our CVA technology, which is our hybrid bonding technology where we're able to combine two different wafers to make a single wafer. By utilizing this innovation, we're actually augmenting a derivative roadmap as I'm showing here. The CBA-enabled roadmap allows us to take an existing technology node and push its performance and power efficiency to the next level by combining the memory array technology of that node with the next generation CMOS technology and combine the two wafers together and get to the next level of performance. So you may ask, why is that relevant? Why is that important? A great example for where this is needed is actually happening in the data centers right now. The storage interfaces in the data centers, And what I mean by storage interfaces, think about our enterprise SSDs, they run on a standard interface called PCIe, and there's a transition that happens industry-wide from, say, Gen 4 to Gen 5 to Gen 6. These transitions used to happen every four or five years in the past. That time allowed us to essentially move from one technology node to the next one, ramp that technology, and maybe ramp the next one as well, so that we would have plenty of supply and transition our products gradually into the next generation of this interface. But with the advent of AI, these transitions started happening significantly faster. And when the demand turns on in the data center with the volumes that we're looking at, you have to enable that transition extremely fast. So you may not even have time to ramp your next generation technology node to meet that demand. So what do we do with this CBA-enabled derivative roadmap? We can take our existing technology node and very quickly move it to the next performance level and make that transition happen, move our entire portfolio very quickly to what our customers need and customize the silicon very rapidly. And the beauty of it is that it can be done with very minimal additional capital spending because we're leveraging the existing nodes, memory array technology, which is where most of our capital sits. So this is a super-efficient way of moving to the next level of performance. It gives us incredible operational flexibility, it gives us great capital efficiency, and it gives us the ability to meet our customers' requirements very quickly. So it's an awesome innovation, a technology competitive advantage that we can leverage and create incredible competitive advantage for our business. So I'm going to talk about BIX9 in a quick bit. But before I get to it, I want to take a quick look at BIX8. Because BIX8 today is the backbone of our current production. And it is the industry's gold standard. We introduced the CBA technology, the hybrid bonding technology, first time with BIX8. And it has given us tremendous competitive advantage in performance, density, as well as power efficiency. And it turns out that these are exactly what our AI data center customers were looking for. So when we compare Bix-8 against some of our peers' performance and power efficiency numbers, we've seen tremendous gap where we had a huge advantage. And here I'm comparing our Bix-8 against our peers' 2xx generation memories. Of course, our peers are moving forward. They're announcing their next generation products, which we call 2YY or 3xx products, and they're improving their performance. But when we look at the power efficiency, we're seeing that their power efficiency is really not moving a lot better. Now, let me put on what's coming, Biggs 10, and show you how Big 10 is going to compare against these. So this is where Big 10 is coming out to be. Big 8 was amazing. Big 10 is going to be even better. And I think it's going to be the gold standard for the AI data centers very soon. So we'll get to Big 10 in a bit. But let's first talk about the latest news. We talked about Big 9. So, Bix9 is the first product where we're essentially deploying this hybrid technology, hybrid bonding technology extension. What we have done for Bix9 is we've taken the Bix8 cellaray, the mature cellaray we have, and combined it with the next generation CMOS wafer, and through that we achieved tremendous performance gains. And we've done so with minimal incremental capital spending. So we essentially are upgrading our Bix-8 deployment supply bases to the next generation performance level with minimal capital. And we designed Bix-9 based on the specification from our large hyperscale customers. They wanted to have incredible performance. they wanted to have a lot of it and they wanted to have it yesterday. So this allowed us to achieve all those objectives very quickly. And I'm very happy to report that this product is already as part of our NBMs and our customers can't wait to have this. So it's going to power our next generation storage SSDs that Khuram is going to talk about. So really looking forward to seeing this powering your AI very soon. Okay let's go to Biggs 10 real quickly. I gave you a sneak preview of this Biggs 10, the first product in the Biggs 10 lineup last year. This is the one terabit TLC die. Again achieving tremendous improvements over Biggs 8 which is the best in the industry. Since then our teams have done a marvelous job with this technology and it progressed ahead of our expectations. So it allowed us to start sampling this dye to our customers as of this month which we just announced. The second product in the BigStan lineup is the 2 terabit QLC dye, QLC being 4-bits-per-cell technology. We're really proud of this technology. It actually is the highest density memory chip in the world. And while delivering this, we have achieved more than 60% density improvements over Bix-8, more than doubled the read and write bandwidths, as well as improved the power intensity and efficiency by 75%. These are truly amazing numbers, considering that we're beating world's best NAND, BIX8. To give you a better picture of the power of scaling, I wanted to show you BIX8 and BIX10 2TB dyes side by side. So this is what we are able to do with the scaling engine. each Big 10 2 terabit wafer has 65% more bits than the Big 10 2 terabit so you have the pictures here but the wafers are sitting outside so I will invite all of you to please go out and check in person and experience that scaling the wafers are sitting out there and you can even touch and play with it if you want So putting this in historical perspective, going back to the data that David showed you earlier, and we're looking at the generations from Big 5 through Big 10 and even projecting into what's coming next, Big 11, we're delivering 27% CAGR on bit growth per wafer annually. 27%. And you already heard that our production plans are based on our long-term demand forecast of about high teens. So this is delivering roughly 50% over that, which means that we have the technology productivity to meet all of our production needs and plans just by scaling the technology alone. as a matter of fact because of this productivity our wafer starts have come down over time and that's another way how we have created value and driven our capital efficiency so with that I thank you all for being here I will be back to talk to you about HPF at the end of this presentation and I will invite Eric to be with you to share his intelligence insights in the market thank you very much and see you soon Please welcome Vice President, Market Intelligence at Sandisk, Eric Churchstrom.

Good morning. I'm excited to be here to give you a brief NAND market update. We expect the flash market to reach 1.2 zettabytes of shipments in 2026. So how did we get there? Well, as you know, this industry started with growth in the consumer and edge. The key drivers were phone scaling to 1.5 billion units of annualized shipments, PCs shifting their storage needs from hard drive to flash, and the emergence of the enterprise SSD. Then, in 2022, ChatGPT was launched. AI propelled the data center segment and increased its share of TAM over time. Data center share of TAM in the early 20s was 20% of bits. Last year, it was 30%. This year, 50%, and continues to outpace the market. So now when you think about the revenue overlaying the volumes from the prior slide, you can again see two distinct periods. Period one, flash was priced as a commodity. ASPs reduced offset volume gains. The historical average during this period of time for the industry was $60 billion, and we measured cycles in $20 billion increments. Now, flash is a critical component of a multi-period data center build-out. And this leads us to believe that the flash market is going to grow to over $300 billion in calendar 26, and again grow to nearly $500 billion in 2027. So what was going on with supply during this same period of time? Well, in the late teens, the industry was targeting over 30% annualized big growth rate. The industry had to invest, growing wafer starts, all the way up to 1,800,000 wafers per month in 2022. At that peak of wafer capacity, COVID-related inventory digestion dramatically reduced flash industry demand. Supply side had to react, underutilizing 500,000 wafers per month, and structurally resetting their capacity to 30% below peak levels. In spite of that reduction in wafer capacity, industry was still able to achieve mid-to-high teens production growth rate via nodal migrations. So let's talk cloud. The chart on the left shows U.S. data center CapEx for select Hyperscale and NeoCloud customers. The chart is showing the projections over time. And as you can see, starting in 2023, we have seen 15 consecutive quarters of upward revisions to this selected CapEx. Current estimates show that $1.9 trillion of capital will be spent by these companies in 2026 and 2027. And we believe monetization is coming. The most recent Amazon earnings call, they talked about not having enough capacity to support near-term demand and the fact that AWS could reach nearly $1 trillion of annualized revenue. Shifting to the edge, the edge is going through a transition period. The chart on the right shows our expectation of unit decline year over year in the mid-teens for both the PC and mobile segment. And as you can see, all of this reduction is being driven by the low-end devices. Our belief is that OEMs will shift their mix to more premium offerings and ASPs and revenues will continue to grow in 26 and 27. And as you can see from the most recent earnings of major OEMs, year-over-year revenues grew between 13% and 24%. So Flash is entering a new reality, A reality where data center is the majority proportion of the share, edge continues to mix to premium devices, and on the supply side, big growth targets are met via nodal migrations. All of this put together gives us the conviction where we see the market opportunity growing over $300 billion in 2026 and can approach half a trillion dollars in 2027. With that, let me pass it over to Kuram to talk about the era of inference.

AI doesn't run on magic, it runs on data. In the era of inference, that data moves through a repeatable loop we call the AI data cycle. Each stage in this cycle is unique and demanding in its own way. Let's go on a journey through the AI data cycle. Stage one is the raw data archive where information lives. Think everything an organization collects. Articles, videos, social posts, business transactions, and internal knowledge. It's massive exabytes to zettabytes, and it's the source of truth. But raw data by itself isn't ready for AI yet. Stage two is model data preparation. Raw data is extracted, transformed, and loaded, also known as ETL, to clean it up, organize it, and make it machine-ready. A key step is creating embeddings, which turn content into numeric vectors so computers can group and search by meaning, not just keywords. And for AI training, the system has to pull data randomly from across the entire enriched dataset fast and at a very high throughput. Stage three is model training. Massive GPU clusters process that prepare data to learn patterns and build a model. Training can run for a long time, so the system regularly saves progress in checkpoints. If something fails, you can restart from the last known good snapshot instead of losing days of work. Because training pauses during checkpointing, the faster those snapshots write and store, the more time the GPUs spend learning. Stage 4 is inference. When trained models are actually used to answer questions and generate outputs, real services may swap models depending on the task, so keeping the right models ready matters. Inference also often uses Retrieval Augmented Generation, or RAC, to pull the most up-to-date, trusted context from a vector database to ground the response. To maximize efficiency, systems use KVCache so GPUs can reuse previously calculated values instead of recalculating from scratch. As more tokens are generated with agentic workflows, the more valuable this cache becomes, and the larger it becomes. These high-capacity KV caches are tiered onto SSDs. Stage five is new content generation. The model's outputs are stored immediately so they can be delivered to users and reused for things like fine-tuning and ongoing consumption. The newest content is accessed the most. Then it ages into long-term archives, becoming tomorrow's raw data. And that's the AI data cycle. Collect, prepare, train, infer, generates, and repeats.

Please welcome Chief Product Officer at Sandisk, Karam Ismail.

Good morning. I'm Khuram, and I'm here to talk about the infrastructure outlook, specifically as it pertains to Flash. The good news is that we are almost at halftime, and since we don't have any breaks for halftime, I get to be your host for the halftime. So let's get into it. So David talked about engineers being at Sandisk for a long time. I'm one of those. I've been in this industry for 27 years, all in memory, and never has been a time more exciting for memory than it is now in AI. I've seen all sorts of peaks and troughs. The pace of innovation that AI is bringing is tremendous, and we all can see that. We see the new frontier models being loaded, right? The system architects are changing the design every six months. So the pace of innovation is quite rapid, but the infrastructure required to deploy that innovation is also being deployed at a very unprecedented rate, right? So what is the role of Flash? In my talk for next 20-odd minutes, I want to leave you with two things. First, like David mentioned, our conviction on the critical role that Flash plays and the size of the opportunity. The second one, I hope you gain the appreciation of Flash is not really a clumpy device sitting at the edge of the infrastructure, but it is actually being proliferated through all the layers of AI infrastructure. So I want to start with some fun facts as I'll be going over some concepts. and I think it'll help us understand those concepts if I draw some analogy to the human brain. Maybe some of you know, I was just doing, you know, chat GPT, Gemini, and I found an interesting fact that each human brain is wired with 2.5 petabytes of memory. Now, you multiply that by entire human intelligence, that's like 20 yotabytes of collective human memory. Now, one yotabyte I had to look that one up to, I work in zettabytes and exabytes, right? So one eurobyte is 1,000 zettabytes. Now you look at the right, at the cloud infrastructure, which I would characterize as being very early in the innings, is only hundreds of exabytes to maybe 10 zettabytes, all memory combined, right? One could argue looking at this, as we are going to scale intelligence, the cloud infrastructure can use more storage, right? The interesting thing about human brain is it works with two types of memory, the short-term memory and long-term memory. And they both work hand-in-hand, utilizing each other to generate intelligence. Turns out the AI intelligence is built on a very, very similar concept. You have a transient short-term working memory that is called ephemeral KV cache, and we'll cover that. And then you have the long-term memory, which is a little bit more persistent, which is known as persistent KVCache. So there is similarity. They both work on the same principle on how humans store data and process data versus AI intelligence. Okay, so Eric talked about the total demand in 2026 to be 1.2 zettabyte for the entire flash market. right here i'm only focused on 2030 ai data center 10 which is equivalent to what we ship as a total output as an industry in 2026 so the opportunity is massive and i'll come back to this slide again as i go through why that is the case but it's important to note as i mentioned flash is not just a single device sitting at the edge of infrastructure there are many workloads that are emerging on flash specifically in ai data center some of what are some of those workloads well you have first fast data lakes we talked about it last year these are the massive data lakes that require massive storage second is staging these are a little bit you know direct attach device close to the gpu for training checkpointing so that market you know is having a tremendous growth. And last is the KVCache, and that'll be the focus of our conversation because that's the fastest growing segment in AI data center. And when we look at the composition of NAND technology, we see that TLC is dominant technology in 2030, and QLC still has a very good decent size share. So how's the infrastructure being viewed today? And the thinking around infrastructure is changing. We are moving from what used to be total cost of ownership to the total value of ownership. In the past, when you deployed the infrastructure, you sort of prioritized cost running at very large scale. And, you know, some of those considerations are listed here. With the total value of ownership, the equation is changing. Infrastructure is no longer being viewed as a cost center, but really a driver of value generation, right? And in this case, the value and the output is intelligence, right? So the race to scale the intelligence is heating up. As you can see, everybody's trying to generate more tokens. They're trying to generate, get more users on their systems or on their AI, but with this scale, there's a lot of challenges that come, right? You know, there's always the challenges of power, right? Where do you store these tokens in volatile media only? There's not enough volatile media. What role does non-volatile media plays, right? And then there's shifting architectures, right? We're moving from training to inference. And it turns out Flash solves a lot of these problems, and that's where I'll be taking you next. So you saw the video, the error of inference. The only point I would make here is a lot of focus in the AI data as it flows through the infrastructure is on inference. So I presented this 18 months ago, last February in 2025, the five-stage data cycle. The first three we focused a lot last year, which are associated with training the model. How you store the data, how you prepare the data, and how you present the data to the GPU for training was the focus, and Flash did quite well. We had our high-cap QLCs that were used in fast data lakes. We had direct-attached TLC SSDs that provided active data sets for the model to train. So a lot of infrastructure got built as a result of this. But now as the focus is shifting to inference, the question to ask, the infrastructure that was built for training, is the same infrastructure relevant? Can that satisfy the growing need of inference? And the answer is no. You can see as we move along, there is disaggregation happening in infrastructure. The infrastructure for training is quite different from the infrastructure of inference. So let's look at what's happening inside the inference. So like I said, we have our existing products that go into the fast data lake staging checkpointing, but inference is something new, and that's where we'll hone in on KVCache. There are two interesting trends that are emerging in inference when it comes to NAND Flash. The first is data augmentation, and the second is context remembrance. A lot of you, I'm sure, use the models, and if you're using the models, it's becoming more persistent. It remembers who you are, right? So that's the second popular use case. So RAG is one of the most popular techniques that is used to provide external data so the model can provide you much more relevant and accurate responses. Second, the users are trying, or the users are wanting richer conversation, smarter conversation, longer conversation and what ends up happening as a result is a kv cache amplification and the kv cache amplification the way to think about is you're having longer context length because you want longer conversations you have longer reasoning chains because you want iterative process you want the model to know about you and then there are multi-modalities associated with this all of these are driving the amplification in kv cache the easiest way to think about kv cache is if i'm having a conversation with you and you're taking notes, as we are having now, you know, so you can refer to the notes rather than listening to my conversation again. And that notebook serves as KVCache. So let's just briefly touch what is KVCache, because that's the most important part in inference. Inference has two major parts. The first is pre-fill, where the model is thinking. The way to think about pre-fill stage in inference is where the model is thinking when you provide the input. The second is decode, where the model is responding, is giving you a response. So as a user puts the input prompt, that gets tokenized, and all that input gets processed simultaneously. So one would think, as that process is driving a lot of parallelism, that is compute bound. You will hear a lot more people say that pre-fill is really compute bound. And then when the pre-fill happens, a context gets generated. And now in the implementation, that gets stored in a memory, which is called KVCache. The way to think about KVCache is it's a working memory of inference or the notebook, memory notebook. It is not important for now for us to discuss that where does that get stored? I'll take you through how the KVCache hierarchy works, but it's important to know that this context is growing, right? It continues to grow. The most important, the most interesting part of inference is the decode process. This is where you generate the response, and as you may know, decode is an autoregressive procedure where one token gets generated at a time to generate the response. Now, to generate the response, the token that gets generated has to know the context of all the previous tokens that were generated. You can imagine if you didn't have the KB cache, that would present a tremendous challenge to the infrastructure, to the power, and computational overhead, which didn't need to happen, right? So that's where you see when we see all the memories boats are rising, it's because this KB cache because it does make the entire AI process much, much more efficient. Okay, so we talked about this context is growing. It gets stored in KVCache. Well, how does KVCache look from a hierarchy perspective? We presented this last Investor Day, and we talked about the system memory hierarchy in a data center system. And the way to think about this memory hierarchy is around the vectors of performance, power, and capacity, right? And for those of us who are in love with Flash, we always made the assertion that Flash is the most scalable technology around these vectors. Well, it turns out, we were right, inference is a perfect use case, right? Inference is a perfect use case for Flash. Why? As we talked about the KVCache amplification and with the deployment of AI-agentic workflows, it's generating a lot of tokens. There's a need to have more pages in your memory notebook, so all these states need to be preserved. The contexts are getting longer. The conversations are getting longer. The reasoning chains are getting longer. And last, all the data that gets generated as part of your input to the system. So we have very close partnership with our customers. The NBMs are a testament. We get to learn a lot from our customers. Now, they're all hyper-focused on optimizing this KVCache because it really solves a lot of problems for them, right? But the way they go about it is different. But there is one common theme that emerges from a KVCache memory hierarchy that is generally applicable to all the AI systems that are getting deployed right now. At the top, taking you back to the human brain analogy, an ephemeral cache, right? There will be a quiz after this. Ephemeral KB cache, right? Those are your HBM and system DRAM. The way to think about this, all the hot context, the current context that the user needs, right, to get the response from, those are stored here. But again, if you look at from top to down, capacities at play, HVM and DRAM are generally smaller, right? Next is the long-term memory, the persistent KV cache. So anything that cannot be stored in the high-tier bandwidth of DRAM and HVM gets stored in flash. And this is what we are calling persistent KV cache. Now there are multi layers, like remember when I said, the flash is proliferated throughout multiple layers of AI data centers. I just want you to remember there are multiple tiers where flash is deployed, right? Because we'll use this later on in the presentation. But it's important to understand that you cannot scale the intelligence without having this persistent KV cache layer. As you can imagine in inference, the model gets trained ones. As the context grows, the interactions are in billions. So you need some kind of persistence in your memory hierarchy. And it provides a nice extendable capacity to the AI systems without having the need to take everything through the volatile ephemeral KVCache. So now that we have covered the KVCache and we talked about KVCache is going to be 35% of the market in 2030, how do we plan? How do we size the opportunity? Like how big can this persistent KVCash can be? If I'm an infrastructure planner, right, I have to think about a few things. And this is Sandisk equation of how to think about the size opportunity for KVCash. But if I am the planner, and this again is this we derive from talking to a lot of our customers, because we have a lot of close relationship people who are actually deploying this at scale. So if I'm planning for this, the first thing that I have to think about is the number of sessions that are going to hit my infrastructure. That's number one. But more importantly, as the sessions hit, how many sessions do I want to retain and for how long? We have customers who tell us, well, when we retain retain the sessions or the context for only a couple of hours. So my friend Luis can go have the coffee and come back and have this context. Or, you know, there are customers who are keeping all the context forever. They want to monetize this somehow, but that's how they're looking at it. But you can think about it. If you retain it forever, there's tremendous opportunity for this KB Cash to grow, right? So that's how they are thinking about it. How many sessions are going to hit my infrastructure, and how long do I keep them? The second part is if you are planning to build out, you obviously have existing infrastructure that has a set of KVCache pools, right? So you want to only plan for the KVCache that your current session may miss, and that's represented in cache miss ratio, right? So that's another important factor. And lastly, you have to figure out how much storage will be required in a session, and that's a function of two things. The first one being what is going to be your session length or size. You know, a lot of people talk about, you know, context length, but it is a series of tokens, right, that determine. And each token, by the way, as we talked about the decode process, generates tremendous overhead on storage needs. So each token is represented in tens of kilobytes to hundreds of kilobytes, depending on which model, which implementation you're using. So as you can see, these variables are what people use to determine how big of a KV cache, or persistent KV cache rather, they need to deploy. And here's our answer. And this is, again, one zettabyte install base. When you multiply all these things up, and thereby the way, these are just four variables underneath. There is second order calculations that come in, right, to make sure that we arrive at the right number. But the SanDisk estimated 2030 install base is one zerabyte. This is, again, this is the fastest growing workload. And this we are saying between now till 2030, we'll have one zerabyte of install base. This, again, our customers are very dynamic. They're changing things. Architectures are changing. There's a lot of optimizations that are happening around Flash, but this gives you a good proxy to think about that, hey, if I wanted to take this case up and if I take the retention time up, the number will be quite large. So we feel pretty good about this because in general, we see the context length growing, right? The average sessions growing, the number of users that use AI is growing. So we see it in a positive place. So coming back to the 1.2 zettabyte number, and I would say this again, that the KVCache number that is represented here, because I didn't cover it first time, is 35% of the overall market. So the previous chart showed the one zettabyte number as an installed base. In 2030 specifically, we see the size of KVCache being 35% of the market, so you can do the math. And this, again, really demonstrates that Flash is present in multiple workloads of AI data center. So let's look at a little bit more physically on how Flash sits in data center, right? This is just showing the various placements of Flash in the data center. At the foundation of it, in the gray box on the right, is a sea of large-capacity drives. These are your fast data lakes that contain your training data, that contain your embeddings, your vectors, your RAG repositories, all the things that enterprise needs to store to make the AI work. For this, from flash point of view, high-capacity QLC drives are perfect because they provide that enormous capacity. Now, you may ask, okay, you say KV cache. You remember the hierarchy that I showed you and they were multi-layers, right? Just think of it as very cold, cold KV cache, the way to think about this. It's great for QLC, and we see that deployed in data lakes. Now, as you get closer to the GPU, the requirements change. This is the second one. These are our direct attached SSDs. Now here, you have actually active training data sets, right? You have checkpointing going on, and you have a lot of data orchestration that is happening from GPU. And for this, you need a very high performance TLC SSD. Now, again, going back to that G3 tier of KV cache, This is what I would characterize as a hot and warmer KV cache. And lastly, what we talked about in persistent KV cache, now there's another rack scale, network scale, data movement that happens between the GPU complex and something that is closer to it via network. This is what we call the persistent KV cache, and I would characterize this as lukewarm G3.5 that you saw in that pyramid. So this is showing the AI data center ESSD placement. And a lot of people talk about AI data center and they talk about, well, it's a GPU factory. But the way to think about it, I hope with these placements that you can see that Flash is living simultaneously in many different places. So I would assert that every AI factory is ultimately a data factory. And it's true when it comes to inference. So I'd be remiss if we didn't talk about, you know, our products. Last year when I was here, we're trying to tell you that we are going to succeed in data center, as David alluded to. Happy to report that both our TLC ESSD and QLC ESSD are qualified at major hyperscalers, major customers, OEMs. And we are shipping both of them. For TLC ESSD, we're shipping it in PCI Gen 5 configuration in all the form factors. and they are great for staging and KVCache that we discussed on the previous slide. When it comes to QLC, it's also, sorry, on the compute, on the TLC SSD, we also demonstrated our next PCIe Gen 6 drive at Flash Memory Summit. It's going to be an industry-leading, high-performance, great power drive, really going to solve a lot of inference bottlenecks. So we demonstrated that at FMS last week. Similarly, on high-capacity ESFD, if you remember, last year we showed you a roadmap of up to one petabyte. We at Flash Memory Summit demonstrated our 256-terabyte drive in E3 form factor, and that was very well received. And you will see a lot of market shifting towards higher-cap drive next year from 128 to 256. So we have a great portfolio. We have great platforms that will serve all the needs of the AI data center growth now, but also for future. So we talked about a lot of close collaboration with customers, applying our thinking, understanding how the KV cash looks like, what does the market size opportunity look like. But we want to become AI practitioners ourselves. We also want to be the AI practitioners. So we started an initiative at SanDisk, it's called AI Lab at SanDisk, where you can imagine we have server-scale, rack-scale type of systems, and we run the workloads, the models, the way our customers do. Because we want to understand, truly understand the bottlenecks, and we want to complement it with what we learn from our customers with our own understanding. So here's, I'm just providing you two metrics. The way to think about this is this data was collected in a server-scale application or system with a cluster of GPUs, multiple SSDs, HBM, DRAM, all the hierarchy that I showed you. We ran hundreds of user sessions, like I explained to you in persistent KVCache equation. We assumed certain things in that equation. And what we see, that a system that has an SSD versus a system that only has volatile media like HBM and DDR consumes 75% less energy. And I'll extend it. This is not published data, but for us to generate 1 million token on a system with SSD versus just the volatile media or no SSD, it takes one-fifth of less power. So different metrics, but you get the idea that for SSD, you consume much less power. Secondly, on the same system that had SSD versus, no, we saw 75% higher throughput in tokens per second. You generate more tokens per second than you would with HBM and DDR. Simply why? Because you don't have enough capacity. You're limited, right? And you have to go recompute. That's an expensive process, both in power and performance. So as you can see, that SSD is not something that is just an afterthought. It's actually an essential. and that's where you see the explosion of KVCache workload in the market. So I want to leave you with one last thought. I don't know what I was supposed to say. Let me pull it from my persistent KVCache, right? So, okay, the persistent KVCache responded. So today, you know, Flash represents the work that is completed, right? And if you look at previous compute cycles, whenever the compute cycle was finished, all the intermediate states or the nodes were discarded, only to be recomputed whenever the compute needed. If you think about the millions and billions of scale of AI, that strategy is very inefficient. It won't work, right? So from that perspective, we like to think about flash SSD as a token battery. right? It's storing the energy to be used later on, right? And Flash truly represents the accumulated intelligence. We believe you cannot build intelligence without persistence, and Flash is great. And in an era where the most valuable output is intelligence, preserving it becomes as important as creating it. So in summary, we have a robust growth outlook. We have conviction In the AI data center market, we have good understanding of where the customers are headed, where things are headed, how the architectures are working. We have the right product portfolio. We have a strong product portfolio that is good for now and for future. We also acknowledge that there are going to be efficiencies when it comes to inference. You all heard quantizations, all the optimization that is happening to reduce the store in KVCash, but that's only going to fuel the Javon paradox. There's going to be more use cases that will come out of it. So we feel pretty bullish that these efficiencies are welcome and they're going to drive more utilization. And last, flash is not something that is an afterthought. Industry is actually innovating around flash. Why? Because it's the most scalable technology. And like I said, you cannot build intelligence without persistence. And flash technology is a great medium to build intelligence. Thank you very much. And I would like to now invite my friend Luis to talk about financials and business planning.

Please welcome Chief Financial Officer at Sandisk, Luis Vistoso.

I thought you may want to look at some numbers. So it's great to be here back after 18 months of launching the company. And frankly, this conversation is about sustainable value creation. Sustainable value creation. We're committed to do that every single year. Our journey, as I said, started in February 25 when we separated from Western Digital. Shortly thereafter, we announced our Q3 25 results. As you may remember those numbers, $1.7 billion in revenue. We lost 30 cents in non-GAAP EPS, and we generated $220 million in adjusted free cash flow. We've come a long way. Hopefully you saw our earnings last week. We reported $9 billion in revenue, non-GAAP EPS of $39.25, and adjusted free cash flow of $5 billion. And this excludes cash we receive from our NBMs as prepayments and deposits. So we've come a long way. Now going forward, what are we going to do? we're committed to creating value for our customers, and as we do that, we're confident that we can create value for our shareholders. So let's look back into the year that we just delivered. These are the metrics that matter the most. We operate in a large, fast-growing market. That market has tripled or will triple in calendar year 26, reaching $300 billion on its way to $500 billion dollars in calendar year 2027. So very large market. Now what's very important is the composition of the market is changing from an edge-centric market to a data center-centric market. That brings very different dynamics and I'll explain some of that. Our revenue for the year, $20 billion, up 175%. That's twice as high as our prior record that we delivered in 2022. So nice growth. And importantly, that growth, that revenue improved every single quarter throughout the year. Gross margin, 71.6%. That's up from 30.3% the year before. Again, Again, our performance improved every single quarter throughout the year. We closed the year with 84.6% gross margin. That enabled our EPS to go to $39.25, up from only $0.29 the year before. So great performance on our financials. And the metric that matters the most, you know, is our free cash flow. We generated $8.7 billion in free cash flow, excluding those new business model prepayments. And that also improved every single quarter. Our run rate, $20 billion. That's our run rate of generating free cash flow from this business. So very good business. Growth is there. The market is growing. We're capturing that value. You may have a few questions about the new business model. So let's go into that. Very importantly, this is our way of strengthening our relationship with our most strategic customers. Why? Because the new business models deliver a fast-growing, profitable, and less volatile business. Going back to what David just said, fast-growing, very attractive, less volatile business. Isn't that beautiful? So we're building these relationships. The way this started is very custom agreements with each of our customers that center around supply and demand certainty. The conversations didn't start around pricing. Obviously, we do get to pricing, but they start with supply and demand. Our customers want to make sure they can get the products they need, and we want to make sure we have somebody on the other side. So that's how this conversation started. They're details by quarter, details by month in most cases. And while they're custom-made, there is a framework that's consistent around all these agreements. They start with a multi-year in most cases. When you have a multi-year, those volumes are growing at a very fast pace, faster than we're growing as a company, and there are fixed and variable components on pricing. And very importantly, every single one of these agreements has a financial guarantee. and I'll talk about that. These conversations go to the highest level of the companies. They require board approval. We're talking to CFOs. We're talking to treasurers. We're talking to CEOs. This is not like a typical conversation of the past. So let's talk about some of the details. So we have eight engagements with customers. These are win-win conversations. As Coram alluded, there is high level, very deep integration from a technical and commercial side. These eight customers, by the way, news to you, include three hyperscalers from the U.S. Three U.S. hyperscalers are part of these eight customers. Now, the oldest deal we signed was only in January of this year. And guess what? Two customers already came back. And they said, hey, guess what? But as I look at my models, as I do the math that Karim was talking about, I need more. So they are already expanding, either extending the duration of their term or adding more bids to the same contract length. So we feel very good about these contracts. In terms of duration, so we're moving from a quarterly price negotiation to large multi-year engagements. Remember, these price negotiations lasted three months, sometimes not even three months. And over that time, when we were operating in that model, we practically generated no shareholder value and made capital investments super difficult because they were very risky. We did not know for how long our customers were going to take our products. We had no commitments. go from there into an average length of our contracts of over four years. The longest contract is five years now. And we're actively in conversations with several customers to go even further. That is very important. So we're allocating a significant portion of our business to these new business models. Why? Because as I said, they are fast-growing, attractive, and less volatile businesses. We like this business model. We want this to be the predominant way of doing business for our company. How does pricing work? Well, pricing will be fixed in some of these contracts. Some of them include variable portions. And very importantly, the variable portions include floors and ceilings. And our financials are very attractive even at floor pricing. We talked about around 80% gross margin for the floor pricing. So we believe there is a subset to that pricing, and therefore we feel very good at financials of the new business models. Now, the non-business model, the rest of these bids, will continue to fluctuate with the market. So if the market continues to go up, obviously we have an ability to capture that. So let's try to quantify the size of these contracts. So if you look at the 93.9, that's the total contract value, TCV. That's how much we expect to collect in revenue from the beginning to the end of these contracts, $93.9 billion, at an average of four years. Now, some of that revenue has already been recognized. So the remaining performance obligation, the RPO, is $91.1 billion. So a lot of the value, a lot of the revenue is still to come. Both of these numbers reflect the forlorn pricing, the minimum pricing we expect from these contracts. We believe that there is upside on both of them as prices will be higher than the floors that we have. So we talked a lot about financial guarantees and is there risk in these contracts where we have secured $16.5 billion in financial guarantees from these contracts. There are two big buckets of this. The biggest ones is financial guarantees held by or provided by third-party financial institutions. The other part, the smaller bucket, exactly $2.9 billion, is deposits and credits from our customers that we have either received or are about to receive. Of the $2.9 billion you will see on this slide, we already have $2.5 billion in our bank. So the vast majority is financial guarantees provided by or held by third-party financial institutions. Very importantly, our customers will pay for their products in ordinary course. So this financial guarantee, other than prepayments, will stay constant throughout most of the time. So that is important, and I'll come to that in the next slide. How do I think about this financial guarantee? How strong of a protection is it? Well, an easy way to think about it is the ratio between your financial guarantee and your remaining performance obligation. You have the numbers. You can do the math, as David said. So if you do that, and if you define that ratio at the beginning of the contract, let's call that the base ratio, as you divide the financial guarantee by the TCV, the total contract value, Some of you are questioning, well, how does that ratio evolve over time? So we looked at our contracts, at multi-year contracts, and we wanted to provide you an illustrative example of how that ratio would evolve over time. So for a three-year contract, two years out, in average, you should expect that ratio from beginning to two years later to be twice as high. So you guarantee your protection as percentage of the revenue to come, significantly increases as the contract goes on. So what are we going to do? Well, we're going to execute these NBMs with excellence. We don't want four-year deals. We don't want five-year deals. We want these NBMs to last decades, right? And therefore, we're going to execute them with excellence. We're going to have the products with quality, on time, just as we agreed with our customers. We want them to fulfill their part of the bargain. We're going to do the same. And you've seen us do some of that. We're increasing some of our safety stocks. We want to make sure that we have the agreements with our JV partners. We buy our agreements with Nandia to make sure that we have the DRAM. But we want to make sure that we can perform very well on these NBMs. Again, the goal is to make them even longer. And we're going to be, number two, we're going to be very patient. We're going to be patient as we continue to evaluate new deals. And just as you saw, we only have eight companies with whom we'll sign an NBM. We'll be very selective going forward to make sure we choose the winners, that value our products, are willing to pay for them, and want to make commitments which are longer term. So how do we think about the model going forward? So going forward, our financials will be the result of a combination of both models. So we will have a proportion of our business will be the NBMs. That would be the largest portion of our business going forward. Why? Sorry to repeat myself, this is a growing, profitable, less volatile business. We like this business. And it has reliable volume. So we're going to keep that MBM, and we know exactly what to expect from that side of the business. And we'll have a portion of the business which will be the non-MBM. We'll continue to support our customers. David alluded to that. Some customers are just too small to have new business models. Some of them, they're very strategic, don't get me wrong, but this business model may just not be the right solution for them. So when you aggregate all of that, for 2028 through 2030, we expect to grow revenue mid-to-high teens, consistent with bid growth. We talked about bid growth in the mid-to-high teens, but we expect revenue to grow at about that same rate. We expect non-GAAP gross margin to be around 80%. We expect non-GAAP operating margin to be 75%. How do we get there? We expect to spend about 5% in OPEX, and we do not expect significant contributions from other income and expense. So you get to that 75%, and then you get to 50% adjusted free cash flow after paying for taxes, working capital, capital spending. And we have, for modeling purposes, I would assume mid-single digits capital intensity as percent of revenue. That's our gross capex. Now, importantly, for 27, we already talked about this as part of earnings last week. We expect bid growth to be somewhere in the mid-teens, and we expect sequential prices to be modest throughout the year. So that's the model. Why do we feel confident sharing with you these numbers? Well, our confidence comes from our customer conversations, It comes from our new business models that we signed based on our conversations will lead us to believe that more NBMs will come. So we feel very good about our new business model, our conversations, and frankly, the growth of the business overall. So what are we going to do with the cash? Right? So we will continue to invest in the business. This is a great business to have, and it requires cash, and we'll continue to invest in it. What does that mean? Well, we'll continue to invest in OPEX, we'll properly fund the business, we'll invest in CAPEX, and we'll continue to do things to strengthen us like the NANIA type of investments, the JV extensions, those type of things that make us more robust, more sustainable as a company. That's super important for us. Number two, which we've done very well this year, will maintain a strong balance sheet. What does that mean? Healthy cash balance. How much? Well, you've seen us operate in a range over the last few quarters, and we will keep operating around that range. Now, that doesn't mean it will be exactly the same number. There are payment terms. There are different things that happen. But within the range that you've seen us operate over the last few quarters, we have no debt. We got rid of the TLB. We intend to keep it that way. Our revolver is unused, and we don't intend to use it either. And we'll continue to improve our credit ratings with the agencies over time. We've made progress this year. We're at double B plus overall, and we intend to continue to make progress. And the rest of the cash, the excess cash, is going to go back to you guys. That's what we're here for. Our value as a company is to create value for our shareholders. And the excess cash, not a portion, 100%, will go back to you. We've done a lot of work to understand what's the best way to do it. And the current moment, we believe that the best way to do it is through a share buyback program. Now, what are we doing? You look at last quarter, right, Q4 of 2006. We generated $5 billion. How much did we return to you? 4.5, right? So we're leaving to whatever we're telling you is exactly what we're executing. So the board authorized a $6 billion program. We executed four and a half. We have one and a half less. So the board authorized another $14 billion program. So now we have $15.5 billion authorized and not spent yet. So we will give you an update as we go on. But we believe that our role is to return the cash to our shareholders. so in closing we're super excited we're super excited not of the value we have already created that's good, don't take me wrong but we're very excited about the value we can create going forward we operate in a very attractive market, it's growing it's profitable and frankly Sandisk is very well positioned to capture that value you saw our technology, we have leading technology with NAND, leading technology with our products, and we have very close relationship with our customers. Those relationships, those NBMs are opening doors that had never been opened as wide as they are today. So we feel very good about where we are in the market. What's our financial model? Super simple. Translate bids to revenue, revenue to profit, and profit to cash. And then the cash goes back to you guys. So that's our model. I hope you guys find it interesting. We do. So what we're going to do next is we're going to talk about HBF. As David mentioned, HBF is not in the revenue projections. We are funding it. It's part of our OPEX. It's part of our CAPEX, but we're not funding, we're not including the revenue projections here. So thank you for that. I'll turn it over to Albert.

Please welcome back to the stage, Chief Technology Officer at Sandisk, Alper Ilkbihar.

Hello again. Oh, nobody left, that's amazing. Okay, so in the second part of our technology presentation, I get to show you our, in a way, to amaze DNA. and I will talk about two technologies that we introduced last year on this very stage. Both of these technologies address the memory wall problem. Memory wall problem is essentially simply DRAM not keeping up with the compute and AI because it just doesn't scale anymore as well as it used to. And to solve that problem, we started working on two technologies, both of which are highly scalable. and can solve this memory ball problem. So the first technology I'm going to cover is the 3D matrix memory. So let's just dive into it right away. Oh, did I? Okay, I'm back. Quick recap first. The 3D matrix memory, we started working on this technology back in 2017 in our research lab. And in 2024, we moved the development to a 300-millimeter facility, a modern facility, at our development partners, iMEC. And last year, when I was here, we had just delivered a development vehicle that we could just essentially pursue the activities at iMEC with, and that's what we had shown you. Since then, we continued making steady progress. We used the development vehicle I showed you, and we started depositing memory layers on top of it, And we delivered 300-millimeter wafers and package parts to test and demonstrated multi-gigabit-level functional memory arrays. And our devices are approaching performance levels that are getting pretty close to our product specs that we have. So steady progress. It keeps going. But this definitely is a project that has a longer time horizon. And we'll keep updating you as we make more progress on this. okay with that let's go on to hpf high bandwidth flash last year again here we introduced high bandwidth flash for the very first time high bandwidth flash delivers the same read bandwidth as hbm yet with 16 or 8 to 16 times the capacity we invented this device with ai inference workloads in mind that actually leverage mixture of experts type sparse models with long context lengths and large kv caches that's what we had in mind and today when i look at some of the most recent developments in the world of ai and the trends actually these do justify the vision we had for HPF two years ago. So on this table here I have summarized some of the latest frontier models and their characteristics. You're going to see very quickly that certain trends are emerging. First the parameter size, the models are growing. Trillion plus two trillion parameter models are no longer amazing they're just commonplace and many of these models actually started utilizing mixture of experts sparse models and they're allowing their users to go up in context lengths all the way to million type of tokens so this is creating a new paradigm the large models as well as the long context length and implied kv cache sizes are driving much higher memory capacities, while the mixture of expert type sparse models are driving the compute needs down. So you're seeing memory needs going up, compute needs coming down, and we call this a new paradigm called memory-centric AI. And in this memory-centric AI, we think HPF is going to play a very critical role. Before I dive into HPF further, I wanted you to hear from somebody who deals with these LLMs and AI inference on a daily basis at a massive scale. So I'm going to take you back to FMS, which is Future of Memory and storage conference in california it was held last week with about 3 000 plus attendees and there were several sessions dedicated to hpf during that conference so i'm going to take you to a panel discussion that we had and going to share with you some of the thoughts from dr xiaoyuma google deep mind so please roll the video real quickly The basic Malina-LM inference is already memory bandwidth hungry.

Now, each of the six trends has a big memory challenge for both bandwidth capacity. And if you look at the state of art large language models or agents, congratulations, you have all of them. And this creates an enormous memory challenge. And this is why we have an industry-wide crisis for large language model inference. okay so dr. Ma is talking about a memory crisis so next let's listen to how he believes we can solve the problem what do I believe I believe in three trends first inference specialization for transformers the reason is because the transformer inference is fundamentally different from training due to its auto-regressive nature and the use of KB cache. Second, I believe in memory heterogeneity. That's because the HBM-only architecture has inefficiencies to scale up the capacity, so that's inefficient for inference. I'm also a big fan for software hardware co-design. I believe we are in a golden age for co-design with many 10x opportunities, and the HBF being one great example.

Okay, so with that obviously Dr. Ma is one of the many researchers who are actually spending a lot of time thinking about HPF as the latest and most exciting memory technology. It is really becoming increasingly an innovation platform and researchers are proposing new architectures showing how one could integrate HPF into AI solutions. So I wanted to share with you some of the architectural proposals that have been published recently. So the first one here is an XPU where we have taken out all of the HPM stacks and chips and replaced them 100% with HPF. So this is an HPF only architecture. Very simple. The second one is where you're sort of mixing and matching depending on the workload needs and replacing part of the HPM chips with HPF. So it's a hybrid architecture. The third architecture is also a hybrid architecture, but in this case, the low HPM chips act as a caching tier in front of the high-capacity HPF. And the fourth one is a disaggregated architecture. In this disaggregated architecture, excuse me, we are disaggregating the two stages of AI inference, the pre-fill and decode, and optimizing the solutions, the hardware solutions for these in a disaggregated fashion. The pre-fill XPU is compute intensive, but doesn't need a lot of memory bandwidth. So what you can do is couple a performant GPU with just regular DDR DRAM, whereas the decode stage, which is very memory capacity and bandwidth intensive, but doesn't require lot of compute, you could take a modest GPU and couple it with HBF. So you get the best of two worlds and combine to optimize the overall solution. So these are a few of the ideas that are coming out and there's many more and results of these have been published. But I want to today double click on the first, the simple architecture, and share with you some of the work, some of the workload simulation work that we have been doing on this architecture. So for this simulation work, what we have done is we've taken a GPU actually that resembles a market-leading GPU today and has 192 gigabytes of HBM on each of them. And then we have created another version of it by replacing all of the HPM chips with HPF, and that gives it about 4 terabytes of HPF memory. So we simulated a benchmark that essentially emulates in a multi-turned agentic workload, it simulates or emulates a code development environment. So what happens is the AI agents start developing code, spawning more jobs, etc., etc. And the underlying LLM model here is a 490 billion parameter QUIN3. So let's see how the two models are sort of, the two systems are comparing. And what we're going to measure is the token output. We're going to compare the token output of these systems. So first off, when we start with the HPM-only system, it turns out that the minimum viable system to run this workload requires use of eight HBM GPUs you just cannot fit the model with less than that so you have to use at least eight GPUs to start this job and here's what the workload looks like on our simulator Okay, so eight GPUs delivering pretty stable token output. Okay, so now we're going to show you how an HPF system compares. So it turns out that we were able to actually run this workload with a single HPF GPU, one GPU alone. And let's look at that. Okay, obviously the performance is not as high as eight GPUs, but if you just wanted to have the minimum capital spend to run this job, all you need is a single GPU. So this is the result. So next I want to show you what four HPF GPUs look like, and here is the result. So with four HPF GPUs, you're able to match the performance of eight HPM GPUs. So we're getting 2x the performance out of our GPUs. So how is this possible? What's happening? Actually, what happens is as the workload starts running, it quickly starts more and more jobs and runs out of the KV cache capacity. It runs out of the high bandwidth memory capacity. The moment you run out of the capacity, you spill into the system memory, and that spill, and losing that bandwidth, essentially costs you roughly half of your performance. Your GPU utilization drops by nearly 50%, and that's why we're able to deliver the same performance with half the number of GPUs. So out of this work, we had two key takeaways. Number one, if you're somebody like, say, a small business or a solo software developer who doesn't need massive amounts of tokens, but you just want to run this job with a minimum capex, we can improve your spending by eight times. You get eight times capex efficiency using HPF. the second takeaway is that at the maximum token output we are able to deliver you 2x the GPU efficiency which means your capital will go twice as far which means you're going to burn half of the energy and all the economics that essentially the benefits that you can gain this is fundamentally going to change the economics of AI. This is the crisis, the memory crisis Dr. Ma talked about, and this is how we intend to solve it. Obviously, we are very bullish on HPF, but we also think that it's not only for data centers. We only believe that we can dramatically change how AI is run on edge devices with HPF. We are envisioning enabling really sophisticated AI models. I'm talking about 100 billion plus parameters, sophisticated models to run on edge devices and enable an AI experience that I like to call AI that never forgets. What I'm talking about here is an AI agent that knows everything about you, that's constantly with you, remembers everything about you on an instant. You don't need to go back and forth many times. Everything is there with you all the time. And we believe that's going to significantly change the way people are experiencing AI in their lives. To that end, we've been working on a second-generation HPF device, which we call HPF for the edge. And this is what you're seeing. and this has been actively in development with multiple customers. So this is what's next on the roadmap that we have for HPF. Obviously, we have a pretty big vision for HPF and having that kind of a vision, you really need to have a vibrant and diverse ecosystem to be successful, to realize that vision. We understood that from the beginning on. And when I was here a year and a half ago, we told you that we intended to create an open ecosystem around HPF. True to our word, last August, we announced a partnership with SK Hynix and talked about our intent to create an open standard around HPF. We followed up in February of this year established a consortium under OCP with participation from Google and TenStorrent. And last week, we celebrated the release of our first public specification that's going to allow XPU designers to incorporate HPF in their systems and designs. The next step is going to be to expand the membership of this consortium. and I have a piece of news to share with you already. We have Meta joining this consortium, so we're very happy with that. And as they and other participants contribute their feedback and input, we're looking forward to incorporate those in the next revisions of the specification over the next two years. One critical element of our ecosystem we view as the advisors that we have, the technical advisory board that we have built. You may remember I talked about this. Professor David Patterson and Roger Kuduri are legendary computer architects. We're very proud to have them on board. And last week I had the honor of introducing our next board member, Jim Keller to our advisory board. Jim is a rockstar chip designer. Over the last four decades, he led teams in some of the most consequential processor designs at DEC, AMD, Apple, Tesla, Intel and I actually started my career as a CPU designer and competed against several of these things and it's not fun I tell you. But Jim brings his expertise and guidance into now leading 10th Torrent as the CEO of the company and today I have the great pleasure a surprise for you Jim is here with us and he's going to join me on the stage for a conversation. So, Jim, would you please come on stage? Please welcome Jim Teller. Jim, thank you so much for coming. And let me hand this over to you. Great to see you here. You flew yourself.

I did.

Thank you very much.

I had the help of an airplane.

Well, that's great.

Not entirely myself.

Well, thank you for being here. We talked about all of the great processors and compute projects that you led, but then now you're taking all of that into the world of AI. How has that journey been for you? And please tell us what you and your teams at TenStorrent have been up to recently.

Yeah, so a couple of years ago, well, it's been obvious for maybe five years now, right, that AI is going to take over most of the data center. And there's going to be heterogeneous computing, so AI compute and general-purpose compute, but it's built on the usual fundamentals. So we built Tense Torrent around that premise. So we build high-end AI processors and high-end RISC-V processors, and we have two businesses. Business one is we license that IP for a variety of projects, so autonomous driving, robotics, a couple of supercomputer companies, and we're looking at some server projects. So we build the IP, but then we put this into our high-end server design. So we're in production today with Galaxy. Galaxy is a scalable AI computer. And one of the things I think it's really interesting, and we're going to talk about this, is computing has always been based on the balance of memory, compute, and I.O., like generally networking. And what happened with AI in the last couple of years came right towards us. So we built a black hole chip that runs AI models, and that's really good for 70 billion parameter models. And we thought we'd build this Galaxy server with 32 chips per server so we can scale up. And in the last three years, we went 70 billion, 300 billion, 700 billion, 1.5 trillion, 2.8 trillion. and the scalability of that has been amazing. So we did something interesting in our boxes. We have 56 800-gigabit Ethernet ports per server, and then we put those together in quad servers, and then hook them today, 36 of our servers all hooked together, and we run models on anywhere from a single chip, now up to 20 servers. We're in testing with 36, and it's scaling really well, right? The other thing we did is this is pretty general-purpose AI. It's a combination. We have Flash in the host, DRAM in the host, AI, DRAM, SRAM, compute, and networking. And that lets us run a wide variety of models on the same hardware. So we announced in May DeepSeq at 400 tokens a second. That's batch 32. This is very high throughput but very high token rates. We do pre-fill decode on the same hardware. We ran WAN 10 times faster than anybody else. It's real-time video. That runs on four Galaxy servers. And recently, we just announced 900 tokens a second on Kibu K3. I guess this is 2.6. 3 is up and running in the lab. And now we have a new higher-resolution video model. So on the same hardware. And the reason we really think about this hard is AI is changing so fast. Who here heard about KV caches two years ago? Anybody? You know, that's a part of an LLM, the fact that we can cache it. Two years from now, something different is going to happen. And everything needs to be flexible, compute memory and I-O. And what we're going to do with really large memory is amazing because memory is one of the most flexible things, right? You can put programs, models, weights, caches, data sets. There's so much to do with that. So we're pretty excited to be here today.

Great. Thank you. Thank you very much.

I can take that if you want or we can just leave it there, perfect so Jim you talked about these super scalable systems that you're building, they go all the way from smaller needs to very large scales when you look at these scalable systems where do you see some of the bottlenecks well depends on how the hardware is built like today's HBM based models, they're limited by the local size of the DRAM which you talked about and they often don't have enough network bandwidth so one thing we did is we have a terabyte per galaxy server 36 galaxies 36 terabytes of DRAM but because the network bandwidth is so high we can serve the memory from one part of the machine to another really flexibly so I think the two biggest bottlenecks today we're doing pretty good on compute but memory scalability and then the network scalability so you can serve the memory everywhere you want those are the big ones great great and And when you talk about memory, there is a lot of AI architectures that people are talking about, and they're highly differentiated by the way they use memory.

Like we see, obviously, the most commonplace GPUs today with HBM memories. And then we're seeing architectures like from Cerebras or GroK that are relying mostly on SRAM. And now we're talking about HBF. How does this whole thing... How do you think about the variety of these memories, and how do you think about HBF in that context?

Yeah, so first of all, GPUs were built for graphics, and they read and write all their data to memory all the time. And that drove them to really push hard on HPM because they don't have enough SRAM on chip. Our processor has 200 megabytes per chip of SRAM, and we can put a large number together. So the balance of SRAM to local DRAM to host DRAM to flash is really important, I think. So, you know, Graken's Freebris exploited essentially a gap and the GPU roadmap, but the thing that we're going to see is the ratios of compute, what we call KV cache to decode the pre-fill today. Those ratios were 1 to 1 to 1, and then it went to 7 to 4 to 1. Now it's 100 to 10 to 1, and people are still moving that. So if you build a machine that has specific processors for different pieces, and the ratios change, what's going to happen to your compute yeah so so how do you think about like where is like HPF it's something like a very high capacity high bandwidth memory relevant and can you think of some examples where it would be really useful yeah definitely so AI is a very high bandwidth problem memory bandwidth network bandwidth compute bandwidth and the limitation flash is great because it's lower cost per bit, much bigger capacity, but it didn't have the bandwidth to really play in that high bandwidth system. So the really cool thing about HPF is now you've brought the bandwidth to the table. So that makes it really great. And the other wild thing is, and to be honest, I didn't see this coming, the fungibility of compute and memory is amazing. Like who knew compute would be so expensive that we should compute it and save the results of the computation in that big format, right? So people don't realize when you send tokens in, it's a pretty small stream. When you embed that and then compute the KVCache side, it's a very large footprint. And with HPF, it's now effective to save that for a very long amount of time. And that unlocks the ability to balance compute memory in a new way. Right, right.

I mean, we've talked a lot about data center, but you do also a lot of work outside a data center in the edge. Do you see any applicability of HPF in the edge devices and edge applications?

Oh, definitely. So today autonomous things look at the world and they have to process everything and they have a model that's trained. Having huge augmentation of KVCache for everything you see in flash on device is going to radically change how robotics work. I was joking this morning, is how many people here would wish GPS worked in New York City? Like, imagine you have a device that actually knows everything around you. It knows exactly where you are. And there's going to be so many transformations, and I don't know if that one particular is going to work out, but one day I was going, if I had enough data, this problem would be solved. And so there's a really interesting thing about robotics is everything you already know, you don't have to compute. And as we make that memory available and robotics autonomous driving so many applications, it's going to be pretty transformational. Memory is way lower power than compute. It's a good trade.

Well, thank you very much. This has been amazing. Thank you for coming and being with us. And I believe you're going to be with us available to answer questions after lunch or during lunch? Yep, you bet. Okay, thank you so much. Thank you very much. Okay. Okay, before I finish, I want to probably address one question that I suspect is top of mind for many of you, which is, when? When are we going to see HPF? So here's the latest update. I'm happy to share with you today that we actually taped out our first HPF memory die. You're seeing, actually you happen to be the very first people in the world outside Sandisk seeing this picture. I apologize, I had to pixelate it because we're still not quite ready to share all the magic that goes into it. But our dye has taped out. But you don't have to wait too long to see the whole thing. a little bit more patience please but we're continuing working on this super hard to deliver our first samples to our customers of inference devices with HPF next year next year, just a little bit more with that I thank you all very much for being here and I'm going to invite back our CEO David Goeckler on stage, thank you and have a great rest of your day I really want to thank Jim for

coming all this way to support us, and more importantly, for joining the advisory board around HBF. This has really been quite, as I said earlier, if I look back over the last year and a half, a lot of really great things have happened at Sandisk. But this one, the ability to make a market and attract people as capable, saying Jim is capable is like a bit of an understatement, But people that are this distinguished in the field to come help drive this technology forward is just really amazing. So we're super happy about where this is, and we will keep you posted on product availability as we continue to drive these milestones forward. As Alper said, the fact that we now have a dye that we've taped out, this product is real. It's going through the fab. We're producing it, and we'll get it back, and then we'll put the systems together and get it in customers' hands, in our partners' hands. I think this point that was made about co-development, I mean, that's what it's all about when you're developing new technology. And if you're co-developing with some of the largest customers in the world, that's a really, really fun place to be. So as we make progress on that, as we get those samples in customers' hands, we'll get a lot more information about what the future of this technology looks like from a market, financial, all of that perspective. So stay tuned as we move through that process next year. All right, we've got, I'm not going to read through all this because you just listened to all of it, but it's a recap of where we are. I think you can tell, hopefully you can tell, we are extremely excited about where we are. Like I said, I've been doing this for six and a half years, really trying to unlock the value of this franchise. I feel like we've made a lot of progress. Since we separated the company, we've seen both companies just bloom and really start to get the valuation. But I really do believe we're kind of now we're entering a very different phase of where we're going to take these franchises. The ability to really recognize the true value of this technology we've been building for decades. As the market changes, we build new customer relationships and we really get this engine running of, again, turning bits to revenue, revenue to profit, profit to free cash flow and returning that cash flow back to you. All right, I'm going to bring everybody back. And I don't know, somebody may have a question in this group. It's been my experience that some of you often have a question. So we're going to open it up for Q&A. We'll bring everybody back. Everybody is fair game. And we'll do our best to answer whatever questions you have. There will be a mic runner. So if you have a question, I guess, raise your hand. Okay, right here. Go ahead, Jim.

Thanks for taking my question. Jim Schneider, Goldman Sachs. I have one business question, one technical question. First of all, on the business question, can you maybe talk a little bit about how you're thinking about the diversity of customers you want to include in the NBMs? You talk about the hyperscale component, the data center component. How do you think about the broader mix and having too much risk in any one given end market? And then maybe secondly, on the technical side, if you think about, you know, we're hearing a lot of discussions about some GPU customers wanting to reduce the amount of HBM content in their systems today. So before HPF comes to the market, how do you think about the amount of HPF or conventional ESSD content you need to add to an existing GPU configuration to deliver the same performance?

Luis, you want to start with the customer mix?

Yeah, Jim, we've been very thoughtful in which customers we sign NBNs with. We're looking at different markets. It includes data centers, as we talked. It includes edge customers as well. So we're looking across. And even when you look at the hyperscalers, their business models are dramatically different. So being very thoughtful in driving that diversity for the reasons you mentioned. But we're betting on winning customers. We believe that they're going to be here with us for many, many years to come. And the level of integration, both technical and commercial, is as strong as it has ever been. But diversity of business models is one of the criteria we look at.

Jim, on the second point, I'll say a few words, and then Alper and Kuram can have a point of view on the very specifics, but I think what you're drawing out is what we're seeing in the market. It's definitely what Jim just said. This is changing at an incredible pace. And that makes it difficult to understand what product to build and how much of it to build, especially with the way the market used to be organized, right? Build it and we'll talk about what the price is later. And so what it says to me is there's just a huge premium on staying very close to your customers because this is going to change it's going to continue to iterate over and over again I mean the scaling of inference is incredible I mean it's one of the most I mean it is the most incredible technology transition I've seen in my career by far and I've been involved in some some pretty big ones so it's going to change very rapidly there's going to be constant innovation you know as Karim said there's going to be there's a constant focus on how do you drive the requirements down how do I use less power how do I use less space how do I make this more efficient because the more efficient you make it you can scale it faster right and more economical and also if there's you know three different people scaling inference around the globe if one of them is twice as expensive as the other that's not going to work very well from the business model so our customers you know the great thing about where we are from a broad technology point of view is you have these companies now that are just spectacular. I mean, they can scale on a global footprint something this complex very, very quickly. And staying very close to them and understanding where they're going is, in my opinion, extraordinarily important for where we're going to drive this franchise. And that's another kind of angle on these NBMs. As Luis said, we have NBMs with some of the largest customers in the world. We have their commitment of what products they're going to deploy quarter by quarter for the next three to five years. That gives us a lot of insight into all of this confusion of what's the product, what's the architecture, how is it going to play out. It gives us incredibly unique insights about how that's going to play out and what are the right products to build and where to put our resources to make sure they're successful. Now, one of you guys want to talk about the specific question?

Yeah, sure. So David is right. You know, our customers provide us with a lot of data, but I'll give you a little bit of long-winded answer on this. Like I talked about AI lab at Sandisk in action. You're absolutely right. You You know, the question is, the system DDR, how much of it you need, you know, as you have the continuum between the volatile and non-volatile media. It was coincidental I was sharing the data with David yesterday from our lab where we ran a 1.2 trillion parameter model. And we ran, again, multiple user sessions, prompts, and an interesting thing emerged from that. when you look at performance and power of the system to execute that workload, what we saw that after, and we ran both the HBM sweep and DRAM sweep. Think of it as a shmoo and running the shmoo around capacity. So you start with, let's say, and I'm making up the number like 4 gig of volatile memory like HBM and DDR and then go all the way up to the maximum capacity of that server scale. And it was interesting to see to run that complex workload, you know, having just the HBM and SSD was good enough, and you really required a minute amount of DDR to run the batch services. And that was a revelation to us, too, by the way. We heard the news that, hey, you're reducing on the Vero Rubin, I think, cutting the system DRAM by half, the SOCAM DRAM. I would contend with the studies that we are doing, you can go even further, right, if you have an ESSD and HBM in the system. And like David mentioned, things are going to change with respect to the ESSD capacity. Again, it's workload dependent, how much you want to run, taking you back to the persistent KV cache pool, right? How big you want to grow it. There's a lot of factors that go in and those are more use case dependent, but certainly one thing is emerging that the system DDR with respect to running the inference, it could be reduced. Now, could it be the half, 1.8? Depends.

Ben, where's the mic? You guys will run the mic up.

Hey, guys. Ben Reitz is with Milius. It's great to be here. Thanks. Thanks for doing this. First, I got an observation, which I hope some people find kind of amusing, and I wanted you to react to it, and then I have a question. I mean, you know, I've been going to tech conferences in analyst days since about 1992, and I've never seen a company guide for a year, three years out, and be trading at less than three times that number. And I just, you know, it's pretty amazing. I just wanted to react to that, you know, trading at less than three times your FY30 number. And second, HBF. So it's not in the model, but you're only growing bits, you know, mid to high teens. I mean, how do we put it in? Like, what if this is a hit and we've got to add it to the model? What are you getting rid of that we just guided for to make room for it? And how do we calculate upside if bits can only grow a certain amount?

Yeah, well, so I appreciate your observation. I may come up with a different word than amazing, my reaction to that. But that's a whole different discussion. But look, this is a conversation I think we'll have as we get the product in customers' hands and we really understand what the demand is. Again, I'm going to sound like a broken record. We keep coming back to these NBMs. We're going to follow our customers, right? When our customers tell us they need something, and this is the way most technology businesses work, right? You work with the customer, they want to buy something, and you build it. You don't, like, talk about what the industry supply is. like, who cares? Like, we have the ability to produce what we can produce, and it's about getting these incentives aligned. And what I would argue, if I go back a year ago, the incentives in this industry were just completely misaligned, right? And that leads to this, all this volatility, and that's good for nobody. But we're clearly walking down a path, you know, in two quarters, We've gone from three months of visibility to four years of visibility. So I think it's just a little bit of wait and see what it's like a year from now when we actually have this product in those customers' hands, what their demand is going to be for that. And we'll then figure out what the production plan is behind that. And remember what I said earlier, we own the whole stack, right? From the NAND IP to the production front end, back end, the whole thing. So we're not ready to go there just yet, but, you know, imagine what could be possible in the future. All right. I'll go to the back next. CJ.

Yeah, thank you, Aaron Rakers. Yeah, Aaron Rakers with Wells Fargo. Appreciate the day and all the details. I want to go down the path with HBF as well because I think it's a fascinating technology. Alpert, you know, one of the evolutions in DRAM that we're seeing, specifically HBM, is this idea of customization, right, driving towards a base die that has certain elements of compute in it. One, do you see that as a roadmap that you could explore on HBF? And then secondly, I guess back to Ben's question, if it's successful and we start to get into next year and we start to see design in, right, and maybe that means 2028 or even 2029 volume, Should we expect the CapEx discussion to change? What's involved in a production of HBF, advanced packaging? Is it a different capital intensity that we should be thinking about?

So, thank you. I agree. I'm also in love with HBF. So, your first question, I apologize, was customization. I'm sorry, yes. Customization that we started seeing. I mean, it is a natural flow of actually compute moving towards memory, right? Because it's just shuffling data around. It's just so expensive, so energy-consuming. I mean, it's something that we had been anticipating in or near memory compute is going to definitely happen, and we're entirely prepared, right? Because we do have all the capabilities. Kouram showed you all the memory management capabilities we have. we only welcome being doing compute next to the memory or in the memory I mean it's our wheelhouse and we'll we love to do that we'll cherish that and we're highly capable of doing that and so it's entirely a customer based discussion what port part of that pre-compute or the compute itself do they want to perform inside the controller that sits next to that memory it's that conversation you need to align but after that you're going to see us being entirely capable of doing that the second question in terms of like how do we think about all the investments certainly I mean one of the reasons I'm in love with HPF is because it entirely leverages all know-how expertise and then flash right it's we have the best flash and we're that's why we were able to create HPF to start with, and in terms of what does it mean for capital and whatnot, you have seen us talk about the productivity of our current NAND roadmap generating, actually giving us the potential to easily increase our output if we choose to do so, and this is a great outlet, right? I mean, the beauty of HPF is that as a business, it's entirely orthogonal to our storage business, so we could definitely entertain that, and as Dave said, as time comes, we will see. Some of the other capabilities certainly are, again, within our capability range, and we're looking forward on working on all of those. So it's going to be definitely very exciting. But let's wait till next year and see how this will get into the market, and I think those are excellent high-class problems that we're willing to work on.

Let's go back.

Hi, thanks so much for doing this today. Mark Newman from Bernstein. Actually, I wanted to ask about the NBM, It seems like great progress that you're sharing here again today. You talked about the target. So you talked about FY28 being 66, two-thirds of volumes on NBMs by FY28. Do you have any kind of target in mind for what percentage of volume that can get to? And related to that, what's the volume in FY29 and beyond? Is it also similar, two-thirds level of commitment on these NBMs? And then a second question. This may seem a silly question, but on the 100% excess cash to shareholders, does that mean 100% free cash flow? Or do you have a different definition of excess cash? Just wanted to clarify that. Thanks very much.

Yeah, I thought I was, I hope I was clear on the free cash flow. Excess cash is defined very simple. The cash we're generating minus whatever we're investing in that bucket number one. investing in the business. There is no trick. It's 100% of the cash, excess cash. We'll go back to shareholders. And if you look at what we just did in Q1, that's exactly what we did. We generated $5 billion and we spent four and a half, right? So pretty much there. I think on your question on the percentage of NBMs, we're going to be optimizing over time. We're going to be learning a lot, and we'll define what that optimal number is. We think that we like the numbers that we see for 27 and the numbers that we see for 20. We think we've optimized based on the data that we have so far. But we'll keep on talking to customers. We'll keep on evaluating the situation, and that may evolve over time. Our goal is to create the maximum shareholder value that we can on a sustainable basis. So that's the filter, and that's what's going to guide that percentage. What is the number for 29? Yeah, I got the question before the session started. It's consistent with the numbers that you are seeing. It's consistent with the 28 numbers so far, but that number will keep on evolving. That's why we didn't put it there, because we'll keep on optimizing our number. Okay?

Yeah, hi. Thanks for taking my question. It's Krish Sankar from TD Common. I have a question for Kuram. Thanks for your interesting presentation on KVCache. The two pushbacks I heard on NAND for KVCache is, one, the tail latency is much longer than average latency, so that impacts system throughput. And number two, it takes a long time to write KVCache onto NAND and so lowers the lifetime of the device. So I'm kind of curious what your answer to that is. Thank you.

I saw your post-FMS commentary on this, and I wanted to send you a chart that I showed to David. Look, when we talk about latency, again, you have to look at the mix between the top tier, high bandwidth tier really suffers from capacity. We all have to acknowledge that first, right? And like Jim was talking about, right? I mean, like the need for the space to have all that context cannot be serviced by that. So you have to go to the next tiers. Now what we see, so far what we see, is that the overall latency is actually quite good, and it's actually workload dependent. Like if you're looking at something that is real time, obviously you won't go to the next tier, right, of the KV cache. But you don't need to. You can put that in the higher bandwidth tier, right? So it's not just one or the other. It's like it depends on what you are running. And what was the second part of your question, sorry? endurance oh the lifetime so look uh you know we uh we are making continuous progress like alper keeps giving me great technology with very high endurance metrics and part of this we are learning a lot not only on our core technology but also an hbf that's allowing us to get like phenomenal endurance number as you saw on the warm cash that we talked about is exactly that That requires, for example, three drives right per day. So we are increasing the endurance at the same time, but again, it's also workload-dependent, where you need to read more. We use our stock standard NAND, but for the warm KVCache, to your point, we are offering a 10x much higher drive right per day as we would on our conventional SSDs. So you're right, but we have tricks on how to do that.

Let's go back.

Joe Morgan Stanley. I also wanted to ask about the business mix between the segments. If you're a third data center now, and by most accounts, data center is going to roughly double in the next 12 months, what does that imply for your mix a year from now? Because if it's 60% data center, you really starve the edge businesses a lot. So just, how do you see that mix? And it just, it seems clear to me that there's not enough supply. Like, how do you just think you imbalance, you balance out that shortage?

Yeah, Joe, I mean, this is what we're talking about now. So, like, there's a couple of things the way we think about this, right? So, and part of it goes back to Mark's question. There's different, we want to engage with customers on longer duration, right? So, there's different contracts or different duration now. And that's why, you know, it's a little hard to say what 29 and 30 are because you're starting to get in a time where some deals are ending and others may start and those kinds of things. and we also want to you know we want to keep some flexibility in the system as well but we'll be very as Luis said we're gonna be very judicious about where we go from here we're gonna follow our customers it's very clear how we want to operate our business we want we want more visibility we think that's better for everybody right it helps us make the best decisions it's very clear that that this model started in data center I think that's fair to say their interests are different as I said earlier we're starting to see now that change pretty dramatically where customers are coming to us and wanting to have these kinds of deals and what I said earlier we want to make sure the whole portfolio stays robust and that means we have flow of products through edge you have it through consumer you have it through data center so we want to keep all of those alive. I want to make sure Keram's engineers stay very busy building the best edge products in the world, just like they're very busy building the best data center products in the world. And of course, they're very busy building the best consumer products in the world. I think it's fair to say our data center mix is going to go up, right? And so we're going to continue to rebalance that to make sure that we keep all of those markets alive and we get the