Sandisk Corporation (SNDK) Earnings Call Transcript & Summary

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 road maps, our new business models and multiyear customer partnerships, our business plans and performance, market trends, 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 in the future for the company. Then we'll go through the technology road map or really the way I think of it is 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 road map and where we see that going to conclude 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 over 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 to be here. Thanks for your support. [Presentation]

Please welcome Chairman and Chief Executive Officer at SanDisk David Goeckeler.

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 -- as I was talking to some of you as we were preparing or just this morning in gathering, we're talking through a lot of stories of stuff that has happened over the 6.5 years, whether it's road shows or conversations we had about this franchise. I joined Western Digital, of course, in March of 2020, actually the same week that Cope had 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 6.5 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 are 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 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 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 we can't just think about 1 thing. There's like 2, 3, 4 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 3 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 at February 2025. We made a bunch of commitments to what we were going to do with the company. I think it's just fair to say like 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 is 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 FY '27. 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 in a bit of an unpolished gem, and we're continuing to work on it. We've gained 2 points of global share in that business. We'll talk a little bit about that business today about why it's so important to the business model as well. 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 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 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. That's -- 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're going to -- we'll talk about that. All right. So let's start with this first -- when we say -- 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 once -- I'll preface this by saying, there are a lot of things we committed to doing back when we launched the company, 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 9 months, where we were presented with some opportunities to really change the business, a lot of momentum. And we took advantage of those. And I think we have fundamentally restructured the business, and we kind of put a pin in the map on this day, early this year, realizing we could see what was happening in the business, we could see we are restructuring the business, and we're going to need to stand up and explain it all to you because it is very different to 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, 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 technologies. 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. Some of 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, 5 years from now, 10 years from now. And this is where I think we -- it just -- when you look at our business, it starts with what Ivan said, kind of the engine of the company is the BiCS road map is the fundamental NAND road map. 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 BiCS 10 I think a couple of weeks ago, by the way, we announced BiCS 9 yesterday, and I'm sure everybody is confused like why did you announce BiCS 10 like 3 weeks ago, and then you announced BiCS 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 very important -- it's a very important point to understand, like the technology strategy is changing based on the fundamental BiCS architecture. And he'll explain to you how we now have multiple ways we can move this technology. So we're investing in BiCS 9, BiCS 10, there's actually people working on BiCS 13 right now. So one thing you should have confidence in, and this is with our partner, Kioxia, we have a long road map of really strong fundamental NAND technology. 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 1/3 of the market. So that means we can invest as much or more than anybody else in the market and 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. The BiCS investment gets you through the wafer through the fab, the wafer comes out of the fab, I have to do something with it. 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 Khurram 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. And I think this is 1 of the big -- if you look at the foundation of the company is the technology, one of the positions that we now are in -- and one of the reasons they have so much conviction about the future is we now have optionality across the entire market. We have a very unique consumer franchise. Ed, we've always been very, very strong, PC, smartphone, IoT, all of the -- so we've got all this optionality, and we're going to keep all that optionality, right? 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 like pick our head up a little bit and look a little further down the field One of the things we talked about 18 months ago as we announced this product or this strategy around high-bandwidth. If you would ask me, like 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 hype-bandwidth flash. And I think the reaction was pretty much what are you guys talking about? Like nobody has any idea what you're talking about. What is high bandwidth flash? And we only knew 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 in the field. Our team this wasn't me, this Alper and his team have a tremendous amount of insight to see that, "Hey, at some point, we're going to move to this inference phase. You're going to have to scale this, and then we're going to have to come up with different memory architecture, 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 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 that's a horizon, and I know the big question is going to be when is it going to ship and all this kind of stuff. But -- 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 ecosystem is being developed, people are coming to the table, a lot of very good discussion. And so we'll -- that is on the horizon. And then we look even further down the field -- we had this idea of 3D matrix memory, a little longer-term project, continue to make progress. So we're basically -- 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 let's get to this idea of a cash-positive balance sheet, right? 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 very happy about. It happened faster than we probably thought. -- market, 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 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 where 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. when he gets up here. But we feel like we're in a very good spot there. All right. Now there was -- that was kind of like things we need to do every day, but 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 would constantly go through a process of how do we systematically derisk the business? How do we systematically make investments? We're basically derisking the future? And we -- 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. One of the first things you saw us do, we invested over $1 billion with our partner, Kioxia, which was a recognition of the scale of this joint venture. But we -- one of the first things we spent our money on was making sure we had production of NAND from 2030 to 2034, right? 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 has gone quite frankly, my personal view is 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, and we have it at scale, and we have all the R&D benefits of that and all the manufacturing benefits that. The other thing I hear sometimes is "Oh, NAND is not that hard, NAND's a commodity. Anybody could do it right?" Well, if that's -- if you actually believe that, I would encourage you to go to Yokkaichi 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 derisking, we knew that, look, we are -- we want to play in the data center market. Why do we want to play in the data center market? 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 5-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. We are 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 took a -- we had the opportunity, we took a 4% equity stake in the company. It's worked out okay. I think we just took Luis' smiling down the our CFO. I think this quarter, maybe one of the few times where our GAAP earnings are higher than our non-GAAP earnings because we recognize like an $800 million gain on that investment. So that's been -- that's been going well. But we didn't invest in it. We had an investment for the return. It's great that we get there. 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 road map. 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 and saying, "Dave, what are you doing, right? Why are you taking this job? Do you realize this industry has never made any money." I'm like, "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 3 imperatives for sustained value creation, right? So is it 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. No matter 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, right? So there's always a debate. But the debate I think 1.5 years 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, like we get it. Like we understand profitability is at a level it's never been before, but it's just a matter of time. Just for 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 9 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 cyclicalities 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 2 things, in every business, you got to grow, you've got to get consistent revenue growth, right? And 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, and this business is actually quite different. All right. So let's dive into these just real quickly -- what, last point, what I said earlier, you have to do this across all time horizons. It's not about maximizing value for the next 2 weeks, right? What's pricing going to do in the next 2 weeks. That's important. You have to do that. You also have to do it across the mid-term, the long term. So we're constantly thinking how do we balance these things across all time horizons to get to this point of sustainable value creation, right? So it's important. So you got to think in 2 dimensions. And a lot of the questions I get, a lot of questions we get are tend to be about one of these things, independent of the other 2 are in 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 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 seems 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 works, 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 staff. 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 now. And I said one thing you should take away from the fact that I just said we have people working on BiCS 13, which is like going to be launched sometime -- I don't -- don't come back and ask me 100 questions about. I just said when BiCS 13 is going to be launched. But you're talking like well beyond 2030. People are working on technology a decade into the future, right? 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, right? So we have all of that. And we have it at the largest scale, again, because of our collaboration with our partner, we have the largest scale of anybody in the world. And as somebody has 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 road map and Alper will go through that. Once you have the NAND IP, we don't call somebody else up to like build the wafers, right? We have our own fabs with Kioxia. Like I said, they're quite spectacular. They're some of the largest fabs in 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 die, put it in an SSD, put 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 manufacturing as well. We 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 home go-to-market piece. So there's a lot of debate there -- I say -- 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, your margins are so high. Your margins are higher than the fabless guys." Well, we do a lot more than have less 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, right? They're not -- these engineers, they're not cheap. They're like expensive. They're a little bit temperamental sometimes. It 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 we've been investing that for years, decades, decades, decades. And quite frankly, one of the things I see today when people say, "Oh, like this is just memory, it's a commodity. I'm like, yes, you try it. 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, Alper will be out here later. He leads this team. Most of the people -- a lot of the people in that team have been at SanDisk like, wait before the Western Digital phase, right? That was just like a little phase of the company. we've got like a 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 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 way we're -- 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? Like what was the -- how did this get unlocked? And again, if I go back 2 years ago, it was like, "Oh, Dave like, can NAND ever be 50% gross margin again? Like, yes, of course, you can, right?" 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 multiyear time frame. All right. Let's talk about cyclicality, the current debate, right? So how do we think about cyclicality? So you may be surprised, as 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 way, it's fun. It's fun in this job because you almost go anywhere in the country, anywhere in the world, then you like people know our brand. There's -- last time I was in China, I like there's a photographer. I pulled them over and said, "Hey, open your camera." He opens his camera. He's got a sanddisk card in it. It was great, right? So people know us all over the place. Billions of products sold in the last decade. 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 state is across the whole thing. But let's look at the financial dynamics of this. We never -- 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 a 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 the 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 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 of 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. 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 of 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, right? And we've talked a lot about this last couple of 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 it. So that should tell you one thing about how important they are. It's like literally the CFO of the company, No, there's a lot of people around. There's a big team, but he's the one that's like got his hands on the steering wheel of what all the terms and conditions are and what we'll agree to and 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, like I've just heard a lot of stuff about these agreements, just incredible. There seems to these people that are very informed about the way these work that have never read the contracts, right? It's incredible. I watch TV and people say, "Oh, there's more holes in these things and you can imagine 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 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? 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's -- 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. I only have to go back like 3, 4 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 3 months of visibility. And we're making investments that are 10 or 15 years long, we have 3 months of visibility. Within 2 quarters, we've gone from 3 months of visibility to over 4 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 2/3 of our supply is under agreements, and we have like average visibility of 4 years. That happened in 2 quarters, 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 are 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, get more visibility. 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 3-year deal? How do we do a 5-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 they're still high-value businesses where customers are not going to sign multiyear agreements, right? 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, right? There's always something new. There's always a new device like there's a magic of innovation. Somebody is always thinking is 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 have -- 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 that just want to do business the way it was done a year ago or 2 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 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 customers. I mean we have like we have like the who's who of technology companies in the world is 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 3 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, like, again, what I tell customers like 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, it is important -- it 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 RIP, we're going to grow supply. We're going to grow volume mid- to high teens, and then we debate is that too big or too low, right? "oh, it could be higher, it could be lower, right?" It's like some -- last year, if we went to February '25, people would probably say, well, it'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 has become clear to me over the last couple of weeks. When we talk about mid- to high teens volume growth, we talked 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, growth volume [ mid-'19 ]. Now 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 2 endpoints and you say, "Oh, you're growing faster, you're going slower." It depends what endpoints you picked, right? CAGRs 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 road map, bit cost scalable road map. We say BiCS all the time, sometimes people don't know what it means, right? It's right there in the title, cost scalable, right? How do we build the scalable technology? So I was 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 about a 10-year period, right? But 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 10-year period or 9 -- let's call it 9 years ,5 nodes over 9 years. All right. BiCS 5, 6, 8, 10, 11, now again, there's a BiCS 9 in there. Alper 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 note every year. So 5 nodes over 9 years, 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. Now all of you can do math, I'm pretty good, but I'm going to do the math for you. And so when you back that out to a yearly CAGR 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 then 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. This growth is primarily driven in this business by the application of intellectual capital, not financial capital. It's the paying of those engineers in those NAND designers to continue to drive that road map 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, right? But you will see -- 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 a 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 NAND road map. As I said, like, you're not going anywhere in this business if you don't have the right road map. And he is expert on this. We have a lot of people working on this. They'll tell you about BiCS 8, BiCS 10, where that's going. We made the 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 talked 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 in the statement that said we think the market is going to be $300 billion this year and $500 billion the year after that. And I was like, "Oh, my guys, we never heard that number before, and they started backing into thinking that was like a revenue forecast for '27. It was it -- 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 in to everything else in the world, and that's an exercise left to the reader to figure out what that is. But Eric [indiscernible], 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 the work Eric does and his team. So we thought we'd give you some visibility into how he thinks about this and kind of how he thinks the big picture of how this market has kind of resettled over the last 2, 3, 4 years. Very important to understand this. Not only is Sandisk is 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. Khurram going to come up. He's going to try. He's going to talk about AI inference. There's obviously a massive tailwind to the business right now. There's a lot of questions, a lot of conversations about cash. How is NAND 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. 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. And 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 1.5 years. 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 Ilkbahar.

Thank you. Good morning. Great to see all of you. 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 #1 priority is to keep our exponential scaling engine running. In semiconductors, especially in memories, scalability is the most critical factor we are 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 scale engine keeps going and how our road map 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 are delivering the most capital efficiency amongst our peers in the industry to deliver superior financial results. And finally, we strive to innovate to. 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 this 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 2 new generations of NAND technology, BiCS 9 and BiCS 10 to our roster. This data tells me 2 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 the 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 in our JV partner, Kioxa 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 bit output. Of course, the higher that 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 3 years and averaging that. In 2025, the industry spend, on average, 2.66x more capital than we did to generate the same amount, 2.66x 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 percentage output bit output of each of the peers in the industry versus the percentage of the CapEx they spend every year. actually, we are looking here in a period of 21 to 25. So this is a 5-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 5-year period, we produced 29% of the industry's beds while spending only 13% of the capital. 29 versus 30. So when we look at these ratios for each player, 1 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 road map, 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 are pushing our technology forward, and pushing the limits of scaling in every single generation, we actually advance the technology across 2 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 4 vectors of scaling. Next -- and you have seen earlier, every generation, we get 50% to 60% more bids, 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 average generation as well through device and design innovation. Now you have seen this road map before. I'm mostly going to talk about BiCS 8 and 10 today on that road map. But as you had earlier, our teams are already working on this and on. And the scaling engine continues running and we see actually no end to the scaling limit 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 CBA technology, which is our hybrid bonding technology, where we're able to combine 2 different wafers to make a single wafer. By utilizing this innovation, we're actually augmenting a derivative road map as I'm showing here. The CBA enabled road map 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 2 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 a standard interface called [ TCIE ]. 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 4, 5 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 you 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 we do with this CBA-enabled derivative road map, 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 big 9 in a quick bit. But before I get to it, I want to take a quick look at BiCS 8 because big 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 BiCS 8 and it has given us tremendous competitive advantage in performance, density as well as power efficiency. And it turns that -- it turns out that these are exactly what our AI data center customers were looking for. So when we compare BiCS 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 big against our peers 2x generation memories. Of course, our peers are moving forward. They're announcing their next-generation product, which we call 2 YY or 3 XX 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 BiCS 10 and show you how BigTent is going to compare against these. So this is where BiCS 10 Stan is coming out to be. BiCS 8 was amazing. BiCS 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 the BiCS 10 in a bit. But let's first talk about the latest news, we talked about BiCS 9. So BiCS 9 is the first product, where we're essentially deploying this hybrid technology bonding technology extension. What we have done for BiCS 9 is we have taken the BiCS 8 salary, the mature salary we have and combine 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 BiCS 8 deployment, supply basis to the next-generation performance level with middle cap. And we designed BiCS 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 Khurram is going to talk about. So really looking forward to seeing this powering your AI very soon. Okay. Let's go to BiCS 10 real quickly. I gave you a sneak preview of this BiCS 10. The first product in the BiCS 10 lineup last year. This is the 1 terabit TLC die, again achieving tremendous improvements over BiCS 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 die to our customers as of this month, which we just announced. The second product in the BiCS 10 lineup is the 2-terabit QLC die. QLC being 4 bit 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 BiCS 8, more than doubled the read and right bandwidth as well as improve the power intensity and efficiency by 75%. These are truly amazing numbers considering that we are beating the world the best NAND, BiCS 8. To give you a better picture of the power of scaling. I wanted to show you BiCS 8 and BiCS 10 2 terabit dies side by side. So this is what we are able to do with the scaling engine. Each BiCS 10 2-terabit wafer has 65% more bits than the BiCS 2 8-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 then 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 are looking at the generations from BiCS 5 through BiCS 10 and even projecting into what's coming next BiCS 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 HBS 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 Cherrstrom.

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 zetabytes of shipments in 2026. So how do 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, PC 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 2 distinct periods. Period 1, 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 and $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 flush 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 the 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 in 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 Neo Cloud 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 will continue to grow -- 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 this put together gives us a conviction where we see the market opportunity growing over $300 billion in 2026 and can approach $0.5 trillion in 2027. With that, let me pass it over to Khurram to talk about the era of inference. [Presentation]

Please welcome, Chief Product Officer at Sandisk, Khurram Ismail.

Good morning. I'm Khurram, 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 half time. And since we don't have any breaks for half time, I get to be your host for the half time. So let's get into it. So David talked about engineers being at Sandisk for a long time. I'm 1 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 sort of peaks and troughs. The pace of innovation that AI is bringing is tremendous, and you all can see that. We see the new frontier models being loaded in, right? The system architects are changing the design every 6 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 the next 20-odd minutes, I want to leave you with 2 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 years of AI infrastructure. So I want to start with some fun facts. As I'll be going over some concept, and I think it will help us understand those concepts if I draw some analogy to the human brain. Maybe some of you know, I was just doing a ChatGPT, 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 Yottabytes of collective human memory. Now 1 Yottabyte, I had to look that 1 up to. I work in zettabytes and exabytes, right? So 1 Yottabyte 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 2 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 very, very similar concept. You have a transient short-term working memory that is called [indiscernible] KV Cache, and we'll cover that. And then you have the long-term memory, which is a little bit of more persistence, which is known as persistent KV Cache. So there is similarity. They both work on 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 zettabytes for entire Flash market, right? Here, I'm only focused on 2030 AI data center TAM, 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 direct attached device close to the GPU for training, checkpointing. So that market is having a tremendous growth. And last is the KV Cache, and that will be the focus of our conversation because that's the fastest-growing segment in AI data center. And when you 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 is 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 those -- 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 is 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? There is always the challenges of power, right? Where do you store? Do you store these tokens volatile media only. There's not enough volatile media, what role does nonvolatile media play, right? So -- and then there is shifting architectures, right? We're moving from training to inference. And turns out Flash solve a lot of these problems. And that's where I'll be taking you next. So you saw a video that 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 5-stage data cycle. The first 3, 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 flashed it quite well. We had our high cap QLC 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 need -- growing need of inference? And the answer is no. You can see as we move along, there is disaggregation happening in infrastructure, right? The infrastructure for training is quite different from the infrastructure 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 late staging checkpointing, but inference something new in that we we'll hone in on KV Cache. There are 2 interesting trends that are emerging inference when it comes to NAND flash. The first is data augmentation, and the second is context remembrance, right? A lot of you, I'm sure, use the models. And if you know 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 1 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 conversations, smarter conversation, longer conversation. And what ends up happening as a result is a KV Cache amplification. And the KV Cache simplification, the way to think about is you're having longer context length because you want longer conversations, you have longer reasoning change because you want iterative process. You want the model to know about you. And then there are multi-modalities associated with us. 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. So you can refer to the notes rather than listening to my conversation again. And that notebook serves as KV Cache. So let's just briefly touch what is KV Cache because that's the most important part in inference. Inference has 2 major parts. The first is prefill where the model is thinking. The way to think about prefill 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 [indiscernible], that is compute bound. You'll hear a lot more people say that prefill is really compute bump. And then when the prefill happens, a context gets generated. And now in the implementation that gets stored in Memory, which is called KV Cache. The way to think about KV Cache, it's a working memory of inference or the notebook, memory notebook. It is not important to -- for now for us to discuss that, where does that get stored. I'll take you through how the KV Cache 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 auto aggressive 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 KV Cache, that will 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 is because of this KV 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 KV Cache. Well, how does KV Cache? What -- how does the KV Cache 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. What 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 KV Cache 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 that all these states need to be preserved. The context are getting longer. The conversations are getting longer, the reasoning changes are getting longer. And last, all the data that gets generated as part of your input to the system. So we have a 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 KV Cache 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 KV Cache 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 is [indiscernible], right? There will be a quiz after this. [indiscernible] cache, right? Those are your HBM and system DRAM. The way to think about this, all the hot context, the current context to the user needs, right, to get the response and those are stored here. But again, if you look at from top to down, capacities at play, HBM 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 HBM gets stored in flash. And this is what we are calling persistent KV Cache. Now there are multilayers. 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 once as the context growth, the interactions are in billions, right? 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 KV Cache. So now that we have covered the KV Cache, and we talked about KB Cache 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 KV Cache 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 KV Cache. But if I am the plan, and this again is -- this we derived 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 a 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, right? We have customers who tell us, well, we 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 the small next or 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 KV Cache 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 KV Cache pools, right? So you want to only plan for the KV Cache that your current sessions, and that's represented in cache risk 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 2 things. The first 1 being what is going to be your session length or size? A lot of people talk about 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, provide generates tremendous overhead on storage needs. So each token is represented in tens of kilobytes to hundreds of kilobytes depending on which mall -- which implementation you're using. So as you can see, these variables are what people used to determine how big of KV Cache or persistent KV Cache, rather, they need to deploy. And here is our answer. And this is, again, 1 zetabyte installed base. When you multiply all these things up, and there, by the way, these are just 4 wearables 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 installed base is 1 zettabyte. This is, again, this is the fastest-growing workload. And this we are saying between now until 2030, will have 1 zettabyte of installed base. This, again, our customers are very dynamic. They are 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 like growing, right? The average session is 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 KV Cache number that is represented here because I didn't cover it first time, is 35% of the overall market. So like the previous chart showed the 1 zettabyte number as an installed base in 2030, specifically, we see the size of KV Cache 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 in how Flash sits in data center, right? This is just showing the placements -- various placements of flash in the data center. As 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 embedding, your vectors your rag depositors, all the things that enterprise needs to start to make the AI work. For this, from flash point of view, high-capacity QLC drives are perfect because they provide that enormous capacity. Then you may ask, okay, you say KV Cache, you remember the hierarchy that I showed you and there were multilayers, 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 the 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, right? This is what we call the persistent KV Cache, and I would characterize this as Lukeworm, G3.5 that you saw in that pyramid. So this is showing the AI data center eSSD placements. 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 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 SSD are qualified at major hyperscalers, major customers, OEMs -- so -- and we are shipping both of them. For TLC SSD, we're shipping it in PCIe Gen 5 configuration in all the form factors, and they are great for staging and KV Cache that we discussed on the previous slide. When it comes to QLC, it's also -- sorry, on the -- on the TLC eSSD, we also demonstrated our next PCIe Gen fix 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 eSSD, if you remember, last year, we showed you a road map of up to 1 petabyte. We at Flash Memory Summit demonstrated over 256 million 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 Cache looks like, what does the market size opportunity looks like. But we want to become AI practitioners ourself. 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 learned from our customers with our own understanding. So here I'm just providing you 2 metrics. The way to think about this is this data was collected in a server scale application or system with the 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 KV Cache equation. We assume certain things in that equation. And what we see that a system that has SSD versus a system that only has volatile media like HBM and DDR consume 75% less energy. I'll extended, 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 1/5 of less power. So different metrics, but you get the idea that for SSD, you're going to do much less power. Secondly, on the same system that had SSD versus no, we saw 75% higher throughput in tokens per second. We generate more tokens per second than you would with HBM and DDR simply why? Because you don't have enough capacity, you are limited, right? And you have compute. That's an expensive process, both in power and performance. So as you can see that SFD is not something that is just an afterthought, it's actually an essential, and that's where you see the explosion of KV Cache workload in the market. So I want to leave you with one last part. I don't know what I was supposed to say. Let me pull it from my persistent KV Cache, right? So okay, the persistent KV Cache responded. So today, 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 notes 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 customer is 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 quantization, all the optimization that is happening to reduce the store in KV Cache, but that's only going to fuel the 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 flat. 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 Visoso.

Good morning, everyone. I thought you may want to look at some numbers. So it's great to be a 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 $0.30 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, reported $9 billion in revenue, non-GAAP EPS of $39.25 and adjusted free cash flow of $5 billion. And this excludes cash we received 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 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% and 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, 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 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 their value. You may have a few comments -- 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 have -- 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. Their details by quarter, details by month in most cases. And while they are custom made, there is a framework that's consistent around all these agreements. They start with a multiyear in most cases. When you have a multiyear, those volumes are growing at a very fast pace faster than we're growing as a company, and they are fixed and variable components of pricing. And very importantly, every single one of these agreements as 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 are 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 engagements with customers. These are win-win conversations. As Khurram alluded, there is high level very deep integration from a technical and commercial side. These customers, by the way, news to you include 3 hyperscalers from the U.S. 3 U.S. hyperscalers are part of these customers. Now the always deal we signed was only in January of this year. And guess what, 2 customers already came back and they said, "Hey, guess what? As I look at my models, and they do the math that Khurram was talking about, I need more. So they are already expanding, either extending the duration of their term or adding more bits 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 multiyear engagements. Remember, these price negotiations lasted 3 months, sometimes not even 3 months. And over that time, when we were operating in that model, we practically generated low shareholder value. and make 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 4 years, the longest contract is 5 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? So 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 sealants, 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 subset to that pricing, and therefore, we feel very good of financials of the new business models. The nonbusiness model, the rest of this bid 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 billion, 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 4 years. Now some of that revenue has already been recognized. So the remaining performance obligation, the RPO was $91.1 billion. So a lot of the value, a lot of the revenue is still to come. Both of these numbers reflect the fuller 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 2 big buckets of this. The biggest one 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 prepayment 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 your question, well, how does that ratio evolve over time? So we looked at our contracts, had multiyear contracts, and we wanted to provide you an illustrative example of how that ratio would evolve over time. So for a 3-year contract, 2 years out, on average, you should expect that ratio from beginning to 2 years later to be twice as high. So you guarantee your protection as a 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 this [indiscernible] with excellence. We don't want 4 year deals, we don't 5-year deals. We won these NBMs to last decades, right? And therefore, we want to execute them with excellence. We're going to have the products with quality on time, just as we agree 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 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 8 companies with will 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, right? So going forward, our financials will be the result of a combination of both models. So we will have an -- 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 NBM, and we know exactly what to expect from that side of the business. And we have a portion of the business, which will be the non-NBM. We 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 they make -- 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 bit growth. We talked about bit growth in the mid- to high teens, where 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, they're paying for taxes, working capital, capital spending. And we have, for modeling purposes, I would assume mid-single-digits capital intensity as a 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 a bit 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 shown with these numbers? Well, our confidence comes from our customer conversations, 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? While we'll continue to invest in OpEx will properly fund the business. We'll invest in CapEx, and we'll continue to do things to strengthen our like the 9-year 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, we'll maintain a strong balance sheet. What does that mean? Healthy cash balance. How much? Well, you've seen us operate in that 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 BB+ 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 where 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 our share buyback program. Now what are we doing? You look at last quarter, right, Q4 of '26, we generated $5 billion. How much we will return to you, 4.5%, right? So we're leaving to whatever we're telling you is exactly where we're executing. So the Board authorized a $6 billion program, which we executed $4.5 billion. We have $1.5 billion left. So the Board authorized another $14 billion program. So now we have 15.5 billion authorized and not spend 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 a 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 debt to revenue, revenue to profit and profit to cash. And then the cash flows back to you guys. So that's our model. I hope that 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, 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. We -- I'll turn it over to Alper.

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

Hello, again. Nobody left. It's amazing. Okay. So in the second part of our technology presentation, I get to show you our innovate to amaze DNA. And I will talk about 2 technologies that we introduced last year on this very stage. Both of these technologies address the memory world problem. Memory world 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 2 technologies, both of which are highly scalable and can solve this memory world 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 -- I'm back. Quick recap first. The 3D Matrix Memory, we started working on this technology back in 2017 in our research path. And in 2024, we moved the development to a 300-millimeter facility, a modern facility at our development partners. And last year, when I was here, we had just delivered a development vehicle that we could just essentially pursue the activities at IMEC West, and that's what we had shown you. Since then, we continued making steady progress. We use the development vehicle I showed you, and we started depositing memory layers on top of it. And we delivered 300-millimeter wafers and packaged 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 had. 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 HBM, 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 16x the capacity. We invented this device with AI inference workloads in mind that actually leverage mixture of experts type, 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 HBS 2 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, 2 trillion parameter models are no longer amazing. They're just commonplace. And many of these models actually started utilizing a mixture of experts, sparse models. And they're allowing their users to go up in context things all the way to million type of tokens. So this is creating a new paradigm. The large models as well as the long contact lens 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 HBF is going to play a very critical role. Before I dive into HBF further, I wanted you to hear from somebody who deals with these LLM 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 helped last week with about 3,000-plus attendees. And there were several sessions dedicated to HBF 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. Xiaoyu Ma, Google DeepMind. So please roll the video real quickly. [Presentation]

Okay. So Dr. Ma is talking about a memory crisis. So next, let's listen to how he believes we can solve the problem. [Presentation]

Okay. So with that, obviously, Dr. Ma is one of the many researchers who are actually spending a lot of time thinking about HBF 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 HBF into AI solutions. So I wanted to share with you some of the architectural proposals that have been published recently. So the first 1 here is an XPU, where we have taken out all of the HBM stacks, chips and replace them 100% with HBF. So this is an HBF-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 HBF. So it's a hybrid architecture. The third architecture is also a hybrid architecture, but in this case, the low-capacity HBM chips act as a caching tier in front of the high-capacity HBF. And the fourth one is a disaggregated architecture. In this disaggregate architecture -- we are disaggregating the 2 stages of AI inference, the prefill and decode and optimizing the solutions, the hardware solutions for these in a disaggregated fashion. The prefill XPU is compute-intensive, but doesn't need a lot of memory bandwidth. So what you can do is couple performance GPU with just regular DDR DRAM, whereas the decode stage, which is very memory capacity and bandwidth intensive, but doesn't require a lot of compute. You could take a modest GPU and couple it with HBF. So you get the best of 2 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 HBM chips with HBF, and that gives it about 4 terabytes of HBF memory. So we simulated a benchmark that essentially emulates in multiturn agentic workload, it simulates or emulates a code development environment. So what happens is the AI agent start developing code, spanning more jobs, et cetera, et cetera. And the underlying LLM model here is a 490 billion parameter Qwen3. So let's see how the 2 models are sort of the 2 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 HBM only system, it turns out that the minimum viable system to run this workload requires use of 8 HBM GPUs. You just cannot fit the model a bit less than that, so you have to use at least 8 GPUs to start this job. And here's what the workload looks like on our simulator, okay? So 8 GPUs delivering pretty stable token output. Okay. So now we're going to show you how an HBF system compares. So it turns out that we were able to actually run this workload with a single HBF GPU, 1 GPU alone, and let's look at that. Okay. Obviously, the performance is not as high as 8 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 4 HBF GPUs look like, and here is the result. So with 4 HBF GPUs, we were able to match the performance of 8 HBM GPUs. So we're getting 2 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 drops 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 cost 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 2 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 the minimum CapEx, we can improve your spending by 8x. You get 8x CapEx efficiency using HBF. 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 HBF. 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 HBF. 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. Remember, 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 HBF device, which we call HBF 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 road map that we have for HBF. Obviously, we have a pretty big vision for HBF, 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 1.5 years ago, we told you that we intended to create an open ecosystem around HBF. True to our word, last August, we announced a partnership with SK hynix and talked about our intent to create an open standard around HBF. 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 HBF 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 day and other participants contribute their feedback and input. We're looking forward to incorporate those in the next revision of the specification over the next 2 years. One critical element of our ecosystem. We view as the advisers that we have, the technical advisory board that we have built. You may remember, I talked about this Professor David Patterson and Raja Koduri, our 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 rock star chip designer. Over the last 4 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 Tenstorrent 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, Jim. Thank you so much for coming, please. And let me hand this over to you. Great to see you here. You flew yourself. Thank you very much.

I had the help of an airplane. Not entirely myself. It's...

Well, thank you for being here. Jim, talking 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 after recently?