Welcome back to The Six Five Summit: AI Unleashed 2026. It is amazing how important and strategic memory has become. Listen, I always thought it was important, but given the age of AI, it is incredible what it has enabled us to do, because, quite frankly, without enough memory, without the right kind of memory, all the amazing stuff that you see around us cannot happen. To have this discussion here, I am pleased to announce Sumit with Micron. Sumit, great to see you.
Great to see you, Pat. Thank you for having me.
Yeah. It is just amazing. I have been in the industry for 35 years, a consumer of memory.
A partner with memory, and now analyzing the memory market and all the corresponding ones around it. But it is amazing how far we have come here. Last year, when we talked, I actually categorized memory as strategic.
Yes.
I'll take a little bit of credit for that.
Absolutely.
But quite frankly, you deserve a lot more credit because you actually do it. I just talk about it. Today, that perspective really seems a lot more relevant, and a lot more people are talking about it. What has changed in the last year, in this age of agentic AI, that the industry thinks more about memory, and why has AI actually accelerated this shift?
Well, great question, Pat, and that was such a good discussion that we had, and I do agree with you. A year has gone by, but it seems like a very long time in terms of how the world has changed in this last year. Certainly, from the perspective of our customers, a lot has changed. We have gone into a situation where memory is in significant shortage.
Right.
Across all market segments. We also, despite our best efforts on supply, don't have line of sight today as to when we will be able to have the supply catch up with demand, because demand signals from our customers, and again, across all segments, continue to escalate in every year that we get these projections from our customers. We have a situation where customers are looking at memory in a different way for a few reasons. One is, of course, this big gap between supply and demand. The other, of course, is also that when you think about AI, the performance requirements from the systems are going up. As you think about what makes a system perform at peak levels, at peak capability, it's not just about the processor.
A lot of it is now driven by the performance of the memory, the bandwidth between the processor and the memory, and the capacity of the memory itself. Because these AI models need to be stored in memory, a lot of data has to move back and forth. That performance, that bandwidth between processor and memory, becomes a key bottleneck. Customers are realizing that they need to plan their roadmaps, define their roadmaps with a different approach to memory. Now it is all about how you can design memory to gain a competitive advantage. How can each customer differentiate their system using memory? If it is going to be the same JEDEC-compliant off-the-shelf memory that everyone else is using, as used to be the case a few years ago, then you cannot so easily get that differentiation.
We are working with customers embedded now in their multi-year roadmaps.
That's right.
They are now focused on how we can eke out a differentiation. How can we change the game on our competition? That means thinking about memory in a different way, partnering with a company like Micron to design a different type of capability that then can be used and leveraged. One example I will give you there is what we have done with NVIDIA on LPDRAM in the data center. We were the first company to introduce that capability in the data center, and we were sole- sourced in that segment for a long period of time. That is now starting to proliferate. More customers are starting to understand that it is so much better to use lower- power DRAM. It helps them through numerous benefits of density, size, performance, and, of course, significantly lower power consumption, which is a critical part in the data center.
That is just one of many examples that are now starting to proliferate as we look out for the next five years.
Yeah, it is interesting. When I try to explain why we have seen such an increased interest in memory, I get back to a college course that I took, which was explained to me, this was in the late 1980s, I am old, where a really basic fundamental is if you can stay inside of memory, you are going to increase performance. This is a little bit even different. By the way, it is true, but you get better results the more memory that you can have and the closer it is to what you are doing, because anytime you go outside of memory, things slow down.
In this age of AI, that is a challenge. It is interesting; I have seen nine memory cycles in my career. Again, I worked as an OEM, I worked at a chip company, and now an analyst firm for the last 15 years. It seemed to be the most cyclical, even though I know there were other curves, some other industries, but also looked at as quite commoditized. You answered a little bit of that in the first question, but why is AI fundamentally changing that perception?
Yeah, it's a great question. If you go back to the 1990s, in the early 1990s, there were upwards of 20 companies that were doing DRAM. DRAM went through a lot of consolidation in the industry. Back then, there were just a couple of processor companies, right? You fast-forward to today.
Right.
And now you have more than 20 processor companies. If you actually sit down and count all the companies that are doing processor design, you'll get to that number pretty quickly. But a very small number of companies were doing DRAM. So when you think about AI subsystem design, not just in the data center, by the way. You're talking about in the data center, of course, training workloads versus inference workloads. Even the inference workloads are going to become more and more fragmented into different types of inferencing. Some can use SRAM in a small category of those, but a large portion still very much needing as much DRAM capacity as we can design into these systems.
Then you go from there to even autonomous cars, industrial systems, look at smartphones and PCs, and you had this whole OpenCL movement with so many of the Mac mini flying off the shelf because of the unified memory architecture. That's really where it all comes down to. AI system performance today is dependent to a first order on memory subsystem performance and memory capacity. These are the two things that really define how well an AI subsystem can do. So when that becomes a core design point in the system, then you have to start thinking about memory in a different way. Then it all becomes, how can you get to market fast with a differentiated product? This is where I think the memory business of tomorrow is going to be very different than the memory business of yesterday.
Combine that with the idea that the whole industry, as it turned out, strangely, got to a point where greenfield capacity expansion was needed. Because the demand growth driven by AI has been so significant, there is no a way to satisfy that demand just through technology transitions, which was how it was done for several years.
That's right.
That tech transitions used to produce enough bit growth to satisfy the demand growth. Now, a dramatic amount of wafer increase is needed in the industry. That means building out new clean room space, and when you run out of clean room space in existing manufacturing sites, which most companies seem to have reached that point simultaneously, you have to build out capacity in greenfield sites, and that just takes a very long time. It's a really time-consuming process, and that process is bottlenecked by a lot of physical aspects of how fast construction can be done in various parts of the world, regulatory approvals can be taken in various parts of the world.
How do you take a place which is barren land or just trees and bring up all of the infrastructure necessary to bring in power, water, and water treatment plants, and chemicals and the whole infrastructure needed to set up fabs? It takes a long time, and that's what the industry is going through. We are now in a situation where we are going to struggle to meet demand. Despite the best efforts from our side and what we are able to see from others in the industry, it's going to take quite a long time for supply to be able to catch up, and that is another factor that's also changing customer behavior. This is where we have been talking about these strategic customer agreements.
That's right.
These strategic customer agreements are fundamentally changing our business model and our approach to business. This is where we are doing these multi-year deals, committed supply to our customers, and they give us committed demand. Unlike previous years, where it used to be like a one-year agreement, they would buy if they feel like it, we would supply as long as the supply was there. These are committed supply agreements, and they have really robust ROIs, so we can underwrite the CapEx needed for a multi-year timeframe. That is how we are looking at it. Of course, this does not even get to the point of agentic AI, which is the next phase of AI growth. After that, you get to even physical AI, which is the next big wave of demand.
If you look out these different waves of demand, they compound on each other. They build on each other. It is not like one replaces the other, but they sort of build on each other, and that is where comes the big challenge of how do you bring so much supply physically online in this period of time to feed all of that demand.
I kind of see your SCAs as a proof point that memory is more strategic. I know it is easy to lump it into, hey, we need to have SCAs to lock in future capacity. It is that, but it is also co-planning technologically. I was really struck, and I am glad you brought up AI on the edge. The most interesting things, whether it is with OpenCL or some of the new designs that the client computing folks are doing with their processors, very tightly coupling memory.
Yes.
With the CPU and the GPU to enhance the bandwidth to be able to do that. You do not see architectural changes a lot in client computers. They have been pretty static for a long time. I want to move on. We talked a little bit about hyperscalers, and that is where a lot of the action is happening, just if nothing more, the investment in the CapEx and, at least right now, the innovation on models and applications. I am curious, though, when it comes to autonomous vehicles, we talked a little bit about PCs and devices, robotics, intelligent edge. How are memory requirements changing those environments?
Yeah, it's a great question, and it all started in the data center, of course. But as we think about how AI will progress, it's not going to just be limited to the data center. That intelligence wave is going to move towards the edge, and ultimately, intelligence will be everywhere, in all devices. So it's all kinds of consumer devices.
Yeah.
Your car, and of course, even the industrial revolution phase 2, where you'll have all of this intelligence distributed around the world in enterprises. And of course, your personal devices, your PC, whether it's at work or at home, as well as your smartphone. And even companies thinking of new devices that may not be the same smartphone or PC form factor, just completely different form factors. Because when you think about building a native AI-first device, you can think of different ways in which it can be done, versus the traditional PC and smartphone approach. In all of these devices, again, it goes back to with the lowest power consumption envelope that some of these devices that need to be battery-powered, for example, can get.
How to maximize performance, how to be able to run the small models that need to be big enough to deliver value to the consumer without frustrating them, yet need to be small enough that they can be run on-device and don't need to go back to the cloud. That is something that is the new frontier of AI, where that experimentation will continue. Those models that are small enough to fit on a device like a PC or a smartphone will get better and better with time. As that happens, you get into the big positives of privacy and confidentiality of data, right? So consumers value that highly, and those companies who can then use these on-device models and give consumers the comfort that everything that they interact with that device is not going to the cloud for somebody to pry on.
That is something that will unleash a whole new set of applications that will bring a lot of value. Then you can think of autonomous driving, which is starting to really do well now and is really being aided by AI. And the next frontier, which is all of the robotics-driven capabilities. And you think about humanoids, that's going to be one of the largest markets of any product that has ever been built. Of course, it will take some time, but we are getting closer and closer to that singularity when you'll be able to talk to a humanoid just like any other human, almost indistinguishable in terms of cognitive capability. And then over time, these robots will get more and more sophisticated with their physical capabilities, and that would be a revolutionary outcome.
Initially, it will start with areas where they are in a factory automation type of environment, a very specific set of tasks, fewer degrees of freedom, and once they graduate from there, the most complex environment is in the home because it is very unstructured, and you will get to that sort of a situation. I am confident that you get into the latter part of this decade and into the 2030s, robotics is going to be a massive growth driver. The thing about memory that is so exciting with robotics is it is going to be a huge amount of content per device. Each humanoid is going to have hundreds of gigabytes of DRAM content and terabytes of SSD NAND content. They are going to be massive drivers of growth in demand for both DRAM and NAND.
Yeah, I think a lot of the time, people can't fully appreciate that it has to be autonomous, and it can't phone home every time.
That's right.
Also, as you were going through that, I was struck by thinking that Micron is enabling picowatts to gigawatts.
That's right.
The thought of having to invent the technology to do that just scrambles my brain a little bit, but I think it's something that people need to keep in mind when they think about Micron. As you've gotten more strategic in the age of AI, whether it's from a cool device that hasn't been invented yet, released in a year, to tap directly into AI and models to gigawatt data centers.
This idea of co-design or co-invention comes up, and I've heard, I've talked to some of your partners about it.
Yeah.
They talk about some ideas of what they're doing. I talked a little bit about the changes in architectures already, and those were traditionally through, I would call it more of a JEDEC standard, a type of thing. Now we’re looking at a serious co-designer. Can you talk about that changing relationships that are going on with your partners?
Absolutely. Our partners are looking to figure out how they gain an edge in their markets.
Yeah.
Against their own competition. The memory processor subsystem has become such a critical portion of the AI subsystem because that memory processor interaction is essentially what defines so many critical parameters of an AI system. It defines how much power is going to get consumed. It defines what kind of models you can run, what size of models, how fast you can run them.
That’s right.
When you think about all of these advances that are happening in LLMs of all shapes and sizes, for all different market segments, to create that differentiation in the hardware, memory cycles on the design side, and processor design cycles are so long that it is imperative that their design start in very close collaboration. This is not a plug-compatible, JEDEC-compliant part that you can plug in after the fact and just do some qualification and run with it. There is some part of the market that will be that, but there is increasingly a need from customers to say: How do I differentiate? I need to be able to create some special capabilities that off-the-shelf products just don't provide to me, and let's partner with a memory company, get into a long-term, five, seven-year type of roadmap discussion on R&D.
We are getting embedded in the R&D roadmaps of a lot of our customers. They are coming up with very interesting ideas. Some of them can work, some of them take longer to create the inventions and innovations needed to create those breakthroughs. We are very excited about the diversity of ideas that we see. Like you said, these span such a broad range of systems. Just like you said, some of these devices consume very low power because they are all battery-operated, all the way to gigawatt-scale data centers.
We have seen innovations come out of several of these segments and some of the leading customers in each of these segments, and we will continue to see that. We are working actively on very exciting projects that stretch the capabilities that we have to deliver some fascinating design concepts that our customers are eager to pursue.
Deeper discussions: they are not able to partner in that deep fashion with multiple companies. It is typically just one or two, and then maybe others would be able to catch up years later. This is another benefit of this model is, you really can partner that closely with just one or two players. Oftentimes, when you go down that path, you become single-sourced for a period of time, or there is just maybe one other source of supply. It changes the relationship to more like an ASIC model of engagement, where the customer takes more of an ownership of the design in terms of the resources, and then there is that longevity that you have on the demand side because you are in that special ASIC-like relationship, and it is not the type of memory- type of relationship that existed years ago.
Yeah, and the big difference that I see too, what is changed so much is you can do this at scale.
That is right.
Meaning the volumes are so big, and quite frankly, the dollars enable investment on both sides to be able to do that. And you hit it when you kick this off, which is about differentiation.
That's right.
People are looking for a way to, whether it's performance, efficiency, or TCO, pick your metric. They want to be different, and I think that's important. So I'd like to look ahead here from an investment point of view. You did talk a little bit about investments on the capacity side . It's funny, if I look at some of what's written, there's almost this expectation that you can vibe code a memory factory. But listen, I used to work for a chip company that had a fab.
Yeah.
Even today, looking, it's a very, very difficult, capital- intensive, and you have to make a bet three or four years before anything-
That’s right.
Is coming on the other side. Can you talk about the investments that you’re making that really solidifies your role in the next generation? Of course, it’s going to be AI, maybe. It’s going to be AI or something different, or a different variation of AI. Again, these investments do take a long time. Audience, you got that out there. Don’t make that mistake.
No, that’s a very good point. By the way, if anyone knows how to vibe code a DRAM factory, we would certainly like to know that because it’ll make our life a lot easier. Our customers definitely want to have the supply quickly, and we are really doing everything we can. We have pulled out all the stops in being able to accelerate all of these investments. If you look at just our CapEx profile, we have gone from just over $13 billion of CapEx in fiscal 2025. Fiscal 2026, we’ll do double of that. Then we have pointed to Fiscal 2027 being above $45 billion even. That number has escalated through time, and we have just recently announced that in the U.S., we have taken our $200 billion investment up to $250 billion and even pulled in the timeline.
Right.
Of by when we expect that $250 billion to be invested. This is across the board. We are investing in Idaho One, what we call ID1 Fab. That's going to have first wafer outs in mid next year. ID2 just follows right after that. It's going to have first wafer outs close to the end of calendar 2028. We have the Tongluo Fab that we purchased in Taiwan. That's going to come out with wafer starts also in 2027. We're making further expansions there. We're making expansions in Japan. We are making expansions in Singapore. So we have 20 investment projects going on around the world of all different shapes and sizes, to bring on front-end capacity, back-end capacity. But it takes a long time to do that.
It does.
For every greenfield fab, for example, whether it is in Idaho or in New York In New York, we have a four-fab cluster. First fab we expect to have output in 2030. We broke ground on it in January. But ahead of schedule, we have had this concrete pour milestone as well. Between that and the Idaho Fabs and the Manassas Fab in Virginia, we have a lot going on as well around different parts of the world. But these projects, you have to do all the permitting. You have to build out the entire infrastructure.
There is a lot of construction talent that is in short supply. If you think about all the construction that is happening around the U.S. and in many parts of the world, you have all these data centers getting built out. These data centers need power, so power plants are being built out. They need semiconductors.
Semiconductor facilities, both front-end and back-end, are being built out. You look at a big crunch in terms of people who are skilled at their job to be able to do all of this very sophisticated construction.
There is not enough technicians to be able to man all of these fabs. We are investing in communities to bring up talent to really ensure that there is that pipeline, not just helping us to make all of these projects happen, but ultimately will be a help for the whole ecosystem. We are making all these investments in human talent in our community colleges, working with towns and communities where we do business. This is a long-term endeavor for us. We expect that there will be a continuous increase in the scale of our facilities over time as we bring up. As you know, these are clusters of fabs, right?
That's right.
You can't just build one fab.
Exactly.
You need that scale, and you need to get past the knee of the cost curve quickly. You have to keep building till you get past the knee of the cost curve, which means you're talking about expansion that will continue for the next decade and beyond, right? These are long-term projects for us, and we really wish we could snap our fingers and just bring capacity up online. But it just doesn't work like that. We think that meaningful supply will start to ramp only in 2028. And that too, it'll be only an initial ramp, right? It'll gather momentum only in the years after. It will take a while for the industry to find a new equilibrium point. But when exactly that happens, we don't have line of sight to yet.
Sumit, that's great example, great information about the investments you're making. One thing, though, that I think some people might get wrong, I've seen it written out there, is the complexity. We know logic is difficult, okay? Talk to me about the complexity of memory.
Memory is one of the most complex semiconductor technologies out there. We talk about logic being on the leading edge, but memory for sure is also very much a leading-edge technology, and it uses EUV machines, which are massive. There are like 2,000 process steps in the fab, and you start to run a wafer in a fab, and by the time you can ship a product to a customer, it's like a five-month cycle time.
That's amazing.
It's like you go.
People have no idea.
Yeah, exactly. It takes three and a half plus months to go through the fab, approaching four months, and then a month on the assembly, packaging, and test, sometimes a month and a half for more complex products. If you think about the complexity of products like an HBM product, it has been called by different people like a marvel. It is a miracle that it actually works.
Right.
Think of stacking 12 high die of DRAM , one on top of the other.
Right.
Having a base die at the bottom that connects to a GPU. That whole thing has a very significant level of thermal challenges and power consumption issues. How do you cool that thing while it is operating at such high bandwidths, moving data back and forth from the processor? The packaging technology alone is like a clean room environment.
Right.
Very, very different from the type of flip- chip packaging that we have done in the industry for a long period of time in the logic domain. No matter which way you look at it, whether it is sophistication on the packaging side, or on the front-end process technology with EUV and each node of DRAM now becoming more and more difficult to scale. Same thing on the NAND side. We are piling up these layers over 200 layers, over 300 layers, over 400 layers, and then being able to do these devices. The process complexity of that is just enormous. Then to be able to have these products on the NAND side store data and do this, a single SSD now can be like 245 TB.
Right.
I mean, just an enormous amount of capacity that we can fit into such a small form factor is just nothing short of a miracle that the engineers and the manufacturing team members perform every day. This is just an incredible amount of complexity. Then to continue to scale this
That's right.
In the years ahead, to go to finer and finer pitches and to actually make this technology work at scale , that is needed is just another marvel, right? It's just something that takes a lot of time and effort, but a huge amount of technological innovation as well.
I'm amazed at how complex it is, and I'm also amazed that you can do it across such a scale of power planes. That really blows my mind. I'm unaware of many people on the planet who can actually do that. Sumit, this is great. It's funny, I have to note, right, people are talking a lot about, "Oh, I can't get enough this," or I look at that as they're not getting enough awesomeness, okay? They want more awesome. Then there's discussion about, " Oh my gosh, the pricing and stuff like that, but it's like I look at this as investment, right? Micron and other folks in the memory industry, it's okay for them to make investments because, quite frankly, if we don't make more investments, the cycle of innovation is going to stop.
I am excited to see what Micron can do in the future, with its investments, not only in the foundries and the fabs, but also in the technology itself. I do believe that robotics could be a 10x, right? It could be the big growth market, again, not after we built out the data center or the data center edge, but it will be another adder that I do not think we have even contemplated in our capacity models. But I know we need to see a little bit more evidence of it, of when it is going to hit, before it hits. Sumit, thank you so much for this conversation. I cannot believe we did this a year ago. The conversation, the situation, where we were and where we are today. I have had a lot of fun, and I know the audience has, too.
Thank you so much, Pat. I really appreciate the dialogue and the great time to have that discussion last year, and what a difference a year makes. Thank you.
I know. It is amazing. I want to thank all of you for tuning in here for The Six Five Summit : AI Unleashed 2026. Yes, it is definitely unleashed. Check out all of our content about Micron, as well as memory . We have covered it a lot. Check out all the analysis on Daniel's and my website as well. Take care. Hit that subscribe button. Bye.