As the AI wave expands from data centers to edge computing and robotics, a senior executive at Micron Technology has warned that memory has become the most critical bottleneck determining AI's performance ceiling. Due to lengthy new capacity construction cycles and extreme process complexity, the industry's supply-demand imbalance won't see meaningful new capacity releases until at least 2028.

On September 15, at the Six Five Summit 2026 semiconductor-focused forum, Sumit Sadana, Senior Advisor to the CEO of Micron Technology, sat down with analyst Patrick Moorhead for a detailed discussion on the supply-demand landscape of the memory industry in the AI era, capital expenditure plans, and key future growth markets.

Over the past year, the tech industry's perception of memory has undergone a fundamental reshaping. This shift stems from the fact that computing power is no longer the sole metric for measuring AI system capability. Sadana pinpointed the underlying logic of current AI hardware: "Today, the performance of an AI system depends first and foremost on the performance and capacity of the memory subsystem. These two factors truly determine the performance level of the AI subsystem."

He explained that because AI models must reside in memory and vast amounts of data must shuttle between memory modules, bandwidth between the processor and memory has become a critical performance bottleneck. Meanwhile, the memory market faces a structural imbalance built up over decades — in the early 1990s, more than 20 companies manufactured DRAM, whereas today only a handful remain; conversely, the number of companies designing processors now exceeds 20. This "few versus many" funnel-shaped supply structure leaves the memory supply chain extremely fragile in the face of explosive AI demand.

Capital Expenditure Surging, Meaningful New Supply Won't Arrive Until 2028

Facing the enormous gap between supply and demand, bit growth driven by technology transitions alone can no longer satisfy market appetite. The entire industry has been forced into a heavy-asset expansion cycle of "building new cleanrooms and wafer fabs."

Micron Technology is embarking on an unprecedented capital expenditure program. Sadana revealed: "Just look at our CapEx plan: in fiscal 2025, our capital expenditure was slightly above $13 billion; in fiscal 2026, it will double; and in fiscal 2027, we expect it to exceed $45 billion. We recently announced in the U.S. that we're increasing our investment from $200 billion to $250 billion, and accelerating the timeline." In addition, Micron Technology is advancing roughly 20 expansion projects simultaneously across Idaho and New York in the U.S., as well as in Taiwan, Japan, and Singapore.

However, distant water cannot quench immediate thirst. Semiconductor fab construction is constrained by infrastructure, regulatory approvals, and a severe shortage of skilled workers. Sadana provided a clear timeline projection: "We believe that truly effective new supply won't begin to be gradually released until 2028, and even then, it's only the early stages. The momentum of capacity growth won't truly strengthen until the following years. Therefore, it will take some time for the entire industry to find a new equilibrium."

Another major reason for the slow capacity release lies in the astronomical complexity of memory manufacturing. Taking the currently hot HBM as an example, Sadana remarked that its ability to function properly is "nothing short of a miracle": "Think about stacking 12 layers of DRAM chips, with a base chip at the bottom connected to the GPU. The entire system faces severe thermal challenges and power consumption issues... There are approximately 2,000 process steps in the fab. From the start of fab production to eventually delivering products to customers, the entire cycle takes about five months."

Bidding Farewell to "Buy-When-You-Want," Long-Term Agreements and Deep Customization Reshape Business Models

The extreme capacity shortage is driving a transformation in the memory industry's business model, with traditional spot trading and the era of JEDEC-standard general-purpose chips undergoing change.

"We're signing multi-year contracts, committing to supply customers, and customers are committing to provide demand forecasts. This is fundamentally different from the one-year agreements of the past — previously, customers bought when they wanted, and we supplied as long as we had inventory," Sadana said. He noted that these "strategic customer agreements" offer exceptionally high returns on investment, providing underlying security for years of capital expenditure at fabs.

An even more profound change lies in R&D engagement. To achieve differentiation in power consumption, model runtime speed, and other aspects of AI systems, customers are incorporating memory design into their 5-to-7-year product roadmaps. Sadana pointed out: "This model transforms the partnership into something more akin to an ASIC collaboration — customers have greater autonomy in design, and the relationship is long-term."

AI Moves to the Edge, Humanoid Robots to Become the Largest Growth Market

Market attention is broadly focused on data center AI demand, but Micron Technology believes this is only the starting point. As AI permeates smartphones, PCs (such as Mac mini with unified memory architecture), and autonomous vehicles, memory demand will become ubiquitous.

Looking to future growth potential, Sadana has his sights set on the robotics sector, believing it will be the next wave of massive demand following data centers.

"Think about humanoid robots — they will become one of the largest product markets in history," Sadana said, emphasizing the staggering memory consumption of edge AI when looking toward the 2030s market: "What's most exciting about robotics is that every device requires massive data storage — each humanoid robot will have hundreds of gigabytes of DRAM and terabytes of NAND flash. Therefore, they will significantly drive DRAM and NAND demand growth... Robots must operate autonomously; they can't send data to servers every time."

Here is the full text of the interview:

Sumit Sadana 00:00

Today, the performance of an AI system depends first and foremost on the performance and capacity of the memory subsystem. These two factors truly determine the performance level of the AI subsystem. Welcome back to the 2026 Summit.

Patrick Moorhead 00:22

The era of AI has arrived. The importance and strategic significance of memory are astonishing. I've always thought memory was important, but given the AI era, what it empowers us to do is incredible — frankly, without enough memory, without the right memory, none of the amazing things around us would be possible.

I'm pleased to announce that Micron Technology is attending this summit. Great to see you, glad you're back. Thanks for the invitation. This is fantastic. I've been in this industry for 35 years — as a consumer of memory, a partner of memory, and now at an analyst firm studying the memory market and everything related. What we've accomplished is remarkable. When we spoke last year, I actually classified memory as a strategic technology — yes, I'll take credit for that. Frankly, you deserve more credit than me, because you're the one taking action; I'm just talking. But now, that perspective seems even more important, with more and more people talking about it. In this AI era, what has changed over the past year that has made the industry take memory more seriously?

Sumit Sadana 01:44

Why has AI actually accelerated this transformation? That's a great question. A year has passed, but given the changes the world has undergone, it feels quite long. The changes have been especially significant from the customer perspective. What we're facing now is a severe memory shortage across all market segments. Despite our best efforts to increase supply, we still can't determine when supply will catch up with demand, because demand keeps growing. Signals from customers across all sectors increase every year.

There are several reasons why customers' view of memory is changing. One is the huge gap between supply and demand; another is that, given AI demands, system performance requirements are also rising. When you think about what makes a system perform at its best, you'll find the processor isn't the only factor. Today, memory performance, bandwidth between processor and memory, and memory capacity itself are all critical. Because AI models need to reside in memory, and massive amounts of data need to move back and forth between memories, the bandwidth between processor and memory has become the key performance bottleneck. As a result, customers are gradually realizing they need to replan their product roadmaps and adopt different memory strategies.

Sumit Sadana 03:44

Now, the key is how to design memory for competitive advantage, how to make each customer's system stand out. If memory remains like it was a few years ago — off-the-shelf, JEDEC-standard memory used by everyone — then differentiation becomes difficult. So now, we're working with customers to incorporate memory design into their multi-year product roadmaps. They're now focused on how to differentiate and change the competitive landscape. This means thinking about memory differently, working with companies like Micron Technology to design new capabilities that can be applied and leveraged.

Sumit Sadana 04:31

For example, our work with NVIDIA on low-power DRAM (LPDRAM) in data centers is a great example. We were the first to bring this technology to data centers, and we've been the exclusive supplier in this space for a long time, and now other companies are gradually adopting it. More and more customers are realizing the benefits of using low-power DRAM — it delivers higher density, smaller footprint, better performance, and significantly reduced power consumption, which is crucial for data centers. This is just one of many examples.

Patrick Moorhead 05:07

Looking ahead five years, these technologies are beginning to flourish. When I try to explain why people's interest in memory is so high, I recall a course I took in college — back in the late 1980s, the basic principle taught was: if you can keep operations within memory, you improve performance. Of course, things are slightly different now. More memory means better results, and the closer memory is to what you're working on, the better — because once you exceed memory capacity, things slow down. In the AI era, this is a big challenge.

It's interesting. I've experienced nine memory cycles in my career. I've worked at an OEM, at a chip company, and now at an analyst firm for 15 years. Memory seems to be the most cyclical, although I know other industries have similar patterns, memory is also considered fairly commoditized. You touched on this a bit in your first answer, but...

Sumit Sadana 06:17

Why is AI fundamentally changing this perception? That's a great question. If you look back at the early 1990s, there were more than 20 companies making DRAM, and the DRAM industry went through massive consolidation. Back then, there were only a few processor companies. Now, there are more than 20 processor companies. If you sit down and count all the companies designing processors, you arrive at that number quickly. But the companies making DRAM number only a handful.

So, when you think about AI design, AI subsystem design — not just in data centers — of course, on the data center side, you need to distinguish between training workloads and inference workloads, and even inference workloads are increasingly being subdivided into different types. A small portion can use SRAM, but a large portion still heavily relies on DRAM. We're designing the maximum DRAM capacity possible into these systems. This trend even extends to autonomous vehicles, industrial systems, and looking at smartphones and PCs — due to unified memory architecture, Mac mini is selling like hotcakes, sparking an "Open Claw" movement. And at the end of the day, all of this is about AI.

Sumit Sadana 07:50

Today, system performance depends first and foremost on the performance and capacity of the memory subsystem — these two factors truly determine the performance of the AI subsystem. Therefore, when memory becomes the core design point of a system, you have to think about memory differently. The core question that arises is: how do you quickly launch differentiated products? I believe the memory business of the future will be fundamentally different from the past, and this ties closely to the following concept...

Sumit Sadana 08:36

The entire industry has ultimately reached a point where new capacity must be built, which is quite surprising, because AI-driven demand growth is so significant. Technology transitions alone can no longer satisfy this demand, whereas in the past few years, bit growth from technology transitions was sufficient to meet demand growth. Now, the industry needs to significantly increase wafer output, which means building new cleanroom space. When existing production sites run out of cleanroom space — and most companies seem to have already hit this point — you must simultaneously expand at new sites, and that takes a long time.

This is a very time-consuming process, constrained by practical factors such as differences in construction speed around the world and the efficiency of regulatory approvals. How do you establish all the necessary infrastructure on a greenfield site — power, water, and water treatment plants? Chemical supplies and all the supporting infrastructure needed to build a fab take a long time, and this is the reality the entire industry is experiencing.

Sumit Sadana 10:01

So, despite our best efforts and those of others in the industry, we're still struggling to meet market demand. It will take a long time for supply to catch up with demand, and this is another important factor influencing customer behavior.

Sumit Sadana 10:27

This is where the strategic customer agreements we've been discussing come in. These strategic customer agreements have fundamentally changed our business model and how we operate. We're signing multi-year contracts, committing to supply customers, and customers are committing to provide demand forecasts. This is fundamentally different from the one-year agreements of the past — previously, customers bought when they wanted, and we supplied as long as we had inventory. Now these are committed supply agreements with very high returns on investment, allowing us to secure years of necessary capital expenditure.

Of course, this doesn't even touch on agentic AI, which is the next phase of AI development. After that comes physical AI, which will be the next massive wave of demand. If you observe these different waves of demand, you'll see they stack on and reinforce each other, rather than replace one another. This presents a major challenge: how to bring this massive physical supply capacity online.

Patrick Moorhead 11:39

During this time, to meet all this demand, I think your long-term agreements demonstrate precisely that memory's strategic significance is rising. I know it's easy to simply attribute this to "we need long-term agreements to lock in future capacity," which is important, but it also involves collaborative planning.

From a technical standpoint, I'm truly impressed, and I'm glad you mentioned edge AI. What's most interesting is that, whether it's Open Claw or some new designs in the client computing space — such as tightly coupling memory with CPU and GPU to enhance bandwidth — architectural changes in client computers are rare; they've been fairly stable for a long time. Next, I'd like to discuss hyperscale data centers. We touched on this a bit earlier, and a lot of the dynamics are happening here — at least in terms of capital expenditure investment, which is enormous, and innovation in models and applications is also concentrated in these areas. However, I'm curious about autonomous vehicles, PCs, robotics, intelligent edge, and so on...

Sumit Sadana 13:00

Is memory demand changing these application scenarios? Yes, that's a great question. Of course, it all starts in data centers. But when we think about where AI is headed, it won't be confined to data centers alone. This wave of intelligence will push toward the edge, and ultimately, intelligence will be everywhere, permeating all devices. So it encompasses various consumer electronics, automobiles, and, of course, even the second phase of the industrial revolution — where all this intelligence will be distributed across enterprises worldwide.

Sumit Sadana 13:43

Your personal devices — whether it's the computer you use at work or home, your smartphone, or even new devices some companies are considering developing — these devices may have form factors completely different from smartphones or computers, with entirely different designs. Because when you consider building a natively AI-enabled device, you can think of implementations completely different from traditional computer and smartphone approaches. This brings us back to how to minimize power consumption, for example, for devices that need battery power. How do you maximize performance, and how do you run models that both deliver value to consumers and are small enough to run on-device without returning to the cloud — this is the new frontier of AI.

This exploration will continue. Models small enough to run on devices like computers or smartphones will get better and better over time. As this happens, the advantages of data privacy and confidentiality become prominent. Consumers value this highly, and companies that can reassure consumers that all their interactions with devices won't be uploaded to the cloud will spawn a whole new range of applications, delivering tremendous value.

Sumit Sadana 15:30

Looking at autonomous driving, it's developing rapidly now, thanks to advances in AI technology; and the next frontier is robotics. Think about humanoid robots — they will become one of the largest product markets in history. Of course, this will take some time, but we're getting closer to that singularity — where you'll be able to converse with a humanoid robot just like talking to another person, with cognitive abilities that are nearly indistinguishable. Over time, the physical capabilities of these robots will become increasingly sophisticated, and this will be revolutionary.

Initially, they'll be deployed in environments like factory automation, performing very specific tasks with lower degrees of freedom. Once they "graduate" from there, the most complex environment is the home, because home environments are highly unstructured. But I believe that entering the 2030s, robotics will become a huge growth driver. What's most exciting about robotics is that every device requires massive data storage — each humanoid robot will have hundreds of gigabytes of DRAM and terabytes of NAND flash. Therefore, they will significantly drive DRAM and NAND demand growth. I think many people often fail to fully appreciate this: robots must operate autonomously; they can't send data to servers every time. That's exactly right.

Patrick Moorhead 17:25

And as you're explaining this, I suddenly realize that Micron Technology is making the leap to gigawatt scale. Thinking about having to invent this technology, I find it somewhat incredible. But I think people should remember this when they think about Micron Technology.

As the AI era arrives, strategic thinking is becoming more prominent, whether it's leveraging frontier devices not yet invented, or gigawatt-scale data centers that will be released soon and can directly connect to AI and large language models.

This concept of co-design or co-invention — I've heard about it, and I've spoken with some of your partners who've discussed some ideas they're trying out. I also touched on architectural changes a bit earlier — these traditionally came through something like JEDEC standards. But now we're talking about truly deep co-design. Can you talk about how this relationship dynamic is changing with your partners?

Sumit Sadana 18:45

Certainly. Our partners also want to figure out how to gain advantages in their markets and outcompete their rivals. The memory-processor subsystem has become such a critical component of AI subsystems because the interaction between memory and processor essentially determines many key parameters of AI systems — it determines the system's power consumption, what types of models can run, model size, and runtime speed.

When you think about the various advances happening in large language models (LLMs) across different sizes and types, you'll find that various market segments must collaborate closely to achieve differentiation in hardware memory design cycles and processor design cycles. This isn't a plug-and-play, JEDEC-standard device that can be used after some post-hoc certification. While that's true for some markets, customers increasingly need to think about: how do we differentiate? I need to create special capabilities that off-the-shelf products can't provide. Therefore, we need to work with memory companies, engaging in long-term discussions spanning 5 to 7 years on R&D roadmaps. We're becoming embedded in many customers' R&D roadmaps, and they're proposing many very interesting ideas — some are feasible, while others will take longer to generate the inventions and innovations needed for breakthroughs.

Sumit Sadana 20:28

We're very excited about the diversified ideas we're seeing. As you said, these ideas span such a broad range of systems — some devices have extremely low power consumption because they're entirely battery-powered, while others are suited for gigawatt-scale data centers. We're already seeing innovation emerging across multiple market segments and among leading customers in each segment, and we'll continue to witness and actively work toward these achievements.

In some exciting projects, we're continuously expanding our capabilities to deliver the innovative design concepts our customers aspire to achieve. Customers can't engage in such deep collaboration with multiple companies, so typically only one or two companies can have in-depth discussions, while others may not catch up for years. The advantage of this model is that you can build extremely close relationships with one or two companies, and typically, when you go down this path, you become a single-source supplier, or have only one other supplier for a period of time. This model transforms the partnership into something more akin to an ASIC collaboration — customers have greater autonomy in design, and the relationship is long-term. This is because it's a special ASIC-style partnership, not the standard memory chip partnership of a few years ago. Another major change I'm seeing is that this model can be applied at scale.

Patrick Moorhead 22:27

Exactly. This means the volumes are so large that, frankly, the capital is sufficient for both sides to invest, and when you start such a project, differentiation is key. People are looking for ways — whether it's performance efficiency or total cost of ownership (TCO) — they all want to stand out.

I'd like to look ahead from an investment perspective. You just talked about capacity investment. Interestingly, some people expect memory fabs to be built as quickly as writing code. But listen, I've worked at a chip company with a fab, and even today, this remains an extremely difficult, capital-intensive project where you have to wait three or four years before seeing any favorable results. Can you talk about the investments you're making that truly solidify Micron Technology's position in next-generation technology? Whether it's AI, other technologies, or some variant of AI. These investments really do take a long time, and you've made that very clear.

Sumit Sadana 23:57

You make an excellent point. By the way, if anyone knows how to build a DRAM fab quickly, we'd very much like to know, because it would greatly simplify our work. Our customers certainly want products as soon as possible, and we're doing everything we can.

We've gone all out, accelerating all these investments. Just look at our CapEx plan: in fiscal 2025, our capital expenditure was slightly above $13 billion; in fiscal 2026, it will double; in fiscal 2027, we expect it to exceed $45 billion. These numbers keep climbing over time. We recently announced in the U.S. that we're increasing our investment from $200 billion to $250 billion, and accelerating the timeline, with $50.2 billion expected to be invested by the end of next year.

These investments cover all areas. We're investing in ID1 in Idaho, which will bring the first production lines online; mid-next year, the ID2 plant will follow, with first wafer production lines expected by the end of 2028. The fab we acquired in Taiwan will also begin wafer production in 2027, and we're further expanding that facility. We're also expanding in Japan and Singapore, so we have roughly 20 investment projects of varying scales underway globally, aimed at boosting both front-end and back-end capacity.

Sumit Sadana 26:06

But this takes a long time. Every new fab is like this. In New York State, we're planning a cluster of four fabs, with the first expected to begin production around 2030. We broke ground in January, and the concrete pouring milestone has been completed ahead of schedule.

Sumit Sadana 26:22

There are also many other projects around the world, but all of them require permits and the construction of entire supporting infrastructure. Construction talent is in extremely short supply — think about the construction projects happening simultaneously in the U.S. and many parts of the world.

Data centers are being built everywhere; they need electricity, so power plants are being built too; they also need semiconductors, so semiconductor fabs, both front-end and back-end, are also under construction. As a result, the shortage of skilled technicians is severe — there simply aren't enough to complete all these incredibly complex construction projects.

Sumit Sadana 27:15

So, we're investing in communities and talent development to ensure an adequate talent pipeline, which will not only help advance all our projects but ultimately benefit the entire ecosystem. We're making various investments, including talent development, community college programs, and partnerships with the towns and communities where we operate. This is a long-term endeavor for us.

We expect facility scale to continue expanding over time, because these fabs are built as clusters — you can't build just one fab. You need scale, and you need to push past the cost curve inflection point as quickly as possible, so construction must continue. This means expansion will persist for the next decade or more. These are all long-term projects for us. We believe truly effective new supply won't begin to be gradually released until 2028, and even then, it's only the early stages. The momentum of capacity growth won't truly strengthen until the following years. Therefore, it will take some time for the entire industry to find a new equilibrium, but exactly when that equilibrium will be reached is something we can't predict at this point.

Patrick Moorhead 28:54

You gave great examples and very informative investment details. However, there's one thing I think some people might misunderstand, and that's the complexity of memory. We all know logic chips are difficult to make. Let's talk about the complexity of memory — memory is currently one of the most complex semiconductor technologies.

Sumit Sadana 29:19

We often say logic circuits are at the technology frontier, but memory is definitely also cutting-edge technology. It uses EUV lithography machines, which are massive pieces of equipment. There are approximately 2,000 process steps in the fab. From the start of fab production to eventually delivering products to customers, the entire cycle takes about five months. That's amazing — many people simply can't imagine it. The fab production cycle alone is at least three and a half to nearly four months, and then assembly, packaging, and testing take another month, sometimes even six weeks.

Sumit Sadana 30:04

For more complex products, like HBM products, the complexity becomes even more evident. Many people call it a miracle, saying it's incredible that it works at all. Think about stacking 12 layers of DRAM chips, with a base chip at the bottom connected to the GPU. The entire system faces severe thermal challenges and power consumption issues — how do you effectively dissipate heat while running at such high bandwidth, moving data back and forth between processors and chips? The packaging technology alone is as complex as a cleanroom environment, completely different from the flip-chip packaging technology we've long used in logic. From any angle you look — whether it's the complexity of packaging technology or advances in front-end process technology like EUV lithography — every DRAM node is becoming increasingly difficult to scale.

Sumit Sadana 31:18

The same is true on the NAND side. We stack layer upon layer — 200 layers, 300 layers, 400 layers — manufacturing these devices this way, and the process complexity is astronomical. And these NAND products can still store data and function properly — a single SSD today can hold up to 245 TB. Fitting such massive capacity into such a small form factor is nothing short of a miracle that engineers and manufacturing teams create every day. The complexity is unbelievable, and in the coming years, we'll continue to scale this technology, achieving increasingly fine pitch, and truly making this technology work at scale — that will be yet another miracle.

Patrick Moorhead 32:23

This requires tremendous time, effort, and technological innovation. The level of complexity astounds me, and the fact that you can do all this across such a broad power range is truly remarkable. I don't know how many companies on Earth can do this.

It's interesting — people often say "I could never get enough of this." I think that's because they want more, more amazing things. Then people start talking about pricing and such. But I think this is an investment — it's entirely reasonable for Micron Technology and other memory industry companies to invest, because frankly, without further investment, the innovation cycle would stop. I look forward to seeing what Micron Technology achieves through its investments in the future, not just in fab construction but also in the technology itself. I believe robotics can deliver tenfold growth; it could become a massive growth market — an additional growth driver that's not even fully accounted for in our capacity models right now. Of course, we still need more evidence to determine when it will truly take off.

Thank you very much for this conversation. It's incredible that we had this same conversation a year ago, and look at where we are now — what a difference a year makes.