Micron CEO Interview: "Storage" is an overlooked bottleneck for AI; supply shortages may continue beyond 2026

Original Author: Li Jia

Original Source: Wall Street Insights

“The AI race is not only a race for computing power; it’s also a race for storage.” Micron Technology CEO Sanjay Mehrotra offered this assessment.

In the podcast episode 《A Bit Personal》 on June 5, Sanjay took part in a rare, in-depth interview recorded at his home. In addition to the usual industry insights, this more personal-style conversation also prompted him to talk proactively about his growth experiences, the influence of his family, and his career choices.

AI is still in the very earliest stage—this is one of Sanjay’s most core judgments.

In his view, as large models, Agent AI, and reasoning applications keep evolving, AI needs not only stronger computing power, but also stronger “memory capabilities.”

Longer context windows, larger model scale, and continually growing Token consumption are all driving storage demand to keep rising.

The essence of AI is data, and data cannot exist without storage; therefore, storage will become one of the most important infrastructures in the process of improving AI capabilities.

At the same time, the supply side has not been fully prepared. Sanjay pointed out that the storage industry is currently facing not a short-term mismatch between supply and demand, but a structural supply constraint. Advanced storage products require more wafers, and building new wafer fabs often takes three to four years; capacity ramp-up afterward is also a long process.

More importantly, as technology nodes advance, the incremental increase in storage capacity output per wafer is declining. He believes that the industry’s tight supply situation is likely to continue beyond 2026.

When explaining why storage technology has been underestimated for a long time, Sanjay was direct: “People often misunderstand memory and don’t know how difficult it is to manufacture memory.” From physics and chemistry to materials science—and ensuring that every one of the trillions of bits behaves correctly, even during large-scale mass production—the technical difficulty behind it is extremely high. He believes the AI race is also a storage race, and that point has been overlooked by the market for a long time.

From a longer-term perspective, Sanjay believes the underlying logic behind success for enterprises and individuals has not changed. Whether it’s pushing a $20 billion investment plan or leading Micron through cycles in the storage industry, the keywords he repeatedly emphasizes are resilience, discipline, and long-termism. Investment must be built on data and fundamentals. Leaders must not only see industry trends clearly, but also deeply understand technical details.

Just as he learned from his father, success requires both resilience to see things through and the ability to seize opportunities at critical moments.

Key takeaways from Micron CEO Sanjay Mehrotra’s interview are as follows:

Storage is the underlying bottleneck that AI has been underestimating; its manufacturing difficulty and strategic value far exceed what the market perceives. AI is expanding from a “computing power race” into a “storage race.” As model scale expands, context windows get longer, and Token consumption surges, AI depends not only on stronger compute but also on stronger “memory” capabilities. Without sufficient storage capacity and bandwidth, even the strongest computing power cannot be unleashed.

Structural constraints on the supply side determine that storage shortages are not a short-term fluctuation, but a long-term condition. Advanced storage products consume more wafers, while building new wafer fabs requires three to four years, and capacity ramp-up is also prolonged. Meanwhile, as technology nodes progress, the output growth margin per wafer declines. Under a supply-demand mismatch, tight supply is expected to persist at least beyond 2026.

People always underestimate the difficulty of manufacturing memory, but that is exactly the industry’s deepest moat. From physics and chemistry to materials science, from design to large-scale mass production, ensuring that not a single one of the trillions of bits is wrong involves extremely high engineering complexity. The manufacturing difficulty of storage chips is no less than— and in many aspects, even more difficult than—any other semiconductor field.

Success comes from resilience, discipline, and long-termism—not from short-term “hot trend” judgments. Whether it’s driving the $20 billion investment or navigating cyclical fluctuations across storage industry cycles, leaders need both to clearly understand industry trends and to go deep into technical details. Just as his father didn’t give up even after being rejected three times when applying for a visa, success requires resilience to carry on to the end, as well as the ability to seize opportunities at key moments.

Storage is becoming the backbone of AI

When discussing storage’s current historical position in the industry, Sanjay said plainly: “I’ve been in this industry for more than 45 years. This is the most exciting time I’ve experienced in the entire industry.”

He further elaborated on the strategic significance of storage for AI:

“Without semiconductors, there is no AI. And storage is the backbone of AI—the key foundation that supports AI’s continued evolution.”

In his view, storage’s role is no longer just a component inside devices; it directly carries “intelligence” itself: “Today, storage is not just about making devices work—it is supporting the ‘intelligence’ itself within AI, helping artificial intelligence become smarter.”

As model scale expands, inference demand explodes, and intelligent agent AI (Agent AI) rapidly takes off, Sanjay sees the logic behind the growth in storage demand very clearly: “As models get bigger, and as inference demand keeps growing, AI moves from training to inference and from data centers to the edge. The demand for storage will only keep getting higher—it needs larger capacity, higher performance, and lower power consumption.”

He also specifically mentioned storage’s dependence on token economics: “When you look at token economics, it also relies heavily on storage. As the number of tokens used increases, the context window gets longer, KV cache demand grows, and the model itself keeps getting bigger. AI needs not only computing capability, but also the ability to ‘remember.’”

Supply tightness will continue beyond 2026

On the supply-demand issue the market cares about most, Sanjay delivered a clear judgment: “The entire industry’s supply tightness will continue beyond 2026, and it will last for quite a long time as well.”

He explained the structural constraints on the supply side: “Building a wafer fab takes a long time. From groundbreaking to the first batch of wafers produced, it usually takes three to four years. After that, there is still capacity ramp-up—bringing output up step by step.”

More critically, the rising difficulty of technology nodes is compressing unit-wafer output efficiency: “The productivity improvement brought by each new generation of technology—meaning the incremental bit increase per wafer—is becoming smaller.”

Sanjay revealed that Micron had already anticipated this trend around 2021.

At the time, high-bandwidth memory (HBM) accounted for less than 1% of the entire storage industry, but they had already seen that future generations of HBM would consume a large number of silicon wafers and would significantly disrupt the supply landscape: “So, as early as 2021, we said that the industry needs to build new wafer fabs from scratch. But no one really predicted that AI would explode at such a fast pace.”

As for the concern in the market about “supply catching up and then becoming oversupplied again,” Sanjay did not directly rule it out, but he emphasized that AI is still at an early stage, and the long-term structural growth on the demand side is the basis for his confidence: “From the demand side, all of this is still at a very, very early stage. We believe AI still has a long way to go.”

The underlying logic of the $20 billion investment: discipline

Micron’s announcement to invest $20 billion in building a storage manufacturing ecosystem in the United States is one of the most attention-grabbing capital decisions in the semiconductor industry in recent years. Regarding the underlying logic behind this decision, Sanjay repeatedly emphasized the word “discipline.”

“Investments are never made blindly; they must be disciplined and based on data. You have to understand the technology, understand the applications, and understand where these applications are going. You also need to work closely with customers, understand where they will go in the future, and what role Micron will play in it.”

He further explained the discipline at the execution level: “Today, we are investing to build a batch of new wafer fabs from scratch. The first step is to build the facilities and infrastructure. Once these facilities are built, when we install equipment and form actual production capacity, we will still keep discipline—continuously assessing demand forecasts, assessing how much growth technological progress can bring, and assessing how product demand will change.”

When asked whether he has ever had self-doubt, Sanjay’s answer was straightforward:

“We don’t have self-doubt. We absolutely believe in the opportunity for storage; today, this is already very clear. Of course, in our business, it’s always important to maintain adaptability and agility.”