In recent years, as the AI industry has expanded rapidly, one company has been drawing attention at an accelerating pace. That company is Micron Technology. To be honest, for a long time it was largely overlooked behind Nvidia and TSMC, but things have started to change significantly.

Looking back a bit in history, in 2012, Elpida—once a source of pride for Japan’s semiconductor industry—went bankrupt. Although it had the technological backing of three major players, NEC, Hitachi, and Mitsubishi, it disappeared completely from the DRAM market in just a little over 20 years. Micron acquired the company at that time. After that, Samsung and SK Hynix from South Korea swept the market and pushed out competing firms one after another. However, Micron survived. Today, it has become the only company in the United States capable of mass-producing advanced memory chips.

Why, now, is Micron’s stock price drawing so much attention? Because it holds the answers to the structural problems in AI computing. GPU computing power has increased dramatically, but there is actually a major bottleneck. The problem is that the time spent waiting for data has become longer than the time spent on the calculations themselves. This “memory wall” cannot be solved with software; it can only be addressed with hardware. And that is exactly what Micron has been working on for 40 years.

In the AI inference stage, the computational workload is extremely low, and the system is fully constrained by memory bandwidth. Even just the KV cache of large language models can require tens of gigabytes of memory, and even with two A100 GPUs, the number of user requests that can be processed simultaneously is limited to only around a dozen. The energy consumed to read data from off-chip memory can be as much as 100 to 200 times the energy used for the actual computation. In other words, a contradiction has emerged: most of the power in data centers is being spent on data transfer rather than on real calculations.

Nvidia manufactures the H100 and B200 GPUs at TSMC, but the high-bandwidth memory (HBM) integrated with these GPUs is manufactured by Micron. GPUs are the “brains,” while HBM is an ultra-fast data channel tightly connected to that brain. Both are indispensable components, and only when Nvidia’s architecture and Micron’s memory technology come together can a true AI accelerator be realized.

Micron’s competitive strategy is completely different from Nvidia’s. Nvidia competes through architecture and the ecosystem, while Micron relies on ongoing improvements in process technology and stacked packaging technology. The evolution to the 1-gamma process has reduced the cost per bit and enabled more chips to be extracted from the same area. As a result, gross profit margins improve.

Micron’s stock price is currently around $600, and its price-to-earnings ratio is 21.44x. That level reflects the market valuing a business model that differs from traditional memory companies. Previously, it manufactured standard DDR memory, with production volumes and selling prices entirely driven by market conditions. Now, however, HBM uses a made-to-order model: by signing irrevocable long-term supply contracts with customers such as Nvidia before production begins, it fixes both price and quantity. It has been reported that the production capacity for HBM in 2026 is already sold out.

Under this model, Micron’s future revenue is no longer determined by forecasts—it is set by contracts. In other words, it is evolving from a traditional cyclical memory stock into an infrastructure provider. Wall Street’s valuation has also changed. Because it has stable contracts, the company’s valuation multiples naturally rise.

In the global DRAM market, the three companies Samsung, SK Hynix, and Micron account for about 95% of the share, and each has different strengths. In terms of process technology advancement, Micron is the fastest; it is often the first to announce the start of mass production of next-generation high-density DRAM. Meanwhile, in the HBM market, SK Hynix is dominant, with a share of more than 50%. However, Micron’s HBM has demonstrated advantages in energy efficiency, with published tests showing it can reduce power consumption by 20% to 30%. In data centers where tens of thousands of GPUs are deployed, this difference translates directly into electricity bills and cooling costs.

Micron was able to enter Nvidia’s supply chain as a latecomer because of this differentiation strategy. Its production capacity is the smallest in the industry, yet by pursuing a technology premium strategy, it is developing the market without relying on price competition.

Even more noteworthy is Micron’s push into CXL (Compute Express Link). HBM solves the bandwidth problem within a single GPU, but when AI clusters expand to hundreds or thousands of GPUs, new challenges arise. Because memory is physically fixed to servers and cannot be shared across multiple machines, some hyperscale data centers experience memory idle rates reaching 20% to 30%. CXL resolves this issue by grouping multiple memory modules into independent memory pools and enabling dynamic mapping to the required compute nodes.

Micron has announced CXL Type 3 memory expansion modules based on the DDR5 process. This is a different level of product from HBM, but using both together allows frequently accessed hot data to be kept in local HBM, while cold data can be offloaded to the CXL memory pool. This makes it possible to achieve extremely long context windows, such as at the million-token level.

The CXL market is still in an early stage, and customer lock-in has not yet been established. As a storage-only manufacturer, Micron has a major opportunity as a new entrant into this market without legacy baggage.

Wall Street’s major investment banks’ 12-month target stock prices are concentrated in the range of $400 to $675, with an average around $500. Based on the current stock price level, further upside is considered possible. However, if the investment pace in AI infrastructure slows or if Samsung re-enters Nvidia’s supply chain with HBM4, then supply-demand dynamics would likely be reassessed.

Micron’s long-term competitiveness will increasingly depend not on being ahead in a single technology area, but on making fewer mistakes than competitors across multiple aspects—improving yields, packaging processes, system integration, and more. A moat is not just a single technology; it is a comprehensive capability to manage all physical constraints at the same time. Accumulating this capability requires decades of manufacturing experience.

Micron’s stock price movement is not just an indicator of corporate performance—it is also a signal of how the AI-era infrastructure is evolving. Recently, more Micron-related stock information has appeared on Gate, and understanding the technical background will enable deeper investment judgment.