Gate Research said in its latest report that the storage industry, long treated as a strongly cyclical sector, is being redefined in the AI era. In the past, storage companies’ earnings were largely tied to supply-demand swings and price elasticity. Today, storage is moving from a supporting component inside general-purpose hardware to a critical resource within AI computing infrastructure. Large model training and inference require not only stronger GPUs and better interconnects, but also storage systems with higher bandwidth, larger capacity and lower latency.
The report notes that the rising importance of storage can be seen across several layers of the AI stack. On the GPU side, high-bandwidth memory, or HBM, has become central to performance delivery. On the server side, DDR5 and enterprise SSDs are also gaining importance as data center workloads intensify. For cloud providers and data center customers, storage is no longer just a cost item. It directly affects model training efficiency, inference throughput and the overall cost of deployment.
Micron as a representative case for AI storage
Micron Technology, Inc. (NASDAQ: MU) was founded in 1978 and is headquartered in Boise, Idaho. It is a major global supplier of semiconductor memory and storage solutions. The company designs, manufactures and sells DRAM, NAND Flash, NOR Flash, HBM, SSDs and storage products for data centers, mobile devices, automotive applications, industrial systems and consumer electronics. Gate Research uses Micron as a research case not to focus the article on a single stock, but because Micron’s product portfolio, customer base, earnings sensitivity and market pricing provide a clear example of how the AI storage segment is evolving.
Within the global memory chip industry, Micron stands alongside Samsung Electronics and SK hynix as one of the main DRAM suppliers, and it is also an important participant in the global NAND market. As demand for large model training and inference continues to grow, AI servers are driving faster demand for HBM, high-capacity DDR5 and enterprise SSDs. Storage chips are no longer merely supporting components inside computing devices. They are gradually becoming one of the key bottlenecks in AI computing infrastructure. In GPU clusters, the bandwidth, capacity and power efficiency of HBM directly influence how much performance an AI chip can deliver.
MU rose more than 800% in one year, with market value near $1.17 trillion
According to Gate market data, as of June 3, 2026, Micron shares were quoted at $1,056. Based on approximately 1.1 billion diluted shares outstanding, the company’s market capitalization was about $1.17 trillion. Over the previous year, MU showed a clear pattern of volatile upward movement followed by an accelerated breakout. The share price started near $110, climbed steadily above $400 as expectations for AI storage demand strengthened, then entered another major upward leg after a period of adjustment, supported by HBM and AI data center demand.
From May to June, Micron’s share price rose sharply in succession and reached a high of $1,076. Compared with its one-year low, the stock gained more than eightfold. From around $110 to near $1,056, the cumulative gain exceeded 800%, while the company’s market capitalization crossed the $1 trillion level. Gate Research said this reflected the continued repricing of AI storage demand and the outlook for Micron’s HBM business.
Micron currently serves four main application areas. The first is data centers and cloud computing, including AI servers, enterprise servers and networking equipment. The second is mobile devices, including smartphones and tablets. The third is storage, including enterprise and client SSDs. The fourth is embedded business, including automotive, industrial and consumer electronics applications. As capital expenditure for AI data centers continues to expand, data center-related storage demand is becoming Micron’s fastest-growing and most profitable business direction.
FY2026 Q2 results show a sharp margin expansion
Gate Research argues that Micron’s trillion-dollar market value breakthrough did not come solely from a rebound in the traditional memory cycle. Instead, it came from a repricing of the company’s strategic value within the AI infrastructure supply chain. Micron’s FY2026 Q2 results showed record revenue, gross margin, EPS and free cash flow, validating an earnings inflection driven by AI demand, tight industry supply and upgrades in high-end storage products.
In its FY2026 Q2 earnings announcement, Micron said the record quarterly performance reflected “the strategic value of memory in the AI era.” CEO Sanjay Mehrotra said memory has become a strategic asset for customers in the AI era. Gate Research reads this as a sign that Micron’s management has elevated the company’s position from a traditional memory supplier to a core participant in AI computing infrastructure.
Micron’s FY2026 Q2 revenue reached $23.86 billion, up sharply from $13.64 billion in the previous quarter and significantly above $8.05 billion in the same period last year. Non-GAAP net income reached $14.02 billion, Non-GAAP EPS reached $12.20, operating cash flow reached $11.90 billion, and adjusted free cash flow reached $6.90 billion.
The quality of earnings also improved. FY2026 Q2 Non-GAAP gross margin reached 74.9%, up from 56.8% in the previous quarter and 37.9% in the same period last year. Non-GAAP operating margin reached 69.0%, compared with 47.0% in the previous quarter and 24.9% a year earlier. For a memory company, a gross margin increase from the 30%-40% range to above 70% indicates a major change in industry supply-demand conditions and in the company’s product mix.
By business unit, Micron’s FY2026 Q2 growth was highly concentrated in AI and data center-related areas. Cloud Memory Business Unit revenue reached $7.749 billion, with a 74% gross margin and a 66% operating margin. Core Data Center Business Unit revenue reached $5.687 billion, with a 74% gross margin and a 67% operating margin. Combined revenue from these two businesses exceeded $13.4 billion, making them the company’s most important growth engines.
HBM, high-end DRAM and long-term agreements reshape the cycle
The report identifies HBM and high-end DRAM as the product categories where Micron has benefited most directly. HBM is a key memory product for AI GPUs and accelerators. It offers high bandwidth, high capacity and high energy efficiency, while its price per gigabyte and gross margin are higher than those of ordinary DRAM. UBS expects Micron’s HBM ASP to rise by about 50% year over year in 2027 and to continue driving HBM revenue growth.
As AI chip platforms iterate, requirements for HBM capacity and bandwidth are increasing. Gate Research said Micron can raise the revenue contribution of high-end products through HBM3E, later HBM products and advanced packaging support. The significance of this product mix upgrade is that Micron is no longer only following movements in the average selling price of industry DRAM. With a higher HBM share, the company gains stronger pricing power, while its overall gross margin and earnings stability improve.
Micron’s strong FY2026 Q2 performance was also supported by tight industry supply. The report said the quarter was driven by a strong demand environment, constrained industry supply and the company’s execution. Some institutions expect DRAM shortages to continue until at least the second quarter of 2028, while NAND shortages are expected to continue until the fourth quarter of 2027. Under constrained supply conditions, DRAM and NAND prices receive continued support, giving Micron a stronger foundation for maintaining high revenue and margins.
This cycle differs from the traditional pattern in which memory suppliers quickly expand capacity after prices rise, eventually causing oversupply and price declines. Demand for high-end memory in AI servers is growing rapidly, while HBM capacity expansion is constrained by manufacturing process requirements, yield, advanced packaging and customer qualification cycles. As a result, supply is not easily released fast enough to catch up with demand.
LTA stands for Long-Term Agreement. In the semiconductor memory industry, it usually refers to a supply arrangement agreed in advance between a supplier and a core customer for a future period. It can include purchase volumes, delivery schedules, product specifications and, in some cases, a pricing framework. Historically, memory purchase agreements tended to lock volume but not price. Customers committed to certain purchase quantities, while suppliers gained some demand visibility, but prices still moved quickly with DRAM and NAND market conditions.
Gate Research identifies LTA as another key logic behind Micron’s valuation re-rating. Newer LTAs not only lock purchase volumes, but also partially lock prices, with terms that can reach three to five years. For Micron, LTAs improve revenue visibility, reduce price volatility and strengthen cross-cycle profitability. For cloud providers and AI customers, LTAs help secure future memory supply and partially lock costs, reducing exposure to higher prices during supply shortages. If LTAs are implemented at scale, Micron’s business model shifts from that of a traditional cyclical commodity company toward a semiconductor supplier with long-term orders, steadier cash flow and higher customer stickiness.
Gate stock service and risk disclosure
Micron’s FY2026 Q2 adjusted free cash flow reached $6.9 billion, and the company’s board approved a 30% increase in its quarterly dividend. Gate Research said the improvement showed not only much stronger profitability, but also better cash flow quality. In capital markets, stable and high free cash flow usually supports a higher valuation. Micron’s valuation was previously held back mainly because the market questioned the sustainability of its earnings. If AI demand, LTAs and the HBM-led product mix upgrade reduce cyclicality, Micron has a basis to move from a traditional memory-cycle valuation framework closer to that of a core AI semiconductor asset.
The report also discusses Gate’s stock service. Unlike stock tokenization or RWA mapping models, Gate stock service emphasizes market access and a compliant trading framework. By connecting with regulated brokers, Gate provides stock and ETF trading services. These products are not on-chain mapped assets and are not tokenized stock derivatives. Users can buy, hold and sell stock assets through a Gate account, while positions, profit and loss, fund flows and corporate action information can be viewed and managed within the account.
In terms of coverage, Gate stock service currently supports more than 10,000 stocks and ETF assets. It covers major securities markets and liquidity networks including NYSE, Nasdaq, NYSE Arca, NYSE American and BATS. Gate stock service currently supports intraday trading, and the report says it will gradually expand toward 24/7 trading to provide global users with a more flexible entry point for U.S. stock allocation.
Within Gate TradFi, stock-related trading tools can be divided into three categories, using MU trading products as an example: stock spot trading, perpetual contracts and CFDs. Gate stock spot trading is separate from the traditional CFD framework. Stock trading does not involve funding rates found in perpetual contracts, and it differs from CFD products that can involve swap fees, overnight fees or other holding costs. Therefore, stock spot trading is more suitable for users seeking long-term U.S. equity exposure, while perpetual contracts and CFDs are more oriented toward directional trading or risk management around short- and medium-term price volatility.
By using a unified crypto asset account system, Gate connects digital asset trading with stock investment scenarios. After completing KYC and meeting regional access requirements, users can enter the stock section through the TradFi area of the Gate App, view market data, and participate in trading after transferring stablecoins through the trading page or asset page. The report says this extends the use case of USDT from crypto trading to global stock asset allocation.
Gate Research concludes that the storage sector can no longer be understood solely through the old framework of a price-cycle stock. A more suitable approach is to treat it as a semiconductor subsector where cyclical features remain, while the weight of structural upgrading keeps increasing. Micron’s case provides a recognizable sample for observing this shift. The report also states that LTAs can help stabilize part of revenue, but the locked-price ratio, execution period and customer commitments remain uncertain and cannot fully eliminate industry volatility. Micron’s share price and market value have already risen sharply, and expectations for an AI storage supercycle and valuation re-rating are already high. If earnings delivery falls short of expectations, share price volatility can intensify. Gate Research adds that cryptocurrency market investment involves high risk, and users should conduct independent research and fully understand the nature of the assets and products they purchase before making any investment decision. Gate is not responsible for any loss or damage resulting from such investment decisions.