Will HBM become the most profitable track in the AI era? Examining new opportunities in the storage industry from Micron to SK Hynix.

Over the past two years, AI investment has been almost entirely centered around GPUs, with NVIDIA being the most typical beneficiary. However, as the scale of large model parameters continues to expand, a more fundamental issue has become impossible to ignore: while computing power grows, data throughput and storage capacity are becoming new bottlenecks.

In the latest round of AI infrastructure expansion, the market has gradually come to recognize a shift: no matter how powerful the GPU, it needs a sufficiently fast "data supply system" to support operational efficiency—and this is the core reason HBM (High Bandwidth Memory) is being repriced.

Micron's latest earnings report shows that the company not only significantly exceeded market expectations but also signed long-term supply agreements worth approximately $22 billion. Management explicitly stated that AI memory demand will remain tight, possibly extending beyond 2027. Meanwhile, SK Hynix, leveraging its leading position in the HBM business, has surpassed Samsung Electronics to become one of the most valuable listed companies in South Korea.

When two memory leaders from different markets simultaneously signal strong growth, a critical question emerges: Is HBM becoming the most deterministic growth track in the AI era?

The Essence of HBM: The GPU's "High-Speed Memory System"

To understand the value of HBM, one must first understand the structure of AI computing.

During the operation of large models, the GPU handles computation, but what truly affects efficiency is whether data can be quickly and continuously fed into the computing units. As model parameters grow, traditional DRAM can no longer meet bandwidth requirements, leading to the emergence of HBM.

The AI chip system can be simply understood as:

• GPU = Computing engine
• HBM = High-speed cache and memory system
• Data center storage = External data warehouse

As models move from training to inference, the frequency of data calls increases further, and the importance of HBM actually grows. This is why the market increasingly refers to HBM as "key infrastructure for AI factories."

From a technical trend perspective, HBM significantly increases bandwidth density through a stacked architecture, allowing GPUs to access data more efficiently, thereby reducing latency and improving overall throughput. This structural optimization is not just a simple upgrade but a reconstruction of traditional memory architecture.

Micron and SK Hynix: Two Main Lines of the AI Memory Cycle

The global HBM market is currently highly concentrated, dominated mainly by SK Hynix, Samsung Electronics, and Micron. Among them, SK Hynix holds a leading share in the HBM market and is in an advantageous position for AI customer orders.

SK Hynix's advantage lies in its early bet on the HBM technology path, with its products deeply embedded in NVIDIA's AI chip ecosystem. Recent data shows that its HBM business has driven a significant increase in profits and pushed its market value to surpass Samsung in the South Korean market.

Micron, on the other hand, represents the cyclical changes in the U.S. market. The latest earnings report shows that not only did revenue and profit fully beat expectations, but it also sent a strong supply-demand signal: AI memory orders have entered a long-term lock-in state, with some customers even signing multi-year procurement agreements.

This indicates a key change is underway: the memory industry is shifting from "cyclical commodities" to "structural demand-driven." In the past, price fluctuations in the memory industry came from supply-demand cycles, but now an increasing amount of demand comes from the long-term expansion of AI infrastructure itself.

Is HBM Entering a "Super Cycle"?

Market divergence on HBM mainly focuses on two issues: Is demand sustainable? Will supply catch up quickly?

From the demand side, AI is moving from training to inference, and inference computing is characterized by continuous online operation and high-frequency access, which requires more stable and longer-term memory bandwidth. Meanwhile, agents, long-context models, and enterprise AI applications are rapidly expanding, further increasing the frequency of data calls.

From the supply side, HBM production processes are complex, yield improvements are slow, and it is highly dependent on advanced packaging and high-end manufacturing capabilities. This makes capacity expansion significantly slower than demand growth. Industry research also points out that HBM may still maintain a tight supply-demand structure for several years, with some manufacturers even locking in capacity for 2026 in advance.

However, a second-layer concern is emerging: once supply expansion accelerates, will prices decline? Historically, the memory industry has experienced similar cycles multiple times, so whether HBM can break free from cyclicality remains debatable.

Asset Logic Shift: From "GPU-Dominated" to "Memory Revaluation"

In the past, the market's AI investment logic was very clear:

Whoever controls computing power captures the highest premium.

But now the structure is changing:

• GPU → Still core, but growth is becoming concentrated
• HBM → Becoming a new source of growth elasticity
• Data centers → Gradually entering infrastructure pricing logic

This shift means that capital markets are beginning to dismantle the AI value chain rather than just pricing around a single leading company. Especially after Micron and SK Hynix simultaneously released strong growth signals, the market is gradually accepting a new narrative: the bottleneck in AI is shifting from "insufficient computing power" to "insufficient data flow capability."

Gate Stock Trading: Participate in the AI Memory Industry Chain 24/7

As AI memory becomes a global capital focus, investor demand for cross-market trading is also increasing. Core companies like Micron, NVIDIA, and SK Hynix are listed on different markets, making it difficult for a single trading session to cover the full market rhythm.

Against this backdrop, Gate Stock Trading has been upgraded to a 24/7 all-weather trading model, supporting U.S., Hong Kong, and South Korean stock trading, covering core targets in the AI memory industry chain.

Users can participate in the same account:

• U.S. stocks: AI infrastructure companies like Micron and NVIDIA
• South Korean stocks: Memory leaders like SK Hynix and Samsung Electronics
• Hong Kong stocks: AI servers, optical modules, and new economy enterprises

It also supports trading with USDT, reducing cross-market capital switching costs and making global asset allocation more flexible.

For a highly interconnected, event-driven market structure like the AI industry chain, 24/7 trading capability means faster responses to earnings reports, supply-demand changes, and industry chain information updates.

Conclusion: HBM Is Not an "End Point" but the Beginning of AI Infrastructure Revaluation

Whether HBM will become the most profitable track in the AI era remains an open question in the market. But what is certain is that it is no longer a "supporting technology"; it is becoming an indispensable part of AI infrastructure.

Micron's earnings and SK Hynix's market value changes essentially reflect the same trend: AI value is redistributing from the "application layer" to the "infrastructure layer."

In this round of structural change, the memory industry is likely still in the mid-to-early stage of the cycle, not at the end.

FAQs

What is the difference between HBM and traditional DRAM?

HBM is a high-performance memory that increases bandwidth density through a stacked architecture, mainly used for AI GPUs and high-performance computing, while DRAM is more for general-purpose computing.

Why does Micron's earnings report affect the entire AI sector?

Because Micron is one of the world's major memory suppliers, and its performance directly reflects the real demand for memory chips in AI data centers.

Why is SK Hynix leading in the HBM field?

Due to its early investment in HBM technology and deep integration with AI chip customer ecosystems, it holds a dominant position in the high-end memory market.

Can HBM price increases continue?

In the short term, it is still supported by tight supply-demand conditions, but whether it is sustainable in the long term depends on the pace of capacity expansion and the development of alternative technologies.

What scenarios is Gate stocks' 24/7 trading suitable for?

It is suitable for tracking AI earnings reports, chip market trends, and cross-market linkage opportunities, improving responsiveness to global market changes.