The Inflection Point for the Storage Industry: From Hardware Stacking to Algorithm-Driven Demand Reshaping

Since 2026, the memory sector has undergone a rapid shift from "extreme euphoria" to "rational revaluation." Among them, SanDisk (SNDK) is undoubtedly the core leader of this market trend. After being spun off from Western Digital, the company's independent operational efficiency has significantly improved. Coupled with the supply contraction caused by the industry's proactive production cuts over the past two years, NAND Flash prices have continued to rise, driving a double beat in both performance and valuation. The stock price's surge from below $50 to around $700 essentially reflects the market's concentrated pricing of its "cyclical peak-like profits." The market generally expects its gross margin for the third quarter of fiscal year 2026 to reach 65%-67%, a level indicating the industry is still in a phase of extreme supply-demand tightness. More crucially, the main production capacity for 2026 has already been locked in by long-term contracts, giving the company's short-term performance strong certainty. Therefore, even though recent technical shocks have caused a pullback, its fundamental logic has not been substantially damaged.

In contrast, Micron Technology's (MU) situation is more controversial. The company recently proactively optimized its balance sheet through a roughly $5.4 billion senior note repurchase, reducing interest expenses and enhancing cash flow resilience. This move essentially prepares for potential industry volatility in advance. However, from a market performance perspective, Micron is more directly impacted by the new technology shock. As its product structure leans more towards DRAM and HBM, which are the most memory-capacity-sensitive components in AI servers, the market's certainty about its future demand has wavered, leading to a noticeable short-term pullback in its stock price. This divergence essentially reflects the market's re-evaluation of a core question: Does the development of AI still necessarily rely on the continuous expansion of physical memory?

The turning point for this question came from Google's TurboQuant algorithm launched at the end of March. This technology compresses the KV Cache of large models to 3 bits, achieving approximately a 6x reduction in memory footprint and delivering significant inference acceleration on high-end GPUs like the NVIDIA H100. Essentially, this breakthrough is not just a simple performance optimization; it changes the functional relationship of "memory resources required per unit of computing power," thereby impacting the entire memory demand model.

In the short term, this technology first weakens the market's previous rigid logic of "hardware stacking." The mainstream narrative in the past held that improvements in AI computing power inevitably accompany linear growth in HBM and DRAM capacity. However, TurboQuant proves that algorithm optimization can, to some extent, replace hardware expansion. This directly leads the market to revise down its expectations for future shipment growth from memory manufacturers. Simultaneously, corporate IT procurement behavior may also change accordingly. When existing servers can support larger-scale models through software optimization alone, the urgency for new memory purchases will decrease, leading to order delays and even phased inventory destocking pressure. This change of "demand postponement" rather than "demand disappearance" is the core source of current market volatility.

However, from a medium-to-long-term perspective, this technological shock does not necessarily constitute a negative; instead, it may trigger a typical "Jevons paradox." When AI inference costs drop significantly, a large number of application scenarios previously unattainable due to cost constraints will be activated, such as on-device large models on smartphones, real-time inference on PCs, and AI processing for video streams. The common characteristic of these scenarios is higher data generation frequency and more decentralized usage, driving overall data volume to grow exponentially. At the same time, data in the AI era is no longer a one-time consumable but needs to be repeatedly called, trained, and stored, significantly extending the data lifecycle. This means that whether it's hot or cold data, the total storage demand will continue to rise.

Looking further ahead, the growth logic of the future storage industry will also shift from "scale expansion" to "structural upgrade." The market no longer merely needs more storage capacity but requires storage solutions with higher performance, lower latency, and smarter scheduling capabilities to adapt to complex AI algorithm environments. This change constitutes a structural benefit for the NAND-centric storage system, while forming phased pressure on DRAM and HBM, which heavily rely on the logic of capacity expansion.

Against this backdrop, the current memory sector is essentially in a phase of "high volatility coupled with logic reconstruction." In the short term, the core of market trading is no longer supply and demand itself, but the uncertainty about the future demand path. Therefore, at the operational level, a more reasonable strategy is to maintain a defensive position, focusing on observing the actual feedback from companies regarding shipment volumes, order cadence, and algorithm impacts in the upcoming earnings window. If leading companies can maintain their shipment guidance unchanged, it would mean the market may have overreacted, creating an opportunity for phased positioning.

From a medium-to-long-term perspective, the investment logic needs an appropriate shift: on one hand, temporarily favoring the NAND direction, which is less impacted by algorithm shocks; on the other hand, focusing on the demand explosion from Edge AI on the device side, including storage upgrades for smartphones, PCs, and various smart devices. Meanwhile, manufacturers with high-performance controllers and system-level optimization capabilities will occupy a more advantageous position in the new round of competition.

Overall, the core contradiction in the current market is not "whether demand disappears" but "how demand is redistributed." Before the technology is fully implemented, the physical constraints on hardware supply still exist, while the efficiency gains brought by algorithms are reshaping the demand curve. For investment, this stage should focus more on verification than prediction, preserving flexibility amidst uncertainty, and waiting for the deterministic opportunities that emerge once the industry logic becomes clear again.

$Sandisk(SNDK.US) $Micron Tech(MU.US)