A surge in AI-driven storage demand has sent NAND prices skyrocketing by over 20 times, yet flash memory manufacturers and hard drive makers alike are exercising restraint regarding large-scale production expansion. This caution is underpinned by a supply discipline built on economic logic, technological barriers, and collective industry memory.

According to insights from a recent industry expert webinar hosted by Bernstein Research, the dramatic spike in NAND prices is reshaping storage architecture choices in data centers. Robert Soderbery, former Executive Vice President of Western Digital and former head of the flash business at SanDisk Corp., pointed out that the price gap per gigabyte between NAND and HDD has now widened to approximately 20-25 times. This far exceeds the critical threshold of 2-3 times required for hyper-scale cloud providers to justify a switch based on Total Cost of Ownership (TCO), making the replacement of HDDs with NAND economically unattractive.

This expanding price gap is prompting AI data center operators to re-evaluate their storage architectures, with some demand expected to shift back from NAND to HDDs. Concurrently, after years of loss-making cycles, NAND manufacturers are generally adopting a cautious stance toward capacity expansion, while the HDD oligopoly is proactively maintaining supply discipline. For investors, this suggests that supply constraints for both types of storage assets will be difficult to break in the short term, and the strong pricing environment is likely to persist.

**AI Reshapes Storage Architecture; NAND Demand Concentrated on Latest Nodes** Data center storage architecture has evolved from a traditional two-tier model to an AI-driven three-tier structure. Robert Soderbery explained that traditional data centers used small-capacity enterprise SSDs for computational tasks and nearline HDDs for capacity storage, with HDDs accounting for about 80% to 85% of the total bit volume.

The rise of AI data centers has disrupted this pattern. The data preprocessing stage in AI workloads—including pre-computation tasks like vectorization and embedding—places extremely high demands on storage performance, which traditional nearline HDDs cannot meet. This has spawned explosive demand for high-capacity enterprise SSDs. Capacity specifications have jumped from previous 2-8TB to 32TB, 64TB, and even 256TB. At one point, NAND accounted for 60% to 70% of new AI storage deployments.

However, NAND demand is not evenly distributed across all production capacity. Soderbery noted that the high-performance, high-capacity NAND required for AI workloads must utilize the latest process nodes (currently the 7th to 9th generations). Due to sustained pressure and insufficient capital expenditure by NAND manufacturers over the past several years, capacity for these latest nodes constitutes only about 30% to 35% of the industry's total output. Demand being highly concentrated on this limited capacity is the core reason for the sharp recent price surge in NAND.

**Insufficient Returns Deter NAND Manufacturers from Rushing to Expand** Why aren't NAND manufacturers accelerating expansion in the face of soaring prices? The answer lies in Return on Investment (ROI) remaining unattractive.

Robert Soderbery explained that the NAND industry experienced sustained losses over the past five to nine years, leaving manufacturers with deep cyclical scars and a lack of confidence in demand sustainability. The cycle from capital expenditure to operational capacity typically takes about 15 months—roughly one year for equipment delivery and several more months for debugging and commissioning. This means prices must rise and be sustained for several quarters before manufacturers gain sufficient confidence to initiate expansion decisions, followed by another five quarters before new capacity comes online.

More critically, even before this recent price surge, NAND manufacturers' gross margins were only around 20%, a return level insufficient to justify large-scale capital investment. Soderbery pointed out that the current focus for NAND manufacturers is on migrating existing capacity from older-generation nodes to more advanced nodes to better serve the high-performance demands of the AI market, rather than simply increasing total wafer capacity. The industry typically targets an annual bit growth rate of 20% to 30%, with a significant portion of this growth coming from density improvements enabled by QLC technology, not from increased actual wafer input.

**HDD Oligopoly Proactively Maintains Supply Discipline** In contrast to NAND, supply constraints in the HDD market stem more from proactive choice than technical bottlenecks.

The HDD market is effectively an oligopoly with Western Digital (WDC) and Seagate Technology PLC (STX) as the main suppliers, while Toshiba's competitiveness is relatively limited. Robert Soderbery stated plainly, "The first rule of an oligopoly is not to add capacity until you achieve your target margin."

HDD manufacturers have strong incentives to maintain this discipline. The industry suffered from prolonged overcapacity over the past decade, making vendors highly vigilant against repeating past mistakes. Furthermore, the inherent complexity of HDD technology—encompassing HAMR (Heat-Assisted Magnetic Recording), read/write heads, mechanical structures, and material science—introduces significant friction costs for expansion,客观上 supporting supply constraints. Soderbery revealed that the number of PhD researchers on the HDD side at Western Digital even exceeds that on the NAND side, underscoring its technology-intensive nature.

This suggests that HDD manufacturers should be viewed as oligopolies capable of sustaining reasonable returns over the long term, rather than purely cyclical plays.

**Long-Term Threat of NAND Replacing HDD is Overestimated** While markets have long feared NAND gradually eroding HDD market share, this logic faces significant economic hurdles.

According to Soderbery's calculations, for NAND to pose a substantive TCO-based threat to HDD, the price gap would need to narrow to within 2-3 times. Achieving this cost level for NAND would require the industry to complete multiple process node iterations, with each node upgrade requiring roughly $50 billion in capital expenditure. Effective replacement of HDD might necessitate cumulative investments of around $150 billion—not including the capital required to serve the AI market itself.

Simultaneously, NAND's cost reduction curve is flattening. The transition from 2D planar scaling to 3D stacking means marginal cost benefits from adding layers are diminishing, slowing the pace of cost decline. In contrast, HAMR technology for HDDs is accelerating cost reductions for HDDs, causing the two cost curves to converge. Robert Soderbery believes the point at which NAND poses a substantive replacement threat to HDD is "still quite far away."

**Shift to Long-Term Agreements Improves Industry Planning Visibility** Another significant change in the storage industry is the transformation of Long-Term Agreements (LTAs) from historically "one-sided contracts" towards truly binding two-way commitments.

Robert Soderbery explained that past LTAs essentially only bound the supplier—who had to reserve capacity—while the customer could refuse to take delivery if needed, yet the supplier faced penalties for failure to deliver. This asymmetric structure made effective capital planning difficult for manufacturers.

SanDisk Corp. recently disclosed signing five new long-term agreements involving approximately $42 billion in procurement obligations, with $11 billion financially guaranteed. Soderbery views this development positively, believing such two-way binding contracts can significantly boost manufacturers' investment confidence and help the industry overcome supply shortages. However, he also noted that in a significant market downturn, customers would still have an incentive to renegotiate by paying penalties, meaning the actual binding power of contracts depends on market conditions. Using the SanDisk data as an example, the $11 billion guarantee represents about 25% of the total procurement obligation, implying that even if prices fell by 50%, the effective price decline would be cushioned by approximately 25%.

**HBF: NAND's New Frontier in AI Compute** Beyond storage architecture evolution, High Bandwidth Flash (HBF) technology represents a potential opportunity for NAND to penetrate the AI compute layer.

HBF adapts the packaging technology of High Bandwidth Memory (HBM), utilizing a stacked structure and wide bus interface designed for co-deployment with GPUs. Its core advantage lies in leveraging the specific characteristics of AI inference workloads—during model inference, large coefficients are streamed into the GPU rather than being frequently read and written. While NAND has slower dynamic read/write speeds and higher latency, once streaming begins, its throughput is considerable, fitting this specific scenario well. Combining HBM-like bandwidth with NAND's higher storage density and differentiated cost structure gives HBF potential competitiveness in AI inference scenarios.

Robert Soderbery considers HBF "very promising" but still in its early stages; whether it can meet commercial requirements for power consumption, performance, and reliability remains to be validated. He suggested that regardless of whether HBF can directly replace HBM, the innovation potential for NAND within the AI pipeline remains vast, and investors might view it as a potential call option on future upside.