AI Drives Semiconductor 'Super Cycle': Storage Chips Move Center Stage

Zhang Haixiao, an equity fund manager at Yongyingfund, stated in a recent appearance on The Paper’s “Chief Connection” program that the current semiconductor ‘super cycle’ is fundamentally different from previous cycles due to a shift in demand sources from AI production rather than inventory-driven factors. According to Zhang, downstream demand has not decreased despite price increases; instead, capital expenditures continue rising, and even token price increases have failed to suppress demand, creating a smooth transmission chain throughout the industry.

Downstream Demand Explosion Reshaping Storage Supply-Demand Dynamics

Zhang traced a clear demand evolution trajectory beginning with ChatGPT’s release in late 2022. Initially, hardware demand focused on training endpoints pursuing extreme performance, with high-bandwidth memory (HBM) achieving large-scale application for the first time. As models introduced new paradigms like chain-of-thought reasoning and extended context to improve results, demand shifted toward inference endpoints, where token consumption experienced double-digit percentage growth year-over-year.

By the second half of 2025, AI Agents capable of autonomous tool invocation and complex task completion moved from concept to reality, further increasing storage dependencies. Based on this series of demand explosions, Zhang concluded: “The starting point of this round of storage chip price increases was Q3 2025.” He attributed the reversal of supply-demand gaps entirely to explosive growth on the downstream demand side, a judgment validated by recent corporate financial disclosures showing strong profit releases in AI hardware, particularly among storage chip price-increase products.

Fundamental Distinction from Previous Semiconductor Cycles

Zhang emphasized that the current “super cycle” differs fundamentally from the 2019-2021 cycle, with the most critical structural difference being a fundamental shift in demand characteristics. The 2019-2021 period was a typical inventory-driven cycle, where terminal demand growth primarily came from work-from-home hardware needs and supply chain disruptions triggering stockpiling behavior. Consequently, after mid-2022, the industry rapidly fell into a “destocking and rapid chip price decline” predicament.

“This round of demand originates from AI production endpoints,” Zhang stated. “Downstream has not reduced purchases due to price increases; instead, capital expenditures continue rising. Even token price increases have failed to suppress demand, and the entire transmission chain is extremely smooth.” He further noted that global AI capital expenditure is expected to reach $700 billion in 2026, with this figure still being revised upward.

Simultaneously, restraint on the supply side has significantly amplified the supply-demand gap. Major storage suppliers plan approximately 50% capital expenditure increases in 2026, yet a substantial time lag exists between capital investment and effective supply formation. Moreover, new funding primarily targets AI-specific high-performance storage, while general storage capacity additions still await future release.

Industry Chain Benefit Sequence: Two Distinct Pathways

Zhang divided industry chain benefits into two pathways: first, the sales chain of original equipment makers, module manufacturers, and design enterprises, which experience prosperity first—already validated by recent financial reports; second, the expansion chain of equipment, materials, and facility construction, where prosperity transmission occurs with relative lag in a gradual progression process.

Notably, upstream original equipment makers’ cautious stance on supply is reflected in strategic adjustments. Zhang revealed that “upstream original equipment makers, as capital-intensive enterprises concerned about future demand falling short of expectations and causing losses, are actively signing long-term supply agreements (LTAs) with customers to lock in future demand.”

Storage Chips Advance to Center Stage

Technological architecture evolution is reshaping the hierarchy of hardware value. Zhang explained this shift using the “barrel principle”: under Von Neumann architecture, computing, storage, and input-output communication constitute three core functions, with one element becoming the performance bottleneck at different stages. In 2023, computing power was the obvious constraint, but as inference endpoint demand exploded, the bottleneck rapidly shifted to the storage end.

Hardware iteration cycles are accelerating. Before 2022, HBM typically updated every three to four years; since 2023, this cycle has compressed to “two-year upgrades.” This acceleration trend also appears in computing chips and communication chips. Simultaneously, computing architecture evolution is driving CPU back to center stage. Zhang cited industry dynamics indicating that future GPU-to-CPU ratios may shift from current 8:1 to 4:1 toward 2:1 or even 1:1, directly driving increased memory demand and imposing higher transmission speed requirements on memory and interface chips—upgrades that will continue pushing up product unit prices.

Domestic Semiconductor Progress and Risk Monitoring

Beyond global cycle synchronization, domestic semiconductor advancement represents another key investment focus. Zhang noted that domestic storage chip upstream original equipment makers have made remarkable progress, with market share growing from zero to approximately 10%, becoming an undeniable industry force. “Improved original equipment maker capabilities will drive development in mid-downstream design and upstream equipment and materials sectors. Every previous transfer in global storage industry history brought comprehensive supply chain upgrades in local regions, and this time will be no exception. However, this is destined to be a long-term process, not something accomplished overnight.”

Regarding potential risks, Zhang identified core variables on both supply and demand sides. On the supply side, tracking production capacity release timing is critical: manufacturers first maximize existing capacity, then add equipment within current facilities (typically requiring 6-12 months for actual output), followed by new facility construction (usually 2-3 years for effective capacity). He cautioned: “Major capacity release is expected to concentrate over the next two years, requiring continuous monitoring of whether release pace exceeds expectations.”

On the demand side, attention must focus on AI capital expenditure pace and technological developments. Zhang concluded: “AI capital expenditure is the most direct tracking indicator, while new technologies open entirely new market spaces, affecting capital expenditure predictions by model manufacturers and internet giants—all requiring sustained observation.”