NAND Shortage Could Peak in 2027
💡NAND may become AI infrastructure’s next major bottleneck after GPUs.
⚡ 30-Second TL;DR
What Changed
Phison CEO KS Pua expects the worst NAND shortage to occur in 2027.
Why It Matters
AI developers and infrastructure operators may face higher storage costs or longer procurement cycles as model training and inference expand. Companies planning large AI clusters should treat NAND availability as a capacity-planning risk alongside GPUs and networking equipment.
What To Do Next
Audit your next 12–24 months of SSD and data-storage requirements, then secure capacity commitments with suppliers before AI infrastructure demand accelerates.
Key Points
- •Phison CEO KS Pua expects the worst NAND shortage to occur in 2027.
- •AI workloads are increasing the strategic importance of NAND flash storage.
- •Phison supplies controller chips for SSDs, USB drives, and memory cards.
- •A prolonged shortage could affect storage availability and hardware costs for AI systems.
🧠 Deep Insight
Background and context from public sources — not the original article. 9 sources cited.
🔑 Enhanced Key Takeaways
- •The NAND market is currently experiencing a structural supply deficit of 4–6% throughout 2026 due to AI-driven demand.
- •A single NVL72 AI server rack requires approximately 1,152TB of NAND capacity, illustrating the massive storage density requirements of modern AI infrastructure.
- •Major manufacturers have prioritized high-margin HBM and enterprise SSD production, leading to a strategic reallocation of wafer capacity away from consumer-grade NAND.
- •NAND contract prices surged by over 55% in Q1 2026, creating a feedback loop that has suppressed demand in the smartphone and notebook PC sectors.
- •China's YMTC is projected to increase its global NAND output share to nearly 19%, acting as a significant factor in the eventual market stabilization by late 2027.
🛠️ Technical Deep Dive
- NAND flash architecture is shifting toward high-capacity enterprise SSDs (eSSDs) to meet the density requirements of AI server racks like the NVL72.
- Production focus has moved toward advanced process nodes to maximize wafer output efficiency.
- Controller chips are being optimized for high-throughput, low-latency data access required by AI training and inference workloads.
🔮 Future ImplicationsAI analysis grounded in cited sources
⏳ Timeline
📎 Sources (9)
Factual claims are grounded in the sources below. Forward-looking analysis is AI-generated interpretation.
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Original source: Bloomberg Technology ↗
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