AI inference is pushing NAND deeper into the memory stack, and Sandisk is betting on it
AI spending in semiconductors has largely been framed around GPUs, high-bandwidth memory, networking and power. This report argues that inference is widening that trade. As AI systems move from training to serving billions of queries, enterprise agents, multimodal workloads and real-time applications, the need to keep large pools of data accessible at reasonable cost is becoming more central. That is giving NAND a larger role as a capacity layer alongside HBM and DRAM, rather than leaving it as a conventional commodity storage product. Sandisk has become one of the clearest public advocates of that view. At its 2026 investor day, the company said enterprise data center flash demand could reach 1.2 ZB by 2030 and projected mid-to-high double-digit annual revenue growth from FY2028 through FY2030, with Reuters cited in the source for related expectations. The company is also developing High Bandwidth Flash, or HBF, for AI inference, while continuing to push denser QLC NAND products. At the same time, Sandisk and other storage makers are leaning more heavily on multi-year customer agreements. Reuters, as cited in the source, reported that Sandisk had signed eight long-term agreements with six customers worth about $93.9 billion in total. The article’s main conclusion is narrower than a permanent re-rating story. AI is unlikely to erase NAND cyclicality. What may change is the amplitude: stronger bit demand, better demand visibility and tighter supply discipline could make future NAND cycles less violent than in the past.
