Global Technology: Memory – How to Play the New AI Bottleneck

Memory sits in a capacity-constrained cycle with unusually long order visibility driven by AI inference. For 2026, the risk is execution and transition, not demand. A steeper pricing climb and favourable conditions likely persist through 2027. Multiples have expanded, but we think stock calls can still work with much higher earnings upside from here. Inference becomes a memory challenge, not just compute. Memory access increasingly determines the performance of longer text, image/video and Agentic AI workflows, with far more robust memory requirements than prior AI models to support context, autonomy and continuous learning. These systems require superior server DRAM and enterprise NAND to function effectively. Memory cycle – a steeper pricing climb. Memory pricing power is shifting at lighting speed. We expect a steeper upcycle with rapid gains in DRAM, HBM, NAND, and legacy memory. Innovation and architectural redesign continue to improve memory efficiency, enabling AI systems delive

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• Mohit Agarwal