Morgan Stanley: AI Won’t Wait for Memory Expansion – Downgrading, Disaggregation, and CXL Are Reshaping the Next Semiconductor Opportunity Morgan Stanley believes that memory shortages will persist throughout the AI cycle. However, the AI industry won’t wait for new DRAM capacity to be built slowly. Instead, it will bypass bottlenecks through three paths: downgrading specs, workload disaggregation, and CXL memory pooling. This could potentially extend the entire memory shortage cycle even further. The most straightforward approach is downgrading specs. NVIDIA has already started providing Rubin with lower memory configurations. For example, LPDDR5 has been reduced from 54TB per rack to 28TB, and HBM has been cut from 288GB per GPU to 192GB. Rubin Ultra might also see its original 1TB HBM4e reduced to 512GB or even lower. While this may seem like a short-term suppression of single-machine memory usage, it essentially shifts the bottleneck from HBM/DRAM to NAND and larger-scale expansion domains. However, as long-context, agents, and coding assistants become more widespread, the room for downgrading will also shrink. The second path is workload disaggregation, separating prefill and decode tasks. Prefill can be handled by GPUs or other compute-intensive architectures, while decode is assigned to systems better suited for memory bandwidth and capacity. This heterogeneous architecture improves the efficiency of scarce memory usage, benefiting players like CBRS and NVIDIA after its integration of Groq. The third path is CXL memory centralization, where external DRAM is pooled into a scalable, shareable, and centralized memory layer via CXL. This can address KV Cache and long-context demands. Morgan Stanley predicts that AI will expand the addressable CXL market from around $4 billion (currently tied to traditional CPU-attached controllers) to approximately $60 billion by 2030. Key beneficiaries include ALAB and MRVL, with Marvell’s management estimating that its CXL business could contribute over $1 billion in revenue by around 2028. From an investment perspective, Morgan Stanley’s core view hasn’t turned bearish on memory despite the downgrading trend. Instead, they continue to overweight MU and SNDK, as this round of downgrading stems from supply constraints rather than weak demand. Once supply eases, spec upgrades can quickly absorb capacity again. What might end this memory cycle isn’t necessarily new supply but rather a slowdown in AI capital expenditures due to bottlenecks like power, land, and data center infrastructure.