Gate News message, April 20 — Morgan Stanley forecasts that autonomous AI systems could significantly lift central processing unit (CPU) demand by 2030, reshaping data center investments and expanding AI spending beyond graphics processing units (GPUs). The bank estimates agentic AI could add $32.5 billion to $60 billion to a data center CPU market projected to exceed $100 billion by 2030, while also driving memory demand.
Agentic AI systems rely on CPUs for general-purpose tasks such as code compilation, software tools, and database queries, roles GPUs are not designed for. Nvidia has introduced its Vera CPU specifically for agentic AI and reinforcement learning applications. SemiAnalysis research points to Microsoft’s Fairwater data centers supporting OpenAI, where a 48-megawatt CPU and storage infrastructure backs a 295-megawatt GPU cluster, a roughly 1-to-6 power ratio. Beneficiaries of this shift are expected to include Nvidia, AMD, Intel, Arm, Micron, Samsung, SK hynix, TSMC, and ASML.
Memory constraints are emerging as a critical bottleneck. Agentic AI systems depend on extended, persistent contexts that can rapidly expand memory requirements. SemiAnalysis projects memory will account for approximately 30% of hyperscaler capital expenditure in 2026, up from roughly 8% in 2023-2024. DRAM prices are anticipated to more than double in 2026, while High Bandwidth Memory (HBM) used in AI servers is expected to remain undersupplied through 2027, positioning suppliers like Micron and SK hynix as increasingly central to system costs and deployment timelines.
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