Morgan Stanley Warns of Multiple Risks in U.S. AI Infrastructure Investment, Predicts 32 GW Power Shortage by 2028

Morgan Stanley points out that the construction of AI infrastructure in the United States faces three core risks: "people, power, and politics," which could impact investment plans worth up to $1.6 trillion. Local governments tightening approvals, a reversal in supply and demand leading to a drop in both quantity and price, and power shortages are the main factors. Politically, the location of data centers, grid connection applications, electricity pricing mechanisms, and tax incentives are influenced by state and local governments. Especially with midterm elections approaching, restrictions on data center construction may affect sentiment in the AI industry. The report mentions that the cancellation of tax incentives has become a bipartisan issue, and regulatory decisions by utility commissions may have long-term impacts on project implementation. Regarding supply and demand, there is currently a shortage of AI computing power, with companies pre-purchasing and accepting premiums. However, once supply catches up with demand or the growth rate of demand slows, upstream products such as chips and memory may face simultaneous declines in price and shipment volume, putting pressure on profit margins. Power is currently the most direct constraint. Morgan Stanley's model shows that even considering power supply options such as nuclear energy, gas turbines, and fuel cells, the U.S. may still face a net power gap of 32 GW by 2028, with the gap expected to widen to about 72 GW by 2029. Delays in power delivery may result in GPUs procured by cloud vendors not being put to use in time, leading to equipment hoarding and inventory backlog. Although Morgan Stanley maintains a constructive view on AI infrastructure, believing that power shortages are more likely to cause project delays, it warns that if political resistance, power bottlenecks, and reversals in supply and demand overlap, it could trigger obstacles in chip procurement and a decline in upstream profit margins, potentially reversing the entire AI capital expenditure cycle.

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