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JPMorgan Chase: AI Capital Spending Raised to $5.5 Trillion, Broadcom Expected to Exceed $150 Billion in AI Revenue by 2027

2026.06.21 10:12:36

JPMorgan Chase has updated its research report, lifting its 2030 forecast for global AI total capital expenditure (capex) from $51 trillion to $55 trillion, with corresponding debt financing scaled to $41 trillion. The report attributes this accelerated spending to surging AI demand and persistent computing power scarcity: Google processes 3.2 trillion AI tokens monthly, while firms like Microsoft and Uber have burned through their full-year AI budgets in just a few months. For the U.S.’ four leading hyperscale cloud providers (Google, Amazon, Microsoft, Meta), their combined 2026 capex guidance is roughly $700 billion to $725 billion, marking a ~75% year-over-year increase and projected to exceed $1.1 trillion by 2027. JPMorgan forecasts these firms will generate over $900 billion in combined operating cash flow in 2027, but that will still fall short of their massive planned spending — making debt and equity financing standard practice. On the chip beneficiary front, JPMorgan expects Broadcom’s AI-related revenue (including ASIC/XPU and AI networking segments) to top $150 billion by 2027. The company’s management disclosed its 2027 order backlog exceeds $100 billion, far outpacing JPMorgan’s prior ~$60 billion estimate for 2026 (a figure the bank now calls conservative). Broadcom’s AI chip financing platform (AI XPV), developed with Apollo and Blackstone, is highlighted as a landmark structural innovation in GPU financing. In terms of financing dynamics, the top five hyperscale providers have raised ~$240 billion in external funding so far this year, with high-grade corporate bonds as the primary channel. JPMorgan projects total AI-related high-grade bond issuance will hit $2.1 trillion over the next five years. On the power front, JPMorgan has raised its data center capacity growth forecast from 122 gigawatts (GW) to 138 GW, but power availability remains the most critical bottleneck. Hyperscale providers are actively exploring alternatives like self-built power infrastructure.
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