Good morning. AI-driven capital spending is scaling rapidly, and the economics remain attractive and increasingly profitable—for now, according to JPMorgan Global Research’s midyear outlook.
Analysts see a broadening AI capex cycle underpinning growth expectations, led by “AI upstream” investments in data centers, chips, and supporting infrastructure. Much of this activity remains concentrated in the U.S., which accounts for about 85% of AI and machine learning venture capital, with spillover benefits expected in China, South Korea, and Taiwan through their roles in semiconductor supply chains.
That broad investment wave is also reshaping corporate strategy, as technology companies race to expand beyond their traditional markets and capture a share of the rapidly growing AI infrastructure opportunity. For example, Qualcomm is the latest major tech company to aggressively expand into the data center market.
While traditionally best known for its smartphone and mobile chip business, the company unveiled a comprehensive AI data center strategy at its Investor Day on June 24, targeting the rapidly growing AI infrastructure market. Qualcomm said it expects its data center business to generate more than $15 billion in annual revenue by fiscal 2029. If the company achieves this goal, the data center unit will become its single largest growth business outside of smartphones.
The company is “delivering a comprehensive roadmap for next-generation AI data centers—entering at a key inflection point with momentum from multi-year, multi-generation agreements with leading customers,” Qualcomm CFO and COO Akash Palkhiwala wrote in a LinkedIn post.
Meanwhile, on June 24, memory chipmaker Micron reported a 346% surge in quarterly revenue and profit of $28.2 billion for the quarter, nearly 15 times higher than the same quarter last year. Micron’s results briefly boosted investor sentiment after a selloff in AI and tech-related stocks began earlier in the week, lifting futures for both the tech-heavy Nasdaq and the broader S&P 500, Fortune reported.
In its midyear outlook, released on June 24, JPMorgan also raised its estimate for global AI-related capital expenditures through 2030 to $5.5 trillion, up from $5.1 trillion, driven by greater capacity expansion and increased debt financing. The bank now projects AI-related debt financing will reach $4.1 trillion as loan-to-cost ratios rise.
Analysts also forecast continued growth in hyperscaler spending, with capital expenditures reaching $650 billion in 2026 and surpassing $1.1 trillion in 2027. Despite the rapid investment, profitability is expected to remain robust, with operating cash flow exceeding $900 billion by 2027. (You can read more about JPMorgan’s outlook here.)
Capital expenditures for hyperscalers like Amazon, Google, and Microsoft are reaching unprecedented levels. For example, Microsoft expects to invest roughly $190 billion in capital expenditures in calendar year 2026, a 61% increase from the previous year.
While hyperscalers continue to report rising AI-related revenue, investors remain focused on whether enterprise adoption and monetization will keep pace with the trillions being invested. For now, the AI investment cycle continues to accelerate, but whether those returns ultimately justify the scale of spending is likely to remain one of the market’s defining questions.
Sheryl Estrada [email protected]
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