In its midyear outlook, the firm points to a broadening capital expenditure cycle anchoring growth expectations. At the center is the “AI upstream” buildout—data centers, chips, and supporting infrastructure—still heavily concentrated in the U.S., which commands about 85% of AI and machine learning venture capital. Spillover benefits are expected in China, South Korea, and Taiwan, given their roles in semiconductor supply chains.

JPMorgan now estimates total global AI-related capital expenditures will reach $5.5 trillion through 2030, up from $5.1 trillion. The increase reflects higher capacity expansion and a shift in financing. The bank raised its estimate for debt financing tied to the AI buildout to $4.1 trillion, citing higher loan-to-cost ratios.

But the scale of spending can raise questions about whether AI demand will grow quickly enough to justify the capacity being built. While cloud providers such as Amazon Google, and Microsoft report rising AI revenue, investors still remain divided on how long it will take for returns to match investment levels.

Regarding financing conditions loan-to-cost ratios average above 85%, with some exceeding 90%, reflecting both favorable credit conditions and perceived value creation. In some cases, investors are assigning outsized equity value to expansion—for example, a $15 million-per-megawatt investment translating into a $25 million increase in market capitalization.

Hyperscalers—the primary drivers of AI infrastructure spending—appear to be entering this phase from a position of strength. JPMorgan expects their capital expenditures to reach $650 billion in 2026 and exceed $1.1 trillion in 2027. Profitability remains intact, with operating cash flow projected to surpass $900 billion by 2027.

Still, the investment cycle remains concentrated among a small group of technology companies, meaning any slowdown could have an outsized impact on industry growth.

Recent equity issuance is reinforcing balance sheets. Analysts suggest elevated leverage reflects a strategic choice: companies are financing projects with debt while conditions are favorable, preserving flexibility to deleverage later.

Credit markets will play a pivotal role. JPMorgan forecasts high-grade corporate debt will account for more than $2.1 trillion in data center financing over five years. In 2026, the bank anticipates $150 billion in U.S. hyperscaler issuance and another $100 billion equivalent abroad.

Additional financing—about $170 billion—is expected from data center and chip issuers outside the core high-grade market, though alternative channels will remain relatively small.

For JPMorgan, the conclusion is clear: AI-driven capital spending is scaling rapidly, and the economics are holding, for now. It will still be closely watched whether adoption will keep pace with the trillions being invested.