- Top tech firms to spend over $1 trillion annually on AI infrastructure from 2027 onward
- Hyperscalers' capital expenditure to exceed 90% of operating cash flow by 2027
- AI investments require large upfront costs, causing a funding gap for tech companies
The AI infrastructure race is pushing top tech firms Alphabet, Amazon, Meta, Microsoft and Oracle to have a combined spending of over a trillion dollars annually from 2027, according to an ICICI Bank Research report.
The report said that these aforementioned firms are expected to spend $729 billion on capital expenditure in 2026, rising to $1.069 trillion in 2027. Hyperscaler capex is projected to remain above $1 trillion through 2030, reaching $1.184 trillion in 2028, $1.172 trillion in 2029 and $1.213 trillion in 2030.
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The spending is increasingly stretching companies' operating cash flows. Hyperscalers' capex was a little over a third of their operating cash flows between 2018 and 2021. That ratio rose to 45% during 2022-2025 and is projected to reach 90% in 2026 and 104% in 2027.
The report attributed the sharp increase to rapidly advancing capital expenditure as companies ramp up AI investments, while operating cash flow is expected to grow more slowly because AI investments will take time to generate returns.
AI infrastructure requires large upfront investments in hardware infrastructure, data centres, power generation, GPUs and networking equipment. The report said the mismatch between the initial investment required and the time taken for these investments to generate returns has created a funding gap for technology companies.
Hyperscalers are increasingly turning to bond markets to bridge the aforementioned funding gap.
US hyperscalers had already borrowed $220 billion through debt instruments in 2026, as per the report. That borrowing is expected to increase as AI infrastructure requirements grow.
The borrowing has largely been an investment-grade phenomenon, with most hyperscalers, except for Oracle, being high-investment-grade companies. AI-related debt is estimated to account for around 15% of US investment-grade debt, while the five hyperscalers alone are projected to exceed 5% of the investment-grade index by the end of 2026.
US investment-grade corporate bond issuance stood at $1.7 trillion during January-July 2026 and is expected to cross $2 trillion for the first time. The report cited estimates that AI-related debt could already account for close to a third of net new investment-grade supply in the US market.
The funding pressure is particularly significant because much of the AI-related borrowing is long-dated.
Around 80% of hyperscalers' bond issuance has a maturity of more than five years, with 23% categorised as "very long term". Technology companies are seeking multi-year funding to finance their infrastructure projects.
This creates direct competition with long-term US Treasury bonds.
The US Treasury's net issuance of long-term debt, with maturities of 20-30 years, stood at around $424 billion in 2025. The report estimates US bond supply in the 20-30-year maturity bucket at $400-500 billion, while AI debt issuance for similar maturities is estimated at around $500 billion.
This means AI debt issuance could be larger than the US government's comparable long-term debt supply.
The report describes this phenomenon as 'reverse crowding out' where private-sector borrowing for AI infrastructure competes with sovereign debt for the same pool of long-term capital.
In contrast with regular 'crowding out,' where government borrowing can reduce the funds available for private investment, the report argues that the surge in private-sector AI borrowing is putting additional pressure on government bond markets.
The funding requirement extends beyond the five major hyperscalers.
The report estimates total AI debt issuance at $489-570 billion in 2026, compared with $220-250 billion for the core hyperscalers. At around $500 billion, AI debt supply would be approximately 120% of the US Treasury's $424 billion net long-term maturity supply.
The report stated that this flood of AI debt is putting pressure on US Treasury yields.
Hyperscalers are also increasingly borrowing in currencies other than the US dollar, especially euros and Canadian dollars. The report said this is increasing competition for debt issuance in those currencies and pushing up bond yields.
The report expects the reverse-crowding-out phenomenon to intensify in the near term as AI capex remains above $1 trillion from 2027 and AI debt issuance peaks in 2027.
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It also anticipates an easing in the borrowing pressure as AI investments begin generating revenue and operating cash flows fund a larger part of capital expenditure.
According to the report, AI borrowing could therefore peak in 2027 and moderate thereafter. This would reduce competition for sovereign debt and eventually reduce the pressure on bond yields.
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