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The borrowing is not, by itself, proof that the sector is in financial trouble. Many issuers remain highly rated and generate substantial cash. But longer maturities increase interest-rate sensitivity, lock companies into payments tied to uncertain AI demand and make investors more exposed to a concentrated technology-and-infrastructure cycle.
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The basic change: technology is becoming more capital-intensive
For much of the past two decades, major technology companies could fund expansion largely from operating cash. Software, online services and digital advertising generally required less physical equipment than industries such as utilities, telecommunications or manufacturing. The OECD reports that the median capital-expenditure-to-sales ratio for global technology companies was 2.2% in 2024, the lowest of the industries it tracked and a position the sector had held consistently since 2003.
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Artificial-intelligence development has changed that equation. Training and serving large models require specialized processors, dense server fleets, high-speed networks, storage and data centers. Those assets are purchased or built in large increments, often years before a project reaches steady utilization. The OECD’s Global Debt Report 2026 says many hyperscalers sharply increased capital-expenditure intensity in 2025; in some cases, capex as a share of revenue rose to more than ten times historical averages and exceeded the aggregate intensity of traditionally asset-heavy utilities. Free-cash-flow ratios declined and leverage increased for some companies, although the OECD still describes sector leverage as broadly manageable.
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“Given the magnitude of the costs associated with AI expansion however, this funding model is increasingly misaligned with future investment needs.”
OECD, Global Debt Report 2026, “The role of debt markets in financing the AI expansion”
How large is the increase in borrowing?
Issuance has accelerated most visibly among hyperscalers and the companies that supply their infrastructure. Vanguard’s calculations, based on Bloomberg data, estimate that five hyperscalers issued about $35 billion of debt annually on average from 2020 through 2024. The estimate rose to $93 billion in 2025 and approximately $132 billion from January 1 through July 31, 2026. Vanguard estimates total 2026 AI-related debt issuance of roughly $300 billion to $570 billion when hyperscalers are combined with chipmakers, data-center developers and utilities. Those are market estimates, not audited final totals.
| Measure | Reported figure | How to read it |
|---|---|---|
| Technology capex-to-sales ratio | 2.2% median in 2024 | OECD measure for global technology companies; lowest among industries tracked |
| Five-hyperscaler annual debt issuance | About $35 billion average, 2020–2024 | Vanguard calculation using Bloomberg data |
| Five-hyperscaler debt issuance | $93 billion in 2025 | Vanguard calculation for the full calendar year |
| Five-hyperscaler debt issuance | Approximately $132 billion through July 31, 2026 | Vanguard year-to-date estimate, not a full-year result |
| Wider AI-related 2026 issuance | Approximately $300 billion–$570 billion | Vanguard estimate covering hyperscalers and the broader infrastructure ecosystem |
Company disclosures show the scale of the underlying spending. Meta’s 2025 Form 10-K reported $69.69 billion spent on property and equipment, including servers, data centers and network infrastructure. Meta forecast $115 billion to $135 billion of capital expenditures in 2026 for its AI efforts and core business. In November 2025, Meta also issued fixed-rate senior unsecured notes that produced $29.91 billion of net proceeds.
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Why issue bonds with long maturities?
Match financing to the life of the assets
A data center and its supporting equipment are intended to operate for many years. A bond maturing in a few years could force the issuer to refinance before the project has generated enough cash. A longer bond spreads principal repayment across the period in which the infrastructure is expected to operate. This is a financing rationale, not a guarantee that the assets will earn enough to repay the debt.
Keep cash available for other uses
Borrowing allows a company to retain cash for payroll, research, acquisitions, working-capital needs and unexpected construction costs instead of paying every infrastructure bill from current operations. Vanguard identifies preservation of liquidity and strategic flexibility as a central issuer motive. That explanation does not establish that every borrower lacks sufficient cash; it describes why a financially strong company may still choose debt.
Use strong credit ratings
Large technology companies often have investment-grade ratings and access to deep bond markets. Vanguard notes that a strong rating can make debt relatively inexpensive compared with issuing additional equity. Debt avoids immediate ownership dilution and lets existing shareholders retain a larger share of future earnings if the investment succeeds. The trade-off is a fixed obligation: interest and principal must be paid even if AI revenue grows more slowly than expected.
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Companies are not uniformly choosing debt over stock. Oracle announced a calendar-year 2026 plan to raise $45 billion to $50 billion of gross cash proceeds, approximately half through debt and half through equity, to expand Oracle Cloud Infrastructure. Oracle said the mix was intended to support growth while maintaining an investment-grade balance sheet. The amount was a forward-looking plan, not a completed issuance total.
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What the borrowed money is paying for
- Data centers: Land, buildings, cooling, power systems and related construction for AI and cloud workloads.
- Servers and accelerators: Specialized processors and the server systems that house them.
- Networking and storage: High-bandwidth interconnects, switches and storage needed to train and serve models.
- Supply-chain capacity: Commitments to equipment makers, data-center developers and utilities that are also expanding for AI demand.
- General corporate purposes: A bond may refinance older debt or replenish cash after capital spending; proceeds are not always legally restricted to one project.
Meta’s filing explicitly tied its projected 2026 spending to both AI and its core business, illustrating why company-level capex cannot always be separated into a single AI line. Oracle likewise cited customer purchasing changes, data-center delays and operational problems among factors that could cause actual results to differ from its financing plan.
Why not fund the entire buildout with equity?
Equity does not require scheduled repayment, which makes it safer for a project with uncertain cash flows, but issuing large amounts of stock dilutes existing owners. It can also be expensive when a company’s share price does not reflect management’s view of long-term value. Debt preserves ownership and can lower the upfront cost of capital for a highly rated issuer.
That advantage disappears if leverage rises enough to weaken a rating or restrict future borrowing. Interest expense also reduces cash available to shareholders and can become burdensome if AI services are commoditized, customers delay purchases or equipment becomes obsolete faster than expected. A balanced debt-and-equity plan, such as Oracle’s stated 2026 approach, distributes those risks rather than eliminating them.
Why are the maturities so long?
Long maturities are a distinctive feature of the AI financing wave. The Federal Reserve Bank of Kansas City reported that AI-related investment-grade bonds issued from 2025 through August 2026 had weighted-average maturities of 16 years for hyperscalers and 17 years for utilities, compared with a 10-year market average. Utilities are included because they are building power and grid capacity that supports data centers.
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| Issuer group | Weighted-average maturity | Comparison |
|---|---|---|
| Hyperscalers | 16 years | AI-related investment-grade bonds issued in 2025 through August 2026 |
| Utilities | 17 years | Same Kansas City Fed sample and period |
| Overall market | 10 years | Market average used by the Kansas City Fed for comparison |
For investors, a 16- or 17-year bond behaves differently from a 10-year security. Its price generally moves more when market yields change, all else equal, because more cash flows remain exposed to the new rate environment. For issuers, the long term reduces near-term refinancing pressure but extends the period during which interest and principal obligations remain outstanding.
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Rates and duration
Long-dated bonds have greater duration. If market interest rates rise, the market value of existing fixed-rate bonds can fall more sharply than that of shorter securities. An investor who holds to maturity may receive the promised payments if the issuer remains solvent, but a fund or household that sells earlier can realize a loss.
Execution and demand risk
AI infrastructure requires spending before future demand is certain. A company can build capacity that is underused, face construction delays or discover that a newer generation of hardware makes existing equipment less competitive. Oracle has identified customer purchasing changes, data-center delays and operational problems as factors that could affect outcomes. Meta’s large capex forecast is also a company projection that can change with business conditions.
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Concentration in bond indexes
Vanguard says AI-related issuance is increasing the technology sector’s weight in U.S. corporate-bond indexes and adding long duration. A broad investment-grade bond fund may therefore become more exposed to the same AI investment cycle that already influences technology stocks. Index diversification by issuer does not necessarily eliminate concentration by theme, industry or maturity.
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Private and off-balance-sheet financing
Some data-center funding occurs through private credit, joint ventures or structures that do not appear as conventional public corporate bonds. Vanguard and the Kansas City Fed warn that these arrangements can make total exposure harder to observe across the AI value chain. Limited visibility is a transparency and monitoring problem; it is not evidence by itself of imminent default.
Leverage and refinancing
Higher debt can reduce a company’s room to respond to a downturn. Even investment-grade issuers must refinance or repay obligations eventually, and a weaker rating could raise future borrowing costs. The OECD says leverage has risen in some cases but remains broadly manageable, while Vanguard describes the major issuers as exceptionally strong credits and does not characterize the issuance surge as a solvency warning.
How to tell whether borrowing is prudent or a warning sign
The headline amount of debt is less informative than its purpose, terms and relationship to cash generation. When reading a company filing or bond prospectus, examine:
- Use of proceeds: Separate new infrastructure spending from acquisitions, refinancing and general corporate purposes.
- Debt versus equity: Check whether management is sharing the funding burden with shareholders or relying almost entirely on borrowing.
- Maturity profile: Look for large repayment clusters, weighted-average maturity and fixed- versus floating-rate exposure.
- Cash generation: Compare operating cash flow and free cash flow with interest expense and planned capital expenditures.
- Credit quality: Track leverage, rating outlooks, covenants and the company’s stated target for an investment-grade balance sheet.
- Project progress: Monitor data-center completion, customer commitments, utilization and evidence that AI services are producing revenue or cost savings.
- Visibility beyond public bonds: Read disclosures about leases, joint ventures, supplier financing, private credit and other obligations that may not be obvious in a simple debt total.
What this means for investors
For shareholders, debt can increase returns if AI infrastructure produces profits greater than the borrowing cost, but it also makes the company less flexible when results disappoint. For bondholders, investment-grade ratings and strong cash flows provide important protection, yet long maturities add duration and leave more time for technology, competition and demand assumptions to change.
The Kansas City Fed’s analysis emphasizes that the financial risks extend beyond individual issuers. Banks, insurers, bond funds, utilities, equipment suppliers and private lenders can all be linked to the same buildout. A portfolio that owns several parts of that chain may be less diversified than its security count suggests.
The most accurate conclusion is therefore conditional: technology companies are borrowing because AI infrastructure has made internal financing alone less suitable for the scale and timing of investment. Long-term debt is a rational tool for matching multiyear assets with multiyear funding, especially for companies with strong ratings. It becomes a concern when spending outruns cash generation, projects remain unproductive, repayment dates cluster or hidden financing makes leverage difficult to measure.
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