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Technology companies are issuing more long-term debt because the artificial-intelligence infrastructure buildout requires far more capital, and for far longer, than the sector historically needed. Data centers, servers, networking equipment and power capacity must be paid for years before their full economic returns are known. Long-dated bonds let companies spread that financing over the expected life of the assets, preserve cash for other uses and, when ratings are strong, raise money without immediately diluting shareholders.

This is not automatically a sign that technology companies are in financial distress or that every new bond funds AI. The OECD says leverage has risen in parts of the sector but remains broadly manageable. The more useful question is how much each issuer is borrowing, what it is funding, when the debt comes due, and whether future cash flow can support the obligations.

The funding model changed when AI made technology more capital-intensive

For much of the modern technology industry, investment could be funded internally. Software, online services and semiconductor design generally required less physical equipment than industries such as utilities, transportation 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. Strong operating cash flow meant many companies did not need to borrow heavily for routine expansion.

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Generative AI changes that equation. Training and serving large models require dense clusters of accelerators, high-speed networking, storage, cooling systems and data-center buildings. Cloud providers also need more electricity and, in some locations, new substations or power contracts. Those are conventional, long-lived capital assets rather than purely incremental software costs.

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In its Global Debt Report 2026, the OECD 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 the traditionally asset-heavy utilities sector. Free-cash-flow ratios declined and leverage rose for some companies, although the OECD describes overall leverage as broadly manageable. It says the magnitude of AI expansion costs is making a funding model based mainly on internal cash increasingly misaligned with future investment needs.

What the borrowed money is paying for

Data centers and servers

The most visible use is construction and equipment. A data-center campus can require buildings, land, electrical distribution, cooling, backup generation and network connections before a customer uses a single hour of compute. Servers and accelerators are purchased in large batches, and companies may commit to additional capacity to secure supply. Debt proceeds can therefore fund a multiyear program rather than one discrete project.

AI and the existing business at the same time

AI spending is not always separable from ordinary cloud and internet operations. Meta Platforms’ 2025 Form 10-K reports $69.69 billion of purchases of property and equipment, including servers, data centers and network infrastructure. Meta forecast $115 billion to $135 billion of capital expenditures for 2026, saying the investment would support AI efforts as well as its core business. The forecast is a company estimate, not a completed spending total.

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Working capital, acquisitions and refinancing

A bond announcement may describe proceeds as general corporate purposes rather than a single building. That money can leave cash available for payroll, acquisitions, research, spectrum or unexpected costs while debt funds the overall capital plan. Existing bonds can also be refinanced, replacing a near-term maturity with a later one. Investors should read the offering document instead of assuming that every dollar is tied to a named AI facility.

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Why choose long-term bonds instead of only cash or short-term debt?

Match repayment to the asset’s useful life

A long-maturity bond allows a company to pay for infrastructure over the years in which the facilities and equipment are expected to operate. This avoids funding a 15- or 20-year asset with a sequence of short-term borrowings that must be rolled over repeatedly. Matching is a financing rationale, not a guarantee that the project will generate enough profit to repay the bond.

Preserve liquidity and strategic flexibility

Using debt rather than all available cash leaves a larger liquidity cushion for operations, acquisitions and downturns. Vanguard describes this as a central issuer rationale, while cautioning that the logic does not prove every borrower lacks sufficient cash. A company may borrow even with a large cash balance because management values optionality or wants to keep cash in particular subsidiaries or jurisdictions.

Use strong credit ratings without issuing as much equity

Large technology companies often have investment-grade ratings and substantial recurring revenue. Vanguard says those ratings can make debt relatively inexpensive compared with equity. Borrowing also avoids selling shares at a price management considers too low and avoids reducing existing shareholders’ percentage ownership. Debt is not free: coupons and principal are contractual, and additional borrowing can eventually raise the cost of all future financing.

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Lock in funding before conditions change

Issuing a long bond fixes the coupon for its entire term when the bond is fixed-rate. That can protect a company from having to refinance during a period of higher interest rates or weaker credit markets. The trade-off is that the issuer may pay a higher coupon than a short-term instrument available today, and the bond’s market value will move when rates change.

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Companies are combining debt with equity

Debt is only one part of the funding response. Oracle announced a calendar-year 2026 plan to raise $45 billion to $50 billion of gross cash proceeds, with approximately half from debt and half from equity. The stated objective was to expand Oracle Cloud Infrastructure while maintaining an investment-grade balance sheet. The range was a forward-looking plan, not a realized issuance total.

An equity component limits the increase in leverage but dilutes shareholders. A debt component avoids that dilution but creates fixed obligations. The appropriate mix depends on cash generation, valuation, credit capacity, project timing and management’s tolerance for balance-sheet risk. A headline amount without the mix can therefore give an incomplete picture.

How large has the increase been?

Vanguard’s calculations, based on Bloomberg data and measured through the dates shown, illustrate the change among five hyperscalers. They are market calculations rather than audited company disclosures.

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Measure Figure Qualification
Average annual debt issuance by five hyperscalers, 2020–2024 About $35 billion Vanguard calculation for that issuer group
Debt issuance in 2025 About $93 billion Vanguard calculation
Debt issuance year to date through July 31, 2026 About $132 billion Vanguard calculation; partial-year figure
Estimated 2026 AI-related debt issuance About $300–$570 billion Vanguard estimate for hyperscalers and the wider ecosystem, including chipmakers, data-center developers and utilities; not a final total
Oracle planned 2026 gross proceeds $45–$50 billion Company plan, approximately half debt and half equity; forward-looking

The wider estimate matters because an AI supply chain is financed in several places. A cloud provider can issue bonds directly, while a data-center developer or utility builds capacity for that provider and raises its own debt. Looking only at technology-company balance sheets can therefore understate the financing tied to the same investment cycle.

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Why the maturities are unusually long

The Kansas City Fed reports 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. A weighted average means the result reflects the size of each issue, not simply the average number of bonds.

Issuer group Weighted-average maturity Comparison period
Hyperscalers 16 years AI-related investment-grade bonds issued in 2025 through August 2026
Utilities 17 years Same issuance window
Overall market 10 years Market average cited by the Kansas City Fed

Long maturities fit assets expected to operate for decades, but they also leave investors exposed to the issuer’s strategy for a long time. A facility can become obsolete, power costs can change, and demand for a particular model or cloud service can fall well before the bond matures.

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What long-term debt means for investors

Duration and interest-rate sensitivity

A long-term fixed-rate bond generally has greater duration than a short-term bond. When market yields rise, its price usually falls more because more distant coupon and principal payments are being discounted for longer. Conversely, falling yields can produce larger price gains. Investors who plan to hold to maturity still face the issuer’s payment risk and the opportunity cost of being locked into the coupon.

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Execution risk before returns arrive

AI infrastructure requires spending ahead of proven demand. Companies must forecast utilization, pricing, customer adoption, power availability and equipment life years in advance. Meta’s filing identifies its 2026 spending as supporting both AI and its core business, while Oracle’s financing announcement lists changes in customer purchasing, data-center delays and operational problems among factors that could make actual outcomes differ from plans. If returns arrive later than expected, cash flow available for debt service may be lower than management projected.

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Concentration in bond indexes

Vanguard says AI-related issuance is increasing the technology sector’s weight in the U.S. corporate-bond index and adding long duration. A broad corporate-bond fund can consequently become more exposed to the same AI investment cycle that already affects technology stocks. Diversification by issuer does not eliminate concentration by industry, project type or economic driver.

Private and off-balance-sheet financing

Some data-center projects are financed through private credit, joint ventures or structures that do not appear as straightforward corporate bonds. The Kansas City Fed and Vanguard both highlight the resulting transparency challenge. Public investors may see only part of the obligations, guarantees or lease commitments supporting the infrastructure chain. Lack of visibility is a monitoring issue, not proof of hidden insolvency.

Does the borrowing signal a technology-sector debt crisis?

Not by itself. The OECD says leverage has increased in some hyperscalers but remains broadly manageable, and Vanguard describes the largest issuers as exceptionally strong credits rather than a solvency warning. A company with durable cash flow, ample liquidity and investment-grade access can borrow heavily for productive assets without being distressed.

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The warning sign would be a combination of rising debt, falling operating cash flow, weak interest coverage, aggressive assumptions about AI demand and repeated refinancing needs. Investors should distinguish a temporary investment surge from a balance-sheet deterioration and examine each issuer’s disclosures rather than infer distress from the sector headline.

A practical checklist for evaluating an issuer or bond

  1. Identify the purpose. Determine whether proceeds fund capital expenditure, an acquisition, refinancing or general corporate purposes. The use affects how quickly the borrowing might generate cash.
  2. Separate planned from completed amounts. Mark company forecasts, such as Meta’s $115 billion–$135 billion 2026 capex outlook and Oracle’s $45 billion–$50 billion plan, separately from amounts already issued or spent.
  3. Check the debt structure. Note maturity dates, fixed or floating coupons, call provisions, secured or unsecured status and any large maturities that could require refinancing.
  4. Measure cash support. Compare operating cash flow and free cash flow with interest expense and scheduled principal payments. Falling free-cash-flow ratios deserve attention even when leverage remains investment grade.
  5. Assess project and customer concentration. Ask whether repayment depends on a few cloud customers, a particular model family, a single data-center region or continued access to scarce power and equipment.
  6. Look beyond public bonds. Review leases, guarantees, joint ventures, private-credit arrangements and other commitments that may not resemble ordinary notes on the face of the balance sheet.
  7. Consider portfolio exposure. Check whether a bond fund already has large technology and utility holdings, because the same AI buildout can affect both issuers.

What could change the outlook

The investment case depends on whether AI services produce enough durable revenue and margin to justify the infrastructure. Faster adoption and high utilization could make today’s borrowing efficient. Delayed data centers, falling service prices, power constraints, equipment obsolescence or a shift in customer spending could reduce returns while coupons remain fixed. Interest rates also affect the market value of long bonds even when the issuer continues paying on time.

For that reason, the key distinction is not “debt versus no debt.” It is whether the maturity, amount and financing structure are proportionate to assets that can generate cash across the same horizon. Long-term borrowing can be a rational way to fund a historic investment cycle, but it transfers more of that cycle’s duration and execution risk to bondholders.

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