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Technology companies are issuing more long-term debt because the artificial-intelligence infrastructure buildout requires far more upfront capital than many firms historically needed. Data centers, servers, networking equipment and power capacity must be paid for years before their returns are fully visible. Long-dated bonds let companies finance those projects over a period closer to the assets’ expected working lives, while preserving cash and avoiding immediate shareholder dilution.
This is a shift in funding mix, not automatic evidence of financial distress. Strongly rated issuers can borrow at comparatively attractive rates, and some are deliberately combining bonds with equity. The trade-off is that investors and companies are taking on longer duration, greater exposure to the success of the AI investment cycle and, in some cases, less visible private financing.
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What changed in technology-company financing?
For much of the modern technology sector, investment could be funded internally. Software, online services and semiconductor design generally required less physical capital than industries such as utilities, manufacturing or telecommunications. The OECD’s Global Debt Report 2026 puts that difference in context: the median capital-expenditure-to-sales ratio for global technology companies was 2.2% in 2024, the lowest among industries, and it had remained the lowest since 2003.
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That model is being challenged by AI. Training and serving large models requires clusters of specialized processors, high-bandwidth networking, storage, cooling systems and substantial electricity. Companies are also building or leasing data centers and, in some cases, securing power and construction capacity years in advance. The spending arrives before management can know exactly which products, customers or workloads will generate the eventual return.
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The OECD reports that many hyperscalers increased capital-expenditure intensity sharply 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 still describes leverage across the sector as broadly manageable.
That is why the OECD says the traditional reliance on internally generated cash is “increasingly misaligned with future investment needs” given the magnitude of AI-expansion costs. Debt is filling part of the gap between current cash generation and the scale and speed of planned construction.
How large is the increase in borrowing?
Market estimates show a sharp acceleration, although the figures cover a defined group and should not be read as a complete census of every technology issuer. Vanguard’s calculations, based on Bloomberg data, estimate that five hyperscalers issued roughly $35 billion of debt annually on average from 2020 through 2024. The estimate rises to about $93 billion in 2025 and approximately $132 billion from January 1 through July 31, 2026.
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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteVanguard estimates that total AI-related debt issuance for full-year 2026 could reach roughly $300 billion to $570 billion across hyperscalers and the wider ecosystem, including chipmakers, data-center developers and utilities. Those are estimates rather than final issuance totals, and the wider figure includes companies outside the conventional technology classification.
| Measure | Figure | Qualification |
|---|---|---|
| Median technology capex-to-sales ratio | 2.2% in 2024 | Global technology firms; OECD, lowest among industries and consistently lowest since 2003 |
| Average annual debt issuance by five hyperscalers | About $35 billion | Vanguard estimate for 2020–2024 |
| Hyperscaler debt issuance | About $93 billion | Vanguard estimate for calendar 2025 |
| Hyperscaler debt issuance | About $132 billion | Vanguard estimate through July 31, 2026 |
| AI-related debt issuance | About $300–$570 billion | Vanguard full-year 2026 estimate for hyperscalers and the wider ecosystem |
What the borrowed money pays for
Data centers and physical plant
AI workloads need buildings with specialized power distribution, cooling and networking. A company may buy a facility, construct one, expand an existing campus or sign long-term arrangements with a data-center developer. Each option creates commitments that can run for many years.
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Servers, accelerators and networking
Servers containing AI accelerators are expensive and become productive only after they are installed, connected and supplied with sufficient power. Networking equipment, storage and replacement capacity add to the initial bill. These assets can generate revenue over several years, but their economic life and resale value are uncertain because chip generations and model architectures change quickly.
Power and related infrastructure
Electricity is a material constraint for large AI clusters. Spending can include substations, transmission connections, backup generation and cooling systems, as well as contracts that reserve future capacity. Utilities and infrastructure developers may also borrow to build assets serving AI customers, which is why AI-linked debt extends beyond the technology sector.
General corporate purposes
Bond proceeds are not always traceable to one server hall. A company can use debt for general corporate purposes, refinance older obligations or preserve cash while directing operating cash toward capital expenditure. The exact use should be checked in an offering document or regulatory filing rather than inferred from the issuer’s AI announcements.
Why choose long maturities?
Match the financing horizon to the project
A long-maturity bond spreads principal repayment over the years in which a data-center program is expected to operate and produce cash flow. This avoids forcing a company to refinance the entire construction cost shortly after the facility opens. The approach is a planning rationale, not a guarantee that the assets will repay the debt or remain economically attractive for the full term.
Keep cash available
Issuing bonds allows a company to keep more cash on its balance sheet for payroll, suppliers, acquisitions, research and unexpected needs. Preserving liquidity can be valuable during a construction cycle in which costs, delivery schedules and customer demand may change. The existence of a bond issue does not prove that the issuer lacked cash; it shows that management preferred to combine cash with external financing.
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Use a strong credit rating
Highly rated companies can often borrow at a lower expected cost than a weaker issuer. Vanguard describes this as one reason established technology companies may prefer debt to issuing additional shares. Debt avoids immediate dilution of existing ownership, but it creates contractual interest and principal payments and can reduce flexibility if operating results weaken.
Spread funding sources
Issuers can combine bonds, equity, operating cash, leases and project-level financing. Oracle announced a calendar-year 2026 plan to raise $45 billion to $50 billion of gross cash through an approximately balanced mix of debt and equity while expanding Oracle Cloud Infrastructure. Oracle said maintaining an investment-grade balance sheet was an objective. The announced amount was a forward-looking plan, not a completed issuance total.
Company examples show different approaches
Oracle: an explicit debt-and-equity mix
Oracle’s plan illustrates that large infrastructure programs do not have to be financed solely with bonds. Approximately half of the planned $45–50 billion was to come from debt and half from equity. The company also identified risks that could change actual outcomes, including changes in customer purchasing, data-center delays and operational problems. That combination of a stated balance-sheet target and disclosed execution risks is more informative than treating any large bond sale as proof of distress.
Meta: very large capital expenditure alongside bond financing
Meta’s 2025 Form 10-K reported $69.69 billion of purchases of property and equipment, including servers, data centers and network infrastructure. The filing projected $115 billion to $135 billion of capital expenditure for 2026 to support AI efforts and the core business. In November 2025, Meta issued fixed-rate senior unsecured notes that produced $29.91 billion of net proceeds. The 2026 spending range was a company forecast and can change with business conditions.
These examples are not directly comparable: Oracle’s figure is a planned calendar-year financing program, while Meta’s figures combine a completed 2025 spending disclosure, completed note proceeds and a forward-looking 2026 capex outlook. Fiscal years, transaction dates and intended uses must be kept separate when assessing issuers.
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What long-term debt means for bond investors
More duration and interest-rate sensitivity
A bond with a long maturity generally has greater duration than a shorter bond with similar credit quality. Its market price can therefore move more when interest rates or credit spreads change. The issuer also remains obligated to pay interest and repay principal over a longer period, even if AI demand, hardware economics or technology priorities change.
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. Those figures describe a defined set of AI-related bonds, not every corporate bond issued by technology companies.
Concentration in bond indexes
As large technology issuers sell more bonds, technology’s weight in broad U.S. corporate-bond indexes can increase. Vanguard warns that this adds long duration and can make a diversified-looking bond allocation more exposed to the same AI investment cycle that already influences technology stocks. An investor who owns both technology equities and a market-wide bond fund may therefore have more correlated exposure than the labels suggest.
Execution risk before revenue arrives
Data-center construction requires billions of dollars before all future demand is contracted or profitable. Delays, cost overruns, equipment shortages, power constraints or lower-than-expected customer usage can reduce returns on the assets while debt service continues. Meta’s filing connects its spending to AI and core-business needs, while Oracle’s disclosures identify customer purchasing changes and construction or operating problems as factors that could produce different results from its plan.
Private and off-balance-sheet financing
Not all AI infrastructure borrowing appears as a conventional public bond issued by a technology company. Private credit, leases, joint ventures and other structures can finance data centers or equipment. Vanguard and the Kansas City Fed both point to the resulting transparency challenge: aggregate obligations across the value chain can be harder to observe than public-debt totals. Less visible financing is a monitoring issue, not evidence by itself of an imminent default.
Does the borrowing signal a financial warning?
Borrowing alone cannot answer that question. The OECD says leverage has risen in some cases but remains broadly manageable, and Vanguard describes the largest issuers as exceptionally strong credits rather than presenting the issuance surge as a solvency warning. A useful assessment combines the debt with cash generation, liquidity, maturity dates, interest costs, credit ratings, committed customer demand and the expected productivity of new capacity.
The key distinction is between financing a credible, long-lived investment program and borrowing to cover persistent operating losses. A company with strong recurring cash flow, substantial liquidity and staggered maturities may be using debt strategically. A company whose cash flow is deteriorating, whose leverage is rising faster than earnings and whose projects depend on uncertain demand faces a more fragile equation. The same bond volume can mean different things for those two balance sheets.
How to analyze a technology-company bond issue
- Identify the purpose. Read the prospectus or filing to determine whether proceeds fund capital expenditure, acquisitions, refinancing, general corporate purposes or a combination.
- Separate completed transactions from plans. Mark whether each number is issued debt, net proceeds, a planned financing amount or a capex forecast. Keep calendar and fiscal years distinct.
- Check maturity and coupon structure. Record final maturity, call dates, fixed or floating rates and any refinancing concentration. Longer maturity increases duration exposure.
- Review balance-sheet capacity. Compare total debt with operating cash flow, cash and marketable securities, interest expense and available liquidity. Examine secured versus unsecured obligations.
- Test the project assumptions. Look for evidence of customer commitments, utilization, power availability, construction schedules and hardware replacement needs. Consider what happens if demand arrives later than expected.
- Look beyond public bonds. Review leases, purchase commitments, joint ventures and other disclosures that may place obligations outside a simple public-debt total.
- Consider portfolio concentration. Determine how much exposure a bond fund, equity portfolio or retirement plan already has to the same hyperscalers, chip suppliers, utilities and data-center developers.
What could slow the debt wave?
Issuance could moderate if AI revenue grows fast enough to fund more investment internally, if companies postpone projects, if interest rates or credit spreads make bonds less attractive, or if boards shift toward more equity financing. Conversely, stronger customer demand, faster model adoption or competition for scarce data-center capacity could keep borrowing elevated. Vanguard’s $300–$570 billion 2026 estimate should therefore be treated as a range around an uncertain investment cycle, not as a promised total.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesThe central change is structural: a sector that once financed much of its growth from operating cash now needs external capital for an unusually large physical buildout. Long-term debt is an efficient way to spread that cost and preserve flexibility, but it transfers duration, execution and concentration risks to investors and keeps companies committed to payments long after today’s AI assumptions may have changed.
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