SAN FRANCISCO | The artificial-intelligence investment cycle is becoming a credit-market story as technology companies and infrastructure providers turn to debt at a scale once associated with national industrial projects. Morgan Stanley expects global AI-related debt issuance to approach $570 billion in 2026, more than double earlier levels, as hyperscalers, data-center developers, utilities and suppliers finance computing capacity that cannot be built from operating cash alone. The expansion is creating business across banking and construction while transferring more of the AI boom’s risk to bondholders and lenders.
The spending requirements extend far beyond buying advanced chips. Data centers need land, buildings, servers, networking equipment, cooling systems, backup generation and long-term power contracts. Grid connections can require new substations and transmission. Fiber routes, water systems and security infrastructure add further cost. Each component has a different useful life and risk profile, which is why companies are assembling financing from bonds, loans, convertible securities, equity and project-specific arrangements rather than relying on a single source.
Amazon’s recent $17.5 billion delayed-draw loan facility illustrates the change. The structure allows the company to access funds over time for general corporate purposes while it expands AI infrastructure. Amazon also completed a record Canadian-dollar corporate bond transaction. A company with enormous revenue and cash generation is choosing to preserve flexibility by borrowing, a sign that the size and speed of planned capital expenditure are altering traditional funding preferences.
Oracle presents a more leveraged version of the same strategy. The company plans exceptionally high capital spending to build cloud capacity for large AI customers and has outlined tens of billions of dollars in debt and equity financing. Its remaining performance obligations have grown sharply, indicating contracted demand, but free cash flow has turned deeply negative as construction accelerates. Investors are therefore being asked to judge whether long-term contracts and customer reimbursements adequately compensate for the balance-sheet strain required to deliver the capacity.
Convertible bonds have become another important tool. They allow issuers to pay a lower interest rate because investors receive the possibility of converting debt into equity if the stock rises. Reuters reported that U.S. convertible issuance accelerated in early 2026 and that a large share was connected to AI. The structure can be attractive for companies with high growth expectations but limited current cash flow. It can also dilute existing shareholders and expose investors to complex interactions between credit risk and stock volatility.
The bond market’s willingness to fund AI depends partly on the perceived quality of the borrowers. Large technology companies can issue debt at relatively low spreads because investors view their businesses as durable and liquid. Data-center developers, energy suppliers and smaller AI firms may face higher costs or rely on private credit. As the financing chain lengthens, the ultimate risk becomes harder to see. A hyperscaler may sign a contract with a developer, which borrows to build a facility, while utilities invest in power generation based on expected demand from that site.
That interconnected structure can support rapid construction, but it also creates dependence on forecasts. If AI demand grows as expected, the infrastructure can generate long-lived revenue and improve productivity. If computing becomes more efficient, customers consolidate or new models require less capital, some facilities may earn less than projected. Debt must still be serviced even when technology changes. The central business question is therefore not whether AI will be important, but whether the specific assets financed today will remain economically valuable for the life of the borrowing.
Power availability is emerging as one of the strongest constraints. Data centers can consume electricity at the scale of industrial plants, and utilities must plan years ahead to build generation and transmission. Long-term contracts can support new investment, but they can also create conflict over who pays for upgrades. Regulators and communities are asking whether households will bear higher rates or whether data-center customers will cover the full cost of expansion. The answer will affect where projects are built and how quickly they can connect.
Construction companies, electrical contractors, engineering firms and equipment manufacturers are benefiting from the spending wave. Demand for transformers, turbines, cooling systems, fiber and specialized labor has increased. Supply bottlenecks can raise project costs and delay revenue, which in turn increases the amount of financing required. A data center that opens a year late may accumulate interest without generating the contracted cash flow that was supposed to support it.
Banks and asset managers see opportunity in underwriting, syndicating and holding the debt. The market can generate fees and provide investors with exposure to AI without buying expensive technology stocks. Yet the concentration of issuance creates portfolio risk. If many borrowers depend on the same assumptions about data-center utilization, power prices and customer demand, a change in those assumptions can affect several parts of the credit market at once.
The growth of private credit adds another layer. Private lenders can finance projects that do not fit public bond markets, often with customized terms and higher yields. That flexibility helps construction proceed, but private deals disclose less information than public securities. Regulators and investors may have a limited view of how much leverage exists across developers, special-purpose vehicles and suppliers. Transparency will become more important as the market grows.
Corporate boards also face governance questions. Management teams must decide how much borrowing is appropriate, how long contracts should run and what protections are required if a major customer reduces demand. They must compare the risk of underbuilding, which could surrender market share, with the risk of overbuilding, which can leave expensive assets underused. The pressure to match competitors can encourage projects that appear strategic even when their financial returns are uncertain.
The accounting can obscure that tension. Capital expenditures are recorded as assets and depreciated over time, while cash leaves the company during construction. Adjusted earnings measures may look stronger than free cash flow. Investors should therefore examine debt maturity schedules, interest expense, customer concentration, lease obligations and the assumptions used to estimate useful life. A server hall and its power connection may last for decades, while the computing hardware inside it can become outdated much faster.
Workers and communities will experience both benefits and disruption. Construction creates jobs, and new facilities can expand local tax bases. Data centers typically employ fewer permanent workers than factories of similar size, however, and their demand for electricity, water and land can generate opposition. Financing terms that require rapid completion may intensify pressure on permitting agencies and local infrastructure. Companies that treat community consent as an obstacle rather than a project risk may face delays that directly affect debt economics.
The record debt wave does not prove that AI investment is a bubble, nor does the technology’s importance guarantee that every financed project will succeed. It shows that the industry has moved into a capital-intensive phase in which growth depends on physical infrastructure and long-duration financial commitments. The next stage will be judged not by announced spending but by whether the assets produce reliable cash flow, useful computing capacity and returns that exceed their cost of capital.
AI financing increasingly resembles infrastructure finance because projects generate cash over long periods and depend on physical assets. Traditional technology investing emphasized software margins and relatively low capital needs. Data centers reverse that model. They require large upfront spending, long construction schedules and continuing hardware replacement. Credit analysts must therefore evaluate power contracts, site quality and tenant obligations alongside software demand.
Special-purpose vehicles can isolate project risk, but they may also make leverage less visible. A technology company can sign a lease or capacity agreement while a separate entity borrows to build the facility. The obligation may not appear as ordinary corporate debt even though the customer’s commitment supports the project. Investors need consolidated disclosure that shows economic exposure across leases, guarantees and take-or-pay contracts.
The length of customer contracts is critical. A ten-year agreement can support long-term financing if pricing, renewal and termination terms are strong. If a contract allows a customer to reduce capacity after a few years, lenders may be left with a specialized asset and uncertain demand. Credit ratings should reflect the enforceability and concentration of contracted revenue rather than the headline value alone.
Interest-rate risk can alter project economics before a site opens. Many developers arrange financing in stages, and a rise in yields can increase the cost of later borrowing. Fixed-rate debt provides certainty but may carry a higher initial cost. Floating-rate loans expose borrowers to policy changes. Hedging helps but adds complexity and counterparties.
Utilities are also entering long financial commitments. New generation and transmission can be justified by projected data-center load, yet regulators must decide whether other customers share the cost. If a project is canceled, ratepayers could be left paying for infrastructure built around demand that never arrived. Contracts and tariffs should place cancellation risk on the companies creating it.
The labor market can constrain even fully financed projects. Electricians, engineers, construction managers and technicians are in high demand. Wage pressure and shortages can delay openings and increase budgets. Workforce development can expand capacity, but training takes time. Companies that announce simultaneous projects in the same region may compete for a limited pool of skilled workers.
Water use has become a financing and permitting issue. Cooling systems can require significant water, particularly in hot or dry regions. Communities may demand conservation technology or reject sites that compete with households and agriculture. A project’s cost of capital should account for environmental constraints and the possibility of tighter regulation.
Cybersecurity and physical security also affect credit quality. Data centers hold valuable computing resources and can become targets for intrusion, sabotage or espionage. Lenders and insurers will expect resilient power, redundant networks and incident-response plans. A major outage can trigger service credits, customer claims and reputational damage even when the building remains intact.
There is a public-policy case for greater transparency. AI infrastructure can influence electricity prices, land use and local budgets while financing occurs through private contracts. State utility commissions, local governments and federal regulators need enough information to evaluate cumulative demand. Individual projects may appear manageable while the combined regional load requires entirely new systems.
The debt wave will eventually separate companies with durable revenue from those relying on momentum. When capital is abundant, many projects can begin. As interest costs accumulate and customers demand performance, weaker structures become visible. The next phase of the AI boom will be determined as much by credit discipline and project execution as by model capability.
Rating agencies will play an important role. Large technology borrowers may retain strong ratings because of diversified revenue, while project entities depend on contracts and collateral. A downgrade can raise borrowing costs across future phases and trigger covenant consequences. Companies should not assume that strategic importance guarantees favorable credit treatment.
Refinancing risk becomes significant when short-term loans fund assets that take years to complete. Borrowers may expect to replace bridge financing with bonds after construction milestones. If markets close or rates rise, they can be trapped with expensive debt. Matching maturities to project timelines reduces that vulnerability but may cost more initially.
Equipment financing can separate rapidly depreciating hardware from long-lived buildings. Servers and accelerators may be leased or financed over shorter periods, while land and power infrastructure support longer debt. That alignment helps, but residual-value assumptions for specialized chips remain uncertain. A new generation of hardware can reduce resale value quickly.
Customer prepayments can reduce borrowing, yet they create obligations that resemble financing. A provider receiving cash in advance must deliver capacity or refund the customer. Disclosure should show how much construction is funded by prepayments and what happens if schedules slip. Otherwise, headline debt figures understate the total financial commitment.
Public pension funds and insurance companies may become major holders of AI debt because they seek long-duration income. That broadens exposure beyond technology investors. Fiduciaries need to understand tenant concentration, project risk and climate or water constraints rather than treating every security from a major technology ecosystem as equivalent.
A healthy financing market should allow failed projects without threatening the broader system. Diversified lenders, conservative loan-to-value ratios and transparent covenants can contain losses. Excessive reliance on the same customers and forecasts can transmit stress. The record issuance forecast makes those safeguards more important before a downturn tests them.
Bank regulators will monitor underwriting standards as lenders compete for AI business. Large fee opportunities can encourage optimistic assumptions about collateral and future demand. Stress tests should examine simultaneous project delays, customer defaults and power-price increases rather than treating each exposure independently.
Local tax incentives should be evaluated against the financing structure. A highly leveraged project may receive public support while creating relatively few permanent jobs. Governments should require performance benchmarks, clawbacks and disclosure of infrastructure costs. Economic-development competition should not transfer private downside to taxpayers without measurable benefit.
Insurance capacity may become a constraint. Builders need coverage for construction, equipment, cyber incidents and business interruption. Concentrated sites with enormous power demand can exceed traditional limits, raising premiums or requiring layered coverage. Financing agreements often depend on maintaining insurance, so availability can influence whether projects close.
The business lesson is that AI infrastructure is no longer an experimental budget line. It is a long-duration allocation of corporate and financial-system capacity. Boards, lenders and communities should demand the same disciplined analysis applied to power plants, factories and transportation networks.
Auditors should test whether capitalization policies and useful-life estimates remain realistic. Extending depreciation schedules can improve reported earnings without changing the physical pace at which computing equipment becomes obsolete. Cash flow remains the clearest check.
The financing boom will be healthiest when capital is selective. Cheap funding for every announced project would encourage overbuilding; disciplined pricing can direct resources toward sites with strong contracts, power access and community support.
Investors should also distinguish corporate debt from project debt. A bond backed by a diversified technology company carries different recovery prospects from a loan secured mainly by one facility and one customer. The shared AI label can obscure those distinctions unless disclosures remain detailed.
The record issuance forecast is therefore a warning as well as evidence of confidence. It shows that companies believe demand will justify enormous construction, and that lenders are willing to fund it. Whether that confidence becomes productive infrastructure or excess capacity will be decided over years.
Financial discipline now becomes part of technological execution. Companies that connect borrowing to durable contracts and resilient infrastructure will be better positioned than those financing growth mainly because competitors are doing the same.
Additional Reporting By: Reuters AI debt report; Reuters Amazon financing; Reuters Oracle spending; Reuters convertible bonds