AI spending is becoming a major corporate-finance question, not just a technology question.
The largest technology companies are building data centres, buying advanced chips and securing electricity at exceptional scale. The International Energy Agency estimates that their capital expenditure exceeded USD 400 billion in 2025 and could rise by another 75% in 2026. That is an estimate, not combined company guidance, but it shows the scale of the investment cycle.
At the same time, companies across the economy are still refinancing corporate debt. OECD data show that global companies borrowed a record USD 13.7 trillion through bond and syndicated-loan markets in 2025, while total outstanding corporate debt remained broadly near USD 60 trillion. This suggests much of the activity was rollover financing rather than a surge in new net borrowing.
For investors and small business owners, the key point is that AI investment is arriving while many borrowers already need to refinance. The risk is not necessarily an immediate funding crisis. It is whether companies retain enough financial flexibility to fund growth, renew debt and handle delays without straining their balance sheets.
AI capex financing is moving from cash flow toward debt
The IMF estimates that AI-related capital expenditure could reach USD 3.4 trillion through 2029, with hyperscalers accounting for roughly 70%. The figure should be treated carefully because estimates differ in scope. Some include chips, data centres, power infrastructure and construction, while others do not.
Large technology groups have so far benefited from strong operating cash flow, high credit quality and deep access to capital markets. The IMF reported that hyperscalers raised more than USD 100 billion in bond financing from January 2025 through its April 2026 assessment.
That does not mean their funding model is broken. The IMF judged investor appetite for investment-grade debt and hyperscaler credit quality to be strong. The more important question is future balance-sheet capacity. If capital spending remains elevated for years, even highly rated companies may have to choose more carefully between debt-funded expansion, shareholder returns, acquisitions and liquidity protection.
The same lesson applies beyond the largest firms. A regional data-centre operator, software company or manufacturer deploying automation may need to finance equipment before expected revenue arrives.
The refinancing cycle raises the cost of getting timing wrong
Refinancing becomes difficult when a company has several funding needs at once: maturing bonds, construction draws, equipment purchases and delayed customer revenue. This is the practical meaning of a debt maturity wall: a concentration of obligations coming due before cash flows are ready.
The wider market may also compete for investor capital. The OECD expects OECD-country governments to borrow about USD 18 trillion on a gross basis in 2026, mostly to refinance existing obligations. One-third of OECD fixed-rate sovereign debt outstanding at the end of 2025 is due to mature between 2026 and 2028.
This is not a global sovereign-debt figure, and it does not mean companies will be shut out of markets. Still, heavy government issuance can influence yields, investor demand and term premiums. Companies refinancing in that environment may face higher costs or shorter maturities, especially if their plans depend on uncertain future AI demand.
Retail investors should look beyond headline revenue growth. A company can announce ambitious AI plans while carrying rising interest expense, near-term maturities or weak free cash flow. Small business owners should make the same distinction: growth spending may be sensible, but it should not leave routine refinancing dependent on optimistic sales forecasts.
Data centre financing requires matching debt to the asset
Not all AI assets have the same economic life. Land, grid interconnections, substations and some power equipment can support long-lived projects. Servers, graphics processing units and other advanced chips may lose economic value much faster as technology changes.
The IMF notes that major hyperscalers have an average implied accounting useful life of about seven years for property, plant and equipment. But it also warns that GPUs and advanced chips may become obsolete sooner. Debt that amortizes slowly against equipment that becomes outdated quickly can leave lenders and shareholders exposed to residual-value risk.
The IEA projects global data-centre electricity consumption rising from 485 terawatt-hours in 2025 to 950 terawatt-hours by 2030 in its central case. Power equipment, grid supply chains and chip manufacturing are potential bottlenecks. A data centre may look attractive on paper yet fail to earn on schedule if energization is delayed. Funding a grid connection and short-life compute equipment should not automatically use the same debt structure or repayment schedule.
Private credit can help, but it is not unlimited capital
Private credit has become an important part of AI and data-centre financing. OECD research describes structures that can begin with syndicated construction loans or private credit, then move to asset-backed financing or private placements after a project is operating.
Private lenders may offer tailored terms where public bond markets are less practical. But private credit is not an unlimited substitute for bank lending and bonds. OECD reported private-credit assets under management of USD 1.8 trillion in June 2025, while dry powder was 28% of assets under management, down from 59% in 2018. Businesses should not assume financing will always remain available at an acceptable price.
The BIS also highlights the need for clearer disclosure. Some AI-linked funding arrangements may involve investors that are also suppliers, commercial counterparties or users of related services. Opaque circular structures, exit clauses and multiple claims on the same assets can amplify losses if expectations change quickly.
A practical CFO treasury strategy
A sound CFO treasury strategy starts with one integrated calendar. Debt maturities, committed capital-expenditure draws, power and interconnection milestones, lease payments and expected customer revenue should sit in the same forecast.
Management should preserve liquidity before assuming an AI revenue ramp will arrive on time. This includes committed credit lines, cash reserves and refinancing headroom. Interest coverage also deserves close attention: if operating earnings fall or borrowing costs rise, a comfortable ratio can weaken quickly.
A useful stress test should include:
- higher credit spreads and refinancing rates;
- delayed energization or construction completion;
- lower customer utilization of computing capacity;
- foreign-exchange mismatches between debt and revenue; and
- shorter economic lives for servers and chips.
Project-level or ring-fenced structures can limit risk, but only when guarantees, lease commitments, termination rights and asset pledges are fully visible. A parent company may still carry meaningful exposure even when a project is legally separate.
Finally, companies should disclose supplier-linked, related-party or circular commitments separately from arm’s-length revenue. Clear disclosure helps lenders, investors and business partners assess whether demand is durable or partly supported by financing relationships.
Growth is valuable, but balance-sheet resilience is the test
AI investment can create real productivity gains and long-term revenue opportunities. Yet financing decisions must be judged alongside the refinancing calendar, not apart from it.
The strongest companies will not necessarily be those spending the most. They will be the ones matching funding to asset life, protecting liquidity, testing downside scenarios and avoiding a debt maturity wall built on assumptions that have not yet become cash flow.