AI capex wave tests bond market depth

Key takeaways

• AI-related capital expenditures are widely estimated to reach $5 trillion, led by high-grade technology issuers.
• Many bond managers expect the scale of financing to pressure spreads moderately, though market demand is viewed as remaining strong.
• Across managers, there is a focus on assessing asset longevity and residual value risk in data center financing, as well as strength of covenants.
• While large, highly rated tech firms may lift overall credit quality as borrowers, the opportunity favors managers with deep expertise in deal structure and direct claims on hyperscalers.

The global race to build AI infrastructure has accelerated sharply. Estimated capital expenditures now total more than $5 trillion, equivalent to the annual GDP of Germany. That magnitude matters for bond investors: roughly $1.5 trillion of this is expected to flow through high-grade credit markets, with the remainder financed via private markets, and cashflows of the hyperscalers. Managers anticipate major technology companies —including Amazon, Meta, Microsoft, Oracle, and Google—to dominate issuance, according to market participants. As a result, the composition of corporate bond indices may tilt toward higher-quality credits. This shift could strengthen the market’s overall credit profile, even as it increases concentration in a handful of large issuers.

The question now is whether the bond market can absorb this scale of issuance. So far, signs are constructive. Recent AI-related bond deals have been well oversubscribed—meaning investor demand far exceeds the bonds available—supported by strong appetite for high-quality issuers. Issuers are offering modest concessions, which have helped drive the strong uptake. 

Still, the debate on spread impact remains open. Some investment managers we work with see single-digit basis-point widening as new supply builds; others anticipate larger moves. Historical precedent has seen large issuance trends previously from banking or telecoms not driving systematic issues.

One recent example illustrates the nuance: the Blue Owl–Meta joint venture, created to fund a hyperscale data center, priced about 100 basis points wider than Meta’s general obligations, based on market pricing at issuance. That differential has since narrowed, underscoring how quickly market pricing adapts as investors grow comfortable with new structures.

Most AI-related borrowers have balance sheets strong enough to handle the leverage. Rating agencies are unlikely to view this issuance as materially risky, aside from select cases where spreads have already widened for issuer-specific reasons. However, the details will matter substantially as to whether the debt raised has effective claims on the balance sheets of the hyperscalers. Asset-backed special purpose vehicles may be attractive, but the distinct risks in the underlying assets make selectivity and deep asset-level expertise essential.

For example, high-powered chips that run AI systems make up about two-thirds of a data center’s value, but lose their worth quickly. Depreciation rates for these chips are fast and the new chip cycles have sped up, raising questions about how long these facilities will hold their value and what that means for long-term returns

Some hyperscalers are responding cautiously—funding capex within cash flow and relying on outsourced partners to maintain financial flexibility. This conservatism reflects uncertainty over both technological turnover and data center resale potential.

The financing landscape is also diverging across segments. Structured credit managers note that the commercial real estate market is assigning higher risk premiums to AI data-center loans than similar asset-backed deals, suggesting investors are evaluating the underlying assets unevenly.

Meanwhile, active managers are adjusting portfolios to navigate this transition. Investors see the surge in issuance as likely to keep yields under pressure for a while, with differing opinions on how much. Most see this pressure as mild to moderate, but not severe. Others view it as a chance to add selectively in new deals and structured credit, even if it creates short-term performance differences across managers.

Across the managers we work with, the AI buildout is widely viewed as a lasting theme for bond markets—large in scale but generally seen as manageable in the initial stages. Strong balance sheets, deep investor demand, and flexible financing options are cited by many managers as reasons the market can absorb the surge in issuance.

The longer-term supply, though, is viewed by many as likely to require concessions, potentially putting some upward pressure on spreads. How and when those opportunities emerge is an area of active debate, with managers differing on timing, structure, and issuer selection.

As more capital is required, many expect an increase in off-balance sheet structures, where deal quality is supported by contractual obligations by the operators and lessors. Manager approaches vary in how they assess the durability of these obligations under a variety of scenarios.

Ultimately, liquid investment grade markets are expected by many market participants to evolve from being dominated by the financial sector toward a more balanced mix that includes a larger technology component. For multi-manager portfolios, this transition is likely to create both challenges and opportunities, depending on how managers navigate concentration, structure, and asset-life risks.