How the new AI economy is reshaping global credit markets

Artificial intelligence (AI) is catalyzing one of the most consequential capital investment cycles in modern economic history. Demand for AI compute capacity is accelerating across hyperscalers, power utilities, semiconductor manufacturers, construction firms, real estate developers, and capital markets. Multiple research providers, including J.P. Morgan Chase and Bank of America Securities, estimate this could result in a USD 5 trillion–USD 7 trillion global capital expenditure (capex) wave over the next five years. Understanding this cycle requires an integrated view across power markets, physical supply chains, digital infrastructure, and global financing ecosystems.

Competition is driving hyperscalers to accelerate capital spending, in some cases ahead of realized revenue. Combined capex for the five largest cloud providers, or hyperscalers,¹ is expected to rise from about USD 240 billion in 2024 to over USD 580 billion in 2026, according to the mean estimate from Bloomberg’s sell‑side analyst survey in early February, an unprecedented two‑year doubling.

Financing the compute economy via four fixed income markets

Given the scale of investment required, capital markets—not just tech company balance sheets—will increasingly provide financing for AI infrastructure going forward. Debt, in particular, is expected to play a major role. Companies with investment‑grade credit ratings can primarily use three distinct fixed income markets to fund AI‑related capex, with some overlap between them: public investment‑grade corporate bonds, securitized credit, and private credit and hybrid debt. Non-investment-grade companies can mainly access leveraged finance.

1. Public investment‑grade corporate bond markets

Technology‑related issuance of investment‑grade corporates has risen sharply since mid‑2025 and could reach USD 350 billion in 2026, according to Wells Fargo Securities, driven by hyperscaler demand. Several large platform companies have executed multibillion-dollar multi‑tranche offerings in recent quarters, in some cases representing their largest‑ever financings. We anticipate that AI‑related bond issuers will use the investment‑grade corporate market to meet the bulk of their funding needs.

As these issuers grow as a percentage of investment‑grade corporate credit benchmarks, credit curves² may steepen as the flood of new supply could more than offset demand for longer‑maturity debt. Within the technology sector, the market may further differentiate credit quality, leading to a tiering of tech credit spreads and creating more prospects for active managers.

The new AI‑related corporate supply could create benchmark concentration dynamics similar to that in the banking industry after the global financial crisis of 2008–2009, when large banks recapitalized by tapping the investment‑grade corporate market. This led to financials becoming the largest sector in the major investment‑grade corporate bond indexes. The upward pressure on spreads from the heavy issuance also opened up opportunities for fundamental credit analysis to find sound credits that had been swept up in the negative technicals.

2. Securitized credit markets

Asset‑backed securities (ABS) and commercial mortgage‑backed securities (CMBS)³ in the securitized credit market provide another avenue for AI‑related funding, albeit on a much smaller scale than the investment‑grade corporate market. ABS or CMBS can be useful for financing data centers that are generating cash flows or for power generation agreements that produce cash flows under contract, for example. As more AI campuses mature, we think that securitization tools will play a growing role in refinancing activity.

3. Private credit and hybrid structures

While investment‑grade public companies historically have not relied heavily on private debt funding, these markets are a deep pool of capital to help finance AI capex. Capital‑intensive AI projects increasingly rely on custom financing structures that combine elements of corporate credit, project finance, and securitization. Issuers can structure these deals as off-balance sheet debt under certain conditions or with amortization schedules that match the cash flows generated by the project. Lease or service payment streams can enhance collateralization, and project operators can share credit risk with debtholders. The Beignet Investor deal, described in more detail below, is an example of a hybrid structure.

4. Leveraged finance: high yield bonds and loans

Leveraged finance (high yield bonds and broadly syndicated loans) is also likely to play a supporting role in funding the AI infrastructure build-out, particularly for non‑investment‑grade participants across the extended ecosystem. While the largest hyperscalers will primarily access investment‑grade and private markets, high yield bonds and loans are well suited for data center developers, power and infrastructure contractors, equipment suppliers, and emerging AI infrastructure platforms whose balance sheets or project risk profiles fall below investment grade.

According to J.P. Morgan, the leveraged finance markets have sufficient capacity to absorb a meaningful share of AI‑related funding over time—potentially on the order of USD 150 billion over the next five years. As a result, high yield bonds and loans are likely to finance discrete projects, growth platforms, and second‑order beneficiaries rather than the hyperscalers themselves, making them an important—but potentially more cyclical and risk‑sensitive—component of the overall AI funding mosaic.

Cross‑asset research is critical

AI infrastructure exposure manifests differently across chips, utilities, data centers, power generation, and industrial components. Because of the resulting nuances, we believe a global cross‑asset research ecosystem where equity and credit analysts collaborate is critical to effectively navigate this transformative era. This allows for insights to be integrated across industries and capital structures, which helps to enhance the depth of analysis.

An integrated research framework allows credit analysts and fixed income portfolio managers to compare corporate credit curves against amortizing private placement debt structures and to assess whether project‑level credit spreads appropriately compensate for construction, technology, or tenant concentration risk. This can also help benchmark data center valuations across real estate investment trust (REIT),⁴ corporate, and securitized markets.

Identifying indirect beneficiaries of AI capex—in the capital goods, utilities, or industrials sectors, for example—is important given that the cyclical uplift they offer may be underappreciated by the market. In investor portfolios, this can help avoid concentration in a single expression of an AI‑related theme while capturing more stable multi‑industry benefits.

Harvesting complexity premium

For example, Meta Platforms brought USD 27 billion of debt to market in late 2025 to finance construction of a huge AI data center in Louisiana. Known as the Beignet Investor deal, the issuer is a joint venture between Meta and private debt firm Blue Owl. A thorough analysis of these new bonds would involve expertise in the quality of Meta’s AI products and their power use as well as accurately predicting their cash flows and the evolution of the deal’s leverage and credit quality over time—among a wide range of other factors.

Like the Beignet deal, some AI‑driven capex debt structures are much more complex than a typical investment‑grade corporate bond with a fixed coupon and maturity date. Investors that do not have the research expertise to analyze these structures often demand additional spread (a complexity premium) to buy the bonds.

Managing risks in an era of transformative change

AI‑related bonds have meaningful risks, which can be managed via credit analysis and selection alongside thoughtful portfolio construction. Examples of risks that are important to monitor include:

Supply: The broadest risks are the potential for overbuilding and oversupply of data centers, chips, and power assets or for AI capex to moderate relative to the current very elevated expectations.

Deal risk: Individual AI‑driven debt issues could be overly dependent on a limited number of hyperscaler tenants or constrained by regulatory or permitting delays. Utilities subject to regulatory price constraints may be unable to fund an elevated capex burden over time.

Portfolio construction: Overlapping AI dependencies across sectors may create portfolio construction risk.

The AI infrastructure supercycle will likely shape market structure, capital flows, and sector performance for years to come. Collaborative research across asset classes to inform credit analysis and disciplined underwriting will be essential to position effectively for this transformative era.