What the AI Infrastructure Boom May Mean for Credit Investors

AI not only drives equity markets, it’s also increasingly important for credit markets. The massive infrastructure spending that AI requires may still be in its early stages, but its implications for credit are already becoming clear: growing capital intensity, expanding credit supply, and rising dispersion across sectors and issuers.

For investors, we think the challenge will be to distinguish companies that can translate AI-related investment into sustainable returns from those that can’t. Careful analysis of capital allocation, financing, and execution risk may be critical as AI investments become a more significant driver of market outcomes.

This capex cycle apears, at least for now, to be less sensitive to modest changes in interest rates than previous cycles.

Financing Needs and Market Constraints

The scale of AI investment implies substantial ongoing financing needs. Large technology firms are already important credit issuers, and continued capital deployment is likely to sustain elevated supply. While many issuers have strong balance sheets, the demand for financing across the ecosystem is significant.

Notably, this capex cycle appears, at least for now, to be less sensitive to modest changes in interest rates than previous cycles. Projects are often evaluated using high hurdle rates⁹ and long-term strategic assumptions. As a result, constraints on investment are more likely to emerge through market-based channels—such as investor demand, credit-spread¹⁰ movements, or equity-market valuations—rather than central-bank policy alone.

FIGURE 1 depicts the steep rise in dedicated data center-related debt issuance across different market segments.

As investment accelerates, execution risks are becoming more visible.

Execution Risks and Supply-Chain Constraints

As investment accelerates, execution risks are becoming more visible. Building data centers requires complex supply chains, including semiconductors, power equipment, and specialized labor. Bottlenecks in any area can delay projects or increase costs.

For credit investors, these constraints introduce the risk of uneven outcomes. Even companies with strong demand may face operating challenges that affect earnings and cash flow. Execution capability is therefore likely to become a more significant driver of credit performance.

High Yield: AI May Create Growth—and Credit Casualties

In high-yield markets, the AI build-out reinforces the theme of resilient spreads alongside rising dispersion. Despite macro uncertainty, spreads have so far remained relatively tight, suggesting continued demand for income and a relatively supportive backdrop.

At the same time, opportunities are becoming more differentiated. Dispersion is increasing across issuers, particularly in lower-rated segments, reflecting both sector-specific dynamics and the uneven impact of AI investment. For some companies, AI-related investment may support growth, while for others it raises capital-intensity or disruption risks.

The result is a market with diverging issuer-level outcomes. While spreads may remain range-bound in the absence of a downturn, greater dispersion underscores the importance of selective positioning and bottom-up credit analysis. Moreover, AI exposure in high yield often lies outside traditional technology companies. Beneficiaries may increasingly include independent power producers, utilities, telecom infrastructure providers, and industrial suppliers that support the AI build-out.

The expanding role of private credit raises questions around underwriting standards and risk selection.

Private Credit: Toll Collectors of the AI Economy

The AI build-out also has implications for private-credit markets, where financing demand may extend beyond public markets. As companies invest in infrastructure, private capital may play a growing role in funding projects that are large, complex, or less suited to traditional bond issuance.

The expanding role of private credit raises questions around underwriting standards and risk selection. As capital flows into the asset class, investors will need to differentiate between transactions supported by durable cash flows and those more exposed to execution risk or optimistic growth assumptions.

As in other areas of credit, AI investment may create attractive opportunities for private lenders, but it demands both rigorous discipline in underwriting and structuring given an environment of increasing capital intensity. As financing demand continues to grow, the ability to structure transactions thoughtfully may become an increasingly important differentiator. Careful structuring and appropriate lender protections may ultimately help mitigate downside risk while allowing lenders to participate in attractive long-term financing opportunities.

Securitized Credit: Collateral Matters

AI investment is creating opportunities in securitized markets but also highlights the importance of structure and collateral quality. In areas tied to data-center and infrastructure financing, investors are increasingly evaluating whether projected cash flows and anticipated takeout¹⁴ financing assumptions will materialize as expected.

A key concern is that some transactions are being structured around optimistic repayment timelines or refinancing assumptions. If projects experience delays, construction challenges, or slower-than-expected cash-flow generation, investors may face significant extension risk.¹⁵ In these cases, an attractive yield may make an investment appear less risky than it is, especially if repayment depends on favorable future market conditions.

As a result, securitized investors are placing greater emphasis on transaction structure, downside mitigation, and the durability of underlying cash flows rather than simply seeking exposure to AI-related growth. The opportunity can remain attractive, but differentiation between well-designed and aggressively structured transactions is becoming progressively more important.

Structural Shift in Credit Markets

The AI capex cycle is still in its early stages, but its implications for credit markets are becoming clearer. It represents a structural shift toward higher capital intensity, a growing source of credit supply, and a key driver of dispersion across sectors and issuers. For investors, it can create significant new opportunities, but careful analysis of capital allocation, financing, and execution risk may be critical as the cycle evolves and increasingly shapes credit-market outcomes.

Talk to your financial professional to learn how AI is transforming market opportunities.

¹ Dispersion measures how far apart asset returns or economic outcomes are from an average or a benchmark.

² High yield refers to investments—most often bonds—that offer substantially higher interest rates than safer government or investment‐grade issues.

³ Securitized credit refers to tradable bonds or financial securities backed by a pooled collection of income-generating loans, mortgages, or other receivables rather than a single corporate entity. Common types of securitized credit include: mortgage-backed securities (backed by residential or commercial real estate loans) and asset-backed securities (backed by consumer or commercial debt such as auto loans, student loans, or credit-card receivables).

⁴ Capex (capital expenditure) is the money a company spends to buy, upgrade, or maintain long-term physical or intangible assets like buildings, equipment, or software.

⁵ Investment grade refers to a bond or debt security with a relatively low risk of default and a high credit quality.

⁶ The term “capital intensive” describes industries needing significant investment to produce goods or services. These industries leverage high fixed assets like property, plant, and equipment, resulting in increased depreciation costs.

⁷ A hyperscaler is a company that provides massive cloud computing and data storage infrastructure across huge, globally distributed data networks.

⁸ Leverage metrics are financial tools that measure how much debt a company uses to fund its operations and buy assets.9 A basis point is a unit that is equal to 1/100th of 1%, and is used to denote the change in a financial instrument. The basis point is commonly used for calculating changes in interest rates, equity indexes and the yield of a fixed-income security.

⁹ Hurdle rate refers to the minimum required return on an investment or project to be deemed acceptable, typically adjusted for risk.

¹⁰ A credit spread is the difference in yield between two debt securities with different credit quality but the same maturity..

¹¹ AI-adjacent refers to companies that support or benefit from the development and operation of AI infrastructure but are not primarily AI companies. These may include power, utility, telecommunications, equipment, and other infrastructure providers..

¹² An asset-backed security (ABS) is a financial investment that derives its value and income from a pool of underlying assets, such as loans or receivables.

¹³ A commercial mortgage-backed security (CMBS) is an investment secured by commercial real estate loans, and which provides investors with regular income through interest payments.

¹⁴ A takeout can refer to either a long-term loan that replaces a short-term loan or, in a colloquial sense, the acquisition of a company through a merger or buyout.

¹⁵ Extension risk is the probability that borrowers remain in their loan longer than investors would like, because this defers the average payment cycle for secondary market product investors. In the primary market, lenders are mainly focused on contraction risk (also known as prepayment risk) which is the risk that a borrower will pay early and thus reduce the interest paid to a lender over the life of a loan.

Important Risks: Investing involves risk, including the possible loss of principal. • Focusing on one or more sectors, including the information technology, consumer discretionary and communication services sectors, may subject investors to increased volatility and risk of loss if adverse developments occur. • Fixed income security risks include credit, liquidity, call, duration, event, inflation and interest-rate risk. As interest rates rise, bond prices generally fall. • Investments in high-yield (“junk”) bonds are considered speculative, involve heightened credit risk and greater risk of price volatility, illiquidity, and default than investment grade bonds. • Mortgage-related and asset-backed securities’ risks include credit, interest-rate, prepayment, and extension risk. 

This information should not be considered investment advice or a recommendation to buy/sell any security. In addition, it does not take into account the specific investment objectives, tax and financial condition of any specific person. This information has been prepared from sources believed reliable, but the accuracy and completeness of the information cannot be guaranteed. This material and/or its contents are current at the time of writing and are subject to change without notice.

The views expressed here are those of the authors and are based on available information and are subject to change without notice. This information should not be considered as investment advice or a recommendation to buy/sell any security. In addition, it does not take into account the specific investment objectives, tax and financial condition of any specific person. Portfolio positioning is at the discretion of the individual portfolio management teams; individual portfolio management teams and different fund sub-advisers may hold different views and may make different investment decisions for different clients or portfolios. This material and/or its contents are current as of the time of writing and may not be reproduced or distributed in whole or in part, for any purpose, without the express written consent of Wellington Management or Hartford Funds.