Financing the Future: Credit Perspectives on the AI Investment Boom
Credit Perspectives | December 2025
AI is powering one of the fastest and largest corporate investment cycles in decades. With global AI data center and infrastructure spending projected to reach $5 – $7 trillion by 2030, companies must commit massive capital long before the payoff is realized. This leaves both equity and debt investors to confront uncertain implications. The gap between investment today and monetization tomorrow is already reshaping balance sheets across some of the strongest global issuers. The core question for debt investors is whether companies can sustain this pace of spending without eroding their credit positions. At the same time, rising issuance across AI-linked sectors is beginning to reverse the technical support that has helped buoy investment grade credit markets over the past year.
KEY TAKEAWAYS
AI-driven capital expenditure (capex) is accelerating sharply, pushing even the strongest hyperscalers from self-funded growth toward increased reliance on public and private credit markets.
Rising issuance from AI-linked corporates is reshaping index composition and applying sustained supply pressure across investment grade technology, media, and telecommunications (TMT) credit.
Wider dispersion favors selective credit exposure to issuers with durable liquidity, balanced funding plans, and clear capex visibility, while an active approach can help offset AI-driven volatility in equity markets.
The Rising Cost of the AI Infrastructure Cycle
For years, hyperscalers like Microsoft, Amazon, and Alphabet comfortably funded growth with internally generated cash. That model is now being tested as AI data centers can cost up to an estimated $50 – $60 billion per gigawatt of capacity,1 and annual AI-related capex at the largest players is already approaching $100 billion.
Consensus expectations for hyperscaler spending have doubled since early 2025, yet actual needs are likely to run even higher as AI infrastructure scales. Even with significant cash reserves, the pace and scale of this build-out are forcing these companies to tap into external financing. We are moving from a long period of self-funded expansion into a clear phase of increased reliance on outside capital.
A New Phase of Capital Markets Reliance
This shift in funding needs is already reshaping how companies access capital. Oracle’s unexpected $18 billion issuance in September marked a clear turning point in AI-related financing. Meta, Alphabet, and Amazon followed with their own jumbo deals to support their AI investments. Meta went even further, partnering with Blue Owl on a $27 billion off-balance-sheet structure (Beignet) to fund a new data center campus. At the same time, the ABS and CMBS markets have taken on a growing share of infrastructure financing, with AI-related issuance nearly doubling so far this year. We expect companies to lean even more heavily on private credit, structured financing, and even hybrids to preserve balance sheet metrics and manage funding costs. These tools might protect headline metrics, but they also add structural complexity and opacity, key considerations for credit investors going forward.
Market Impact: Strong Fundamentals But Rising Risks
These financing shifts are unfolding against a broader backdrop of solid fundamentals and rising structural risks. While some comparisons can be drawn to the late 1990s telecom build-out, today’s AI leaders enter this cycle on much stronger footing. They have healthier balance sheets, stronger cash generation, and clearer demand visibility. For investors, the key question becomes how well companies balance intense shareholder pressure to invest in growth with the need to preserve credit quality.
At the index level, the concentration is becoming impossible to ignore. JP Morgan estimates AI-linked issuers now make up nearly 15% of the US investment grade index, with the cohort potentially surpassing 20% by 2030.2 At that scale, AI-related credits would represent the single largest component of the index, meaning continued supply growth will materially shape market technicals.
We are already seeing the impact. Since the start of the third quarter, rising issuance has contributed to almost 10 basis points of relative underperformance across the technology, media, and telecommunications industries as investors rebalance exposure and reassess both fundamental and technical risks. This supply pressure is likely to remain elevated and will reduce the technical support that benefited markets over the past year, setting the stage for the year ahead.
Outlook & Positioning
From here, greater credit dispersion will mean investors must assess which companies can sustainably fund outsized capex and shareholder returns without eroding liquidity, leverage capacity, or balance sheet flexibility.
With currently limited spread compensation available across these highly rated issuers, we maintain only modest exposure. AI-linked issuance has driven broad pressure across TMT and is creating opportunities for active managers who can differentiate on fundamentals and relative value.
Our focus remains on forward-looking credit quality. We favor issuers with clearly disclosed, internally balanced funding strategies and avoid those showing early signs of leverage drift or liquidity strain as AI spending accelerates. Within this group, we would look to add exposure selectively where spreads fully reflect both the scale of the capex cycle and the issuer’s ability to absorb increased spending without compromising financial strength. At the same time, we seek to enhance active, risk-adjusted returns through diversification across issuers and structures, including opportunities in securitized markets where AI-related issuance should continue to surge in the coming year. Further, we see an active, multi-sector approach to fixed income as a valuable source of diversification as AI continues to drive equity market volatility.
1 Nvidia second fiscal quarter 2026 earnings call, 8/27/25.
2 AI Capex — Financing the Investment Cycle,” Tarek Hamid, C. Stephen Tusa, Jr., Harlan Sur, et al., JP Morgan, published 11/10/25.
This represents the views and opinions of GW&K Investment Management and does not constitute investment advice, nor should it be considered predictive of any future market performance. Data is from what we believe to be reliable sources, but it cannot be guaranteed. Opinions expressed are subject to change. Past performance is not indicative of future results.
Indexes are not subject to fees and expenses typically associated with managed accounts or investment funds. Investments cannot be made directly in an index. Index data has been obtained from third-party data providers that GW&K believes to be reliable, but GW&K does not guarantee its accuracy, completeness or timeliness. Third-party data providers make no warranties or representations relating to the accuracy, completeness or timeliness of the data they provide and are not liable for any damages relating to this data. The third-party data may not be further redistributed or used without the relevant third-party’s consent. Sources for index data include: Bloomberg, FactSet, ICE, FTSE Russell, MSCI and Standard & Poor’s.
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Financing the Future: Credit Perspectives on the AI Investment Boom
Credit Perspectives | December 2025
AI is powering one of the fastest and largest corporate investment cycles in decades. With global AI data center and infrastructure spending projected to reach $5 – $7 trillion by 2030, companies must commit massive capital long before the payoff is realized. This leaves both equity and debt investors to confront uncertain implications. The gap between investment today and monetization tomorrow is already reshaping balance sheets across some of the strongest global issuers. The core question for debt investors is whether companies can sustain this pace of spending without eroding their credit positions. At the same time, rising issuance across AI-linked sectors is beginning to reverse the technical support that has helped buoy investment grade credit markets over the past year.
KEY TAKEAWAYS
The Rising Cost of the AI Infrastructure Cycle
For years, hyperscalers like Microsoft, Amazon, and Alphabet comfortably funded growth with internally generated cash. That model is now being tested as AI data centers can cost up to an estimated $50 – $60 billion per gigawatt of capacity,1 and annual AI-related capex at the largest players is already approaching $100 billion.
Consensus expectations for hyperscaler spending have doubled since early 2025, yet actual needs are likely to run even higher as AI infrastructure scales. Even with significant cash reserves, the pace and scale of this build-out are forcing these companies to tap into external financing. We are moving from a long period of self-funded expansion into a clear phase of increased reliance on outside capital.
A New Phase of Capital Markets Reliance
This shift in funding needs is already reshaping how companies access capital. Oracle’s unexpected $18 billion issuance in September marked a clear turning point in AI-related financing. Meta, Alphabet, and Amazon followed with their own jumbo deals to support their AI investments. Meta went even further, partnering with Blue Owl on a $27 billion off-balance-sheet structure (Beignet) to fund a new data center campus. At the same time, the ABS and CMBS markets have taken on a growing share of infrastructure financing, with AI-related issuance nearly doubling so far this year. We expect companies to lean even more heavily on private credit, structured financing, and even hybrids to preserve balance sheet metrics and manage funding costs. These tools might protect headline metrics, but they also add structural complexity and opacity, key considerations for credit investors going forward.
Market Impact: Strong Fundamentals But Rising Risks
These financing shifts are unfolding against a broader backdrop of solid fundamentals and rising structural risks. While some comparisons can be drawn to the late 1990s telecom build-out, today’s AI leaders enter this cycle on much stronger footing. They have healthier balance sheets, stronger cash generation, and clearer demand visibility. For investors, the key question becomes how well companies balance intense shareholder pressure to invest in growth with the need to preserve credit quality.
At the index level, the concentration is becoming impossible to ignore. JP Morgan estimates AI-linked issuers now make up nearly 15% of the US investment grade index, with the cohort potentially surpassing 20% by 2030.2 At that scale, AI-related credits would represent the single largest component of the index, meaning continued supply growth will materially shape market technicals.
We are already seeing the impact. Since the start of the third quarter, rising issuance has contributed to almost 10 basis points of relative underperformance across the technology, media, and telecommunications industries as investors rebalance exposure and reassess both fundamental and technical risks. This supply pressure is likely to remain elevated and will reduce the technical support that benefited markets over the past year, setting the stage for the year ahead.
Outlook & Positioning
From here, greater credit dispersion will mean investors must assess which companies can sustainably fund outsized capex and shareholder returns without eroding liquidity, leverage capacity, or balance sheet flexibility.
With currently limited spread compensation available across these highly rated issuers, we maintain only modest exposure. AI-linked issuance has driven broad pressure across TMT and is creating opportunities for active managers who can differentiate on fundamentals and relative value.
Our focus remains on forward-looking credit quality. We favor issuers with clearly disclosed, internally balanced funding strategies and avoid those showing early signs of leverage drift or liquidity strain as AI spending accelerates. Within this group, we would look to add exposure selectively where spreads fully reflect both the scale of the capex cycle and the issuer’s ability to absorb increased spending without compromising financial strength. At the same time, we seek to enhance active, risk-adjusted returns through diversification across issuers and structures, including opportunities in securitized markets where AI-related issuance should continue to surge in the coming year. Further, we see an active, multi-sector approach to fixed income as a valuable source of diversification as AI continues to drive equity market volatility.
1 Nvidia second fiscal quarter 2026 earnings call, 8/27/25.
2 AI Capex — Financing the Investment Cycle,” Tarek Hamid, C. Stephen Tusa, Jr., Harlan Sur, et al., JP Morgan, published 11/10/25.
M. Seamus Ryan, CFA
Principal, Director, ResearchDisclosures
This represents the views and opinions of GW&K Investment Management and does not constitute investment advice, nor should it be considered predictive of any future market performance. Data is from what we believe to be reliable sources, but it cannot be guaranteed. Opinions expressed are subject to change. Past performance is not indicative of future results.
Indexes are not subject to fees and expenses typically associated with managed accounts or investment funds. Investments cannot be made directly in an index. Index data has been obtained from third-party data providers that GW&K believes to be reliable, but GW&K does not guarantee its accuracy, completeness or timeliness. Third-party data providers make no warranties or representations relating to the accuracy, completeness or timeliness of the data they provide and are not liable for any damages relating to this data. The third-party data may not be further redistributed or used without the relevant third-party’s consent. Sources for index data include: Bloomberg, FactSet, ICE, FTSE Russell, MSCI and Standard & Poor’s.