How Private and Public Markets Are Combining to Fund the AI Buildout
Increasing numbers of investment-grade corporate borrowers are tapping private credit to finance major infrastructure projects. We think there are clear benefits to sharing funding needs across public and private markets. Here’s why.
Growth Is Happening Everywhere
Private credit isn’t what it used to be. Once a niche corner of finance focused on direct lending to high-risk, private equity–owned companies, it’s since blossomed into a thriving, diverse market where billions also flow into asset-backed loans and bespoke investment-grade deals for large corporations.
The growth has been driven in part by borrowers that need bespoke financing arrangements, often for vast infrastructure investment. Demand is also robust among a wide array of market participants, from retail investors to insurance companies eager for quality assets tailored to their liabilities.
The result is a market that—while less liquid than public markets—is beginning to rival them in depth and breadth. Thus, companies today navigate an increasingly efficient range of funding options that allows them to choose between public and private lenders. Many choose both.
Balancing Out the AI Investment Cycle
That makes sense. Flexibility in funding solutions is especially valuable for capex-intensive sectors and those involved in them, such as the hyperscalers that are rapidly building out artificial intelligence (AI) infrastructure. These firms—primarily Amazon, Microsoft, Alphabet Inc., Meta Platforms and Oracle—have an insatiable need for financing. S&P estimates that spending on global AI information technology will grow between 28% and 36% annually over the next five years.
When it comes to data centers and the energy sources needed to power the growth of AI, we believe in an “all-of-the-above” approach to capital formation. In our view, it reinforces a more sustainable and balanced AI investment cycle.
Earlier this year, Meta Platforms raised nearly $60 billion to support its data center build-out through two major transactions. The first, issued by a joint venture between Meta’s affiliate and a private lender, was tailored to support the project’s construction. Although executed as a private placement, the notes are tradable in the public investment-grade market. Meta then supplemented this financing with a $30 billion public investment-grade bond issuance, rated Aa3/AA–, further broadening its funding base.
In coming years, these hyperscalers are expected to issue significant amounts of debt to fund two core workloads: large-scale training superclusters, which require heavy upfront capex, and globally distributed inference capacity, which drives more recurring infrastructure investment. These companies generally have healthy cash flows and strong balance sheets that make it possible to fund major AI infrastructure projects with both public and private debt at a massive scale. Most have the ability to absorb significant incremental leverage without causing imminent credit downgrades, in our analysis.
In public credit markets, liquidity depends on the ability of investors to buy and sell bonds easily. This is only possible when there’s a steady pipeline of new and existing bonds from a diverse set of issuers. The scale, speed and duration of public issuance have shown that the market remains active, liquid and resilient.
There are other potential benefits, as well. Hyperscaler credit spreads have already moved wider as investors react to the sheer volume of expected supply, balance-sheet growth and a longer-dated payback timeline. This presents the opportunity to lock in durable income with issuers whose balance sheets can weather a more volatile AI-adoption path.
