Private Credit Fuels AI Boom, Raising Concerns of Overheating and Bubble Risks

Private Credit-Powered AI Boom at Risk of Overheating, UBS Says

Private credit lenders, and their deep pockets, are rapidly becoming an important source of capital for artificial intelligence development. That's raising concerns at UBS Global Research. As private credit grows beyond its roots of lending to smaller, heavily indebted companies, large-scale tech firms have started to see the asset class as a way to fulfill their growing capital needs. Private debt had about $450 billion loaned to the technology sector as of early 2025, up $100 billion from 12 months earlier, according to UBS estimates. For business development companies -- or funds that hold direct corporate loans -- tech lending has nearly doubled to $150 billion from $80 billion, a bank report showed.

Credit fuels the AI boom -- and fears of a bubble

Credit investors are pouring billions of dollars into artificial intelligence investments, just as industry executives and analysts are raising questions about whether the new technology is inflating another bubble. JPMorgan Chase & Co. and Mitsubishi UFJ Financial Group are leading the sale of a more than $22 billion loan to support Vantage Data Centers' plan to build a massive data-center campus, people with knowledge of the matter said this week. Meta Platforms Inc., the parent of Facebook, is getting $29 billion from Pacific Investment Management Co. and Blue Owl Capital Inc. for a massive data center in rural Louisiana, Bloomberg reported this month. And plenty more of these deals are coming. OpenAI alone estimates it will need trillions of dollars over time to spend on the infrastructure required to develop and run artificial intelligence services. At the same time, key players in the industry acknowledge there is probably pain ahead for AI investors. OpenAI Chief Executive Officer Sam Altman said this week that he sees parallels between the current investment frenzy in artificial intelligence and the dot-com bubble in the late 1990s. When discussing startup valuations he said, "someone's gonna get burned there." And a Massachusetts Institute of Technology initiative released a report indicating that 95% of generative AI projects in the corporate world have failed to yield any profit. Altogether, it's enough to make credit watchers nervous. "It's natural for credit investors to think back to the early 2000s when telecom companies arguably overbuilt and over borrowed and we saw some significant writedowns on those assets," said Daniel Sorid, head of U.S. investment grade credit strategy at Citigroup. "So, the AI boom certainly raises questions in the medium term around sustainability." The early build-out of the infrastructure needed to train and power the most advanced AI models was largely funded by the AI companies themselves, including tech giants like Alphabet Inc.'s Google and Meta Platforms Inc. Recently, though, the money has been increasingly coming from bond investors and private credit lenders. The exposure here comes in many shapes and sizes, with varying degrees of risk. Many large tech companies -- the so-called AI hyperscalers -- have been paying for new infrastructure with gold-plated corporate debt, which is likely safe due to the existing cash flows that secure the debt, according to recent analysis from Bloomberg Intelligence. Much of the debt funding now is coming from private credit markets. "Private credit funding of artificial intelligence is running at around $50 billion a quarter, at the low end, for the past three quarters. Even without factoring in the mega deals from Meta and Vantage, they are already providing two to three times what the public markets are providing," said Matthew Mish, head of credit strategy at UBS. And many new computing hubs are being funded through commercial mortgage-backed securities, tied not to a corporate entity, but to the payments generated by the complexes. The amount of CMBS backed by AI infrastructure is already up 30%, to $15.6 billion, from the full year total in 2024, JPMorgan Chase & Co. estimated this month. Sorid and a colleague at Citi put out a report on Aug. 8 focusing on the particular risks for the utility firms that have boosted borrowing to build the electrical infrastructure needed to feed the power-hungry data centers. They and other analysts share a commonly held concern about spending so much money right now, before AI projects have shown their ability to generate revenue over the long term. "Data center deals are 20 to 30 year tenor fundings for a technology that we don't even know what they will look like in five years," said Ruth Yang, global head of private market analytics at S&P Global Ratings. "We are conservative in our assessment of forward cash flows because we don't know what they will look like, there's no historical basis." The stress has begun to appear in the rise of payment-in-kind loans to tech-oriented private credit lenders, UBS Group noted. In the second quarter, PIK income in BDCs reached the highest level since 2020, climbing to 6%, according to UBS. But the fire hose of money is unlikely to stop anytime soon. "Direct lenders are constantly raising capital, and it has to go somewhere," said John Medina, senior vice president in Moody's Global Project and Infrastructure Finance Team. "They see these hyperscalers, with this massive capital need, as the next long-term infrastructure asset."

UBS Strategists Warn of 'Overheating Risk' Around AI Investments | PYMNTS.com

Private debt loaned to the technology sector increased by $100 billion over the past 12 months, reaching a total of $450 billion, while business development companies' tech lending leapt from $80 billion to $150 billion, the UBS note said, per the report. The demand for this capital is driven by tech companies' spending on AI data center construction and other AI development costs, according to the report. "This phenomenon could sustain significant growth plans for AI and other hyperscaler companies, sowing the seeds of an upside scenario and increasing overheating risk," the UBS strategists led by Matthew Mish wrote in the note, per the report. Microsoft said in a July 30 earnings report that its capital expenditures are projected to exceed $30 billion in the first quarter to meet the rising infrastructure needs driven by strong demand for cloud and AI. During the fiscal year ended June 30, Microsoft opened new data centers across six continents and now has more than 400 facilities in 70 regions.

Credit Flows Into AI Projects Amid Bubble Worries | PYMNTS.com

By completing this form, you agree to receive marketing communications from PYMNTS and to the sharing of your information with our sponsor, if applicable, in accordance with our Privacy Policy and Terms and Conditions. For example, there is a $22 billion-plus loan sale led by JPMorgan Chase and Mitsubishi UFJ Financial Group, Bloomberg News reported Saturday (Aug. 23). Sources with knowledge of the matter say that funding will help Vantage Data Centers' plan to build a massive new campus. Meta, meanwhile, is getting $29 billion from Pacific Investment Management and Blue Owl Capital for another gargantuan data center in rural Louisiana. More of these deals are expected, the report added, as OpenAI projects it will ultimately need trillions of dollars to develop the proper AI infrastructure. This is all happening at a time when fears of an AI bubble are driving down tech stocks, with companies like Amazon, Apple, Google and Nvidia also seeing declines last week. "Pundits pointed to two culprits: Comments by OpenAI CEO Sam Altman that AI could be in a bubble, and a report from MIT saying that 95% of companies it studied are getting "zero return" from generative AI," PYMNTS wrote Friday (Aug. 22). "Private credit funding of artificial intelligence is running at around $50 billion a quarter, at the low end, for the past three quarters," Matthew Mish, head of credit strategy at UBS, told Bloomberg. "Even without factoring in the mega deals from Meta and Vantage, they are already providing two to three times what the public markets are providing." In other AI news, PYMNTS spoke last week with Discover® Network Director of Payments Innovation Kate Lybarger about efforts by businesses to both use this new technology, while also deploying in ways that align with evolving customer expectations and regulatory scrutiny. Lybarger contended that AI isn't a revolution for revolution's sake. It's a relatively new tool, although a powerful one, that must be employed precisely, transparently and responsibly. "If you're building anything with AI, nothing matters more than trust," she said. "These solutions require a lot of data, potentially your customers' information."