Carlyle warns: Private equity is competing for trillion dollars in AI infrastructure financing, and concentration risks may repeat the software loan crisis

The Zhitong Finance App learned that Carlyle Group said that private equity credit institutions are competing to finance artificial intelligence (AI) infrastructure construction, or will repeat the mistakes of concentrated credit exposure in the software industry.

A white paper released by Carlyle on Thursday indicates that the industry may need to provide around $1 trillion in capital to finance AI computing power infrastructure. This scale is equivalent to more than half of the total assets currently managed by private equity credit.

According to the white paper, failure to set clear limits on concentration in the field of AI computing power may become the “biggest mistake.”

Mark Jenkins, Co-President of Carlyle and Head of Global Credit and Insurance, said in an interview: “We are in a period where revenue models are still uncertain until now. In such an environment, as credit investors, it's hard for us to say 'OK, we've taken it all. '”

Private equity credit management agencies are increasingly being asked to finance large-scale expansions of AI infrastructure. According to estimates, related capital expenditure is expected to exceed $5 trillion by 2030. There are various forms of financing, including data center construction and electricity financing, loans secured by chips that support this technology, and loans to special purpose carriers.

The white paper points out that unlike software, the credit risk of data centers and other AI-related assets is more speculative and is more likely to be related to overall economic trends, while many of the financing structures currently in use have not been tested to a large extent.

For Carlyle, this doesn't mean shying away from investing in AI. “We want to take risks, but we want to do it in a balanced way,” Jenkins said.

He said that one of the biggest challenges facing lenders is that it is still unclear where AI's final profits will be accumulated — in chip makers, data centers, or application development companies.

The white paper shows that the software industry experienced a similar boom between 2020 and 2022, accounting for about half of private equity transactions during the same period. The influx of lenders into software companies is partly due to their recurring subscription revenue being viewed as stable and relatively less vulnerable to economic downturns.

But the rise of generative AI challenges this assumption, leaving software companies facing the common threat of technology obsolescence. Since then, software loans have struggled in the syndicated loan market, making it difficult for borrowers to refinance, while some private equity credit funds have experienced an increase in redemption requests.

Jenkins believes that AI infrastructure financing is repeating a similar lesson: on the surface, financing projects may be scattered, and the underlying capital may eventually be concentrated in a few leading companies. He observed that the vast majority of lower-level financing on the market is concentrated on seven or eight high-quality companies.

He said it is particularly important to understand the final counterparty, the contracts supporting the financing, and the value of the underlying assets.

Jenkins said, “As investors, people need to think very, very carefully about what your counterparty's risk exposure is, how to write contract terms, and what the final asset value is. In a crisis scenario, all of this will be critical. And when everything is going well, they just don't seem to matter.”