Source: Zhitong Finance

Carlyle Group stated that private credit institutions are competing to provide financing for artificial intelligence (AI) infrastructure, which may repeat the concentrated credit exposure seen in the software industry.

The white paper released by Carlyle on Thursday pointed out that the industry may need to provide about $1 trillion in funding to finance AI computing infrastructure. This scale is equivalent to more than half of the current private credit assets under management.

The white paper states that failing to set clear limits on the concentration in the AI computing power sector could become 'the biggest mistake'.

Carlyle Co-President and Global Head of Credit and Insurance Mark Jenkins said in an interview: 'We are in a period where the revenue model is still uncertain. In such an environment, as credit investors, it is hard for us to say, 'Okay, we are all in.''

Private credit management institutions are increasingly being asked to provide financing for the large-scale expansion of AI infrastructure. It is estimated that by 2030, related capital expenditures are expected to exceed $5 trillion. The forms of financing are diverse, including loans for data center construction and power financing, loans secured by chips that support this technology, and loans to special purpose vehicles.

The white paper points out that, unlike software, credit risks of data centers and other AI-related assets are more speculative and are more likely to be related to the overall economic trend, while many of the financing structures currently in use are largely untested.

For Carlyle, this does not mean avoiding AI investments. Jenkins said, 'We want to take risks, but hope to do so in a balanced way.'

He stated that one of the biggest challenges facing lenders is that it is still unclear where the ultimate profits from AI will accumulate—whether in chip manufacturers, 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. Lenders poured into software companies, partly because their recurring subscription revenues are seen as stable and relatively less affected by economic downturns.

However, the rise of generative AI challenges this assumption, putting software companies at a common risk of technological obsolescence. Since then, software loans have struggled in the syndicated loan market, borrowers have faced difficulties refinancing, and some private credit funds have experienced increased redemption requests.

Jenkins believes that AI infrastructure financing is repeating similar lessons: seemingly decentralized financing projects may ultimately see underlying funds concentrated in a few leading companies. He observes that the vast majority of underlying financing in the market is concentrated in seven or eight high-quality targets.

He stated that it is particularly important to understand the ultimate counterparty, the contracts supporting the financing, and the value of the underlying assets.

Jenkins stated: 'As an investor, people need to think very, very carefully about what your counterparty exposure is, how the contract terms are written, and what the ultimate asset value is. In crisis scenarios, all of these will be crucial. And when everything is going smoothly, they seem irrelevant.'

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