By Leeja van Bezouwen, Credit Analyst, and Victor Verberk, CIO
Something unusual is happening in credit markets. Companies with negative free cash flow, capex running at multiples of their operating cash and sometimes even revenues, and projected debt growth into the hundreds of billions are receiving investment grade ratings.
We’ve spent a lot of time on AI infrastructure financing as part of our research, looking at the unit economics of AI models, the financial position of the labs, and how the underlying deals actually work. Last time we looked into the accounting practices and circularity of company ownership[1]. Now we turn to our investment process.
Some AI infrastructure providers (hyperscalers) have made a strategic decision to position themselves at the centre of the AI buildout. These are taking on long-term obligations and investing heavily in specialised hardware to capture a share of what they believe could be a generational market. But when we look at the characteristics of the activity being funded, what we see looks very different from what the credit rating implies. That is important knowledge as our investment process includes a corporate analysis comprising five pillars: Business Position, Corporate Strategy, Sustainable Investing, Financial Position and Structure. This process has helped this credit team avoid a single default across our investment grade portfolios for over 18 years. Let’s have a look.
These providers typically lease facilities on terms of 15 to 19 years, install the hardware, and rent out computing capacity to tenants (AI labs) on contracts of roughly five years with renegotiation provisions. The lease obligations are long and fixed. The customer revenue is shorter and conditional. The hardware, however, is no longer frontier-grade within two years as new chip generations arrive. The depreciation is very punitive. Pricing for compute is not static either: industry data suggests rental rates for previous-generation chips soften as newer hardware comes to market. This combination, long fixed costs set against shorter revenues and fast-eroding assets, is what weighs on the Business Position, since reinvestment and the matching of cash flows become critical.
The major AI Labs remain heavily loss-making, with some reporting negative operating margins exceeding 100%[2]. Their ability to meet multi-year infrastructure commitments depends on continued access to equity capital markets to plug the gap. Some financing arrangements also have a circularity to them, with chip suppliers investing in the same companies whose infrastructure commitments ultimately fund chip purchases, making the Structure opaque. The investment race is now so fierce that debt is being issued as well, which changes the Financial Position materially.
The datacentre facilities are also less fungible than they appear. Much of what’s being built is purpose-built for frontier AI training: gigawatt-scale clusters with specialised cooling and networking designed for workloads that only a handful of organisations run globally. The broader economy’s AI demand is predominantly for inference, a different workload that runs on standard cloud infrastructure. Re-tenanting assumptions depend on a pool of replacement demand that is, for now, very narrow. A risky strategy since nobody can tell us whether re-tenanting is possible.
We invest according to five Investment Principles[3]: Managing behavioural biases, Mean Reversion of markets, understanding the global macro, Sustainable Investing and Winning by not Losing while nurturing a safety margin (for error). This last one is important for Investment Grade clients.
Investment grade has always meant a margin of safety, room for things to go differently than planned without the creditor getting impaired. What the current datacentre structures require to be and remain profitable is the opposite: everything needs to go right, simultaneously, over a long time horizon. Demand has to arrive at scale to provide real external client money flows. Hardware has to stay current and depreciation speed is unclear. Tenants have to stay funded and the structure of funding requires transparency. Pricing has to hold to prevent holes in the capital expenditures programs. In our view, this doesn’t imply a prudent margin of safety, nor an investment grade rating.
[1] https://www.osmosisim.com/a-credit-perspective-on-ai-infrastructure-accounting/
[2] Financial Times – “OpenAI spending hit $34bn last year ahead of planned IPO”
[3] Bias, Victor Verberk 2023
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