Executive summary
Should economic conditions in the tech sector sour, the burgeoning artificial intelligence (AI) boom may evaporate—and, with it, the economic activity associated with the boom in data center development.
Policymakers concerned about the deployment of clean energy and compute-focused infrastructure over the long term need a framework for managing the uncertainty in this sector’s investment landscape—and for understanding the local and regional impacts of a market correction that strands data centers and their energy projects. This framework requires understanding how a potential downward…
Executive summary
Should economic conditions in the tech sector sour, the burgeoning artificial intelligence (AI) boom may evaporate—and, with it, the economic activity associated with the boom in data center development.
Policymakers concerned about the deployment of clean energy and compute-focused infrastructure over the long term need a framework for managing the uncertainty in this sector’s investment landscape—and for understanding the local and regional impacts of a market correction that strands data centers and their energy projects. This framework requires understanding how a potential downward market correction in the tech sector might occur and, if so, how to sustain investment in critical energy infrastructure assets during potentially recessionary conditions.
Data centers themselves are an asset with the characteristics of both real estate and infrastructure: Data centers have tenants, chiefly large tech companies, that are undertaking expensive long-term capital investment plays with fast-depreciating assets and minimal cash flow to show for them. A careful review of these characteristics suggest that the sector faces the following salient risks:
- Cash flow uncertainty persists as the cost of providing AI inference services continues to rise. Leading AI inference service providers are not particularly differentiated from one another; this competitive market structure suppresses market participants’ pricing power and prevents them from recovering rising costs.
- The collateral value of a graphical processing unit (GPU), the sector’s keystone asset, looks poised to fall in the near-term. The value of chips fluctuates depending on uncertain user demand as well as the supply dynamics and technical specifications of new GPUs, now released yearly. The cash flow that GPU collateral can demand is suppressed due to the sector’s competitive market structure and the uncertain depreciation schedule of existing GPUs.
- Data center tenants will undertake multiple cycles of intense and increasingly expensive capital expenditure within a single lease term, posing considerable tenant churn risks to data center developers. This asset-liability mismatch between data center developers and their tenants will strain developers’ creditworthiness without guarantees from market-leading tech companies.
- Circular financing, or “roundabouting,” among so-called hyperscaler tenants—the leading tech companies and AI service providers—create an interlocking liability structure across the sector. These tenants comprise an incredibly large share of the market and are financing each others’ expansion, creating concentration risks for lenders and shareholders.
- Debt is playing an increasingly large role in the financing of data centers. While debt is a quotidian aspect of project finance, and while it seems like hyperscaler tech companies can self-finance their growth through equity and cash, the lack of transparency in some recent debt-financed transactions and the interlocked liability structure of the sector are cause for concern.
The first half of the report describes how those four trends interact across the industry: In the short-term, cash flows in the AI sector are wholly insufficient to service liabilities, and, as the AI sector grows, it does not look like this condition will change in the near-term. Given hyperscalers’ consistent ability to self-finance new investments out of equity, and (for the most part) their low debt-to-equity ratios, it is not yet clear that the data center boom is yet in such a dire situation. To be sure, these leverage ratios do not account for off-balance sheet vehicles—and debt is playing an increasing role in backstopping some of the latest deals, particularly for non-investment grade issuers. Each subsection of the first half of the report describes these trends in detail and, aided by T-Chart visualizations, showcases how assets and liabilities are distributed across the relevant market participants.
The second half of the report proceeds to explain how a market correction might occur and how it would cascade through the sector, making use of a consolidated T-Chart and drawing on the work of pioneering financial economist Hyman Minsky.The report concludes with a strategic framework for how policymakers ought to evaluate the consumer, regional and energy infrastructure-related impacts of a market correction. Policymakers should be wary of extending tax incentives that do not pay off or yield any benefit, as well as of hitching local budgets to this one growth industry. More importantly, though, policymakers should prepare an investment strategy centered on acquiring distressed energy infrastructure assets and repurposing them to serve future demand.
