Tech Giants Shift Over $120 Billion in AI Data Center Costs Off Balance Sheets

Several of the world’s largest technology companies have quietly shifted more than $120 billion in artificial intelligence (AI) data center expenditures off their own balance sheets, using specialized financial structures that are raising questions among investors and analysts about transparency, risk, and the long-term stability of the burgeoning AI infrastructure boom.

This trend reflects the immense capital demands required to build and operate the sprawling data centers that power advanced AI models, such as generative AI systems and large-scale machine learning platforms. Instead of reporting these liabilities directly, companies are increasingly using special-purpose vehicles (SPVs) and similar entities backed by private credit firms and Wall Street lenders to finance construction and related costs. 

What’s Happening Behind the Scenes

Tech leaders, including Meta Platforms, Elon Musk’s xAI, Oracle, and CoreWeave, have employed SPV-based financing to raise vast sums earmarked for AI data center build-outs. These vehicles borrow money from private capital sources—such as Pimco, BlackRock, Apollo, Blue Owl Capital, and JPMorgan—and then lease or finance data center assets back to the companies. This keeps the associated debt off the parent company’s balance sheet, preserving key financial ratios like debt-to-equity and avoiding an apparent spike in corporate leverage. 

For example, Meta led one of the largest deals of its kind, securing about $30 billion through an SPV backed by private capital. Oracle has arranged over $60 billion in similar financing tied to data center facilities leased to AI partners. Many of these arrangements fund not only construction but also advanced cooling, power infrastructure, and specialized networking needed to support high-intensity AI workloads. 

 Off-Balance-Sheet Financing

There are clear financial incentives behind these strategies:

• Improved reported credit metrics: By moving liabilities off their main financial statements, companies can maintain stronger balance-sheet health in the eyes of investors and credit rating agencies.

• Greater borrowing capacity: SPVs backed by dedicated assets make it easier to attract large amounts of private capital, spreading risk across external lenders rather than concentrating it within the corporate parent.

• Flexibility to scale rapidly: Data-center build-outs require huge up-front investment. Off-balance-sheet constructs enable firms to tap new pools of capital without triggering investor concern over rising corporate debt. 

Concerns and Market Risks

Despite the appeal of these financing techniques, experts warn of hidden vulnerabilities:

• Lack of transparency: Off-balance-sheet commitments can obscure the true scale of corporate obligations. If AI demand weakens or expected revenue fails to materialize, companies or their SPVs could struggle to service debt, with knock-on risks for lenders and broader markets.

• Concentrated risk: Many of these SPVs are backed by the same group of private credit firms. If the AI build-out falters, losses could cascade across lenders exposed to multiple deals.

• Potential systemic impact: While hyperscale companies such as Google, Amazon, and Microsoft have mostly self-financed their own data centers to date, the growing use of private credit to fund AI infrastructure highlights how the tech boom is increasingly tied to large-scale financial engineering.

Analysts caution that the massive capital flows into AI data center financing underscore both the technological ambition and the financial complexity of the current AI expansion. They also note that the structures being deployed echo past financial innovations that obscured risk prior to major market downturns, prompting calls for more rigorous disclosure and oversight.