Analyzing Oracle’s AI Exposure

You may have seen the Financial Times’ excellent article the other day analyzing Oracle ($ORCL), picking apart the tech giant’s exposure to OpenAI and the many ways that mammoth deal might turn sour for Oracle.

Or maybe you already knew Oracle’s potential risk here because you’ve been studying those same disclosures yourself — disclosures that are all readily available in Calcbench.


Don’t get us wrong; we appreciate the FT’s article, which is packed with good points and insight. But consider the key metrics that the article cites:


  • Segment-level revenues

  • Debt levels

  • Cash and short-term investments as a percent of total assets

  • Free cash flow

  • Leases

  • Debt-to-equity ratio


Calcbench tracks all those metrics for public filers. So if you had wanted to run your own analysis of Oracle’s exposure to OpenAI, perhaps comparing that exposure to other AI “hyperscalers” such as Microsoft ($MSFT), Google ($GOOG), and Amazon ($AMZN) — well, we’ve had all that data all along, there for the picking.


Always in the Footnotes


For example, the article notes near the bottom that Oracle “has signed at least five long-term lease agreements for US data centres that will ultimately be used by OpenAI, resulting in $100 billion of off-balance-sheet lease commitments.”


That’s absolutely right: if you look at Oracle’s most recent annual report (filed last May), you’ll see $147.4 billion in total liabilities, and only $11.5 billion in operating lease liabilities. 


So where is that disclosure of $100 billion in off-balance sheet lease commitments? It’s in the footnotes! Specifically, the number is tucked away at the bottom of a note blandly labeled “Leases.” See Figure 1, below.



You’ll notice that the exact number is $99.8 billion, and that number is tagged in XBRL. This means that you can use our See Tag History feature to see how that number has changed over time. We did exactly that — and those Calcbench fans with heart conditions may want to sit down and take a few deep breaths before reading further. Ready? See Figure 2, below.



Holy poop, Oracle’s off-balance sheet leasing commitments have increased by 24,822 percent in five years — from $411 million in 2020, to $43.4 billion at the end of the company’s fiscal 2025 six months ago, to $99.8 billion as of Aug. 31.  


Oracle’s off-balance sheet commitments more than doubled in its summer quarter. Presumably all of that is due to the deal that Oracle reached with OpenAI in the same period, where OpenAI has agreed to purchase $317 billion of computing power from Oracle for years to come. 


That computing power has to come from somewhere, which means Oracle needs vastly more data center resources. Hence its leasing commitments are soaring. 


The company will file its next quarterly report in early December, and lord only knows what the off-balance sheet leasing commitments will look like then. But as soon as Oracle does file those numbers, Calcbench will have them indexed and ready for analysis within minutes. 


Other Metrics


So that’s the off-balance sheet commitments. We also mentioned a squadron of other performance metrics cited in the Financial Times article: cash, debt, free cash flow, debt ratios, and so forth. Where can you find those? 


One great place to start is our Bulk Data Query page, which lists just about every financial disclosure a company might ever make. That includes individual items on the income statement, balance sheet, and statement of cash flows; plus other non-standard disclosures and even important liquidity ratios that you’d typically need to calculate yourself. Calcbench has all that for you.


For example, if you go to the Bulk Data page and scroll to the bottom, you’ll see more than two dozen profitability, liquidity, and solvency ratios. Figure 3, below, lists them all, with debt-to-equity highlighted since that one was mentioned in the FT article.



All these metrics can be calculated and then exported to your desktop in a tidy spreadsheet. You can then fiddle with them to your heart’s content, especially if you have our Excel Add-in. Alternatively, power users can have the data piped directly into your own models using our API. If you need help with either of those, email us at us@calcbenc.com any time.


Our point is simply that you don’t need to wait for the business press to write an in-depth article exploring possible AI bubbles or any other corporate financial scenario. If the data is out there, we have it, and you can consume it as soon as you’re ready.

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