Dr Mythili KolluruK V Ch Madhu Sudhana Rao
As global leaders race to develop and deploy artificial intelligence (AI), it is important to understand the financial commitments behind the technology boom. The question is not whether AI will change the world, but whether the companies borrowing to build AI infrastructure can convert the opportunity into cash in time to meet their financial obligation.
AI debt is a descriptive term that refers to companies’ borrowings and related financial commitments that are utilised to build the needed infrastructure for AI processes, data centres, servers and computing capacity. Financing pathways include equipment financing, bank loans, corporate bonds, leases, and borrowing through separate project companies. Building secure AI computing capacity creates strategic tension and risk. Companies have two options: risk losing customers or expanding slowly, or expanding rapidly and risk taking on larger financial borrowings. The basic business model is:
Raise financing acquire computing infrastructure sell computing services collect cash pay operating costs, interest and principal.
The model looks straightforward, but in this investment race the financial arrangements are increasingly complex; uncertainty remains about whether cash arrives on time and in sufficient amounts to meet borrowing commitments. An important distinction to be understood here is that revenue growth is not the same as cash available to repay lenders. Even if a company sells more computing services, cash generated from operations after capital expenditure can remain negative during rapid expansion.
Oracle’s recent financial debate illustrates the gap between revenue growth and cash generation. Oracle is an enterprise software and cloud-services company whose reported $300 billion agreement to supply computing services to OpenAI has intensified scrutiny of its infrastructure spending. The agreement represents expected customer revenue, not Oracle’s investment expenditure or debt. According to FactSet figures cited in CNBC’s October 19, 2026, report, Oracle had approximately $118 billion in outstanding long-term debt and $39 billion in capital and lease obligations. Oracle is currently facing a financing test.
S&P’s reported warning was conditional: it could lower Oracle’s credit rating if leverage remained too high or Oracle could not demonstrate a credible path to positive free operating cash flow by fiscal 2029. The question is whether future AI cash flows will justify today’s financial commitments.
The AI debt dilemma is a timing problem: companies borrow and invest heavily to build AI infrastructure without the cash needed to justify the investments. Another company that illustrates this dilemma is CoreWeave, which shows why enthusiasm for artificial intelligence investment is viewed differently, and confidence in its financing is uncertain. CoreWeave is a US-based company that supplies a powerful computing infrastructure, particularly for AI. CoreWeave's business model is:
Finance infrastructure acquire processors and data-centre capacity rent computing services collect customer payments.
CoreWeave rents access to Nvidia-powered infrastructure primarily together with the software and services. Recently, it has expanded rapidly to meet growing demand for AI computing. It took on substantial borrowing and obligations to finance infrastructure. Reuters reported on October 15, 2026, that its shares and bonds had slumped as investors questioned the borrowing behind the AI buildout. This raises concerns about financing and repayment risks.
The Oracle and CoreWeave case shows a highly demanding and complex AI financing ecosystem, not a reliable, predictable crash date. Today, companies increasingly rely on external funding for AI expansion rather than internal cash flows (IMF, 2026). As AI financing expands and financial linkages grow more complex, policy frameworks should remain attentive to the accumulation of AI-related systemic risks across financial systems.
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