By OBBM Network Editorial Staff

Derived from an episode of Economic War Room.

Artificial intelligence is reshaping the economy, but a hidden cash vortex may be inflating a bubble that could burst the broader market.

The Circular Nature of AI Funding

Kevin Freeman opens the discussion by noting a startling statistic from the Bank for International Settlements (BIS): between 2021 and 2025, 55% of the investment flowing into AI firms came from other AI firms. In nearly half of those deals, the investor was also a supplier or customer of the company it was investing in. This creates a loop where the same dollars are counted as investment, revenue, and demand, obscuring the true source of cash.

Freeman illustrates the loop with a simple analogy: a chipmaker invests in an AI startup, which then rents computing power from a cloud provider that purchases the chipmaker’s products. Each step records revenue, boosting stock prices and enabling further investment. Multiply this process across hundreds of billions of dollars, and the financial picture becomes distorted.

Why Companies Embrace Self‑Financing

The scarcity of critical inputs—chips, power, and computing capacity—drives AI firms to lock in supply and customers simultaneously. As Freeman explains, “If you can’t get chips, power, or computing time, you can’t compete.” By investing in each other, firms secure the resources they need while appearing to generate external demand.

These arrangements are legal and can serve real economic purposes, according to the BIS. However, the repeated counting of the same money makes it difficult to assess how much demand originates from genuine, outside customers.

NVIDIA and the Scale of the Loop

NVIDIA sits at the center of this phenomenon. Its latest quarterly filing reported $99 billion in equity investments, many of which are stakes in AI model makers and cloud companies that purchase NVIDIA products. The company also disclosed $279 billion in supply and capacity commitments, up sharply from $119 billion a quarter earlier, and a $105 billion credit support tied to a data‑center campus in Ohio for OpenAI.

Freeman cautions, “If outside customers don’t show up, NVIDIA itself becomes the customer of last resort.” While he stops short of alleging fraud, he highlights the classic risk when a supplier finances its own buyers: the need to scrutinize underlying demand.

OpenAI’s Grand Projections and the Funding Gap

OpenAI reported roughly $13 billion in revenue for 2025 and announced a plan to spend $600 billion on computing power through 2030—a scaled‑back figure from an earlier $1.4 trillion commitment. To justify such spending, OpenAI projects revenues exceeding $280 billion by 2030, implying a 24% annual growth rate. Freeman asks, “Is it possible? Maybe. Is it guaranteed? No.” The gap between projected spending and realistic revenue underscores the fragility of the AI financing model.

He draws a parallel to past technology booms, noting that while the dot‑com surge produced massive productivity gains, many investors who funded the early wave suffered losses when the bubble burst.

Warning Signals for the Broader Market

Freeman outlines five warning lights: a slowdown in big‑tech spending, higher borrowing costs for AI‑related debt, decelerating NVIDIA order growth, reduced cash flow from AI customers, and the potential for hidden debt to surface as defaults. He explains that because AI‑heavy stocks now make up roughly a third of the S&P 500, a correction in this sector could reverberate throughout the entire market.

“If you own an index fund, you may own a much bigger AI bet than you think,” Freeman warns, emphasizing that the concentration of AI exposure amplifies systemic risk.

Historical Context and the Path Forward

The episode places today’s AI financing in historical perspective, comparing it to the railroad overbuilding of the 1870s and the telecom bust of the late 1990s. In each case, over‑investment financed by debt eventually led to a correction, yet the underlying infrastructure survived and later powered new growth.

Freeman concludes that AI itself is a transformative technology—writing software, answering calls, reading medical scans, and accelerating research. The challenge lies in ensuring that the financing mechanisms do not outpace real, sustainable demand.

Understanding the AI money loop is essential for investors, policymakers, and anyone watching the next wave of technological disruption. As the market grapples with the balance between innovation and financial prudence, the lessons of past bubbles may prove decisive.

The full episode of Economic War Room is available on OBBM Network TV.