The current trend of AI infrastructure providers investing in their largest customers, who then use that capital to buy their products, mirrors the risky vendor financing seen in the dot-com bubble. This creates circular capital flows and potential systemic risk.
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Major tech companies are investing in their own customers, creating a self-reinforcing loop of capital that inflates demand and valuations. This dangerous practice mirrors the vendor financing tactics of the dot-com era (e.g., Nortel), which led to a systemic collapse when external capital eventually dried up.
The AI boom is fueled by 'club deals' where large companies invest in startups with the expectation that the funds will be spent on the investor's own products. This creates a circular, self-reinforcing valuation bubble that is highly vulnerable to collapse, as the failure of one company can trigger a cascading failure across the entire interconnected system.
Tech companies are acquiring essential AI hardware through complex deals involving stock warrants. The deal announcement inflates the chipmaker's stock, giving the warrants immediate value. This value is then used as capital to complete the original purchase, creating money "out of nothing."
Current AI investment patterns mirror the "round-tripping" seen in the late '90s tech bubble. For example, NVIDIA invests billions in a startup like OpenAI, which then uses that capital to purchase NVIDIA chips. This creates an illusion of demand and inflated valuations, masking the lack of real, external customer revenue.
Instead of simple cash transactions, major AI deals are structured circularly. A chipmaker sells to a lab and effectively finances the purchase with stock warrants, betting that the deal announcement itself will inflate their market cap enough to cover the cost, creating a self-fulfilling financial loop.
The AI ecosystem appears to have circular cash flows. For example, Microsoft invests billions in OpenAI, which then uses that money to pay Microsoft for compute services. This creates revenue for Microsoft while funding OpenAI, but it raises investor concerns about how much organic, external demand truly exists for these costly services.
A new risk is entering the AI capital stack: leverage. Entities are being created with high-debt financing (80% debt, 20% equity), creating 'leverage upon leverage.' This structure, combined with circular investments between major players, echoes the telecom bust of the late 90s and requires close monitoring.
The memo flags deals where money is "round-tripped" between AI players—for example, a chipmaker investing in a startup that then uses the funds to buy its chips. This practice, reminiscent of the 1990s telecom bust, can create illusory profits and exaggerate progress, signaling that the market is overheating.
Current financing deals in AI, sometimes viewed as risky, are analogous to the General Motors Acceptance Corporation (GMAC) funding car dealers in the 1920s. This isn't a sign of fake demand like the dot-com bubble, but rather a necessary mechanism to fund infrastructure for red-hot, genuine customer demand.
Large tech firms invest in AI startups who then agree to spend that money on the investor's services. This creates a "circular" flow of cash that boosts the startup's perceived revenue and the tech giant's AI-related sales, creating questionable accounting.
