OpenAI's series of hundred-billion-dollar deals has propped up the market caps of its numerous infrastructure partners. This creates a systemic risk, as these partners are making huge capital expenditures based on OpenAI's revenue projections. A failure by OpenAI to pay could trigger a cascade of financial problems across the tech sector.
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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.
While AI represents the largest segment of corporate debt, the risk is not yet systemic. The current build-out is primarily financed by the massive free cash flow from operations of megacap tech companies, not excessive leverage. The real danger emerges when this shifts to debt financing that cash flow cannot support.
OpenAI's CFO hinted at needing government guarantees for its massive data center build-out, sparking fears of an AI bubble and a "too big to fail" scenario. This reveals the immense financial risk and growing economic dependence the U.S. is developing on a few key AI labs.
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.
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.
Markets can forgive a one-time bad investment. The critical danger for companies heavily investing in AI infrastructure is not the initial cash burn, but creating ongoing liabilities and operational costs. This financial "drag" could permanently lower future profitability, creating a structural problem that can't be easily unwound or written off.
OpenAI's aggressive partnerships for compute are designed to achieve "escape velocity." By locking up supply and talent, they are creating a capital barrier so high (~$150B in CapEx by 2030) that it becomes nearly impossible for any entity besides the largest hyperscalers to compete at scale.
The massive OpenAI-Oracle compute deal illustrates a novel form of financial engineering. The deal inflates Oracle's stock, enriching its chairman, who can then reinvest in OpenAI's next funding round. This creates a self-reinforcing loop that essentially manufactures capital to fund the immense infrastructure required for AGI development.
The AI boom's sustainability is questionable due to the disparity between capital spent on computing and actual AI-generated revenue. OpenAI's plan to spend $1.4 trillion while earning ~$20 billion annually highlights a model dependent on future payoffs, making it vulnerable to shifts in investor sentiment.
The AI infrastructure boom is a potential house of cards. A single dollar of end-user revenue paid to a company like OpenAI can become $8 of "seeming revenue" as it cascades through the value chain to Microsoft, CoreWeave, and NVIDIA, supporting an unsustainable $100 of equity market value.
