Private credit has been financing the real economy for over a decade.

Factories, warehouses, data centers are all long lived projects with predictable counterparties.

But AI infrastructure does not fit that mold.

It is too CapEx intensive to be funded like software but too dynamic for traditional project finance, since hardware turns over in months.

This is why institutional capital flows overwhelmingly to the top of the stack. Private credit firms finance massive GPU clusters with offtake agreements from hyperscalers like Meta, Google, and Microsoft.

The long tail of AI companies sits below the threshold where bespoke deals make sense. They do not lack demand or revenue but they need access to financing that matches their cadence.

GPUs are financeable not because of their LTV but because they generate predictable inference cash flows. Most deals still assume 5-7 year GPU lifespans.

NVIDIA has moved to 12 month product cycles with 10-20x improvements in tokens-per-watt between generations. The effective economic life is closer to 12-24 months.

GPU backed debt is now estimated at $20-25 billion outstanding. The risk is not that AI demand disappears. The risk is that debt has been written against optimistic assumptions about hardware lifetimes and utilization.

@USDai_Official targets this narrower window. It does not assume GPUs will be productive for five or six years. It assumes there is a finite inference phase where cash flows are predictable and redeployment is possible if a borrower fails.

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