AI Debt is more like 2008 not 2000 - Silicon Subprime loans
u/BearWithMeGM ·
Reddit — r/ValueInvesting
· June 11, 2026 at 19:23
· ⬆ 15 pts
· 💬 15 comments
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Summary
The post warns that $200B+ in loans backed by AI hardware (e.g., H100 GPUs) face default risk due to rapid obsolescence and artificially long amortization schedules.
Author compares this to the 2008 subprime crisis, arguing that falling rental rates for old chips, trade tensions, and war could trigger widespread non‑performance (“Silicon Subprime”).
Quality assessment: Well‑reasoned speculation with a specific, data‑backed thesis on structural risk in AI infrastructure debt; not a deep “DD” on a single company but a macro‑level warning.
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In the end of 2025 there was $200B worth of debt which used AI hardware as collateral. With $800B expected to be added over the next two years.Those are 10-15 year long period loans.
AI hardware's amortization rate has been artificially prolonged to 6-8 years so that it wouldn't drag earnings too much. And even if you think that the physical life of a modern compute rack is 6-8 years, which is not really tested or guaranteed. The rate of innovation makes hardware real amortization speed much faster. Don't go any further than the same neoclouds to prove the point. Rates for H100 changed from $4.7-$8.0 per hour in 2024 to $2.0-$2.5, that's a 60% drop in just 2 years.
And with Vera Rubin being so much more efficient in compute per unit of energy, it might come to a point that it is simply economically unviable to run H100s anymore. Those H100s that someone took a loan for the next 15 years.
Also with so many datacenters being postponed to 2028, I can imagine some of that hardware will get old without being even used.
Not to mention the trade wars, the Iran war, the export ban of rare earth metals. Like I can imagine a lot of those loans being non performing. Silicon Subprime loans.
AI hardware rental rates for H100 dropped 60% in two years; new Vera Rubin chips make older hardware uneconomical to run, yet 10‑15 year loans use that hardware as collateral. Loan defaults would force distressed sales of used AI hardware, accelerating price declines and impairing asset values for all semiconductor companies exposed to AI compute demand. Short SMH as a proxy for the sector that will suffer from write‑downs and oversupply as the “Silicon Subprime” cycle unwinds. Innovation could keep older hardware viable for inference; government subsidies or war‑driven demand may extend useful life; loan restructuring could delay defaults.