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Name Thesis (plain English)

For two years the data centre buildout was financed out of Microsoft/Google/Meta/Amazon free cash flow. That phase is ending. Morgan Stanley estimates roughly $3 trillion of data-centre capex is coming and less than 20% has been deployed. The remaining 80%+ increasingly gets funded with debt, vendor financing, SPVs, and circular vendor-customer deals (chipmaker invests in cloud startup, startup buys chips, revenue gets booked). Tech-sector debt issuance is spiking and even sell-side strategists expect investment-grade spreads to widen because of it.
The mispricing: the equity market treats every node in the AI supply chain as a beneficiary of the same secular tailwind. But once the buildout is leveraged, the supply chain bifurcates into two completely different risk profiles, and they should not trade at correlated multiples.
The short side (the interesting half). Look for the marginal, debt-funded layer:

"Neocloud" / GPU-rental businesses whose model is borrow-to-buy-GPUs, where the asset (the GPU) depreciates faster than the debt amortises. A Blackwell-era GPU bought on 5-year debt but economically obsolete in 3 is a structurally short-able balance sheet.
Data-centre developers and REIT-like names with backlog that assumes power interconnection and permitting timelines that physically cannot be met. Backlog is not revenue if the substation isn't energised.
Companies with aggressive server "useful life" assumptions in their 10-Ks. This is empirically checkable: pull the depreciation schedule, compare stated useful life to the real GPU refresh cycle. A widening gap is a future earnings air-pocket. Several hyperscalers extended useful-life assumptions in 2023-24 to flatter earnings; that reverses.
Anyone whose customer concentration is itself an unprofitable AI startup. Circular revenue.

Claude will parse this into structured screening criteria automatically.

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