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AI infrastructure is no longer just a technology buildout. It is becoming a capital structure question.

Aggregate hyperscale capex has crossed above free cash flow. At the same time, physical constraints around power, memory, and compute are becoming visible across the stack.

Recent utility consolidation and stronger memory pricing signals are not isolated developments. They point to the same pressure surfacing in different layers of the AI infrastructure system.

That is where the analytical work now sits.

A portfolio may appear to have broad “AI exposure,” but that exposure can sit in very different parts of the stack — infrastructure, compute, energy and grid capacity, hardware supply, or the instruments used to hedge cost volatility.

Each layer carries a different margin profile, financing structure, and return horizon.

The question is not whether the buildout continues. It is whether the framework used to evaluate AI exposure can distinguish where the capital is deployed, where the risk is financed, and where the return is captured.

For investors, “AI exposure” is no longer a single category.

The harder question is whether the valuation framework can separate where capital is being spent from where returns may ultimately accrue.

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