ā¢ Power generation capacity has emerged as one of the most critical structural constraints limiting AI scaling.
ā¢ Dispatchable power providers such as Vistra and Constellation demonstrate asymmetric positioning due to their ability to deliver immediate energy capacity.
ā¢ Electrification and grid infrastructure providers including Eaton, Quanta Services, and Schneider Electric form essential expansion layers for AI deployment.
ā¢ GE Vernova represents a unique scaling layer due to its role in enabling incremental power generation capacity globally.
ā¢ Energy infrastructure scarcity differs from compute scarcity in that expansion timelines are measured in years, not quarters.
ā¢ Index Average sPEG: 1.27
This reflects that energy scarcity is now partially recognized by markets, but valuation dispersion remains across the constraint layer.
Read the full Definition of the sPEG (Scarcity Adjusted PEG ratio)
Read the AI Inrastructure Scarcity Index
Read the AI Memory Scarcity Index
The Scarcity-adjusted PEG (sPEG) framework evaluates valuation efficiency through the lens of structural scarcity.
Unlike traditional valuation metrics, sPEG incorporates both growth expectations and structural bottleneck positioning.
sPEG Formula:
sPEG = Forward P/E Ć· (Growth Rate Ć Scarcity Multiplier)
Forward P/E reflects consensus forward earnings estimates aligned to the index baseline date.
Growth Rate is a proprietary estimate developed through institutional analysis of sector expansion trajectories, infrastructure deployment timelines, and structural demand growth.
Scarcity Multiplier is a proprietary measure developed by exmxc.ai to quantify structural constraint intensity, replacement difficulty, and ecosystem control.
Lower sPEG values indicate stronger scarcity-adjusted valuation positioning.
This methodology reflects institutional valuation principles applied in mergers and acquisitions, where assets controlling constrained capacity command disproportionate strategic value.
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