sPEG (Scarcity-adjusted PEG) is a proprietary valuation metric developed by exmxc.ai to measure how efficiently a companyâs valuation reflects its growth potential after accounting for structural scarcity within the AI infrastructure stack.
Traditional valuation metrics such as P/E and PEG treat all growth equally. However, in the AI era, growth occurring within structurally scarce layersâsuch as compute, fabrication, memory, lithography, power, and networkingâcarries disproportionate strategic value.
sPEG corrects for this distortion by incorporating scarcity into valuation efficiency analysis.
Core Formula:
sPEG = Forward P/E á (Growth Rate à Scarcity Multiplier)
Components:
Forward P/E: Market valuation relative to expected forward earnings
Growth Rate: Proprietary exmxc.ai growth estimate reflecting structural AI demand exposure
Scarcity Multiplier: Proprietary exmxc.ai coefficient quantifying structural bottleneck intensity, replacement difficulty, and infrastructure criticality
Interpretation:
Lower sPEG indicates higher valuation efficiency
Higher sPEG indicates lower valuation efficiency
Companies controlling structurally scarce infrastructure layers typically exhibit superior sPEG profiles due to durable demand, constrained supply, and long replacement cycles.
sPEG serves as the foundational valuation framework for exmxc.ai indices, including the AI Infrastructure Scarcity Index, AI Compute & Acceleration Index, and AI Energy & Power Constraint Index.
sPEG is designed specifically for the AI era, where valuation efficiency is determined not only by growth, but by a companyâs position within the global infrastructure bottleneck hierarchy.
Read about the AI Infrastructure Scarcity Index