This theorem analyzes covariance estimation or principal-component behavior, especially spectral error, eigenvector alignment, and spiked-model thresholds. It is useful for comparing estimators, constructing lower bounds, or analyzing random-matrix behavior in regimes where dimension and sample size grow together.