Mean-Variance Optimization remains the intellectual cornerstone of modern portfolio theory, yet its real-world deployment via plug-in MVO often delivers unstable, over-leveraged portfolios that collapse out-of-sample. The core insight from VertoxQuant’s analysis is profound: raw plug-in MVO does not merely propagate estimation error—it systematically amplifies it. This error-maximization phenomenon occurs because the optimizer’s inverse-covariance operator assigns extreme weights to directions that appear low-risk, which, in finite samples, are dominated by noise rather than signal. For academics, this reveals a fundamental statistical pathology; for practitioners, it explains why backtests sparkle while live portfolios bleed.