A long-form systems essay arguing that machine learning fails when used as an automated decision-maker in unstable environments. It reframes ML as an early-warning instrument that exposes pressure, instability, and shrinking intervention windows, preserving human judgment instead of replacing it with late, brittle decisions.

A long-form systems essay arguing that most metrics fail because they measure outcomes instead of accumulated pressure. It reframes collapse as a consequence of debt, buffer depletion, and delayed feedback, and explains why early warning depends on measuring pressure rather than predicting final events.

🔍 Analyze pressure metrics to uncover system truths, prevent failures, and enhance early warning mechanisms for better decision-making.

⚠️ Warn against automated decision-making in machine learning; empower users with awareness and early signals for better outcomes.