While AI tools attract growing attention, their predictive accuracy often relies on large datasets and high computational cost. Fast, physics-based numerical simulations can achieve comparable or better predictions with far greater efficiency and transparency.This presentation demonstrates how reduced order models and fast implementations enable real-time, reliable predictive modeling. We argue that such physics-driven approaches remain the most effective and trustworthy alternative to purely AI-based methods.