Is the end of insight in sight?

The rise of deep learning challenges the longstanding scientific ideal of insight—the human ability to understand phenomena by uncovering underlying mechanisms. From a physics perspective, we examine this tension through a case study: a physics-informed neural network (PINN) trained on rarefied gas dynamics governed by the Boltzmann equation. Despite strong physical constraints and a system with clear mechanistic structure, the trained network’s weight distributions remain close to Gaussian, showing no coarse-grained signature of the underlying physics. This result contrasts with theoretical expectations that such networks might retain structural features akin to discrete dynamical systems. We argue that high predictive accuracy does not imply interpretable internal representations and that explainability in physics-informed AI may not always be achievable—or necessary. These findings highlight a growing divergence between models that predict well and those that offer insight.