Is the end of Insight in sight?

Jean-Michel Tucny^1*Mihir Durve²Sauro Succi^2*

• ¹Department of Engineering, Roma Tre University, Rome, Italy
• ²Istituto Italiano di Tecnologia Center for Life Nano- & Neuro-Science, Rome, Italy

The rise of deep learning challenges the longstanding scientific ideal of insight-the human capacity to understand phenomena by uncovering underlying mechanisms. In many modern applications, accurate predictions no longer require interpretable models, prompting debate about whether explainability is a realistic or even meaningful goal. From our perspective in physics, we examine this tension through a concrete case study: a physics-informed neural network (PINN) trained on a rarefied gas dynamics problem governed by the Boltzmann equation. Despite the system's clear structure and well-understood governing laws, the trained network's weights resemble Gaussian-distributed random matrices, with no evident trace of the physical principles involved. This suggests that deep learning and traditional simulation may follow distinct cognitive paths to the same outcome-one grounded in mechanistic insight, the other in statistical interpolation. Our findings raise critical questions about the limits of explainable AI and whether interpretability can-or should-remain a universal standard in artificial reasoning.