Today, AI can diagnose illnesses, sift through vast amounts of data, and generate text with an uncannily human tone. However, the rationale behind its outputs often eludes us. Explainable AI arose to make such systems interpretable rather than leaving them as black boxes. Earlier algorithms, such as linear regressions or decision trees, were almost self-explanatory, with decision paths that could be traced step by step. Deep learning models, by contrast, distribute their “reasoning” across millions of parameters, producing a form of conceptual opacity: we can measure what they do, but it is harder to grasp why they do it. Interpretability research has begun to uncover striking internal structures in these systems: patterns of attribution, latent representations, and emergent behaviors that seem to echo human-like reasoning. However, these findings open as many questions as they resolve. Are we genuinely uncovering something like a machine’s thought process, or are we simply projecting our own mental models onto intricate statistical correlations? In trying to explain AI, we confront the limits of explanation itself, and perhaps the unsettling idea that some forms of “understanding” might always remain beyond our reach.