Neural networks are often described as black boxes, and for good reason — their internal representations have resisted principled mathematical characterization. But a new research program applying persistent homology from algebraic topology to neural network weight spaces is beginning to crack that open. Researchers can now precisely characterize the topological complexity of representation manifolds, identify phase transitions in training dynamics, and predict generalization behavior from geometric properties of the loss landscape. The mathematics is hard. The results are concrete.