Modern neural networks are typically explained through optimization, statistics, and linear algebra, which describe how models learn and transform tensors. This article argues that modal logic offers a complementary mathematical framework for interpreting what those transformations represent. Using Layer Normalization, embeddings, attention, residual connections, and hidden representations as examples, it explores how different numerical states can preserve the same semantic structure and how ne...