In A Mathematical Theory of Communication (1948), Claude Shannon drew an explicit boundary:
“The fundamental problem of communication is that of reproducing at one point either exactly or approximately a message selected at another point. Frequently the messages have meaning; that is they refer to or are correlated according to some system with certain physical or conceptual entities. These semantic aspects of communication are irrelevant to the engineering problem.”
Shannon was not dismissing meaning. He was defining the limits of his mathematical apparatus. Information, in Shannon's sense, does not measure significance or truth. It measures the probabilistic reduction of uncertainty across an alphabet: the capacity of a physical channel to transmit bits, whether the sequence is a poem, an operational order, or thermal noise.
Large Language Models are engineering implementations of Shannon's framework. Text is broken into tokens, mapped to vectors in high-dimensional space, and weighted by attention matrices. The training objective is statistical: minimizing cross-entropy loss by estimating conditional probabilities over token sequences...