We introduce Semantic Compression Work: the drop in the next-token Shannon entropy of a language model across a token sequence. Each sequence either compresses the model's uncertainty (a funnel), leaves it approximately unchanged (equilibrium), or expands it (a fan-out). The framework treats the model's probability space not as a black box producing outputs, but as a measurable system whose uncertainty evolves sequence by sequence.