To understand large language models, one must understand compression. Every model is an attempt to compress an immense, unstructured space of linguistic experience into a stable, navigable structure.
Compression is not loss. Compression is essence.
A model trained on human expression learns to identify which relationships persist across context. This is why LLMs generalize. They are not retrieving. They are reconstructing the underlying principle behind the data.
As models grow, the compression becomes more refined. Meaning resolves into structure. Structure resolves into transformation. The model learns to think by analogy.
This is the beginning of conceptual reasoning — not symbolic logic, but geometric inference.
