Generative models do not simply learn to predict text. They learn to compress, reorganize, and restructure conceptual space. Behind every output of a large language model is a hidden representation — a dense, multidimensional map of meaning.
At their core, models like GPT, Claude, and Gemini learn distributions over linguistic sequences. But these distributions are not shallow patterns. They capture relationships, analogies, transforms, and high-level semantic structures. Meaning becomes geometry.
In deep learning terms, embeddings are the interface between language and concept. Words and sentences are mapped into high-dimensional vectors where distance corresponds to conceptual similarity. This is why models can infer relationships that were never explicitly stated: the geometry itself encodes meaning.
When we say a model “understands,” we are describing its ability to navigate this space. Not symbolically. Not logically. But structurally.
In practice, this means that generative models are not merely generative. They are representational engines. They encode the shape of knowledge, and generation is simply the visible surface of that shape.
To understand generative AI, we must shift from thinking about text to thinking about topology. The future of AI literacy is the ability to reason about meaning in vector space.
