The roots of today's large language models stretch back over a century to linguists like Ferdinand de Saussure and philosophers like Gottlob Frege, who redefined meaning as relational rather than literal. Their insights laid the groundwork for modern semantic search and embeddings.

Early symbolic AI tried to encode meaning through logic, but failed with natural language’s ambiguity. The shift to statistical NLP and models like Word2Vec allowed machines to infer semantics from text patterns, echoing Saussure’s structural view.

With BERT and GPT, context and scale enabled AI to grasp shifting word meanings and even exhibit emergent reasoning. These systems don’t “understand” in a human sense, but they approximate meaning well enough to power real-world applications.

For developers building semantic systems, this evolution explains why similarity-based approaches outperform exact matching. Meaning is no longer stored—it’s inferred through vast data flows and neural relationships.