LLMs and the "Storage Tax"

Everyone is talking about fine-tuning Large Language Models, but few are talking about the "Storage Tax" that comes with Retrieval-Augmented Generation (RAG).

RAG is essentially a search problem masked as an AI problem. To make it work at scale, you need vector databases that can handle millions of embeddings with sub-millisecond retrieval.

This isn't just about saving bits anymore; it's about the geometry of data. How do we store meaning? How do we index context? We are moving from storing files to storing vectors, and the infrastructure requirements are catching many by surprise.