RAG Was Always a Temporary Workaround. What is Next?

RAG (Retrieval-Augmented Generation) has always been a workaround, not a true memory system — it converts neural states into text, stores them, then expensively rebuilds them later. As AI moves toward autonomous agents and edge devices, this high-latency process becomes a real problem. The future points toward directly transferring neural states between models, though significant technical challenges around compatibility remain unsolved.