RAG Was Always a Temporary Workaround. What is Next?

Retrieval-Augmented Generation (RAG) is positioned as a temporary and inefficient workaround for AI memory, not a final architectural solution. The current process involves a high-latency chain of converting neural states to text, embedding them in vector databases, and then reconstructing the state upon retrieval. The future of AI memory will likely involve the direct persistence and transfer of a model's latent neural state, which would eliminate the need for text-based pipelines and significantly reduce latency. While this approach is more efficient, it faces major research challenges related to model compatibility, precision matching, and state alignment. RAG's role will likely evolve into an interoperability layer for communication between different AI systems, rather than serving as the primary memory mechanism.