My original point was that language models will almost certainly be the glue that holds a variety of models together in a post-AI world. We communicate with ot

My original point was that language models will almost certainly be the glue that holds a variety of models together in a post-AI world. We communicate with other humans using language, we will use language with AI as well. In order to understand and debug model networks they will probably be built using a common embedding framework that allows you to derive textual descriptions of the things being passed between them. LLMs already have the ability to generate text to dispatch to other services and synthesize results, all that is required for it to act as an "model executive" is a framework that can ensure security/correctness of code before building and executing it. Even if LLMs never progressed past the current crop of transformers, there are a lot of small architecture optimizations that could reap big performance improvements, and compute/data scaling alone basically guarantees a level of functionality sufficient for a lot of very interesting applications. I agree that trying to scale larger and larger language models to build "the one model to rule them all" is probably not going to play out.