There are approaches to add "memory" to LLMs. Either the memory can be used to find relevant information and append it to the prompt - I believe LangChain can d

There are approaches to add "memory" to LLMs. Either the memory can be used to find relevant information and append it to the prompt - I believe LangChain can do this - or the LLM can be used as a controller to retrieve and store information in a database, for example by using ChatGPT's tool integration. I don't think it works amazingly, but purely technically you could argue that gives it access to unbounded external storage. I don't think an LLM can be considered as capable of becoming an AGI on its own with the current architecture, but potentially combined with other techniques, models and supervision, I can see the potential for it to evolve in that direction. Frankly, for what LLMs are right now, they're _surprisingly_ effective. If you chopped the Broca's area out of my brain and hooked it up to an API, I suspect it wouldn't do half as good of a job.