I think there are two different layers that get frequently mixed.
(1) LLMs as models - just the weights and an inference engine. These are just tools like hammers. There is a wide variety of models, starting from transparent and useless IBM Granite models, to open-weights Llama/Qwen to proprietary.
(2) AI products that are built on top of LLMs (agents, RAG, search, reasoning etc). This is how people decide to use LLMs.
How these products display results - with or without citations, with or without attribution - is determined by the product design.
It takes more effort to design a system that properly attributes all bits of information to the sources, but it is doable. As long as product teams are willing to invest that effort.
Protecting and preserving dying languages and culture is a great application for natural language processing.
For the record, I'm neither against LLMs, nor AI. What I'm primarily against is, how LLMs are trained and use the internet via their agents, without giving any citations, and stripping this information left and right and cry "fair use!" in the process.
Also, Go and Python are a nice languages (which I use), but there are other nice ways to build agents which also allows them to migrate, communicate and work in other cooperative or competitive ways.
So, AI is nice, LLMs are cool, but hyping something to earn money, deskill people, and pointing to something which is ethically questionable and technically inferior as the only silver bullet is not.
IOW; We should handle this thing way more carefully and stop ripping people's work in the name of "fair use" without consent. This is nuts.
Disclosure: I'm a HPC sysadmin sitting on top of a datacenter which runs some AI workloads, too.