> Even if LLMs fail spectacularly
Haven't they already proven to be extremely useful? In some areas they are definitely here to stay, coding/software and search (retrieve and summarize information). There's a bunch of places where they are surely shoehorned in, overhyped, and don't belong, but there's also equally many places where they might still be transformative but aren't used yet.
But overall I think the technology is well proven.
I always leave room open for failure, and that approach has generally served me well personally and professionally. I have never been punished for having an exit strategy.
Besides, the marketplace is still in its infancy for LLMs, with a lot of unanswered questions. A lot of those questions surround the commercial viability of frontier models on bespoke hyperscaler data centers with limited usage outside of LLMs specifically should those economics be non-viable. Since that's where the memory is being tied up into, that means it's a critical question to answer in order to determine long-term investment needs into further memory fabrication.
They work well functionally, but financially anything can still happen.
> Haven't they already proven to be extremely useful?
Most certainly not. The accuracy issues mean that they can't really be used effectively for coding or search, the two things you mentioned.