Earlier quoted context omitted.
I disagree. Crypto people kept suggesting that crypto was a solution to an X problem while ignoring that a database was a better solution the problem. I’ve yet to hear any good use cases for crypto, and I’ve been asking for years on here. Meanwhile there are a bunch of AI tools out there that are working and helping.
AI is a gigantic landscape with tons of different applications to different problems, and there are many solutions which work for a given problem. However, if we narrow what AI is to LLMs, we have a stochastic parrot which needs to be fed the world literally to enable it to create semi-coherent sentences about something being asked. More importantly, what that parrot says doesn't have to be true, it can't be guarante…
People are spending all that money training because they are trying to fix the problems you're complaining about, and this includes fixing the power consumption problem. If we can create 3B parameter models that have capabilities on par with today's 405B parameter models, that's worth spending a lot of energy training. But nobody knows what is possible, so they have to try. I feel like you're basically arguing nobody should try because you don't believe they will ever improve, but that seems contradicted by the general trajectory of how things have been working the past decade. More resources spent on training means more efficient and useful models.