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this post was submitted on 28 Jul 2024
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I think there are legitimate uses for this tech, but they're pretty niche and difficult to monetize in practice. For most jobs, correctness matters, and if the system can't be guaranteed to produce reasonably correct results then it's not really improving productivity in a meaningful way.
I find this stuff is great in cases where you already have domain knowledge, and maybe you want to bounce ideas off and the output it generates can stimulate an idea in your head. Whether it understands what it's outputting really doesn't matter in this scenario. It also works reasonably well as a coding assistant, where it can generate code that points you in the right direction, and it can be faster to do that than googling.
We'll probably see some niches where LLMs can be pretty helpful, but their capabilities are incredibly oversold at the moment.
AI is great for asking questions, not answering them
We might eventually get to a point where LLMs are a useful conversational user interface for systems that are actually intrinsically useful, like expert systems, but it will still be hard to justify their energy cost for such a trivial benefit.
The costs of operation aren't intrinsic though. There is a lot of progress in bringing computational costs down already, and I imagine we'll see a lot more of that happening going forward. Here's one example of a new technique resulting in cost reductions of over 85% https://lmsys.org/blog/2024-07-01-routellm/