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I do all the time as well. Has changed my life in a very useful way. But as you said, knowing what it's place is, how to use it, and what it's limitations are (as well as my own) are key. I have solved many many problems I've been working on for years on in the digital world.
I also sympathize with the AI hate, and really struggle with the energy usage as well as the bubble. It has power and capability, but not what the "public" think it does.
I just deal with the online hate as it's not shit people says to my face, and it's driven of ignorance like much is these days.
And as you said there are development in the pipe which will further change our lives. Knowing how it works and why, as in using the critical thinking in synthesis with an LLM and what comes next is going to be valuable.
The power usage isn't even that much on an individual basis once it's trained, it's that they have to build these massive data centers to train and serve millions of users.
I'm not sure it's much worse than if nvidia had millions of customers using their game streaming service running on 4080s or 4090s for hours on end vs less than an hour of AI compute a day.
It'd be better if we could all just run these things locally and distribute the power and cooling needs, but the hardware is still to expensive.
You have apple with their shared GPU memory starting to give people enough graphics memory to load larger more useful models for inference, in a few more generations with better memory bandwidth and improvements, the need for these data centers for consumer inference can hopefully go down. These are low power as well.
They don't use CUDA though so aren't great at training, inference only.