1. We've barely scratched the surface of this solution space; the focus only recently started shifting from improving model capabilities to improving training costs. People are looking at more efficient architectures, and lots of money is starting to flow in that direction, so it's a safe bet things will get significantly more efficient.
2. Training is expensive, inference is cheap, copying is free. While inference costs add up with use, they're still less than costs of humans doing the equivalent work, so out of all things AI will impact, I wouldn't worry about energy use specifically.
Humans don't require immense amounts of energy to function. The reasons why LLMs do is because we are essentially using brute force as the methodology for making them smarter for the lack of better understanding of how this works. But this then gives us a lot of material to study to figure that part out for future iterations of the concept.
Are you so sure about that? How much energy went into training the self-assembling chemical model that is the human brain? I would venture to say literally astronomical amounts.
You have to compare apples to apples. It took literally the sum total of billions of years of sunlight energy to create humans.
Exploring solution spaces to find intelligence is expensive, no matter how you do it.
