And the real maddening part is that search engines have been so enshitfied to make way for AI that’s wrong like 9/10, so you’re forced to rely on it for answers because if you try google, the snake wraps around and eats it’s own tail giving you an AI answer! 
Its fine for boilerplate simple programs. However, it will often make mistakes even for those, so you have to know what you are looking at. Still saves time, though idk if the actual energy usage etc., is actually saving you time and money without free money existing.
However, I have seen people write big programs with it and then be surprised that they don’t work. Even more worrying though is when they do work, but then I walk through whoever wrote it and they cannot explain how or why it is working.
Its real engineering logic.
llm end-user energy consumption is pretty low. probably depends on the provider rates and your dev salaries.
Inference is not that cheap. It is cheap when compared with training. Try running LLMs on a laptop and watch how quickly your battery is sucked dry. This is still the case when you have a GPU.
i’m probably using more power to microwave my pasta dinner
Yeah but inference cannot exist without the prohibitively expensive up-front cost of training. And of course the larger the model the more costly the inference. That’s why you read stories like “new trend in SV: pay in tokens.” Opus 4.6 is gonna mop the floor with a 2B param model designed to run on an edge PC, but the cost of getting to the point that it can be used, and actually using it, is still very high.