Individual LLM requests are vanishingly small in terms of environmental impact; inference providers use a
lot of batching to do lots of work at once. Furthermore, LLMs and diffusion models are not the only ML workload. While generative AI tickles investors, most of the ML actually being deployed is more mundane things, like recommendation systems, classifiers, and the like; much of which is used for adtech purposes adversarial to that of users. If LLMs and diffusers were the
only thing companies used ML for, but efficiency gains from new hardware remained constant, we'd still be at the 2017 baseline for environmental impact of data centers.
Likewise, I doubt that USENET warning was ever true beyond the first few years of the networks' lifetime. Certainly if everything was connected via dial-up, yes, a single message could incur hundreds of dollars of cost when you added the few seconds of line time it took to send up across the whole world. But that's accounting for a lot of Ma Bell markup. Most connections between sites and ISPs on USENET were done through private lines that ran at far faster speeds than what you could shove down copper phone wiring back then.
Likewise, I doubt that USENET warning was ever true beyond the first few years of the networks' lifetime. Certainly if everything was connected via dial-up, yes, a single message could incur hundreds of dollars of cost when you added the few seconds of line time it took to send up across the whole world. But that's accounting for a lot of Ma Bell markup. Most connections between sites and ISPs on USENET were done through private lines that ran at far faster speeds than what you could shove down copper phone wiring back then.