I'm not sure your premise even makes any sense here, because it doesn't take an artist much more resources to produce art then it took them to just exist for the same amount of time. They're still just eating, sleeping, making basic usage of the computer, using heating and light, and so on either way. Whereas someone using dall-e is doing all of that plus relying on the immense training costs of the artificial intelligence. That basic usage of the computer in order to use the machine learning model might be shorter than the basic use of the computer to use procreate or something, but they'll still be using the computer for about the same amount of time anyway, because the time not spent not making art will just be shifted over to other things. So it doesn't seem to me like having machine learning models do something for you instead of learning a skill and doing it yourself will really decrease emissions or energy usage noticeably at all.

Furthermore, even if there is some decrease in emissions using pre-trained machine learning models over using your own skills and labor, the energy costs of training a powerful machine learning model like you're thinking of are way higher than I think you are imagining. The energy and carbon emission cost of training even a 213M parameter transformer for 3.5 days is 626 times the cost of an average human existing for an entire year according to [this study](https://arxiv.org/abs/1906.02243). Does using a pre-trained machine learning model take that much emission out of people's lives? Or a day's worth out of 228,490 lives, perhaps? I doubt it.

But we aren't even using such a small transformers anymore either — they actually aren't that useful. We're using massive models lile GPT-4, and pushing as hard as we can to scale models even further in a cargo cult faith that making them bigger will fundamentally qualitatively shift their capabilities at some point.

So what does the emissions picture look like for GPT-4? The study above found that emissions costs scale linearly with number of parameters and tuning steps as well as training time, so we can make a back of the napkin estimate that GPT-4 is 8,592,480 times more expensive to train than the transformer used in the study, since it is rumored to have 1.76 trillion parameters versus the 213 million of the model in the study, and GPT-3 was said to take 3640 days to train (despite using insane amounts of simultaneous compute to scale the compute up in conjunction with the scale of the model) versus 3.5 days. This in turn means it is 5,378,892,480 times more expensive to train a GPT-4 than it is for a human to live for one year. And again, to reiterate, no matter what work the humans are doing, they're going to be living for around the same amount of time and using roughly the same amount of carbon emissions as long as they're not like taking cross country or transatlantic flights or something. So it's more expensive to train gpt4 then it is for almost 6 billion people to live for a year. I don't think it's taking a year's worth of emissions off of 6 billion people's lives by being slightly more convenient than having to type some things in or draw some art yourself. And there are only 8 billion people on the planet, so I don't think there's enough people to spread smaller gains out across to justify the training of this model (you'd have to take a days worth of emissions off of 1,963,295,755,200 people to offset that training cost!), especially since in my opinion the decrease in emissions of using machine learning models would necessarily be absolutely miniscule.