These tools may not be turning a profit yet, but as many point out, this is simply due to deeply subsidized free usage to capture market share and discover new use cases.
However, their economic potential is undeniable. Just taking the examples in TFA and this sub-thread, the author was able to create economic value by automating rote aspects of his wife's business and stop paying for existing subscriptions to other apps. TFA doesn't mention what he paid for these tokens, but over the lifetime of his apps I'd bet he captures way more value than the tokens would have cost him.
As for the energy externalities, the ACM article puts some numbers on them. While acknowledging that this is an apples/oranges comparison, it points out that the training cost for GPT-3 (article is from mid-2024) is about 5x the cost of raising a human to adulthood.
Even if you 10x that for GPT-5, that is still only the cost of raising 50 humans to adulthood in exchange for a model that encapsulates a huge chunk of the world's knowledge, which can then be scaled out to an infinite number of tasks, each consuming a tiny fraction of the resources of a human equivalent.
As such, even accounting for training costs, these models are far more efficient than humans for the tasks they do.
However, their economic potential is undeniable. Just taking the examples in TFA and this sub-thread, the author was able to create economic value by automating rote aspects of his wife's business and stop paying for existing subscriptions to other apps. TFA doesn't mention what he paid for these tokens, but over the lifetime of his apps I'd bet he captures way more value than the tokens would have cost him.
As for the energy externalities, the ACM article puts some numbers on them. While acknowledging that this is an apples/oranges comparison, it points out that the training cost for GPT-3 (article is from mid-2024) is about 5x the cost of raising a human to adulthood.
Even if you 10x that for GPT-5, that is still only the cost of raising 50 humans to adulthood in exchange for a model that encapsulates a huge chunk of the world's knowledge, which can then be scaled out to an infinite number of tasks, each consuming a tiny fraction of the resources of a human equivalent.
As such, even accounting for training costs, these models are far more efficient than humans for the tasks they do.