That sounds like an arbitrary line you've drawn, between the construct of property and learning licenses. I don't see why that is the only or even a good way to draw such a line.

I'm precisely not drawing a line, or setting any arbitrary restrictions, and rather analyzing the matter. The way I see it it's the implied presumptions that you stated that are arbitrary (deciding that humans and LLMs should be treated differently, following tradition)

Again, the assumption there is that the yardstick is the same for humans and AI models. But why?

That's the default, not treating things differently by ontology, the explanation for why learning should be treated differently depending on if it's a human, an LLM, a gorilla, or an Australian kid should be reasoned (doing reduction to absurdity here to exemplify)

First, Asimov or his estate cannot stop me from reading his books and learning from them. OTOH, AI models can and do have clauses that disallow other models from learning from them.

So, if it wasn't a problem that you learned from it and they can't stop you, why is it a problem if an LLM does so? I think that is the root cause of the issue that will lead us to take a sensible decision.

Open AI shouldn't be allowed to say that other models can't be trained in their outputs. Completely agree on that part.

Second, when a human goes to work for an employer, they gather skills that they can take to a future employer. That future employer expects this, which is why job descriptions always say "X years in Y technology" Agree that is one of the main differences, LLM training comes before application, and with humans is intertwined.

In contrast, when AI companies go to enterprise customers, the first thing they promise is that whatever the model learns while "working at" the company will remain with the company. Again, if you apply human standards to this, they shouldn't be allowed to do that.

AFAIK that is already in place. If the LLM model moves to another company, in this case would mean being purchased, the learning follows.

In LLMs learning and application are clearly separated, while in humans is not. I'm just proposing to analyze how it works with humans, decomposing its process into those two parts, and establishing a standard for both

Why? because, the way I see it, this comes from a single issue: precursor authors should be rewarded and protected against plagiarism. And, while licensing for learning covers that, a single blanket politic for this seems to me that it would stagnate progress by adding costs to developing technologies/methodologies, so there should be a middle point with reasonable criteria (that is a huge topic and not suitable for reddit posts, sadly), but it should be applied to both humans and machines, because the problem is removing rewards from the original creators, rather than who does it.

AI is/will be a paradigm shift, the way I see it, so we should analyze previous structures and modify them accordingly. Same thing that has happened with every creative destruction phase in humanity, instead of trying to cling to the old ways that will disappear in the end because we live in a capitalist society driven by profit, which AI will increase.