Comment by neilv | Hacker News Reader

Comment by neilv | Hacker News Reader

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> I don't think anyone who's not monetarily incentivize to pretend there are IP/Copyright issues actually thinks there are.

Why do you think that about people who disagree with you? You're responding directly to someone who's said they think there's issues, and not pretending. Do you think they're lying? Did you not read what they said?

And AFAICT a lot of other people think similarly to me.

The perverse incentives to rationalize are on the side of the people looking to exploit the confusion, not the people who are saying "wait a minute, what you're actually doing is..."

So a gold rush person claiming opponents must be pretending because of incentives... seems like the category of "every accusation is a confession".

No, I think they have a monetary incentive they have failed to disclose or a limited at best understanding of basic copyright law.

They can have a moral view that AI is "stealing" but they are claiming there is actually a legal issue at play.

Putting aside the notion that a monetary incentive is invalid for some reason, there are also plenty of open source authors who are pretty unimpressed that they only ask for attribution and even that's being laundered away via weights. If their licence isn't being respected that's certainly a legal issue.

Pretty weird motivation for writing open source software. Just saying.

I think there are ramifications to making the argument that learning is stealing.

I don't want my children to pay a license fee to their school or their textbook publishers for what they learn in school.

Learning isn't stealing, but learning can absolutely lead to stealing. (Edit: This is why bring able to demostrate that you did not learn from a copyrighted work when trying to replicate and compete with it can be an important defense.)

The amount of IP risk caused by USING (not training) AI models to produce code, especially wholesale commercial code that competes with code that was contained in the training data, is poorly understood.

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