Incorrect. In the absence of new legislation case law is how these things get worked out, but new statutes could be passed and could void prior case law.

https://www.copyright.gov/title17/title17.pdf

Let me expand on this a bit... if you read through the text (feel free to search for the below terms) of the current laws around copyright you will notice that there is no discussion of:

https://en.wikipedia.org/wiki/Structure,_sequence_and_organi...

https://en.wikipedia.org/wiki/Abstraction-Filtration-Compari...

https://en.wikipedia.org/wiki/Idea–expression_distinction

These legal doctrines are the result of the details are hashed out in case law.

This is distinct from countries that use civil law jurisprudence. Common law jurisprudence relies heavily on case law.

Funny way to admit that your original statement was flat-out wrong.

There is no separate "civil law jurisprudence" and "common law jurisprudence". Common law by itself is by definition not jurisprudence. Civil law combines aspects of statutory and common law (as one form of precedent) into a single system. Some aspects of common law even make their way into criminal law. Instead of just Googling for buzzwords, learn what they mean before you try to bluster your way through an argument with them.

The law moves slow. Even were that to happen eventually, the laws will very likely be challenged in courts, and those will take a while to be resolved.

Finally, even if the US outlaws this, there'll be plenty of other countries where it'll be legal. There's plenty of infringement of US copyright in China, for instance. The same is likely to happen in regards to AI that's illegal in the US but legal elsewhere.

By the time the law catches up, model creation may become so easy to create by individuals instead of just by deep-pocketed corporations, it may be practically impossible to stop.

But sure, let's talk about outputs as well. From the second source we can see this from Tyler Ochoa:

"If there's only one good way to do it, OK, then that's probably not eligible for copyright. But chances are that there's just a lot of code in [the training data] that has used the same open source solution, and that the output is going to look very similar to that. And that's just copying."

I have seen some probable copyright violations from the output of Copilot, such as comments and some certain structural similarities that might be protected, although it is hard to say. But focus on the first part of what Mr. Ochoa is saying here, which is also laid out in this quote:

“In computer programs, concerns for efficiency may limit the possible ways to achieve a particular function, making a particular expression necessary to achieving the idea. In this case, the expression is not protected by copyright."

https://en.wikipedia.org/wiki/Abstraction-Filtration-Compari...

This allows for verbatim copies if they are utilitarian in nature!

As for why we should allow verbatim copies of utilitarian features... First, let's preface this with the substantial similarity of the structure, sequence and organization as established in Whelan v. Jaslow which amongst other things says that you cannot merely change the variable names if the expressive structure of the code remains the same. Now let's imagine 10,000 software developers who all implement Dijkstra's algorithm in C and then run it through clang-format. Aside from variable names, isn't it safe to assume that many of the implementations are going to be exactly the same?

As for why it was carefully selected... more often than not when I bring these things up people who feel upset about Copilot go off to cherry-pick some random quote out of context in order to support their upset feelings. Therefore I'm highlighting the important parts as to help people look beyond their upset feelings.

This is a complicated and nuanced matter. Attempting to channel everything through the lens of "this makes me personally feel bad and must be completely wrong" does not help the discourse. It may make you popular to a certain crowd but it might be unpopular to the public at large and it might also be incoherent from a legal standpoint, akin to bashing your head against a wall at a weekly meetup of the local heads-bashing-against-walls club.

There is plenty of room for discussion on what constitutes not only the legal interpretation of fair use and the idea/expression dichotomy but also the bigger picture. The knife always cuts both ways. Would it be acceptable to the open-source community if Microsoft could stop anyone from publishing Dijkstra's algorithm in C# because they wrote it first?

That's a very petulant way to defend cherry picking. I wasn't asking you to support one particular view; in fact that's the problem I was identifying. Your sources presented a balanced view, which you misrepresented by citing only the part that supported your own conclusion.

> focus on the first part

No, because the second part matters too. Here's Lemley and Casey again (emphasis mine):

<<<some purposes—say, ... a translation program that produces a translation of an entire copyrighted work—seem more substitutive than transformative, so that if they run afoul of the ever-broadening definition of similarity in music, fair use is unlikely to save them.>>>

Or the Register:

<<<"I actually think there's a decent chance there is a good copyright claim," said Tyler Ochoa ... the functional nature of the code means that reproducing it in a suggestion may not be seen as particularly transformative, which is one of the criteria for determining fair use>>>

Those are your own sources undermining - if not outright contradicting - your one-sided interpretation.

The limitation to market harms in the four-factor test for fair use should not be considered permanent. Law is, after all, a social construct. There's ample precedent for considering harms to the commons, to communities, and so on in other areas of law. Also, there might indeed be market harms. If a company open-sources some of their code but also hopes to profit by selling it in pre-packaged form or as a service, then AI-washed copying could constitute harm in even the most market-myopic terms. The "transformative" test is also pretty suspect in the context of AI-assisted copying, but this is getting long enough so I'll not go down that rabbit hole just yet.

> <verbosity about "utilitarian" copies which are not the issue here>

Enjoy your red herrings. I don't share your taste for them.

When Tyler Ochoa is saying that there is a decent chance of a copyright claim he is specifically talking about the output of the model.

Here is the full quote:

"[Machine learning] systems should generally be able to use databases for training, whether or not the contents of that database are copyrighted," they wrote, adding that copyright law isn't the right tool to regulate abuses.

But when it comes to the output of these models – the code suggestions automatically made by the likes of Copilot – the potential for the copyright claim proposed by Butterick looks stronger.

"I actually think there's a decent chance there is a good copyright claim," said Tyler Ochoa, a professor in the law department at Santa Clara University in California, in a phone interview with The Register.

The use of the word "but" marks the transition from a discussion around the model itself to the outputs of the model.

Oh, you mean the very first thing I had to explain to you at the beginning of this exchange because you seemed to be ignoring it? Very little of these discussions has been about the models. Most of the discussion is about the outputs, and there the fair-use cases is - as Lemley/Casey and Ochoa both concede - much weaker.

But by all means keep going on about feelings. We can all tell it's not others' feelings that are being hurt by mere disagreement.

IANAL but I would take any opinions on this right now with a huge grain of salt and treat them more as advocacy than actual predictions of any legal outcomes.

Whether there is a good case for it being considered fair use doesn't matter at all until its actually litigated and historically the result with fair use in relation to new technologies has always been a crapshoot.

The result could easily be affected by the actual cases that get litigated, and one well chosen lawsuit where machine learning software is shown to produce output that's too close to the material it was trained on could result in a completely different outcome.

Once patent trolls figure out there's gold in these hills, it's downhill at that point.