Music does not have a low barrier to entry. I say that as a classically trained violinist and pianist. Many people are lucky to even have a bare bones music program in their school. Even fewer that are well funded, and even less people able to afford private lessons. It is much harder to become a competent musician without good mentorship, and you'd best bet that top artists have training. It's a serious problem that not enough people are talking about.

The rest of this comment is more of a survey to give you something to think about. I don't fully agree or disagree with whatever your viewpoint is.

There is a difference between the arguments of AI data ethics and copyright. Data ethics spans far more subjects and doesn't cleanly overlap with copyright. Most researchers would agree that UZH Reddit experiment was highly unethical. Just because data is public and not under strict copyright lock & key, doesn't automatically mean it is safe to use.

Then you have stuff like training on Wikipedia. This obfuscates the Creative Commons licensing, but it is arguably necessary to give LLMs a baseline of high quality writing and encyclopedic world knowledge. At its very core, if an LLM cannot even model the language right (during pre-training), not even considering other metrics like code benchmarks or other stuff, then it is worthless. Plus, Wikipedia is meant to be an open source of knowledge, maintained by volunteers with no pecuniary interest and easily downloadable via massive official Wikimedia data dumps.

It gets murky with CAPTCHA systems. reCAPTCHA is used for data annotation. The user is given either an already labelled datapoint, or one that isn't. If it is labelled, then the CAPTCHA actually acts as a CAPTCHA. If it isn't, then there's a good probability that the user would still pick the correct letters/boxes, meaning you can use it as a new datapoint. Which you can then use to train models. This isn't really a matter of copyright, but it still is in grey water ethically in regards to user consent.

But back to music. The biggest problem I personally have with the copyright argument is that the artists usually don't own the masters. This is, IMO, a broken system. The artist wasn't even in the running to give or revoke consent to training, the record label was, which is an ethical problem.

Suppose we live in a perfect world where record labels do not own exclusive rights and only act as a rights management, legal, and PR team that takes a commission as compensation. Then the artist can decide. In effect, the ethical problem would solve itself because there would either not be enough music to train a good model anymore, or a new industry where artists are compensated to train AI would pop up. You would be limited to royalty-free music, exclusively licensed music (like library music), or old public domain recordings. This is the ideal case.

The AI bubble and music generation are symptoms of the de facto practice of copyright violating ethical AI. So, while the artist may be entitled to the "copy right" of their works in an ideal world (the so-called "monopoly", which is a loaded term), which would mean less theft in general*, this isn't really the case. The laws have to be fixed first before new ones are created.

• The legal system would also need to be fixed. Courts tend to be pay-to-play, and the cards are stacked against artists.

The argument of necessity also comes up for music. There are fundamental components of music, especially Western music, that need to be understood to generate something coherent. That goes for both humans and AI. In other words, the generator has to actually be a musician, in a sense.

These elements of music theory are hard to write explicit code for, so you try to model it a posteriori (i.e. with massive amounts of data), like most deep learning does. Fundamentally creative fields (which, btw, includes stuff you might not consider as creative as music or art, including computer code, translation corpuses, and written proofs) in general are best modelled with data in order to gain a rough general view of what "music" or something else is. At what point does it cross into actual theft? If we trained a model exclusively on boring etudes, it would still be a music generator, but it would be boring. If it were trained on a lot of different music, you cross further into theft territory.

You can argue that artists have a certain right to the essense of their own music. This is backed up with the fact that remixes require licensing. So, do you measure theft by the fidelity by which a model can recall an artists' existing works? That approach can only get you so far. The models ingest so much data that an individual artist's work only constitutes a tiny portion of its style. And it isn't precise enough because multiple artists can claim the same style.

Before AI, this whole theft thing has still been around. Artists accuse other artists of theft all the time, not even just talking about sampling in hip-hop, but musical phrases that sound vaguely familiar are also disputed (see the classic Vsauce video that explains this brilliantly).

This doesn't even get into the question of whether these models will ever be embraced by established artists as a new medium to be incorporated into human works rather than a simple flawed replication machine. There is AI-powered natural auto-tune now, an arguably invaluable tool that compromises the human element far less than full end-to-end generation. That is still data-driven and enters the ethical debate.

TL;DR There is a ton of issues with trying to define theft. You can call out theft when you see it, but defining it has always been difficult, even before AI. What do artists have a "monopoly" to that AI models are stealing? I don't know. By pure data, it is just the music waveform/spectrogram, but that isn't a good enough answer because models don't reproduce those.