Those claiming AI training on copyrighted works is “theft” misunderstand key aspects of copyright law and AI technology. Copyright protects specific expressions of ideas, not the ideas themselves. When AI systems ingest copyrighted works, they’re extracting general patterns and concepts - the “Bob Dylan-ness” or “Hemingway-ness” - not copying specific text or images.
This process is akin to how humans learn by reading widely and absorbing styles and techniques, rather than memorizing and reproducing exact passages. The AI discards the original text, keeping only abstract representations in “vector space”. When generating new content, the AI isn’t recreating copyrighted works, but producing new expressions inspired by the concepts it’s learned.
This is fundamentally different from copying a book or song. It’s more like the long-standing artistic tradition of being influenced by others’ work. The law has always recognized that ideas themselves can’t be owned - only particular expressions of them.
Moreover, there’s precedent for this kind of use being considered “transformative” and thus fair use. The Google Books project, which scanned millions of books to create a searchable index, was ruled legal despite protests from authors and publishers. AI training is arguably even more transformative.
While it’s understandable that creators feel uneasy about this new technology, labeling it “theft” is both legally and technically inaccurate. We may need new ways to support and compensate creators in the AI age, but that doesn’t make the current use of copyrighted works for AI training illegal or unethical.
For those interested, this argument is nicely laid out by Damien Riehl in FLOSS Weekly episode 744. https://twit.tv/shows/floss-weekly/episodes/744
Here’s an experiment for you to try at home. Ask an AI model a question, copy a sentence or two of what they give back, and paste it into a search engine. The results may surprise you.
And stop comparing AI to humans but then giving AI models more freedom. If I wrote a paper I’d need to cite my sources. Where the fuck are your sources ChatGPT? Oh right, we’re not allowed to see that but you can take whatever you want from us. Sounds fair.
It’s not a breach of copyright or other IP law not to cite sources on your paper.
Getting your paper rejected for lacking sources is also not infringing in your freedom. Being forced to pay damages and delete your paper from any public space would be infringement of your freedom.
I’m pretty sure that it’s true that citing sources isn’t really relevant to copyright violation, either you are violating or not. Saying where you copied from doesn’t change anything, but if you are using some ideas with your own analysis and words it isn’t a violation either way.
I mean, you’re not necessarily wrong. But that doesn’t change the fact that it’s still stealing, which was my point. Just because laws haven’t caught up to it yet doesn’t make it any less of a shitty thing to do.
When I analyze a melody I play on a piano, I see that it reflects the music I heard that day or sometimes, even music I heard and liked years ago.
Having parts similar or a part that is (coincidentally) identical to a part from another song is not stealing and does not infringe upon any law.
The original source material is still there. They just made a copy of it. If you think that’s stealing then online piracy is stealing as well.
Not to fully argue against your point, but I do want to push back on the citations bit. Given the way an LLM is trained, it’s not really close to equivalent to me citing papers researched for a paper. That would be more akin to asking me to cite every piece of written or verbal media I’ve ever encountered as they all contributed in some small way to way that the words were formulated here.
Now, if specific data were injected into the prompt, or maybe if it was fine-tuned on a small subset of highly specific data, I would agree those should be cited as they are being accessed more verbatim. The whole “magic” of LLMs was that it needed to cross a threshold of data, combined with the attentional mechanism, and then the network was pretty suddenly able to maintain coherent sentences structure. It was only with loads of varied data from many different sources that this really emerged.
This is the catch with OPs entire statement about transformation. Their premise is flawed, because the next most likely token is usually the same word the author of a work chose.
And that’s kinda my point. I understand that transformation is totally fine but these LLM literally copy and paste shit. And that’s still if you are comparing AI to people which I think is completely ridiculous. If anything these things are just more complicated search engines with half the usefulness. If I search online about how to change a tire I can find some reliable sources to do so. If I ask AI how to change a tire it would just spit something out that might not even be accurate and I’d have to search again afterwards just to make sure what it told me was even accurate.
It’s just a word calculator based on information stolen from people without their consent. It has no original thought process so it has no way to transform anything. All it can do is copy and paste in different combinations.
