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
{{labeling it “theft” is both legally and technically inaccurate.}} Well, my understanding is that humans have intelligence, humans teach and learn from previous/other people’s work and make progressive or create new work/idea using their own intelligence. AI/machine doesn’t have intelligence from the start, doesn’t have own intelligence to create/make things. It just copies, remixes, and applies the knowledge, and many personalities and all expressions have been teached. So “theft” is technically accurate.
I think what you’re forgetting is that intelligence, in general, is an emergent property of recording information and learning what actions to take based on them. The current work on AI is essentially trying to take this evolutionary behavior, make it less random, and compress the cycles of iteration down so that intelligence emerges quickly. This whole argument “It’s not smart like I’m smart” with only surface level observation about it’s current state and no critical observation about how intelligence came to be, just sounds really insecure.
I get it. Humans will likely not be the smartest thing in the arena soon. But stating matter-of-factly that AI is inherently different is born from an emotional viewpoint. I understand there ARE differences, but no more so then how there are differences between a human and a dog. Which if you’re honestly looking at the situation is impressively close to human intelligence in such a short time.
“Theft” is never a technically accurate word when dealing with the so called “intellectual property”, because the digital content being copied without authorization is legal in tons of cases, and because, come on, property is very explicitly exclusive. I cannot copy my house or my car, but I can make copies of my works for virtually 0 cost.
Using data for training ML models is even explicitly allowed in some jurisdictions (e.g. Japan), and is likely to be fair use everywhere else. LLMs are very transformative, and while they often can produce verbatim copies of fragments of copyrighted works, they don’t store the whole works or significant pieces of them.
Don’t get me wrong, I don’t like big companies making big money. I would not mind a law that would force models to be open sourced. But restricting them to train their models on public data by restricting fair use, it would harm them very little (they could pay something if they are making some profit), while small researchers or companies would never be able to compete, because they would not have the upfront costs, nor the economic engineering to disguise profits and pay less.
