Remember when putting something online felt simple? Nobody called themselves an influencer, or expected a masterpiece, so people posted a song recorded in their bedroom or a story written late at night and shared it with family and close friends. We never imagined that years later, these innocent posts would become part of the backbone of what generative AI can do today.
Fast forward to now: somewhere in the training data of the model you used this morning is probably a stranger’s blog post or a photograph someone took on holiday in 2014 and forgot about. Did anyone ask these people whether their work could be ‘borrowed’? Most of them will probably never know that it helped a machine learn how a sentence needs to flow or how light falls across a face.
We worked on this essay with this fact in mind, because it makes the usual question about AI and ownership feel incomplete. People tend to ask who owns what they create with AI, which is a fair question, but it skips the part where the AI itself was assembled from other people’s creativity. Ownership is contested at the beginning of the process as much as at the end, and the two problems are closely connected.
Where did the AI learn to do that?
Generative models learn by processing enormous quantities of existing material, images, music, and code. Developers argue that this resembles what people have always done, since every writer learned from books by other authors, and every illustrator was inspired by the styles of the artists who came before.
A lot of creators see it very differently, pointing out that a person reading a novel is hardly the same as a company copying millions of works to build a commercial product that may later compete with the creators of those same works.
Few stories capture the scale of this better than the one that came out of a US courtroom. Documents in a case brought by authors against Anthropic revealed that from early 2024, it had bought millions of used books from online sellers, sliced off their spines, and scanned the pages before discarding the originals. Internally, the operation was called Project Panama, and a planning document described it as an effort to destructively scan all the books in the world.
What makes the case so interesting for the ownership question is how the judge split it. Training on books the company had legally bought was found to be fair use, while its earlier downloading of pirated copies was treated very differently, and that part of the dispute ended in a $1.5 billion settlement with authors.
Booksellers from the Netherlands to Japan have since reported unusually large bulk orders that appear to be linked to AI training, so the practice is clearly not limited to one company. Some people look at this and see a company that at least paid for its books, while others see a library being fed into a shredder, and both reactions show how unsettled the idea of ownership has become.
What we find interesting is that neither side’s argument is absurd. Learning from existing work is how culture moves forward, but scale changes the nature of the act. That is why the ownership question cannot be answered by analogy alone: the issue is simple for one artist and far less simple when the ‘reader’ is a system trained on a meaningful share of everything published online.
What happens to the work AI produces?
In March 2026, the US Supreme Court declined to hear Thaler v Perlmutter, the case of a computer scientist who wanted copyright for an image generated autonomously by his AI system. The refusal left in place the lower court’s ruling that copyright requires a human creator, meaning a work produced entirely by AI in the United States has no copyright owner at all. It does not belong to the person who typed the prompt, nor to the company that built the model.
There is an irony in that outcome, isn’t there? A machine built on millions of people’s protected work can make something no one is allowed to own.
Where does AI-assisted work end and AI-generated work begin?
The US Copyright Office has taken the position that a work can be protected when a person exercises meaningful creative control over its expressive elements, and that a prompt on its own usually does not meet that bar. Europe seems to be moving in a similar direction, with Italy’s new AI law stating that AI-assisted works are protected only where there is a substantial human intellectual contribution.
That sounds reasonable until you try to apply it. Let’s say you write a detailed prompt, reject 40 versions, combine elements from a few of them, and then repaint half the image by hand; in that case, you have clearly contributed something. However, the law protects only the parts you made yourself, and almost nobody keeps the records to prove which parts those are.
Who owns it according to the fine print?
Even where copyright is uncertain, the platform’s terms of service still shape what users can do with their outputs. Some services assign rights in the outputs to the user, while others maintain broad licenses for themselves or state plainly that users should not assume they own what they generate. We suspect very few people have read those terms for the tools they use every day, which echoes something we wrote recently about AI memory and personalization, where the decisions that matter most tend to sit in settings and documents that nobody opens.
Platforms are drawing their own lines. At the start of 2026, Bandcamp announced that music generated wholly or in substantial part by AI is no longer allowed on its site, to give fans confidence that what they find there was made by people. Spotify has removed tens of millions of spam tracks and banned AI voice clones, but still allows AI music and, since August, labels artist profiles representing AI-generated identities as “AI Personas,” keeping their music out of recommendations unless a listener chooses to follow them.
What this means for companies
A company that relies heavily on AI for marketing copy or product visuals may have no legal way to stop a competitor from running the same copy tomorrow. Also, under US Copyright Office guidance, code written entirely by AI belongs to no one, so a competitor can copy it freely.
This connects to an argument we made earlier this month about which abilities professionals should continue to practice. Human contribution used to be taken for granted, and now it has become more of a business asset, because the part of the work a person actually shaped may be the only part the organization can call its own.
Owning it vs. earning it
The law decides who can stop others from copying something, but that’s it. Our position is simple: if a model produced most of a report or an illustration, presenting it as your own without saying so is misleading, even if the copyright rules allow it. Disclosure costs very little, and it is the least we owe the people reading our work.
The same goes for the writers and artists whose work trained these systems are a different problem, and disclosure won’t fix it. Knowing that a company used millions of books doesn’t give a single author anything back. A fair use ruling decides whether they can sue, not whether they were treated fairly, and we don’t think they were. They were never asked, and in most cases they have no real way to say no.
What strikes us most is that human originality sits at both ends of this story. These systems learned from it, and current law requires it. Human contribution matters. It is what trained the tools and what users can legitimately claim from them.
So, we would like to ask you one last question: if a machine did most of the work, which part of the result is actually yours?