Generative AI poses a puzzle for copyright lawyers, and many others besides. How can a soulless mechanical process lead to the creation of new expression, seemingly out of nothing, or if not nothing, very little?
This essay will help you understanding where the apparent creativity in generative AI outputs comes from, why a lot of AI works are not copyrightable, and why the outputs of generative AI are mostly very different to the works those AIs were trained on.
Who is the author of Skater Beagle?
The image below was created by one LLM (Google Gemini) using a long prompt written by another LLM (Anthropic’s Claude) following the instruction “draft a prompt for an arresting image of a beagle on skateboard.”
If I took this photo in real life, I would be recognized as the author. Likewise, if I painted it as a picture. But because the image was created by a process that involved very little direct human contribution, it is uncopyrightable. For many people, this seems odd. How can an image that looks creative not be recognized as copyrightable, just because it was created with AI rather than an iPhone camera, or a set of water-based paints? After all, artists use tools to make art all the time?
No copyright for the AI
The first question to address is whether Google’s image generation model is the author of Skater Beagle. The answer is no, for many reasons, but let’s focus on the copyright issues, because they are the most interesting.
The AI can’t get copyright protection because the AI itself is not creative in any of the ways we generally understand that term (at least if you are a copyright lawyer) because it lacks any desire or intention to express. In Burrow-Giles Lithographic Co. v. Sarony (1884) the U.S. Supreme Court recognized that a photograph could be copyrighted, but only because the photographer’s creative choices made the image an “original intellectual conception[] of the author” rather than a mere mechanical capture. LLMs are impressive, but they don’t have any intentions separate from the math that makes them predict one thing and not another. LLMs don’t have original intellectual conception they are trying to express.
No copyright for the simple prompt engineer
If not the AI, then maybe the person who writes the prompts should be credited with the resulting expression? After all, isn’t choosing the right words in the prompt a creative act?
That doesn’t work either. Sure, choosing the right words in the prompt might be creative in some senses, but copyright law doesn’t protect creativity in the sense of “hey, that’s a good idea,”— it protects creativity that manifests in original expression. This idea-expression distinction is one of the foundations of copyright law. Copyright attaches to the final expression, not the upstream idea or instruction that triggered it. Even if you think my idea to get one LLM to write a prompt for another LLM “for an arresting image of a beagle on skateboard” is creative, its really just a simple idea and nothing copyrightable.
Surely, it must be one or the other?
But still, many would say, if Skater Beagle exhibits all the tell-tale signs of subjective creative authorship, that creativity must come from somewhere. So it’s either the AI or the person who wrote the prompt?
This line of thinking is half right, the generative AI is doing something important, it’s creating something from nothing, but its not “creativity” in the relevant sense. If you want to think of all of the details of the skater-beagle picture as expression, that expression does not magically appear from the ether, it comes from the latent space implied by the training data as processed by the model during training. In some ways it’s fair to say it comes from the collective efforts of all of the authors of all of the works in the training data. But not in the sense of a simple remix or cut-and-paste job.
Not from nothing, but not a remix
Generative AI systems come in different kinds, GANs, diffusion models, multimodal large language models, and more. The common feature of all these systems is that they trained on a large volume of prior works, and through a mathematical process, they are able to produce new works, often with very limited additional human input. But that doesn’t mean Skater Beagle belongs to the millions (10s of millions, 100s of millions?) of authors of the works in the training data. This beagle is not simple remix or collage. Although generative AI models are data dependent, they don’t just remix the training data, they produce genuinely new outputs.
AI Creativity comes from latent space
Generative AI models learn an abstract model of the training data, a model that is in many ways more than the sum of its parts. When you prompt a generative AI model, you are not querying a database, you are navigating a latent space implied by the training data.
What do I mean by “navigating a latent space implied by the training data”? Let’s start with a simple analogy. When you fit a linear regression to a handful of data points you generate a line of best fit implied by the data as seen in the figure below. Think of the dots as the training data and the line as the model implied by the training data.
The line illustrated above is simple, it is in fact an equation that you can use to answer the question, “if y is 6, what is x?” The point 6,6 is not in the data, but it is implied by the data and the model we used to fit the data. When you plug y=6 into the model you are navigating to a point implied by the data that tells you x=6, as seen in the figure below. That is what I mean by navigating the latent space.
But of course, if we used a different model, the data would imply a slightly different latent space, as illustrated in the figure below. Here the model is not linear its quadratic and just changing that starting assumption gives us a different line of best fit.
The difference between the straight line and the curved line here is analogous to the difference between different LLMs. Obviously, generative AI models are much more complicated than a two-dimensional regression model. Generative AI models have thousands of dimensions, and so they constructs a much richer latent space, but the analogy holds. Any number of dimensions above 3 is hard to conceptualize, don’t bother trying to imagine thousands of dimensions, your brain might melt.
Does Latent Space Solve the Creativity Puzzle?
Understanding latent space helps resolve the creativity puzzle. The image of Skater Beagle looks original because the model has generated a point in a vast space of possible images implied by its training data — not because a human author made free and creative choices about the details. The model navigates to a statistically plausible combination of features, but no person decides where the beagle’s ears should fly, how steep the hill should be, or what the sunset should look like. Understanding latent space helps explain why the output of a model can feel creative but still lacks the human authorship copyright law requires.
But wait, …
But in practice, it seems like almost any photo you send to the Copyright Office will be deemed creative enough to meet the requirements for registration. If I can get copyright for just pointing my iPhone at a beagle on skateboard and pressing a button, why can’t I get copyright in an image of a beagle on skateboard that I created using generative AI?
This seems inconsistent at first blush, but only because the question overlooks the difference between the “thin” copyright that attaches to photos based in reality and the thick copyright that typically attaches to illustrations drawn from imagination.
Small jumps versus big jumps
When you take a photo, you are making a copyrightable selection and arrangement from reality. You get no rights in the underlying reality, just a specific photographic representation therein. In most copyrightable photos there is only a small jump between idea and expression and so the resulting copyright is limited to that jump. Taking a photo does not give you exclusive rights on the underlying ideas, subjects, locations, etc.
There are two critical differences between the typical iPhone snap and an image generated with AI.
The first difference is that there is a much more significant jump between idea and expression in the transition from text prompt to final image, compared to the jump from a real life scene to photo capturing the scene. The second difference is that in photography, a human still makes some minimal creative decisions (framing, timing, composition) that manifest in the look of the resulting image. The human makes the jump, even if it’s only a small jump. In AI generation, the algorithm fills in the details that transform the prompt into a specific visual expression. The AI makes the jump between your idea for a photo and the details of the photo itself.
There is no copyright in the Skater Beagle image Gemini made for me. The work of bridging the gap from abstract concept to concrete image was done entirely by algorithms trained on trillions of words and millions photos. The details that we might think of as expression in the image didn’t come from nothing, they didn’t come directly from any particular photo featuring low angle action shots, beagles, dogs with ears flying, skateboard riders, steep hills, urban settings, “golden hour” sunsets, city skylines, etc. The details that we might think of as expression don’t reflect the free and creative choices of any human mind. They are details implied by a model trained on millions of photos, but those details don’t come from those photos either. The come from the universe of possibilities those photos imply, they come from latent space.
Skater Beagle is an extreme example
Generative AI lets us navigate a latent space implied by works too numerous to count so that we can create genuinely new digital artifacts. I began this essay with the promise that understanding this would shed light on how copyright applies to AI-generated works, but Skater Beagle is an extreme example drawn from one end of the continuum. Understanding why Skater Beagle is not a copy of beagles in the training data, but is also not my creative expression tells us that the Copyright Office is right to deny copyright to some generative AI creations. But it does not tell us at what point a user would cross the line from commissioning editor to guiding hand or creative mastermind. It’s hard to imagine crossing that line with a single text prompt, but it’s easy to see how you would leap over it in an iterative process as in A Single Piece of American Cheese. Iterative interactive use of generative AI will often be an act of authorship, so long as it is more than just choosing a winner in a beauty pageant of AI creations.
[This essay was adapted from Matthew Sag, Copyright Law in the Age of AI (2025)]