Greg Rutkowski, a digital artist known for his surreal style, opposes AI art but his name and style have been frequently used by AI art generators without his consent. In response, Stable Diffusion removed his work from their dataset in version 2.0. However, the community has now created a tool to emulate Rutkowski’s style against his wishes using a LoRA model. While some argue this is unethical, others justify it since Rutkowski’s art has already been widely used in Stable Diffusion 1.5. The debate highlights the blurry line between innovation and infringement in the emerging field of AI art.
It doesn’t change anything you said about copyright law, but current-gen AI is absolutely not “a virtual brain” that creates “art in the same rough and inexact way that we humans do it.” What you are describing is called Artificial General Intelligence, and it simply does not exist yet.
Today’s large language models (like ChatGPT) and diffusion models (like Stable Diffusion) are statistics machines. They copy down a huge amount of example material, process it, and use it to calculate the most statistically probable next word (or pixel), with a little noise thrown in so they don’t make the same thing twice. This is why ChatGPT is so bad at math and Stable Diffusion is so bad at counting fingers – they are not making any rational decisions about what they spit out. They’re not striving to make the correct answer. They’re just producing the most statistically average output given the input.
Current-gen AI isn’t just viewing art, it’s storing a digital copy of it on a hard drive. It doesn’t create, it interpolates. In order to imitate a person’t style, it must make a copy of that person’s work; describing the style in words is insufficient. If human artists (and by extension, art teachers) lose their jobs, AI training sets stagnate, and everything they produce becomes repetitive and derivative.
None of this matters to copyright law, but it matters to how we as a society respond. We do not want art itself to become a lost art.
This is factually untrue. For example, Stable Diffusion models are in the range of 2GB to 8GB, trained on a set of 5.85 billion images. If it was storing the images, that would allow approximately 1 byte for each image, and there are only 256 possibilities for a single byte. Images are downloaded as part of training the model, but they’re eventually “destroyed”; the model doesn’t contain them at all, and it doesn’t need to refer back to them to generate new images.
It’s absolutely true that the training process requires downloading and storing images, but the product of training is a model that doesn’t contain any of the original images.
None of that is to say that there is absolutely no valid copyright claim, but it seems like either option is pretty bad, long term. AI generated content is going to put a lot of people out of work and result in a lot of money for a few rich people, based off of the work of others who aren’t getting a cut. That’s bad.
But the converse, where we say that copyright is maintained even if a work is only stored as weights in a neural network is also pretty bad; you’re going to have a very hard time defining that in such a way that it doesn’t cover the way humans store information and integrate it to create new art. That’s also bad. I’m pretty sure that nobody who creates art wants to have to pay Disney a cut because one time you looked at some images they own.
The best you’re likely to do in that situation is say it’s ok if a human does it, but not a computer. But that still hits a lot of stumbling blocks around definitions, especially where computers are used to create art constantly. And if we ever hit the point where digital consciousness is possible, that adds a whole host of civil rights issues.