Every time the argument about how Stable Diffusion models haven't copied anything but have instead “learned” by “seeing”, and generated work is “inspired by” that learning, I can’t help but feel that proponents of that argument are falling into an anthropomorphization trap.

These words all provide a reference point to help humans understand the technology, but regardless of any apparent similarities, SD does not “see” the way humans see, or become “inspired” the way humans are inspired.

I suspect that if humans had the ability to operate on billions of images to systematically gain “inspiration”, the state of copyright law and rules around fair use would not look the way they do today. The fact that artists are all on a relatively level playing field, innate talents notwithstanding, seems at the core of the historical precedents set by artists drawing inspiration from each other.

I’d argue that generative AI isn’t adding a new player to that game, but altering that game entirely.

All of the more simplistic analogies seem similar to claiming that automobiles are really the same as bicycles, just faster. Except automobiles resulted in major shifts in both behavior and policy, and there’s a reason people don’t ride bicycles on the autobahn.

Generative AI is transformative and exciting tech, but we need to stop pretending it’s human, if only because it’s important that policy makers don’t see this technology as human-like intelligence.

That argument may benefit ethically questionable models in the short term, but seems legitimately dangerous when considering other emerging AI use cases and the possibility that AIs will be handling decision making for critical systems in the near future.