until they give you images/text almost identical to the sources. Also would do you really want to compare an algorithm to a person? Because i doubt they are the same at learning through observation.
The fact that you have any doubts tells me that you don't actually know how AI work and are just guessing.
Diffusion models are basically image recognition run in reverse.
If you look at the visual "mind map" of a diffusion model, you can actually see much better how it works,
It basically starts with analyzing simple shapes and looking for hard edges (characterized by drastic changes in pixel value)
and then if you follow any given direction on the map you can see where it begins to associate specific shapes and colors with specific tokens.
For instance, if you looked along the vector for "fruit" you would see that it associates "small multiple fruit" like grapes or berries on one end and "large singular fruit" like melons on the other.
During it's training it looks at a billion different instances of any given token, and tries to find the common elements that define that token.
much in the same way that a human, over years of their early life, sees different cat or dog shaped things and has to figure out what the differences and similarities are.
If the extent of your ability to use the AI is "throw in a few tokens and make it spit out 1,000 results so I can pick one I like" then true it might not have enough data to make a completely unique version of that thing every time.
but like "The machine that can make any image has the capability to produce forgeries" isn't the mind-blowing gotcha you seem to think it is.
Also, your brain is just a really complex electro-chemical algorithm. (Well, really like 500 different algorithms piled on top of each other but still.)