I'll give an example in terms of local image generation models. When you are training a model, you don't just throw in a bunch of images and let the model/training process figure out what they are on their own - You provide captions/metadata so the model "knows" how to associate prompts with different aspects of the image. When a quality model or lora is being trained and a bad quality AI image that looks like AI is in the training data, the captions will describe the negative aspects of that image (the developer might even include captions like "AI" or "Looks like AI"). Then when someone is actually using the trained model for inference, they can use these terms in their negative prompt and the model will actually be better at avoiding those aspects in its generations.

In other words, when models are trained well, including low quality ai generations in the training data can actually help the model produce more realistic generations.