I'm not sure this is much better than the state of the art. Training a model on data and then having it generate new, fake data, is not only easy, it's a standard tool for model boosting.
I wouldn't immediately call creating synthetic data 'poisoning the well' unless it is actually distributed as such. For training models with a minimal amount of quality data, it is a viable method for generating more data to increase the quality of the models. But any legit organization will obviously label synthetic data as such.
