[EsssM7QVMehFPAs]

You are ignoring the fact that generative AI is not closed-loop algorithm. You can synthesize expected features in a data set and feed them to the detector - out of bounds of the generative neural network that rather serves the purpose of mapping into (a subset of) the proper input space.

The power of synthesis is not within the GAN or VAE, it is in the outside mechanism that guides the creation of content with specific domain knowledge about the feature space.

This might not replace the value of real data, but it will allow to accelerate bootstrap, improve coverage (at cost of accuracy), or provide free environments for auxiliary processes like CI/CD in many deep learning applications.

There is a lot of published material on synthetic data augmentation if you actually look for it.

[missosoup]

Everything you said doesn't dispute the above comment and agrees with its core premise:

"In terms of model improvement, yes synthetic data can help. In terms of the arms race? No. True examples provide knowledge that is unique. "

[EsssM7QVMehFPAs]

I was rather commenting on the first part implying that training a neural network with the statistical distribution that comes out of a GAN or VAE does not add value beyond that generative model capabilities.

I do not agree on that because as I explained, with domain knowledge it is very much possible to shape the data generated for augmented learning - beyond the plain statistical variations of GAN and similar, which are obviously of very limited value in training.