Yes, I think most of the misunderstanding comes from people who are not in ML. A lot of the times you can't release the training pipeline without getting into g

Yes, I think most of the misunderstanding comes from people who are not in ML. A lot of the times you can't release the training pipeline without getting into grey areas of training data copyright. A ML model is a high dimensional distribution that can be sampled from infinitely, given enough time and money you can train another model (possibly even with a different architecture) that closely models the original model in generation capability.