Whether or not you care about your code being used, other people do care about theirs. They have a right to represent these concerns and see how they fare in the legal system.

At any rate, let me try to cut through your visceral anger and invite a discussion. :-)

"Is ML training sufficiently transformative that the model should be considered fair use?" - a burning question in many of our minds (maybe)

Firstly, this isn't yet clear from a legal perspective. Whether or not you agree with the class action, we need to clarify how IP law views ML training as it regards source -> model.

Anyway, here's a thought experiment: given an extremely overfitted model (to keep it simple, we can achieve a basic overfit by training only on a single piece of source data), predictions will generally replicate the training data. Indeed, one could accuse an overfitted model of being an over-engineered copying algorithm.

Therefore, the question of whether outputs are "sufficiently transformative" must at least consider the fit achieved by an individual ML implementation.

But what about a well-fitted model that generalizes to novel data? Back to the thought experiment. Let's expand and diversify the training source. In other words, we're cutting the training data with other training data to end up with a less "pure" (more general) result. Training time and the complexity of intermediate layer data (e.g. weights) skyrockets but, if you can reduce loss enough (which happens to be the goal of ML algorithms), we'll still end up with a model which reflects the aggregate training source. There's no magic: ML algorithms simply create an incredibly large/complex statistical model based on mathematically visible patterns in the source material. If the goal is to descend the loss gradient, the identity of a model is (100 - loss)% relative to the aggregate training source.

So, as for the "sufficient transformation" question, I think this question is clarified by considering ML training to be a form of "dilution" (in pursuit of model generality): if I dilute source material with other source material from different authors and compose a resulting work, am I cleared of all copyright responsibilities to the individual source works? How much dilution is necessary to put me in fair use territory? Personally, I think these sorts of cases can only be litigated on an individual basis. Also, a fairly obvious solution to clear this question is to generate your own training data, whether or not you believe this should be required.

But all that stuff isn't really what this class action case is specifically about. The root question of the case is this: do FOSS license holders reserve the right to prevent (or at least opt-out) other entities from building commercial products out of their licensed code without honoring those licenses and/or providing any compensation/attribution?

I believe we do reserve this right, and it doesn't require quashing Copilot or kneecapping the (IMO limited) potential of AI/ML. It simply requires clarifying laws and licenses accordingly and then determining whether damages have already occurred. The class action lawsuit is an assertive step to check this via the legal system, particularly considering that Microsoft is a giant corporation making gobs of cash off of this model. It's a way to answer questions and solve problems in the real world.