I see a fundamental issue issue from systems theory here, but do note that I'm by no means an expert at that, just had a bachelor course covering it together with control theory. The issue is that you would train a model on historical data, then you would use the model's predictions to make financial decisions. But, the moment you are using those predictions, you modified the system the model learned to predict. Now, maybe we can argue that, if you only invest a little money, you aren't really influencing the stock market. But I think the case would be very different if we're talking about big investors.

Yes, it is well studied that markets are not, in general, predictable but that doesn't mean that you cannot gain an advantage if you can extract some extra meaning, even a tiny one, that is what hedge funds, and other kind of sophisticated trading firms actually do. Here we are saying that they have an extra pattern recognition tool that they can use with a probability rate.

there probably already is, and it's deployed with a kill-switch one would hope at the HFT firms.

I don't have any insider knowledge but i'm pretty sure that one Quant group that everyone knows about probably figured out most of the "tricks" used to get llms and SD to really take off a decade before anyone else. iirc they consistently get like 30% or greater returns, which is practically black magic and means that they have something that can see a pattern(s) in the data.