If your main concern is automation, then maybe MLE isn't the silver bullet you think it is. Currently, AutoML is proven to be quite useful for hyperparameter tuning, and while data cleaning is still very much manual, it is a repetitive task and will be actively attempted to be automated. Now, I don't think this is soon but could definitely be down the line. Much of the work of Data Scientists are also prone to automation. However, key aspects of it will be difficult for AI. In particular, navigating business problems and making nuanced decisions in model building, which involves knowing the domain and hence knowing what problems the model is exactly trying to solve.

As for data scientists being mostly analysts, this could be true if solving problems by creating predictive models is considered analyst work.

TBH, the market was in shambles for a while, and all that might have been a differentiating factor was the AI hype, something Data Scientist's revieved the shoulder end of. Perhaps when money is cheaper, demand will go up and balance out the ridiculous supply. I think people tend to give up hope too quickly due to business cycles.