I'm an ML engineer, and I agree with you- deep learning is by far the most common approach for new problems in informatics.

Imo deep learning is so popular because it "works". For a classification problem, if you try a linear baseline and a deep learning model, and you do a reasonable job of hyperparameter tuning and experimental design, it's likely you will outperform a simpler model. This holds true across many problem spaces.

I think the issue is that modern DL frameworks make it a little too easy to get pretty good performance on new problems. Other techniques generally require more background knowledge to make reasonable modeling assumptions, and still frequently perform worse than a naively applied DL approach.

I think DL will remain, in practice and education, a very popular tool. But it is essential to learn traditional statistical inference and other background to appropriately contextualize DL models so it isn't just some form of black magic.