Fully agreed that in simple use cases simple solutions make sense; I've been arguing similarly for the NoSQL movement for years (i.e. NoSQL being great for large scale systems; but for most companies day-to-day needs SQL wins out).
However, it would be good to have a bit more in the article to say what AI/ML* is in this context, and a couple of scenarios where it beats SQL; i.e. otherwise it just sounds like the rantings of an old man "in my day we only had turnips; you needed a snack: turnip; you needed a pillow: turnip". By showing a few good use cases allows you to better contrast the product / get an understanding of where the boundaries are between the technologies.
*NB: When I first read this I assumed the author was talking about AIML (artificial intelligence markup language) rather than AI/ML (artificial intelligence / machine language)... as though the slash was included, there was no use of the full terms.
However, it would be good to have a bit more in the article to say what AI/ML* is in this context, and a couple of scenarios where it beats SQL; i.e. otherwise it just sounds like the rantings of an old man "in my day we only had turnips; you needed a snack: turnip; you needed a pillow: turnip". By showing a few good use cases allows you to better contrast the product / get an understanding of where the boundaries are between the technologies.
*NB: When I first read this I assumed the author was talking about AIML (artificial intelligence markup language) rather than AI/ML (artificial intelligence / machine language)... as though the slash was included, there was no use of the full terms.