Well as I said it takes extremely intricate and not all that well performing SQL to perform signal processing and latent trend analysis, as it's not really desi

Well as I said it takes extremely intricate and not all that well performing SQL to perform signal processing and latent trend analysis, as it's not really designed with matrix multiplication or iterative transformation. So anything with noise, seasonality, ARIMA etc shouldn't be done in SQL, it should be done in a proper ML tool, because 99% of the time your developer will miss something and you'll improperly assign value to something that is in fact no pattern at all. You can't recreate the Netflix Prize in SQL, you can't really do NLP, you can't do pretty much any clustering or unsupervised learning. Again, "can't" meaning "can't do it easily or in a sustainable manner (i.e. shouldn't.)" And if we add in AI, I don't need to explain that SQL has no abilities to generate new content - it can't write a new song or headline, understand context and provide feedback, or drive a car. Now what OP is arguing is that you can get a lot of lift with SQL and basic exploratory data analysis and I totally agree. But ML can take you further out into the speculative realm and can work on a much less explicit and prescriptive model than SQL. Basically in SQL if you don't write it (whatever it is, a pattern, an anomaly, a correlation, a context) in your query you will never learn a single thing about it.