The title is deliberately provocative but if I'm reading this article right it seems to push an argument that I've made for ages in the context of my own busine

The title is deliberately provocative but if I'm reading this article right it seems to push an argument that I've made for ages in the context of my own business, and which I think - as the article itself suggests - actually represents the best practice from before the age of ChatGPT, to wit: have lots of ML models, each of which does some very specific thing well, and wire them up, wherever it makes sense to do so, piping the results from one into the input of another. The article is a fan of doing this with language models specifically - and, of course, in natural language-heavy contexts these ML models will most or all be language models - but the same basic premise applies to ML more generally and, as far as I am aware, this is how it used to be done in commercial applications before everyone started blindly trying to make ChatGPT do everything. I recently discovered BERTopic, a Python library that bundles a five-step pipeline of now pretty old (relatively) NLP approaches in a way that is very similar to how we were already doing it, now wrapped in a nice handy one-liner. I think it's a great exemplar of the approach that will probably emerge from the hype storm on top. (Disclaimer: I am not an AI expert and will defer to real data/stats nerds on this.)