Comment by LoganDark

I don't view LLMs as a substitute for thinking; I view them as an aid to research and study, and as a translator from pseudocode to syntax. That is, instead of trawling through all the documentation myself and double-checking everything manually, an LLM can pop up a solution of some quality, and if that agrees with how my mental model assumes it should work, I'll accept it or improve on it. And if I know what I want to do but don't know some exact syntax, like has happened in Swift recently as I explore macOS development, an LLM can translate my implementation ideas into something that compiles.

More to the point of the article, though, LLM-enthusiasts do seem to view it as a substitute for thinking. They're not augmenting their application of knowledge with shortcuts and fast-paths; they're entirely trusting the LLM to engineer things on its own. LLMs are great at creating the impression that they are suitable for this; after all, they are trained on tons of perfectly reasonable engineering data, and start to show all the same signals that a naïve user would use to tell quality of engineering... just without the quality.