You can say it about becoming collectively overdependent on any technology. And I think there's a good case to be made that a similar phenomenon is true for other technologies e.g. over-reliance on (normal) computers has done similar things in my opinion, at least in my field of physics, where you get a more precise result but much less insight.
I also wondered why an LLM would be the right starting point. Why would Balzac or billions of lines of rwir code or reddit be relevant to mapping this smallish, well-defined language (SQL) to this other tiny constrained specification language (the query plan suggestions)? You could make a (relatively) tiny network and then actually pass it some relevant features of the actual data, like as numbers, not just as text returned from a tool call.
You're misunderstanding the setup here. The LLM doesn't modify the query, just some details about how to choose between different ways to break the query into basic operations on the tables. The SQL doesn't change. It's still up to postgres to guarantee that the results match the query. If the proposed plan were nonsense that didn't amount to carrying out the query, postgres would ignore it.
From your linked post: "LLMs sometimes struggle to give legal moves. In these experiments, I try 10 times and if there’s still no legal move, I just pick one at random."
You're pointing out that the goalposts are not fixed in the problem statement above, and gp's interpretation is not the most generous possible. But as the interpretations get more generous, the claim becomes more and more absurd. Maybe a properly-harnessed model would download the most advanced chess engine and query it to find the best move in each position, but that's not really demonstrating the model's intelligence anymore.
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