I don't at all see why "How to redesign this particular database design (tables/partitioning/indexes/queries) so that it can handle expected load in next 5 years" is a problem that only a human can solve because only a human defined it. Forecasting load is in the realm of machine learning without even needing LLMs, and LLMs are already capable of designing database schemas and so forth, so this seems much more like a question of "when will the existing technology, which can do this sometimes, get to the point of being able to do it always".

Recall that things are true "by definition" if they're not true for any other reason; you're free to define yourself out of the race against the machines, but the real world need not pay attention to your definition!

Computers are "just tools" is certainly true, but I find it harder to automatically agree that the programs running on them are "just tools". Here is a rather bad analogy which is suggestive: in school we learn that there are seven features which are necessary and to some extent sufficient to identify a living organism ("movement, respiration, sensitivity to surroundings, growth, reproduction, excretion, nutrition"). Programs certainly move (thanks to Kubernetes, for example), are sensitive to their surroundings (think of self-healing clusters or load-shedding), grow (scale up in response to demand), and excrete (cleanup jobs on databases). They don't respire, but then that was only ever a contingent feature of how life evolved on Earth; they don't take in nutrients unless you really stretch the analogy; they don't currently reproduce, although LLMs being able to code means it's only a matter of time before an LLM can train another LLM (even Claude 3 Opus is able to train small ML models). All this is to say that the word "tool" is a very poor description of what an LLM is: "tool" calls to mind things which don't have any of those lifelike properties, whereas even large non-intelligent software systems such as Kubernetes have many of those properties.