> The place to find them would be performance profiles, query plans, telemetry.

Tbh, once this information is available (which is the tricky part), in 99% of cases there's really no AI needed to analyze the data, since the 'low hanging fruits' will usually stand out anyway. And once you get into the area where optimization hotspots are no longer obvious, you're already deep in the diminishing returns area and optimizations for one use case or hardware configuration may degrade performance on others. That's my experience anyway.

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dewey 9 hours ago | parent | next [-]

> there's really no AI needed once this information is available

Most people are not arguing that problems are too tricky for a human to solve once presented with it, but AI can look at 1000 things at the same time across a whole code base and then just come back with the results a human can review.

	▲	flohofwoe 9 hours ago \| parent [-]
		True, but at least IME, once you get beyond fixing the 'obvious bugs' (how many there are depends on the initial quality of the code base - but I agree that AI is really useful to find those), you'll get into a murky grey area of "maybe false positives" which require a lot of time to validate. As a result you sink a lot of time creating and tweaking code-base-specific rules to try filtering out false positives, and IMHO this is exactly the tipping point where the whole thing becomes pointless because it becomes a bureaucratic monster. E.g. a good code analysis tool needs to work predictably at button press on any code base. Still better than nothing of course, e.g. I actually think bug scanning / code analsysis is indeed the one area where LLMs are actually useful, but it suffers from the same 'diminishing returns' problem as traditional approaches, if not worse (e.g. still no silver bullet, but a mostly useful additional tool in the toolbox).

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duskdozer 3 hours ago | parent | prev [-]

>in 99% of cases there's really no AI needed to analyze the data

As I've seen it, there's a large chunk of those getting the most out of AI doing so because they weren't aware of or couldn't be bothered by already existing, more reliable alternatives.