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Stuff that works well with AI seems to correlate pretty well with high churn changes. I've had good luck using AI to port large numbers of features from version A to version B, or getting code with a a lot of dependencies under mocked unit tests.

It's easy to see that adding up quickly to represent large percentages of the codebase by line, but it's not feature development or solving hard problems.

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Same things I use it for as well - crap like "update this class to use JDK21" or "re-implement this client to use AWS SDKv2" or whatever.

And it works maybe... 80% of the way and I spend all my time fixing the remaining 20%. Anecdotally I don't "feel" like this really accelerates me or reduces the time it would take me to do the change if I just implemented the translation manually.

Amazon is publicly claiming that they have saved hundreds of millions on jvm upgrades using AI, so while it feels trivial - because before that work would end up in the "just don't do it" pile - it's a relevant use case.

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