In my experience AI reduces maintenance costs. Though, context might matter here, I'm working on a multi decade set of projects, while there is a lot of greenfield feature development, the old code / older projects have suddenly become a lot easier to work with, modernize, and in a bunch of cases, eliminated. Dependency on old libraries, build tools, in some cases updated, in other cases just eliminated, builds are faster, easier for developers, etc. End to end testing has become a lot easier to setup and automate. DevOps have been improved a lot, diagnosing production issues drastically improved, we have a ton of logs and information, and while we have various consolidated dashboards / monitoring to capture critical things, now we can do a lot more analysis on our deployed system (~50 ish projects)
This rings true for me too, but I don't think it counts if your just using AI to aid maintenance. The basic argument in the article is around how many hours of maintenance you have to do for each hour of "value-add" feature development. So A. your only measuring maintenance costs not the ratio and B. The "old code" whp wasn't written with AI in the first place.