I'd say if you're a semi-competent developer, as probably many people reading the article and commenting already are, this comment adds nothing new to the discussion and would already be a very vanilla usage example of "AI".
I think the point is that while you can "do things" like extracting the stripe integrations out into their own service in ten minutes, you're not stepping into other problems, such as how do you handle failures, how do you scale the stripe service, how do you structure all your other micro services so they can communicate in a coherent way, basically you're speed running yourself into harder decisions when using AI.
on the contrary, I freed myself from the burden of having to find all the places in the code base where we used stripe and patched them in one go along with the tests to prevent regressions. That represents DAYS of work that I condensed into a few hours.
who cares if it can't know good structure and how to handle failures? I know how to do that. I have a skills file I created that tells stripe our policy for handling error failures, defaults for structures as well as guidelines for how we should deal with communications between different systems. Before i spent hours building this stuff out. now I just spend 20-30 min reviewing a pr to make sure it follows my directives and move onto other problems.
Thats said, i agree with you on principle. I hand coded an app from a solo dev to now managing a team and gettin ready for an imminent series A. AI doesn't save you from scaling issues, you still need to have a clear idea of what you want from the ai and build processes that give it the context to do its job.
I call that job security :)