The engineers who critique AI are the ones who see the garbage code the LLMs write. Just look at the source dump for Claude Code; that code was a rat's nest of epic proportions.
Over the years I’ve worked with a few engineers who talked this way. Ironically, they often ended up being a bigger drag on the team than the “lower skilled” developers they looked down on. Dismissing entire groups of engineers rarely produces much insight.
My experience is that the loudest voices tend to be at the extremes. One side treats LLMs as magic and attributes every productivity gain to AI. The other contributes little beyond “LLMs are garbage and make mistakes.” Neither position is particularly useful.
The reality is probably somewhere in the middle. LLMs are genuinely helpful for many tasks and can make good engineers more productive. They also make mistakes, sometimes serious ones, and still require judgment, design skills, and review. Most engineers I know who use them regularly seem to understand both sides of that tradeoff.
Framing this disagreement as a fundamental misunderstanding of the technical capacity and appropriate use cases, for me, completely misses the plot. Both sides have compelling reasons for their beliefs and the cold rational analysis of the tech is as likely to further entrench the extremes as it is to enlighten.
I will also note that in your comment, you lament the dismissal of entire groups of engineers while doing exactly this when you dismiss the loudest voices (as well as those who think highly of their own ability) and imply that it is the loudest voices who are inherently extreme and therefore inferior to the pragmatic engineer who understands tradeoffs and cost benefit analysis.
But it can also help Sr engineers, differently. They tend to use it in smaller, more tightly scoped use cases. Well scoped re-factoring, boilerplate stuff, improving personal tools, etc. The improvement is not nearly as visible or measurable to managers.