This is the key point. Should we stay vigilant and review all the code, ensuring we have a decept grasp of what it does. Or just green light everything and treat the code itself as a black box. I would hope we do the former, but many are pushing for the latter.
Example from my workplace: pull requests that were already too long before LLMs by a few hundred or a couple of thousand lines are now several thousand lines longer and they get opened much more frequently. The primary source of these PRs is a well-respected senior team member with a ton more experience than everyone else by a measure of decades so nobody dares tell him to stop. Other team members aren’t far behind in the PR opening rate and admittedly I am one of them, but I try to mitigate the issue by doing multiple LLM reviews and anticipating when it’s ok to just approve it myself (have never had an issue in making this judgement thankfully). Part of this IMO is almost certainly because there’s now peer-pressure within the team to churn out a ton more code and that’s adding to the already clear message from leadership that we need to leverage AI to deliver things at unprecedented levels.
My point is that it’s not just that some people are pushing for greenlighting everything without due diligence, it’s also that they’re pushing others to toward that outcome with their extreme AI enthusiasm which they don’t see the ramifications of or just don’t care.
Would recommend your senior team look into stacked PRs, which was made to handle this scenario exactly.
Those skills are often taught in an idealized situation like very small projects. In the real world, there are a lot more practices and techniques to develop software in a pragmatic way. Those "engineering practices" are much more useful than the above, unless you're in some specific domains.