I agree with what is said. When we lean into agents and want to use them for productivity, we need to accept the fact we will not fundamentally understand the code as we have designed and written it, otherwise might as well write the code ourselves. We already used to ship code with different levels of quality and technical debt to production before ai and developed a taste for what is "good enough" depending on the requirements, our long term workflow (how we close technical debt, and handle bugs), and our own personal capabilities. AI gives us more hands in the process and allows us to do more with less time, and less low level understanding. We should still question the code, and get our hands dirty in some sections, slow down and write ourselves small testing environments and chunks of code to get fundamental understanding of code which is critical. Otherwise we should just ship and forget and handle tech debt and "understanding debt" only when relevant. A lot of production code delivered pre ai already had tech debt that even the devs were not aware of, and yet a lot of it never fails and gets the job done, probably will be replaced in the future as those features become obsolete. Just because ai can surface those kinds of issues better does not mean straight away those have to be addressed meticulously.

The same way we do not understand already what the compiler does and how the code we have written runs against the machine, it's ok to delegate some of the lower level of understanding to AI, as long as the process produces the required results.

Different projects will have different sections where tech debt and code understanding debt are more critical, and we should with time acquire the intuition to know where we should rely more, and where we should rely less on ai to fill in the gaps.