I agree, but some (most?) software being written doesn’t require a deep understanding to verify because the domain is small enough or you’re not required to solve for all of its intricacies. E.g. prototypes, internal tools, low scale CRUD apps, personal projects, etc.

I believe this is where the huge divide in perceived AI productivity in SW comes from. It’s folks working on low-understanding-required domains talking to folks working on high-understanding-required domains.

> E.g. prototypes, internal tools, low scale CRUD apps, personal projects, etc.

My experience has been that small-ish projects like these are the most likely to contain code with bond-villain level of complexity (and success).

More than once have I been stuck going on Da Vinci Code-esque adventures to uncover bugs in prototypes years after the fact because the business pivoted away from what it was trying to do and later decided to pivot back, only to discover that despite the prototype and systems/libraries it worked with not changing, somehow it mysteriously doesn't work at all or it fails in convoluted ways despite being perfectly functional when it was originally shelved.

> E.g. prototypes, internal tools, low scale CRUD apps, personal projects, etc.

Those things shouldn't be hugely complex. They sound actually very simple.