Earlier quoted context omitted.
Expert reviews are just about the only thing that makes AI generated code viable, though doing them after the fact is a bit sketchy, to be efficient you kinda need to keep an eye on what the model is doing as its working. Unchecked, AI models output code that is as buggy as it is inefficient. In smaller green field contexts, it's not so bad, but in a large code base, it's performs much worse as it will not have acces…
In my experience, inefficient code is rarely the issue outside of data engineering type ETL jobs. It’s mostly architectural. Inefficient code isn’t the reason your login is taking 30 seconds. Yes I know at Amazon/AWS scale (former employee) every efficiency matters. But even at Salesforce scale, ringing out every bit of efficiency doesn’t matter. No one cares about handcrafted artisanal code as long as it meets both…
We know from experimentation that agents will change anything that isn’t nailed down. No natural language spec or test suite has ever come close to fully describing all observable behaviors of a non-trivial system.
This means that if no one is reviewing the code, agents adding features will change observable behaviors.
This gets exposed to users as churn, jank, and broken work flows.