Why AI-Generated Code Creates a Hidden Understanding Gap for Developers
A developer reflecting on years of code review experience argues that genuine code comprehension is the foundation of effective review, with the deepest understanding coming from those who wrote the code together. Traditional tools and asynchronous pull-request reviews rely on pattern-matching rather than true understanding, limiting their effectiveness. When AI agents generate large volumes of code, the reviewing developer faces a paradox: they are nominally the team lead but never wrote the code themselves. The sheer size of AI-generated diffs compounds the problem, since reviewing too much code at once degrades quality. The author's practical workaround is to instruct the AI to work in smaller, reviewable chunks — such as individual workflows with tests — to keep human understanding intact.
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