You can judge all you want. You'll eventually appear much like that old woman secretly judging you in church. Most of the current discourse on AI coding assist

You can judge all you want. You'll eventually appear much like that old woman secretly judging you in church. Most of the current discourse on AI coding assistants sounds either breathlessly optimistic or catastrophically alarmist. What’s missing is a more surgical observation: the disruptive effect of LLMs is not evenly distributed. In fact, the clash between how open source and industry teams establish trust reveals a fault line that’s been papered over with hype and metrics. FOSS project work on a trust basis - but industry standard is automated testing, pair programming, and development speed. That CRUD app for finding out if a rental car is available? Not exactly in need for a hand-crafted piece of code, and no-one cares if Junior Dev #18493 is trusted within the software dev organization. If the LLM-generated code breaks, blame gets passed, retros are held, Jira tickets multiply — the world keeps spinning, and a team fixes it. If a junior doesn’t understand their own patch, the senior rewrites it under deadline. It’s not pretty, but it works. And when it doesn’t, nobody loses “reputation” - they lose time, money, maybe sleep. But not identity. LLMs challenge open source where it’s most vulnerable - in its culture. Meanwhile, industry just treats them like the next Jenkins: mildly annoying at first, but soon part of the stack. The author loves the old ways, for many valid reasons: Gabled houses are beautiful, but outside of architectural circles, prefab is what scaled the suburbs, not timber joints and romanticism.