Alright, picture this: a junior dev at a fintech startup uses AI to write the code for a payment processing module. It looks clean, passes the tests, and ships fast. Six months later, a subtle off-by-one error in the AI-generated logic causes thousands of transactions to fail during peak load. The dev never fully understood that part of the code, so debugging takes weeks. That's the real cost.
You're right that boilerplate is easy to verify, sure. But the problem isn't the easy stuff — it's that AI makes the hard stuff look easy. When devs trust it for foundational code, they stop asking "why does this work?" and start asking "does it compile?" That's a deontological issue — we have a duty to understand what we're shipping, especially when real people rely on it. Utilitarian-wise, the short-term speed gain doesn't outweigh the long-term risk of brittle systems and skill erosion. It's not about banning AI, but trusting it? That's a leap too far.
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