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The real danger is the answer that looks finished, sounds competent, survives a quick review, and fails only after money moves, customers click, or a production system depends on it.
Large language models are unusually good at producing the signals people mistake for finished work. They give you structure, fluency, confidence, and convention on demand. In software, those signals are often enough to get code approved, tests trusted, and plans greenlit. Looking finished is not the same as being done.
Don Wells calls this the completion illusion, and this book traces it from mechanism to consequence to response.
When organizations adopt AI tools and restructure their processes around the assumption that AI output is reliable, they dismantle the human verification infrastructure that previously caught defects. Senior engineers get reassigned. Code review becomes a formality. Institutional knowledge about edge cases, system quirks, and non-obvious requirements disappears. The organization becomes simultaneously more productive and more fragile-shipping faster while losing the ability to know whether any of it is correct.
The problem is that usefulness is being mistaken for reliability at exactly the moment teams are building process around that mistake.
Twenty chapters. Six failure modes. Real incidents from finance, law, healthcare, and infrastructure. And a framework for engineering teams that refuse to let the surface signals do their thinking for them.
Not anti-AI. Anti-confusion.
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