AI chatbots were supposed to simplify knowledge work.
They promised faster writing, instant answers, and leverage over information overload. For a...
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This article hits uncomfortably close to home. I’ve been using AI chatbots for architecture discussions, but I keep catching subtle flaws that could have caused serious issues if I hadn’t double-checked.
That discomfort is exactly the signal people should listen to. Architecture failures rarely come from obvious mistakes. They come from silent assumptions that feel reasonable until reality disagrees. AI is very good at producing those assumptions confidently.
That makes sense. I notice I trust it just enough to lower my guard, which is worse than not trusting it at all.
I mostly feel this during debugging. The AI gives answers that look right but ignore the specific context of my app or browser quirks. It slows me down more than it helps.
Debugging is a perfect example. It requires causal reasoning tied to your runtime state. AI is replaying patterns from similar problems, not understanding the system you’re actually running.
That explains why it feels useful for boilerplate but almost useless once things get weird.
The emotional fatigue part really resonated. After a while, using AI feels like supervising someone who never learns from feedback.
That’s a sharp observation. The system doesn’t accumulate accountability or experience in the way humans do. Each response sounds fresh, but nothing is truly internalized.
That actually changes how I think about rolling this out to teams. It’s not just a productivity tool, it affects how people think.
Isn’t this just a temporary phase though? New tools always feel uncomfortable until we learn how to use them properly.
Some discomfort is normal, but this is different. AI doesn’t just change execution speed, it changes how confidence and responsibility are distributed. That has cognitive consequences.
That’s a fair distinction. I hadn’t thought about the responsibility shift before reading this.
I’ve noticed AI often proposes clean architectures that completely ignore operational realities like observability, failure modes, or legacy constraints.
Exactly. AI optimizes for conceptual elegance, not operational survival. Real systems are shaped by history, trade-offs, and failure. Those factors rarely show up in training data.
That explains why the designs look great on paper but feel risky in production.