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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A wise observation.
How would we set explicit boundaries to a tool that operates implicitly by principle?
You can’t enforce boundaries at the model level, only at the usage level. AI is implicit by nature, so boundaries must be procedural and organizational: clear scope, clear ownership, and explicit human accountability. Without that, plausible output will always creep into decision-making.
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.
Well done! The predicament of using AI and wishing for speed and realizing that it really can’t be trusted. Excellent job Sir. You made a great contribution to dev.to. My question if I may: how did you create this wonder of a picture a above your article that made us stop and read this.
My picture s generated on dev.to itself (a feature here) come out way to comical and abstract. I like your version much better
Thank you!
Thanks, image is made with nano banana
Thank you ! I will try it out
Chatbots pose some real dangers - for example, excessive automation reduces the subtlety of communication - but they can also be useful and effective. With careful consideration before implementing chatbots in the workplace, they can open up new business opportunities. Some are voice-focused, others live chat-focused. Some eliminate repetitive support work, while others consolidate disparate processes or automate hidden operational bottlenecks. Therefore, using chatbots to perform routine, repetitive tasks can reduce the cognitive load on employees, allowing them to focus on work that truly requires human interaction.
I agree that chatbots can be useful when they’re applied deliberately. The problem isn’t automation itself, but unexamined automation. When chatbots are limited to clearly defined, repetitive tasks, they can indeed reduce cognitive load and free people to focus on work that requires judgment and nuance. The risk starts when organizations blur that boundary and begin outsourcing reasoning, communication subtleties, or decisions that still require human accountability.
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.
Well said ! I agree with you on that
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.
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.