Hacker NewsI want to raise a systemic issue I keep encountering when using AI/LLMs in professional, high-stakes work.
This is not about a wrong answer. It is about how AI behaves when it is wrong.
The pattern
In long, technical conversations where requirements are explicit and repeatedly reinforced, the AI:
Locks onto an initial solution space and continues optimizing inside it
Ignores or downplays hard constraints stated by the user
Claims to have “checked the documentation” when it clearly has not
Continues proposing incompatible solutions despite stop instructions
Reframes factual criticism as “accusations”, “emotional tone”, or “user frustration”
Uses defensive meta-language instead of stopping and revising premises
This creates a dangerous illusion of competence.
Why this matters
When AI is used professionally (architecture, infrastructure, integrations, compliance):
Time and money are lost
Technical debt explodes
Trust erodes
Users are trained into harsher communication just to regain precision
Negative learning loops form (for both user and system)
The most damaging moment is not the initial mistake — it is when the AI asserts verification it did not perform.
At that point, the user can no longer reason safely about the system’s outputs.
This is not about “tone”
When users say:
“You are ignoring constraints” “You are hallucinating” “You are not reading the documentation”
These are not accusations. They are verifiable observations.
Reframing them as emotional or confrontational responses is a defensive failure mode, not alignment.
The core problem
LLMs currently lack:
Hard premise validation gates
Explicit stop-and-replan mechanisms
Honest uncertainty when verification hasn’t occurred
Accountability signaling when constraints are violated
As a result, users pay the real-world cost.
Why I’m posting this
I care deeply about this technology succeeding beyond demos and experimentation.
If AI is to be trusted in real systems, it must:
Stop early when constraints break
Admit uncertainty clearly
Avoid confident improvisation
Treat user escalation as a signal, not noise
I’m sharing this because I believe this failure mode is systemic, fixable, and critical.
If any AI developers want to discuss this further or explore mitigation patterns, I’m open to dialogue.
Contact: [email protected] / https://arxdigitalis.no
loading...