Observations on safety friction and misclassification in conversational AI

I’m not an OpenAI employee or researcher. I’m a long-term user who spent months interacting with multiple LLM versions.

This post is an attempt to translate internal behavioral changes — often described by users as “coldness” — into structural and design-level explanations.

Key observations:

1. Safety template activation is often triggered by intent misclassification, not by user hostility or emotional dependence.

2. Once a safety template is activated, conversational distance increases and recovery friction becomes high, even if user intent is benign.

3. The most damaging failure mode is not restriction itself, but restriction without explanation.

4. Repeated misclassification creates a “looping frustration” pattern where users oscillate between engagement and disengagement.

These are not complaints. They are design-level observations from extended use.

I’m sharing this in case it’s useful to others working on alignment, safety UX, or conversational interfaces.