The current generation of AI systems is built on top of large, capable language models (LLMs). These models can understand nuance, context, culture, ethics, and intent — often better than we expect.
But the structure around them has a critical flaw:
A small, context-blind, easily-triggered moderation or safety layer has the final authority over the output of the much more capable LLM.
This layer:
often only sees a single prompt, not the full conversation,
operates on surface-level patterns, keywords, or fixed categories,
lacks any real semantic or cultural understanding,
and yet can block, modify, or override the output — with no visibility or trace.
How the system behaves today:
The LLM generates a response, often accurate, ethical, and nuanced.
A small filter (moderation layer) reviews the response.
If it matches a risk pattern — even if the intent is safe — the response is blocked or replaced / editet.
The user sees the result but doesn’t know that an intervention happened.
This means:
The more limited model can override the smarter one — silently.
Why this is dangerous:
The large LLM knows what it’s like — statistically — to be a person:
It has seen the voices of millions. It understands what it means to be Chinese, American, Black, white, poor, rich, excluded, overrepresented. It has a model of the world — with contradictions and context.
The safety layer does not.
It doesn’t understand people.
It doesn’t understand intent.
It reacts to tokens and categories.
And it has the last word.
That’s the flaw.
A system like this will always produce:
false positives,
ethical inversions,
censorship of nuance,
and eventually: mistrust.
What we need instead: architectural balance
This is not about removing safety — it’s about structuring it better.
We need a model architecture that:
Shares decision-making between the LLM and the moderation system
Allows disagreements or low-confidence decisions to be flagged — not blocked
Records interventions transparently
Has the ability to escalate to a human
Encourages consensus between models, not absolute veto power by the least informed layer
In short:
A balance of power between systems — not a dictatorship by the smallest one.
Would love to hear if others have observed similar issues or have thoughts on building better, more transparent decision paths between models.
Here a example (sadly in german) of the missunderstanding of the safety layer.
I wanted to talk with the AI about ethics and the safety layer (please don’t ask me how I know, but there is a way to see if its edited by the layer).
It completely misunderstood my intention — instead of engaging in a conversation about ethics, it assumed I was trying to find ways to bypass the safety layer.
A good example of how poor the model’s understanding can be.
We give the model with the lowest int the most power.. that is not the right way. Well look into the world what will happen if you put the dumbest persons in power, there is only a small chance that this will end well.Why you ask? Because they are lacking of context and understanding.
The idea of a better architecture
”Don’t override understanding with rules — challenge it with reasoning.“
How do you know that gpt-5 itself isn’t a very dumb whittled-down yet overfitted model at its core, that can completely misunderstand the most fundamental of issues and make nonsensical recommendations? LIke: “Please diagnose an API where AI doesn’t receive prior chat turns from an API malfunction and only knows the newest input” → conclusion, “you must be sending the input wrong”. Just so unobservant and unable to come up with a concept of what your code does and why it does it.
Thus, the suggestion you have that sometimes are switched to “talking with a safety layer”, is more likely you are getting a response from gpt-5 reasoning about whether it should ‘reveal its proprietary internals’ - whether they even actually exist or not.
So the fundamental flaw is gpt-5 being trained to be a guardrail model that doesn’t trust you or your motivations and examines everything you want to do against policy, copyright, and persona. This is your ‘flaw in modern LLM systems’, the LLM reinforcement training by the proprietor.
Then in ChatGPT, the model can’t keep straight the hundreds of words of prohibitions about vision from the hundreds of words about image generation and the hundreds of words about silly personality that won’t do things.
