AI Hallucination Is Only a Symptom: What’s Really Missing Is a Semantic Law for Our Civilization” by Wen-Yao Hsu #1172
Description
Over the past two years, “AI hallucination” has become a global buzzword.
Some people blame model quality.
Some blame messy training data.
Others believe a few clever prompts can “reduce hallucinations” and make AI safer to use.
If we step back, a different picture appears:
Most AI hallucinations are not caused by a broken model.
They come from the fact that we took an inherently unstable human language system, scaled it into global infrastructure, and never added the missing layer it always needed:
a Semantic Law—a structural backbone that makes language auditable, replayable and accountable.
AI hallucination is just a symptom.
What’s missing is a civilization-level semantic framework.
- Hallucination is not mainly an AI problem.
It’s a language-civilization problem.
We are used to speaking in natural language.
But if you look at it from a structural, semantic perspective, human language has several built-in failure modes:
- Missing subject (no real “who”)
“Everyone knows that…”, “People say…”, “Experts believe…”
These sound authoritative, but who exactly is speaking?
Who is responsible for this claim? Usually: no one.
- Broken causality
Conclusions appear without their reasoning chain.
“This market will definitely grow,” “This approach is safer” — why?
The underlying logic is rarely explicit or reconstructable.
- Blurred responsibility
“For reference only”, “Use at your own discretion”, “No liability assumed”
We invented an entire layer of polite phrasing whose main function
is to push responsibility back to the listener.
- No replayability
Most conversations cannot be reconstructed later:
“Why exactly did we decide this way at that time?”
Sources, versions, assumptions, confidence levels are almost never encoded.
Large Language Models did not create these issues.
They simply act as a high-gain amplifier,
making 3,000 years of linguistic bad habits visible as engineering and governance failures.
When we ask a model to “talk like a human”,
it also inherits all our ambiguity, evasiveness, and responsibility gaps.
In that light, a “hallucination” is often just this:
Given no semantic constraints, the model produces a fluent sentence
to fill a gap that was never structurally defined in the first place.
- Why prompt tricks cannot “cure” hallucinations
(and often just hide the risk)
Popular advice today includes:
Add “If you don’t know, say you don’t know” to your prompts.
Restrict answers to a given set of documents.
Ask the model to self-critique or rate its own reliability.
Use a second model to cross-check the first.
These may help in local, short-term scenarios.
But they share one structural problem:
They turn governance into personal prompt style,
instead of baking it into system design.
The most critical missing piece is this:
Nobody formally defines what “don’t know” actually means.
Is it:
No relevant documents found?
Conflicting information among sources?
A broken reasoning chain?
Risk level above an acceptable threshold?
In those different cases,
should the model always say “I don’t know”?
Who decides the threshold? Where is it encoded?
If “I don’t know” is not a well-defined, detectable state in the system,
then the popular prompt “If you don’t know, say you don’t know”
is itself a kind of hallucination.
There is another side effect:
Within the same organization, teams use very different prompt patterns.
When something goes wrong, it becomes hard to tell:
was it a model failure, or a prompt-design issue?
Multi-model cross-checking can easily become
“multiple hallucinations reinforcing the nicest-sounding answer.”
From a systems-engineering point of view,
using prompt hacks to fight hallucination is like
putting duct tape on a cracked high-pressure pipe,
instead of re-examining the pipe’s pressure spec and material design.
- What we actually need is a Semantic Law,
not just bigger models
If we accept that human language does not come with enough built-in structural responsibility,
then the next generation of AI systems must add something deeper than more parameters.
We need a Semantic Law layer—
not to replace natural language,
but to attach a minimal, enforceable structure to every statement.
At the very least, this means four additional dimensions:
- Subject Layer – “Who is speaking?”
Is this the model’s statistical prediction?
An official corporate position?
An individual’s subjective interpretation?
Without a clear subject, there is no clear liability.
- Causal / Reasoning Layer – “How did we get here?”
Which data, assumptions and steps led to this output?
Can this be simplified into a replayable reasoning trace,
suitable for sampling and audit?
- Boundary Layer – “Where must the system stop or downgrade?”
In which domains must the model refuse, defer, or respond in a safer mode?
(medicine, finance, law, safety-critical control, security, etc.)
When a query crosses that line,
the system should automatically fall back to retrieval, FAQ, or human handoff—
not force a confident-sounding answer.
- Audit / Evidence Layer – “Can we trace this back later?”
For each output: what are the sources, timestamps, confidence levels, and risk markers?
If harm occurs, can we reconstruct the chain of responsibility?
A real Semantic Firewall is not just a buzzword.
It is a system-level semantic layer,
sitting between the model and the user, doing at least three things:
Translate human, fuzzy prompts into internal structures
with explicit subjects and boundaries.
Check model outputs for missing subjects, broken chains, and boundary violations.
When risk is too high, automatically downgrade, annotate, or block the response
instead of silently letting it through.
- If we don’t add Semantic Law now,
the bubble and the trust crisis will arrive together
Right now, global capital is mostly chasing:
Bigger models
More compute
More demos and “AI use-cases”
Without a semantic law layer, what we are really doing is:
Turning a language system with no responsibility backbone
into a cloud-scale, API-driven, industrial dependency.
In entertainment, marketing, or light-weight productivity,
this may look fine for a while.
But once we begin to rely on these systems for:
Medical advice
Financial decisions
Supply-chain orchestration
Industrial control
Public communication and policy
then hallucinations stop being a UX issue and become
a civilizational risk factor.
At that point, we will likely face, at the same time:
An economic bubble (AI spending misaligned with real, safe value), and
A semantic trust crisis (no one really knows which sentences can be trusted,
and who is accountable when they cause harm).
When that day comes, the key market question will shift from:
“Whose model is smarter?”
to:
“Who can clearly answer this:
If this sentence causes damage,
where does responsibility land,
and can we replay and audit that path?”
That is the true boundary
between a “language-model civilization”
and a semantic-law civilization.
Conclusion:
From model worship to a civilization that can handle truth
AI hallucination is only a symptom.
The deeper disease is our lack of a civilization-level framework
that forces language to be structurally accountable.
We should absolutely keep improving model architectures, datasets, and tools.
But without Semantic Law,
every bigger, faster model
is just a more powerful way to swing the same old stone
against the foundations of our own civilization.
The next truly meaningful question is no longer:
“How do I write a better prompt?”
but:
“Are we willing to design AI systems
where every statement lives inside
a replayable, accountable, auditable semantic structure?”
When that question is taken seriously,
AI will stop being just an engineering and marketing story,
and become a moment of civilizational self-correction.
Semantic Law is the piece that has been missing all along.
Now we are finally being forced to bring it on stage.
Truth persists. Illusions collapse.
——
Wen-Yao Hsu
Founder, Semantic Firewall
Researcher in AI Semantic Governance & Semantic Law
Taichung, Taiwan
[email protected]