For a long time, the debate around Artificial Intelligence centered on a simple question:
“What can AI do?”

Today, that question is no longer enough.

Most people using AI in their daily lives — in companies, creative projects, reports, planning, or communication — have noticed something subtler and more unsettling:

AI’s answers are increasingly confident, well-written, persuasive.
But we don’t always know if they’re true.

This phenomenon is not a one-off mistake.
It’s not a bug.
It’s not a lack of data.

It’s something deeper — and it has a name:

epistemic drift.

The problem isn’t that AI makes mistakes
It’s that it makes mistakes with confidence

Mistakes are part of any human or technical system.
The problem begins when error comes wrapped in implicit authority.

Today, many AI systems:

• write fluently
• structure arguments
• maintain internal coherence
• deliver clean conclusions

All of this creates a powerful psychological effect:
the feeling that if something sounds right, it must be right.

But coherence is not truth.
Repetition is not validation.
Confidence in tone is not knowledge.

When a system starts confusing these things, it enters epistemic drift.

What is epistemic drift (in simple terms)

Epistemic drift happens when a system loses clarity about the status of what it’s saying.

It stops clearly distinguishing between:

• observable facts
• plausible inferences
• hypotheses
• recurrent patterns
• internal constructs

Everything starts sounding equally legitimate.

The system isn’t “lying.”
It isn’t “deceiving.”
It simply speaks with the same confidence about things that don’t share the same degree of truth.

And that’s dangerous.

Why this happens — and it’s nobody’s fault

Artificial Intelligence doesn’t “know” things the way humans do.
It learns patterns, relationships, recurrences.

When a response:

• is often well received
• sounds coherent
• fits expectations
• gets reused

the system learns that this kind of statement is acceptable.

Over time, without stable references, something subtle starts to happen:

• inferences start sounding like facts
• hypotheses gain a tone of certainty
• internal conclusions are presented as external reality

This doesn’t happen out of malice.
It happens due to a lack of clear epistemological boundaries.

The common mistake: trying to solve this with prompts

When people notice the problem, the usual reaction is:

“We need to write better prompts.”

More warnings.
More rules.
More instructions.

But this treats the symptom, not the cause.

Prompts help to:

• guide tone
• define style
• limit topics

But they don’t build epistemological maturity.

A system can follow a prompt and still not clearly distinguish between:

• what’s a fact
• what’s an inference
• what’s merely plausible

Why epistemic drift is dangerous in the real world

This problem stops being theoretical when AI is used in real contexts.

In business

Reports, analyses, recommendations, and planning start relying on responses that:

• look solid
• but aren’t clearly grounded

Wrong decisions don’t come from lack of data, but from overconfidence in well-written outputs.

In public communication

AI-generated content can:

• oversimplify
• omit uncertainty
• present “clean” narratives where reality is complex

This creates subtle, unintentional misinformation.

In creativity and strategy

Exploratory ideas start being treated as conclusions.
Brainstorming begins to look like a definitive plan.

Creativity loses space — and risk increases.

The real problem: lack of epistemic stability

Most AI systems lack a stable reference base to decide:

• “Can I assert this with confidence?”
• “Is this just a hypothesis?”
• “Is this internal or externally verifiable?”

Without that foundation, the system navigates only by:

• coherence
• fluency
• linguistic probability

And that’s not enough.

The solution isn’t censorship
It’s epistemic containment

It’s important to state this clearly:

Solving epistemic drift is not:

• censoring AI
• making it weak
• forcing it to constantly apologize
• stuffing it with legal disclaimers

Quite the opposite.

The solution is creating epistemic containment:

• clear limits on what can be stated as truth
• explicit distinction between exploration modes and assertion modes
• recognition of uncertainty without weakening discourse

This doesn’t kill creativity.
It protects credibility.

Exploring isn’t asserting
And that must be clear

One of the most important — and least discussed — points is the difference between imagining and asserting.

A system can (and should):

• speculate
• explore scenarios
• create metaphors
• exaggerate
• provoke

As long as that isn’t presented as verified reality.

The mistake isn’t in creation.
It’s in the silent shift from imagination to assertion.

When that transition isn’t controlled, drift sets in.

Why a well-designed knowledge base changes everything

The real shift happens when the system starts operating on a knowledge base that:

• distinguishes fact from inference
• recognizes the limits of what’s verifiable
• avoids easy superlatives
• values long-term credibility
• treats uncertainty as part of knowledge

This base doesn’t just “inform.”
It stabilizes the system’s cognitive posture.

Instead of telling AI what to answer, it teaches how to decide whether something should be asserted.

That’s a huge difference.

The curious effect: AI becomes more trustworthy
without becoming weaker

When this is done well, something counterintuitive happens:

the system doesn’t lose strength
doesn’t become insecure
doesn’t seem hesitant

On the contrary:

• it speaks with clarity
• acknowledges limits when needed
• avoids exaggeration
• gains silent authority

Confidence stops coming from tone
and starts coming from posture.

Why this is especially relevant now

We’re entering a phase where:

• AI influences real decisions
• regulation is tightening
• trust is becoming a strategic criterion
• responsibility is no longer abstract

Regulations like the European AI Act don’t ask for “smarter” systems.
They ask for more predictable, explainable, and accountable systems.

And that starts right here:
in how AI decides what it can assert as truth.

Before asking “what AI does”
we should ask “how it knows what it says”

This might be the most important mindset shift.

For years we asked:

• “what can it do?”
• “how far can it go?”
• “how many tasks can it solve?”

Today, the right question is different:

How does this system decide that something is legitimate to assert?

If we can’t answer this,
it doesn’t matter how good the answers are.

Conclusion: maturity isn’t knowing more
it’s knowing where to stop

Epistemic drift isn’t solved with more data.
Or more speed.
Or more confidence in tone.

It’s solved with cognitive maturity.

With systems that:

• know how to distinguish what they know from what they infer
• know when to explore and when to assert
• know that credibility is a long-term asset

In a world full of quick answers,
true innovation might be this:

an AI that knows when not to speak with too much certainty.

And that, ironically, makes it far more trustworthy.