We’re entering a strange phase of modern knowledge work.

In software, startups, and technology-driven organizations, it has never been easier to sound intelligent.

Explanations are instant.
Answers are confident.
Language is polished.

And yet, understanding feels thinner than it used to.

Not because knowledge disappeared.
But because sounding like you know something has become easier than actually knowing it.

The gap between the two used to be small.
Now it’s wide enough to build careers on.

For most of history, expertise came with friction.

You couldn’t explain a system convincingly unless you had spent time inside it.
Unless you had internalized its constraints.
Unless you had failed often enough to know where intuition breaks.

Language lagged behind understanding.
That lag acted as a filter.

Not everyone who wanted authority could perform it.

That filter is gone now.

AI didn’t eliminate expertise.
It separated it from appearance.

Tools like AI didn’t suddenly make people smarter.
They made competence legible without comprehension.

You can now explain architectures you’ve never deployed.
Speak confidently about tradeoffs you’ve never faced.
Summarize domains you’ve never struggled through.

The output is fluent.
The reasoning is often missing.

This isn’t a failure of the technology.
It’s a failure of how people are choosing to use it.

What many people actually want isn’t knowledge.
It’s authority.

Not authoritarian power.
Social authority.

They don’t want to defer.
They don’t want to admit uncertainty.
They don’t want to accept hierarchies of understanding.

But they still want correct outcomes.
They still want credibility.
They still want respect.

So instead of learning, they borrow the surface of knowing.

AI makes this tempting because it offers something very specific.

Confidence without accountability.

You can sound right without being tested by consequence.
If an answer is wrong, nothing pushes back.
The language still holds.
The tone remains assured.

There is no feedback loop to correct judgment.

Real expertise is shaped by consequence.

When you’re wrong in real systems, things fail.
Products break.
Costs compound.
Decisions propagate downstream.

That pressure is uncomfortable.
But it’s what sharpens understanding.

AI-generated fluency has no such pressure.
It allows certainty to exist without exposure.

Over time, sounding right starts to replace being right.

The skill being rewarded shifts.

Accuracy matters less.
Depth matters less.
Restraint matters less.

Speed, coherence, and confidence take their place.

But coherence isn’t correctness.
And confidence isn’t understanding.

When those distinctions blur long enough, systems degrade quietly.
Nothing collapses overnight.
Things just stop working the way they should.

Borrowed authority always comes due.

People who skip understanding eventually make decisions they can’t defend.
They scale ideas they can’t diagnose.
They lead systems they don’t actually control.

When something fails, they don’t know why.
They only know that it did.

That’s usually when expertise suddenly matters again.
And by then, it’s too late to improvise it.

This isn’t an argument against AI.

AI is useful.
It accelerates drafting.
It accelerates summarizing.
It accelerates exploration.

But there’s a difference between using AI to compress thinking
and using it to replace thinking.

One builds leverage.
The other builds illusion.

The real divide forming isn’t between humans and machines.

It’s between people who understand why things work
and people who only know how to describe them.

The second group will be louder.
More visible.
Everywhere.

The first will move slower.
And speak less.

And when outcomes start to matter again — they always do —
the difference will become obvious.

Sounding smart was never the point.

It just used to correlate with knowing.

That correlation is gone now.

When sounding right is rewarded, being right becomes optional — and in technology, optional correctness eventually becomes systemic failure.