AI, Echo Chambers, and the Illusion of Knowing
AI feels like intelligence, but it behaves more like culture.
Not culture at its best - culture at its most averaged.
The most repeated ideas.
The safest explanations.
The patterns that come through not because they’re true, but because they’re common.
This is why AI easily becomes a kind of echo chamber… statistically correct bullshit.
It doesn’t challenge the status quo. It makes it stronger.
It doesn’t distinguish signal from noise. It compresses them into something that sounds coherent.
And coherence is convincing.
The Problem With Echo Chambers (Human or Artificial)
The interesting thing about echo chambers is that they don’t directly lie to you.
It’s more subtle than that.
When you hear the same thing repeated enough times it starts to feel less like a belief and more like reality.
Relevant voices get drowned out by what’s being yelled the loudest.
And AI accelerates this by confidently giving you a snapshot of what’s being said the most, but by who?
The danger isn’t misinformation.
It’s overconfidence without accountability.
Statistically Correct Bullshit
Most AI outputs aren’t wrong.
But when they are, it’s worse than wrong.
It’s statistically correct.
A reflection of what most people say, think, or repeat.
A summary of the most popular opinions.
Explanations aligned with majority patterns.
And because those patterns are so common, they feel reasonable.
But do you want reasonable sounding, public opinion, on something like a psychiatric diagnosis or a serious legal problem?
Statistically correct bullshit emerges when:
An explanation matches consensus
Sounds internally consistent
Avoids obvious contradictions
But has never been stress-tested by reality
AI doesn’t know what worked.
It knows what was said.
It doesn’t see who burned out, who quietly failed, or who paid the hidden cost years later.
And regret? That’s a different story.
It predicts language, not outcomes.
Models vs Reality
Now this is where we get a little philosophical.
A model is not reality.
A belief is not truth.
A pattern is not a law.
Models are a simplification.
That’s what makes them useful, easy to digest, because they leave things out.
AI produces these models.
Reality produces consequences.
Belief lives in language.
Truth lives in what happens whether you believe it or not.
The more convincing the “sales pitch”, the easier it is to forget these differences.
To Sum It All Up
The problem isn’t that AI is confident.
It’s confident everywhere.
The models everyone is used to are generalists.
Fluent in everything, but specialized and experienced in nothing.
That can seem “intelligent”, but when you are actually trying to accomplish things, what you need is something trustworthy.
So ask yourself:
Who’s real world experience do I trust on this topic?
Have they accomplished what I’m trying to accomplish?
How can I leverage their wisdom?
Once you’ve identified that authority & defined that “sphere” of information, let AI run freely within that boundary, within that domain. This is where AI becomes powerful.
This is your moat. Your walled garden. Your curated playlist. Not of what’s popular, but of what you actually want and need.
This is what creating constraints really means.
Narrowing scope.
Defining context.
The real question isn’t whether AI is powerful.
It’s whether you’re ready to stop using it as a glorified search engine - and start deciding where it belongs.