Sensemaking: Part 2
Why People Stopped Listening to the Experts
Part 1 of this series explored a simple shift.
Institutions controlled two things.
Information.
And interpretation.
They didn’t just report on events.
They explained what they meant.
If something major happened, everyone waited for institutions to interpret it.
The newspaper.
The evening news.
A university expert.
A government report.
Someone official would eventually show up and explain the reality.
Then the internet happened.
And information is everywhere.
News breaks almost instantly.
Millions of people publish commentary.
Data moves faster than institutions can react.
Artificial intelligence can generate reports faster than entire newsrooms.
The problem humans face today is no longer access to information.
It’s figuring out what the fuck it means.
And when information becomes abundant, interpretation becomes valuable.
That’s why the people gaining influence today aren’t always the ones reporting events.
They’re the ones helping others make sense of them.
Which raises a bigger question.
If institutions spent centuries building authority,
why did people stop trusting them?
The answer isn’t that institutions suddenly became corrupt or incompetent overnight.
Trust slowly started to fade.
One contradiction at a time.
For a while, the institutional model actually worked pretty well.
They filtered information.
They verified claims.
They helped the public understand complicated events.
In a world where information was scarce, that system worked well.
People trusted institutions because they seemed stable.
Professional.
Confident.
Extremely confident, as it turns out.
But that confidence turned out to be a bit misplaced.
Because the authority of institutions depended on something subtle.
The assumption that they were mostly right.
Or at least mostly honest about uncertainty.
Then people started noticing contradictions.
Experts would confidently explain one thing,
and years later explain the opposite.
Often with the same level of confidence.
Apparently certainty ages well.
Predictions disappeared.
Narratives shifted.
Policies once described as necessary became mistakes.
Experts have always been wrong.
Reality is complicated.
The real damage came from something else.
Certainty was often presented where uncertainty actually existed.
People can handle experts saying “we don’t know yet.”
What they struggle with is watching confident explanations change later with no acknowledgement.
Before the internet those contradictions were easy to miss.
Newspapers printed once.
Television broadcasts aired once.
Public memory moved on.
Institutions moved on with it.
Problem solved.
But the internet introduced something institutions were never designed to deal with.
Permanent memory.
Every interview.
Every article.
Every prediction.
Archived.
Searchable.
Suddenly anyone could pull up a years old clip and ask the awkward question.
“Didn’t you say the opposite back then?”
The internet didn’t just open access to information.
It opened access to institutional memory.
And once everyone could see the contradictions, something else became obvious.
Experts disagreed constantly.
Organizations competed with one another.
Narratives shifted depending on incentives.
The landscape started looking less like a single source of truth,
and more like a crowd of competing explanations.
Which, if we’re honest, is exactly what it always was.
The difference now is that everyone can see it.
At the same time something else was happening.
Expertise itself exploded.
Not that long ago the label “expert” was hard to come by.
There were fewer universities.
Fewer research institutions.
Fewer professional interpreters explaining the world.
Today there are millions.
Which is great news if you enjoy watching experts argue with other experts.
Every major issue now has economists, policy analysts, consultants, think tanks, academics, media commentators, and online creators explaining it.
Sometimes hundreds of them.
All extremely confident.
Often saying completely different things.
Which is not ideal if you were looking for clarity.
Paradoxically, more expertise didn’t produce more agreement.
It produced more visible disagreement.
And that left people asking,
If all the experts disagree,
how do I know which one is right?
Authority fragmented.
Institutions didn’t lose interpreters.
The system multiplied them.
At the time, authority was signaled through credentials.
Degrees.
Titles.
Institutional affiliations.
But the internet introduced something new.
Competence that could be demonstrated publicly.
Someone could publish analysis.
Explain patterns clearly.
Build a track record of predictions.
Refine their thinking in public.
Readers started paying attention to something different.
Not just who had credentials.
But who actually seemed to understand what was happening.
Authority started moving from institutional signals to demonstrated understanding.
Meanwhile, algorithms rewired how information spreads.
The modern information system does not optimize for truth.
It optimizes for attention.
Which explains the internet.
Content that spreads fastest tends to be emotional, surprising, or outrage inducing.
Not necessarily accurate.
Which means the most visible interpretations are not always the most reliable ones.
They’re simply the ones that travel fastest through the network.
Instead of one shared national conversation, we now have thousands of overlapping ones.
Two people can live in the same city and experience completely different versions of reality depending on what their feeds show them.
Meaning now emerges from competing interpretations.
And interpretation itself has become competitive.
It used to happen behind institutional walls.
Now it happens in public.
Ideas compete for attention.
Interpretations compete for trust.
Communities decide which voices they keep returning to.
Which leads to an uncomfortable realization.
Society has entered an open market for interpreters.
And like every market, some voices rise.
Others disappear.
This is also why so many people feel disoriented.
Human beings evolved in small information environments.
Village talk.
Local news.
A few trusted authorities.
Today we face 24 hour news cycles, algorithmic feeds, millions of opinions, and increasingly AI generated analysis.
The human brain wasn’t built for this.
So people respond in predictable ways.
Some retreat into ideological tribes.
Preferably tribes where everyone already agrees with them.
Some stop trusting anything at all.
Others begin assembling their own small circle of interpreters they trust.
Writers.
Analysts.
Researchers.
Thinkers who seem to notice patterns early.
Who explain complicated developments clearly.
Who adjust their thinking when new information appears.
Most people can feel that something about the information environment has changed.
They just struggle to describe exactly what.
The internet solved the information problem.
Unfortunately it created new ones.
Information exploded.
Interpretation became the advantage.
The most valuable voices today are increasingly the ones who can connect dots, notice patterns, and explain complex systems clearly enough that others understand them too.
Because helping others navigate it has become the real skill.
And that creates a new challenge for everyone living inside this environment.
If interpretation is now decentralized,
how do you decide which interpreters to trust?
That question may turn out to be one of the most important skills of the information age.
Who consistently notices real patterns?
Who explains systems clearly?
Who changes their mind when the evidence changes?
Who admits uncertainty instead of pretending to know everything?
The ability to evaluate interpreters may become just as important as understanding the events themselves.
When everyone can publish interpretation, the real skill becomes deciding who is actually worth listening to.
The internet gave everyone a microphone.
Unfortunately it did not give everyone judgment.
