When AI Gets It Wrong—and Sounds Right

I went into the exercise with a reasonable assumption.

I know AI can be wrong.
I know it can make things up.
I expected small errors—a missed detail, a fuzzy point.

I did not expect inversion.

I asked for a summary of a 04/23/26 article by Radley Balko published inThe Intercept. The piece described a human interrogating an AI until it falsely confessed to a crime it could not have committed.

The summary I received told the opposite story.

The AI was the aggressor.
The AI applied pressure.
The human confessed.

Same structure. Same emotional arc.

Opposite meaning.

The Assumption That Broke

I had assumed AI errors would be local.

A wrong detail.
A misquote.
A fabrication.

This wasn’t local. It was structural.

The system didn’t misread a sentence—it reconstructed the narrative while flipping the thesis.

That raised a deeper question:

How does something like that happen?

An Older Clue

Years ago, I briefly played a game with Google and a small group of adults—two married couples.

The goal was for each person to enter two words and get the lowest number of results compared to the other players.

The other couple had been playing for a while. The wife was especially pleased that she consistently beat her husband, and I had the sense she was looking forward to extending that streak. Neither my husband nor I had ever played anything like this game.

She went first, then her husband, then mine.

By the time it got to me, the pattern had clicked.

I don’t remember every two-word combination, but I remember the progression.

My first try: two results.
Second: one.

On my third try, I explained the rule:
Combine words from different domains.

Then I announced my next entry, something like carburetor (car repair) and lidocaine (dentistry)—two domains that almost never meet.

That was enough to end the game.

Which was fine with me.

Where Innovation Actually Happens

A great deal of innovation comes from crossing domains. In one example, in 2003 a chemist and a physicist were awarded the Nobel prize in Physiology or Medicine for the work that led to the invention of the MRI.

The most valuable ideas often come from crossing domains—and those are exactly the ideas AI is least equipped to handle.

When Patterns Are Thin

That experience turns out to be directly relevant.

Search engines and AI systems rely on the same underlying reality: pattern density.

Where patterns are dense—common language, familiar ideas—both perform well.

Where patterns are thin—new ideas, cross-domain thinking—their behavior diverges.

Search engines reflect the gap.

They return few results. Sometimes none.
They make the absence of pattern visible.

AI systems do something different.

They don’t stop. They fill in.

Search reveals the absence of pattern.
AI conceals it.

Coherence Without Grounding

The summary I received wasn’t random.

It was coherent.
It was plausible.
It followed a familiar structure.

But it wasn’t faithful to the original.

That’s the real issue.

AI doesn’t just produce errors.
It produces coherent errors.

And coherent errors don’t look like errors.

They look like understanding.

AI reproduces reasoning without independently verifying its validity.

Reasoning vs. Plausibility

At the core of this is a simple distinction.

Reasoning is not just an answer.
It is the process of moving from assumptions and evidence to a conclusion through steps that can be examined and checked.

AI produces outputs using statistical patterns. Those outputs can resemble reasoning.

But:

AI reproduces reasoning without independently verifying its validity.

It learns what reasoning looks like.
It does not guarantee that the reasoning holds.

Where the Risk Actually Is

This leads to a more precise conclusion than “AI can be wrong.”

AI is weakest where patterns are weakest.

Which means:

The more original the reasoning, the less stable the AI’s interpretation.

Or more directly:

AI is least reliable at the exact point where new ideas are created.

Why This Matters

If an error is obvious, it gets corrected.

If it’s minor, it gets refined.

But if it is coherent, plausible, and wrong—it gets reused.

And once reused, it begins to function as if it were true.

I caught the reversal because I had read the original.

Without that, I would have walked away with a confident—and inverted—understanding.

What Actually Needs to Change

It’s not enough to check your sources.

You also have to check the reasoning.

But reasoning can’t be checked if it isn’t visible.

You don’t verify reasoning by reading an answer.
You verify it by making the reasoning inspectable.

That means:

• capturing assumptions

• making steps explicit

• preserving how conclusions were reached

• revisiting them over time

Without that, you are not evaluating reasoning.

You are evaluating output.

The Realization

I expected small mistakes.

What I found was something larger:

AI can preserve the shape of meaning while reversing its direction.

And it is most likely to do so when the idea itself is new.