The Invisible Work Holding AI Together

A recent AI test circulating online poses a simple question:

What happens when you remove one hand from a pen held horizontally?

Ask what happens, and you’ll get a mix of answers.
Some people say it falls. Others aren’t sure. Some describe what they think they see.

AI systems tend to give a clean answer:

The pen falls.

That’s correct.

But it’s not the whole story.

What actually happens

If you try the experiment yourself, something subtle shows up.

Consciously or unconsciously, you do one of two things:

• You make small adjustments—tightening and loosening your grip just enough to keep the pen level.

• Or you don’t.

If you stop stabilizing the pen and let gravity work, it begins to rotate and drop immediately.

Whether you’re aware of it or not, stabilization is the key in this test.

The physics doesn’t change.
The stabilization does.

What looks like stability is often just continuous correction.

The hidden variable

Viewed this way, the test isn’t really about whether the pen falls.

It’s about what was keeping it from falling.

That missing factor—the stabilizing force—is easy to overlook because it is quiet, continuous, and human.

But it is doing real work.

Where this shows up in AI

AI systems often appear more reliable than they are.

Not because they are inherently stable, but because people are continuously stabilizing them:

• rephrasing prompts

• discarding incorrect outputs

• retrying

• adjusting inputs

• interpreting ambiguous responses
  
  

None of this work is tracked.
None of it is visible.

But it is essential.

The system appears stable because people are stabilizing it.

When the work disappears—and then comes back

Over the past year, some companies reduced headcount in anticipation of AI-driven productivity gains.

In several cases, they later had to rehire.

This isn’t a contradiction.
It’s a misdiagnosis—and a skills gap.

The missing piece wasn’t just labor.
It was the ability to recognize and perform stabilization work.

The assumption was:

AI would replace the work.

What was missed:

Much of the work was never in the output. It was in the stabilization.

• checking results

• interpreting ambiguity

• correcting drift

• maintaining continuity across tasks

When those roles were removed, the system didn’t become more efficient.

It became less stable.

The work didn’t disappear. It became visible—by failing.

Not the same kind of thinking

Humans and AI systems do not “think” in the same way.

Humans:

• track context across time

• notice inconsistencies

• apply judgment under ambiguity

• stabilize evolving situations

AI systems:

• generate responses based on patterns

• do not preserve reasoning unless externally supported

• can drift across prompts and sessions

This difference matters.

Because many of the most valuable AI-related skills are not about using new tools.

They are about:

• recognizing when an output is unstable

• knowing what to question

• maintaining continuity across interactions

• supplying the structure the system does not retain

The skill is not just using AI.
It is stabilizing it.

Recognizing how AI “thinks” is a learned skill

Humans don’t automatically understand how AI systems generate answers.

In practice, people often:

• take outputs at face value

• assume consistency where none exists

• miss when stabilization is required

Recognizing how AI systems behave is a learned skill. It includes:

• knowing when an answer is likely to drift

• recognizing when context has been lost

• distinguishing between a correct statement and a stable one

• understanding when additional structure is needed

This is not about learning a new tool.

It is about learning how to work with a system that does not maintain its own continuity.

Organizations that treat this as intuitive will struggle.
Those that treat it as a skill can train for it.

AI literacy is not just knowing what the system can do.
It’s knowing when it needs to be stabilized.

The opportunity

Instead of asking:

“How do we make AI smarter?”

Executives should also ask:

“Where is human stabilization doing the real work—and how do we make it visible?”

Because once you can see it, you can:

• measure it

• support it

• reduce unnecessary effort

• prevent silent failure
  

In practice, this means moving beyond one-off interactions and toward approaches that preserve reasoning across time—so work doesn’t have to be recreated and re-stabilized from scratch.

A note on implementation

One approach to this problem is what I’ve been calling a Documentation Archivist (working title) model:

Not storing conversations for reference, but preserving reasoning so it can be revisited, inspected, and extended.

The goal is simple:

reduce the amount of stabilization work that has to be done manually, over and over again.

Bottom line

What looks like stability is often just unacknowledged correction.

The pen didn’t wait to fall.
It was being held steady.

AI systems behave the same way.

The question isn’t whether they work.

It’s whether the people using them know how to keep them working.