By Jiayu Li

Introduction

I was stopped on the street without warning.

No explanation. No context. Just a gesture—and I was expected to comply.

It was a small moment, almost insignificant. Nothing serious happened. I showed my ID and moved on.

But the feeling stayed.

Not because of what was done, but how it was done.

We are entering an era in which artificial intelligence can process vast amounts of data, predict behavior, and coordinate systems at a scale never before possible.

Yet in everyday life, the way we use these capabilities often looks strangely familiar.

We still interrupt people instead of guiding them.

We still rely on authority instead of structure.

We still treat individuals as objects to be managed, rather than participants in a system.

In other words:

we are using the most advanced technology ever created to reinforce the most primitive forms of control.

This is not a technical limitation.

It is a conceptual mistake.

Artificial intelligence is often framed as a tool for better decision-making:

• more accurate predictions,

• faster responses,

• improved efficiency.

But this framing misses something essential.

AI does not only improve decisions.

It changes the conditions under which decisions need to be made.

If systems can anticipate behavior, structure environments, and provide continuous feedback, then the role of intervention should diminish.

Instead, what we see is the opposite.

AI is increasingly used to:

• monitor,

• evaluate,

• and intervene.

It amplifies the reach of authority, rather than reducing the need for it.

This creates a paradox.

The more intelligent our systems become, the more intrusive their application often feels.

Not because AI itself is inherently controlling,

but because we are applying it within an outdated model:

a model based on interruption, enforcement, and reaction.

This essay argues that the problem is not how powerful AI is, but how we choose to use it.

We are asking the wrong question.

Instead of asking:

“How can AI help us control behavior more effectively?”

We should be asking:

“Why do we still need to control behavior at all?”

Because if AI is used to its full potential, the goal should not be stronger enforcement.

It should be something more radical:

to make enforcement unnecessary.

Part II

What We Are Doing Wrong

The mistake is not difficult to see.

Look at where AI is being deployed today.

In many cases, it is used to:

• monitor behavior more closely,

• detect deviations more quickly,

• and intervene more efficiently.

Governments use AI to expand surveillance.

Companies use it to track performance and enforce compliance.

Platforms use it to moderate, filter, and control interactions.

At first glance, this seems reasonable.

If systems can see more, process more, and react faster, then control becomes more precise.

But this logic carries an assumption that is rarely questioned:

that behavior must always be corrected after it occurs.

This assumption belongs to an older model of society.

A model where:

• information is limited,

• coordination is difficult,

• and control is necessary to maintain order.

In such a world, enforcement makes sense.

You cannot anticipate everything,

so you react.

But AI changes this condition.

For the first time, systems can:

• anticipate patterns,

• model likely outcomes,

• and shape environments in advance.

Yet instead of using these capabilities to reduce intervention,

we use them to intensify it.

We build systems that:

• predict what people will do,

and then

• step in to correct them anyway.

This is not an improvement.

It is an amplification of the same logic.

The result is a system that is:

• more efficient,

but also

• more intrusive.

People are no longer just observed.

They are continuously evaluated.

They are not only guided by rules.

They are shaped by invisible processes they cannot fully understand.

This creates a new kind of friction.

Not physical, but psychological.

You are never quite sure:

• what is being measured,

• how decisions are made,

• or when intervention will occur.

And this uncertainty matters.

Because when systems become unpredictable,

people do not feel guided.

They feel controlled.

This is the core mistake.

We are using AI to extend control,

instead of using it to remove the need for control.

We are solving the wrong problem.

Instead of asking how to enforce behavior more effectively,

we should be asking:

why behavior needs to be enforced at all in a system that can already predict and structure it.

Until this question is addressed,

AI will not reduce friction in society.

It will simply make existing systems more powerful—and more difficult to question.

Part III

What We Should Be Doing Instead

If the problem is not the power of AI, but the way we apply it, then the alternative is surprisingly simple.

We should stop using AI to control behavior.

And start using it to shape the conditions under which behavior happens.

This is not a call for less technology.

It is a call for a different logic.

Instead of building systems that react to people,

we can build systems that guide them.

If AI can:

• anticipate patterns,

• detect risks,

• and model outcomes,

then it can also be used to:

• make rules visible in advance,

• reduce uncertainty,

• and align behavior without interruption.

In such a system, people do not need to be stopped, corrected, or monitored constantly.

They act within an environment where:

• expectations are clear,

• consequences are predictable,

• and actions naturally lead to outcomes.

This changes the role of the system.

It no longer needs to:

• enforce behavior,

• impose authority,

• or intervene repeatedly.

Instead, it becomes something else:

a structure that makes certain actions easier, and others harder.

The difference may seem small, but it is fundamental.

In one model, order is maintained through control.

In the other, order emerges through design.

This shift also changes how individuals experience the system.

They are no longer treated as objects to be managed.

They become participants who:

• understand the environment,

• anticipate outcomes,

• and act with greater confidence.

When this happens, something important disappears.

Not technology.

Not rules.

But the need for constant enforcement.

This does not mean that all problems vanish.

But it changes the direction of effort.

Instead of investing in:

• stronger surveillance,

• faster intervention,

• and tighter control,

we invest in:

• clearer structures,

• better-designed environments,

• and systems that reduce friction rather than create it.

In this sense, the real promise of AI is not that it can make control more efficient.

It is that it can make control less necessary.

Conclusion

We are at a turning point.

Artificial intelligence gives us the ability to see, predict, and organize human behavior at an unprecedented scale.

The question is not whether we will use this power.

It is how.

We can use it to strengthen systems of control,

making them more precise, more pervasive, and more difficult to resist.

Or we can use it to design systems that reduce the need for control altogether.

The difference lies not in the technology,

but in the assumptions behind it.

If we continue to believe that behavior must be enforced,

AI will only make that enforcement more powerful.

If we recognize that behavior can be shaped through structure,

AI can help create systems that are:

• more predictable,

• less intrusive,

• and easier to live within.

The real question is therefore not:

“What can AI do?”

But:

“What kind of system do we want to build with it?”

Because in the end,

the future of AI will not be defined by its capabilities,

but by the way we choose to use them.

Jiayu Li is a student at Chung-Ang University. His writing focuses on artificial intelligence, governance, technology and society, and the human consequences of emerging intelligent systems.