As AI technology spreads, writing methods are also rapidly changing, but the subjects of questions and judgments are still humans. [Photo=Nastia Petruk]

① The Boundary Between AI Writing and Responsibility

② Bias That Appears Neutral

③ The Silent Structure of AI Usage

④ Sentences Resembling Each Other

⑤ The Difference Between Analysis and Judgment

⑥ The Limits of AI Information and Evidence

⑦ Creative Standards in the Age of Recombination

⑧ Question Structure Creates Skill

⑨ Redefining Media Ethics in the AI Era

⑩ Do Editors Disappear, or Evolve?

Lately, when I read articles and reports, I feel a strange sense of déjà vu. Even though they are clearly written by different people, the flow of sentences is similar, the methods of logical development are alike, and the direction of the conclusions is not significantly different. The expressions vary slightly, but the structures are similar.

In the past, there were also popular writing styles. However, the change happening now is different from a simple trend. It’s not just a specific writer’s style spreading; the entire writing environment appears to be converging into a single pattern.

Many people immediately attribute this to "AI." However, to be more precise, the core of the problem lies not in the technology itself, but in how it is used.

AI does not impose a writing style on its own. Rather, it is a tool that can produce entirely different results depending on the questions asked by the user and the framework of the request.

In reality, however, this potential is often not fully utilized; instead, the opposite often occurs. As efficient prompts, methods for quickly generating drafts, and question structures with a low probability of failure are shared, people increasingly use AI in similar ways.

The problem starts precisely here. When question structures become the same, the structure of the results also begins to resemble each other. The issue is not using the same technology, but asking questions in the same way.

At first, it might seem like a matter of a few similar sentence expressions. However, as time passes, this resemblance expands beyond writing style to encompass the entire logical structure.

Introduction methods become similar, transitional sentences become similar, and even the balanced stance taken in conclusions becomes similar. In many cases, even the length and rhythm of paragraphs are standardized into a certain framework. This phenomenon results in the same skeletal framework despite different content.

On the surface, this phenomenon appears to be a victory for efficiency. This is because well-organized, quickly readable articles with few flaws are produced.

Indeed, many readers evaluate such articles as "neat" or "well-organized."

However, that very advantage creates another problem.

Within an overly smooth structure, the writer's individual perspective tends to weaken, and traces of judgment are easily erased. The sentences are stable, but the voice becomes faint.

A paradox arises: information increases, but perspectives decrease.

This issue is even more sensitive in journalism.

In an environment where article drafts need to be created quickly and complex issues need to be organized in a short time, AI becomes a very useful tool.

In many newsrooms, it is becoming increasingly natural to use AI to generate headlines, organize lead sentences, and supplement background information.

The problem is that as this process is repeated, articles from different media outlets begin to exhibit a surprisingly similar flow.

Expressions that mitigate conflict, sentences that strive for neutrality, and safe conclusions that avoid excessive assertions are repeated. Of course, this tendency is not necessarily all bad.

However, if all articles begin to avoid risks in similar ways, readers will eventually feel a sense of fatigue, thinking, "Aren't they all saying the same thing?"

Here, an important question arises.

Why has technology become more sophisticated, yet expression has not become more diverse? Why has access to information expanded, yet the direction of sentences has narrowed?

The answer is surprisingly simple: as humans desire faster results, they repeatedly use proven questions rather than novel ones.

People prefer frameworks that do not fail. Instead of asking in an unfamiliar way, they choose structures that are known to work well. In this manner, questioning methods become increasingly standardized, and those standardized questions, in turn, yield similar conclusions.

Ultimately, the reduction in diversity is not an inescapable fate of technology but stems from the repetition of human choices.

At this point, the homogenization of sentences is not merely a stylistic issue but leads to a problem of trust.

As texts become more alike, readers look at the source before the content itself. When the same structures, similar sentences, and familiar conclusions are repeated, "Who said it?" becomes more important than "What was said?"

This can weaken the individuality of media outlets, blur the distinctiveness of research reports, and even diminish the persuasiveness of corporate documents.

AI can generate countless sentences based on diverse data, but the moment humans invoke it in the same way, the results become uniform.

Therefore, what is needed now is not to reject AI.

As the same question structures are repeated, the logic and conclusions of the writing become increasingly similar.

The key is not how much you use AI, but how differently you use it.

Distinctiveness does not come from securing more information. Most information is now accessible to everyone at a similar pace.

The real difference arises from the questions you ask when faced with the same data, what you consider the core issue, and by what criteria you choose your conclusions.

In other words, writing in the AI era is becoming closer to the ability to design structures than to the skill of producing sentences.

Ultimately, the distinctiveness of writing comes not from the amount of information but from judgment.

In an era where everyone can access similar data and everyone can produce sentences above a certain level, what remains are perspective and criteria.

What should be emphasized, what should be omitted, where should the line be drawn, and what responsibility should be taken? Writing devoid of these judgments, however smooth, is unlikely to endure.

It may be an article that is read, but it is unlikely to be a memorable one.

The fact that writing styles are becoming similar does not simply mean that the styles have become alike.

It can be a sign that the channels of thought are narrowing.

Technology expands choices, but when humans use it in the same way, the results converge.

 Therefore, what is needed now is not a new tool but a new way of asking questions.

When AI-generated sentences are accepted as they are, writing becomes easier, but perspective diminishes. Convenience cannot replace depth.

In the next installment, we will delve a step further into this point.

AI can assist with analysis. It excels at organizing data, extracting patterns, and comparing possibilities.

However, analysis is not judgment. Judgment entails responsibility, and responsibility ultimately rests with humans.

In the next installment, we will examine why AI is good at analysis but cannot substitute for judgment, and why that boundary will inevitably become more important in future writing and decision-making.