AI Is Hollowing Out the Foundations on Which Chinese Education Was Built

China’s biggest education crisis today is not excessive competition, rampant tutoring, or the repeated failure of “reducing academic burden.”

It is that this education system is still desperately training people for an old era, forcing vast numbers of young people to compete for a ticket that is rapidly losing its value.

When people talk about education, they usually see only the surface: exhausted children, anxious parents, the spread of tutoring, and failed reforms. But those are still just symptoms. The real problem lies deeper. A system exists not because it merely looks reasonable, but because it was genuinely useful in the past. The reason modern Chinese education took its current shape is not accidental, nor the result of someone’s temporary confusion. It was originally a talent-producing machine adapted to the industrial age, the age of organization, and the age of examinations.

What it does best is not inspire people, but filter them; not ignite them, but classify them; not help a person become more fully themselves, but make them increasingly fit to be arranged into a larger system. It does not first ask who you are. It first asks whether you can be processed, whether you can be ranked, whether you can be inserted into a bigger machine and operate there steadily.

When it needs engineers, it emphasizes standard answers, deductive ability, problem-solving speed, and obedience to rules. When it needs large numbers of stable mid-level executors, it emphasizes discipline, rankings, diplomas, qualifications, and layer-by-layer screening. When it needs a society capable of rapid industrialization, urbanization, and organization, it must build an educational machine capable of processing large numbers of people into “usable people” in a short period of time.

So why does Chinese education always feel like an assembly line?

Because that is exactly what it is.

It has never been the same thing as education in the truest sense. Real education awakens a person’s sense of inquiry, trains judgment, and helps people form their own interpretive framework and inner order for facing the world. Real education is more like a match: it lights the fire within a person. But modern Chinese education has long been more like a mold: its task is not to ignite, but to shape.

That is also why slogans such as “reducing burdens,” “banning tutoring,” and “quality-oriented education” are so difficult to truly implement. It is not because policymakers do not want change, nor because parents are naturally foolish, but because the underlying logic of survival in society has not changed. If you do not score high enough, you cannot get into a better school; if you cannot get into a better school, you cannot obtain a better diploma; without a better diploma, it becomes much harder to enter the systems that can offer stability, dignity, and position. So everyone knows something is wrong with this arrangement, yet no one dares truly step away from it.

Because in the old era, a diploma was not just a piece of paper. It was a ticket. It proved that you had been screened, had obeyed rules, and were fit to be placed into a larger machine and keep it running. So what many people competed for was not knowledge, but qualification; not growth, but position; not what they truly understood, but whether they could be recognized by the system.

The problem is that AI is hollowing out the very foundations on which this entire education system rests.

Many people understand AI only in terms of better tools, faster models, or hotter industries. But AI’s deepest significance is not merely that it improves the efficiency of certain jobs. It is that, from the demand side, it is cutting away the core practical foundation that made this old education system viable.

Why could this education system function in the past? Because industry, companies, organizations, and institutions genuinely needed huge numbers of standardized, trained people. Knowledge moved slowly, information was expensive to distribute, and the cost of organizing ability was high. So society had to rely on large numbers of intermediaries: teachers, textbooks, schools, exams, diplomas, companies, platforms, and hierarchical management. Layer by layer, they explained, filtered, certified, and allocated, placing people where they were supposed to go.

But what AI brings is not just greater efficiency for part of the workforce. It is a rapid expansion of productivity itself. And every step forward in productivity compresses another layer of intermediaries in the old system. In the past, to obtain high-quality knowledge, a person had to go through teachers, textbooks, schools, tutoring, institutions, and diplomas. Now, as long as an ordinary person knows how to ask questions, how to judge, and how to use AI, they can approach knowledge itself at an unprecedented speed. In the past, a company needed departments, hierarchies, meetings, approvals, and many people working together to get things done. Now, more and more tasks that once required three people can be done by one person equipped with a suite of AI tools.

Just look at the world’s leading AI companies today, and you will see how far this has already gone. The tools they build—Claude Code, Copilot, and all kinds of coding agents—are not merely “assisting engineers.” They are directly rewriting the production process itself. In the past, a product feature often had to pass from product managers to developers to testers to documentation teams, layer by layer. Now, one person can open a dozen terminal windows at once, and behind each window stands an on-demand, nearly tireless, top-engineer-level agent capable of executing tasks immediately. For the first time, large-scale engineering power—once available only to the very best companies—is beginning to open up to individuals.

What is truly frightening and thrilling about this is not simply that “AI can write code.” It is that AI is beginning to compress the organization itself. In the past, you first had to enter a large company before you could borrow its people, processes, knowledge, tools, testing capacity, and delivery capabilities. Now, for the first time, these things are becoming directly callable by ordinary individuals. To put it even more bluntly: before, you had to join the army to have firepower; now, firepower itself is beginning to slip out of the hands of organizations.

So the real question is no longer whether AI will replace a few programmers. The real question is this: when even the most advanced companies are using agents to reorganize their production processes, the old assumption that only large organizations can possess large capabilities also begins to shake. When a company can become an army of one, an individual can also become an army of one. A person is no longer just a person. For the first time, they have the chance to enter reality accompanied by an invisible engineering team, research team, and execution team.

Once intermediaries are compressed, the authority of the old education system begins to hollow out.

This means young people are still being pushed by the education system into brutal competition for an old ticket whose value is collapsing. It means middle-aged people who once believed they were selling experience, credentials, and organizational position are now discovering that when companies cut costs and boost efficiency, all of those things are being revalued. It means many supervisors and teachers still hold pockets of power within the old system, but find it increasingly difficult to prove that they remain irreplaceable in the distribution of knowledge. As a result, relationships between graduate students and advisors will become more strained, relationships between companies and employees more cold-blooded, and the old exchange relationship between diplomas and jobs more unstable.

This is why more and more graduate students today feel that they are not being trained, but being used. Projects need people. Papers need people. Research topics need people. Administrative affairs need people. Presentations need people. Errands need people. Yet the value of the student as a human being is becoming weaker within the system. Many advisors speak in the language of cultivation, but what they really hold in their hands are research projects, resources, authorship, graduation, and the power to decide who stays and who leaves. Thus, students are institutionally called “talent,” but in reality are often little more than low-cost, obedient, repeatedly callable intellectual laborers.

This is not merely a matter of individual morality. It is an ugly form of self-preservation that emerges when the old structure of education begins to loosen. When education becomes less and less about education itself and more and more about serving as the front-end talent-processing plant for a larger system, those who control the processing power become more likely to treat people downstream as costs, resources, and tools. The hollower the system becomes, the more brutal localized power tends to grow. Because as genuine intellectual authority declines, the hardest power left is process power, evaluation power, graduation power, and allocation power.

The same is true for companies.

In the past, many companies hired people not because people were so irreplaceable, but because under older technological conditions, human labor was the cheapest way to keep the system running. A large workforce did not necessarily mean people were important; often, it merely meant that under the production conditions of the time, people were still cheaper than machines. Now AI is changing that calculation. Once a company realizes that work that used to require ten people, twenty people, or an entire department can now be done by three people—or even by one person supported by a suite of agents—it will of course recalculate.

So layoffs are often not simply a matter of an “industry winter.” They are the first open admission by organizations that they no longer need so many people to maintain their former dignity, hierarchy, and processes. Yesterday you were “talent”; today you are redundancy. Yesterday your experience was an asset; today your salary is a burden. This is not because you suddenly became worse. It is because productivity changed, old organizations began shrinking their demand for people, and for the first time people saw nakedly that within the larger system, they too were merely a cost item to be recalculated.

This is not the choice of a few individual companies. It is a recalculation by the age itself.

So what is truly frightening is not whether a specific job will be replaced by AI, but how vast numbers of people will prove their value anew when society as a whole no longer needs so many people trained by the old educational system.

But the story cannot end there. If all we see is AI’s destruction of old education, then this article tells only half the story. Because while AI is hollowing out the practical foundation of old education, it is also, for the first time, returning real education to the individual.

The most positive significance of AI is not that students can find answers more quickly, nor that teachers can prepare lessons more efficiently. Its truly positive significance is that, on a massive scale, it weakens the old structure in which knowledge had to be distributed through intermediaries. In the past, an ordinary person could access high-quality knowledge only by attaching themselves to elite schools, famous teachers, institutions, textbooks, and authoritative publishing systems. Now, that threshold is rapidly falling. Knowledge is no longer guarded as tightly by a small number of intermediaries as it once was.

This will bring about a deeper shift: the importance of memorization will decline, while the importance of questioning, judgment, connection-making, and action will rise sharply.

Because when knowledge itself becomes easier and easier to access, what is truly scarce is no longer “how much you know,” but “what exactly you are asking,” “whether you can tell what is true,” “whether you can organize scattered knowledge into your own understanding,” and “whether you can turn understanding into action.”

In other words, real education finally has a chance to return to the human being.

Education should not merely stuff ready-made answers into a person’s head. Education should help a person develop a sense of inquiry, a framework for interpreting the world, and an inner order for facing uncertainty. Its purpose should not be to stuff people into a system, but to help them first become full human beings. What AI is destroying is the most industrialized layer of the old education system. What it is releasing, however, may be the true meaning of education itself.

In the future, education in China will likely split more and more clearly into two layers.

The first layer is the old system, which will still exist. It will continue to examine, filter, stream, and fight over elite schools and diplomas, because the old order will not collapse overnight. The second layer will consist of more and more people outside the system, reorganizing their own learning paths through AI, self-study, projects, communities, and real problems. They will no longer study merely for assessment, but for understanding, creation, collaboration, and survival.

These two educational systems will coexist for a long time: one will continue training the kind of people the old era needed, while the other begins training the kind of people who will be truly scarce in the future.

And who are those people?

They are not the ones best at test-taking, nor the ones best at memorizing standard answers, nor the ones best at scoring highly within the old system. The truly scarce people of the future will be those who can learn by themselves when there is no ready-made path, make judgments for themselves, organize their own rhythm, call on AI themselves, and turn problems into results.

To put it even more plainly: the real divide of the future will not show up only in who scores higher on exams. It will show up in who can learn more proactively, who can organize themselves better, and who can still assemble themselves into a productive small unit even as the system contracts.

That is also why phrases like “one-person company” and “one-person army” will become increasingly common.

They are not motivational slogans. They are realistic outcomes of changing productivity. In the past, many capabilities could only be possessed by organizations. Now, for the first time, more and more of those capabilities are opening up to individuals. Companies can use AI; individuals can use AI too. Platforms can use AI; ordinary people can use AI too. When a person no longer has to depend on a massive system in order to access knowledge, tools, productive capacity, and expressive power, they gain, for the first time, the chance to truly reorganize themselves.

This does not mean everyone will succeed, nor that organizations will immediately lose their significance. It simply means that the old single path—“you must first pass through this educational system, then obtain that diploma, then enter that system, and only then be recognized”—is being broken open.

So the deepest impact of AI on Chinese education is neither “will exams still be necessary” nor “will teachers lose their jobs.” It is that AI is forcing society to answer a far more fundamental question:

When the old intermediaries are failing, what exactly should make a person needed?

That question is the master question that China’s education, employment, intergenerational relationships, and broader social reform will not be able to avoid over the coming decades.

Old education is losing what was once its most essential practical meaning. That is dangerous. The most dangerous thing is not that a few subjects will become outdated, nor that a few exams will stop working, but that an entire generation may still be training itself desperately in the ways of the old era, only to discover too late that the old era has already begun to leave the stage.

And yet true education may be getting its first real chance to begin again precisely because of this collapse.

It is no longer just about sending people into systems. It is beginning to force people to relearn how to ask questions, how to judge, and how to organize themselves when there is no ready-made path.

That is the deepest divide of the AI age.

Not who is better at solving test questions. Not who is better at obeying. Not who is better at getting high scores within the old system.

But who can see earlier than others that the old ticket is losing value, the old intermediaries are failing, and the old path is collapsing—

and then, one step ahead of everyone else, reorganize themselves.