When a major power decides to alter more than 30% of its university offering in just five years, it is not merely intervening in the administration of its education system. It is choosing which skills and forms of knowledge it considers essential to its position in the world. This is what is happening in China, where between 2021 and 2025 some 12,200 undergraduate degree programs were revoked or suspended, while approximately 10,200 new ones were introduced. The scale of the operation is revealing in itself: more than one third of the national academic offering has been affected by a revision that concerns not only universities, but also the relationship between education, industry and national strategy.
Viewed in isolation, the figure might appear to describe a large bureaucratic exercise. In reality, it points to something more delicate: the way a society decides which forms of knowledge deserve to be cultivated through public resources, family expectations and years of students’ lives. Humanities, foreign languages, the arts and several traditional management programs are among the areas most affected. During the same period, programs dedicated to artificial intelligence, robotics, semiconductors, advanced automation and embodied intelligence have expanded, marking a frontier where algorithms and physical systems increasingly operate within the same environment.
Within this framework, the university gradually ceases to be regarded solely as a place for the transmission of knowledge. It also becomes part of the country’s productive machinery, while formally remaining outside both factory and corporation. It shapes skills before they become employment, directs expectations before they become careers, and selects priorities before the market makes them fully visible. This function is less visible than frontier research, yet it often exerts a deeper influence over the long term.
China’s decision did not emerge in a vacuum. The country faces significant employment pressure among young graduates, while millions of students enter the labor market each year and many companies report difficulties in finding advanced technical profiles. The paradox is familiar well beyond China: an abundance of qualifications alongside a shortage of skills that can be effectively deployed in the fastest-growing sectors. The issue is not always the quantity of education. More often, it is the misalignment between what is taught and what the economy has begun to demand.
In an economy driven by data, automation and computational capacity, the value of an educational path is increasingly assessed through its effectiveness in generating employment. Such a measure may appear reductive, and to some extent it is, because no university should be transformed into a mere pre-emptive placement office. Yet ignoring it would be equally naive. When entire generations invest years of study in skills that struggle to find practical application, the gap between education and work becomes a political issue rather than a purely pedagogical one.
Educational planning thus enters the sphere of industrial policy, not as a technical appendix but as one of its material foundations. A country that aims to lead in artificial intelligence, build semiconductor capacity, automate supply chains and reduce external dependencies must first ask what kind of graduates it is producing. Part of the factory of the future already takes shape within university curricula, laboratories and the courses that are approved or discontinued.
For businesses, the Chinese experience offers a highly concrete lesson. For years, TSMC has devoted enormous investments to advanced semiconductor production, while demand for specialized expertise continues to outpace available supply. Siemens, on a different front, is tying a significant portion of its industrial strategy to the integration of software, automation and artificial intelligence within production processes. In both cases, the principal constraint is not merely financial capital or technological infrastructure. The most persistent limitation often remains human.
The same dynamic can be observed across many organizations introducing artificial intelligence into decision-making processes, predictive maintenance, design activities or data management. Purchasing a platform is not enough to create competitive advantage. Organizations need people capable of understanding its limits, integrating it into operational workflows, governing its effects and translating its outputs into credible decisions. Without those capabilities, innovation remains an expensive promise, sometimes little more than a sophisticated layer built around managerial practices that have otherwise remained unchanged.
Universities therefore become the first link in a productive chain that begins long before an individual enters an office or a factory. This does not mean subordinating every field of knowledge to the immediate needs of business. It means recognizing that human capital formation has become part of the architecture of competitiveness. Many organizations experience this daily: the challenge lies not so much in imagining new business models as in finding the people capable of turning them into reality rather than leaving them as declarations of intent.
The geopolitical dimension broadens the picture without necessarily making it more abstract. For more than a century, economic power was primarily associated with control over raw materials, energy infrastructure, trade routes and manufacturing capacity. Today, an increasing share of global competition revolves around the ability to produce applicable knowledge. Artificial intelligence models, advanced chips, robotic systems and digital platforms represent the visible surface of a deeper chain composed of research, computing power, data, talent and industrial continuity.
At its foundation stand researchers, engineers, mathematicians, computer scientists and professionals capable of transforming research into competitive advantage. Those who control the formation of these individuals control a significant share of the knowledge that will eventually become products, infrastructure, dependency or sovereignty. Global hierarchy is no longer measured solely through quantities of steel, energy or capital, but also through the ability to convert learning and research into functioning systems.
For this reason, the revision of China’s university offering resembles an infrastructure investment more than an educational reform. The highways of the twentieth century carried goods, workers, vehicles and industrial growth. The universities of the twenty-first century carry skills, codes, mental models and decision-making capabilities. The difference is that the latter operate over longer horizons and with effects that are less immediately visible. They shape the decisions that will be taken in laboratories, corporations and institutions once today’s emerging technologies become routine components of economic life.
Interpreting this evolution as a simple victory of technology over the humanities would be convenient, but insufficient. The world’s most innovative organizations demonstrate the opposite. Microsoft and Nvidia do not compete solely because they possess superior technology. They compete because they combine innovation, leadership, organizational culture, strategic vision, trust management and relationships with complex ecosystems of partners, developers, customers and institutions.
These elements do not belong exclusively to engineering. They concern the understanding of contexts, language, responsibility and the ability to assign meaning to decisions taken in environments increasingly mediated by intelligent systems. The central issue is not deciding which disciplines should survive and which should disappear. The real challenge lies in recomposing them within an economy where artificial intelligence is altering the relative value of skills.
Traditional management does not vanish by decree. Its function changes. An executive overseeing processes assisted by algorithms cannot simply delegate the selection of options to a machine. That executive must understand where the data originates, which assumptions are embedded within the models and which responsibilities remain human even when recommendations appear technically sound. Corporate culture does not become less important in this transition. It becomes more exposed.
The question of which professions will be needed ten years from now confronts governments, universities and businesses across the Western world with equal urgency, even if their institutional tools differ. The answer concerns far more than technical occupations. It involves the capacity to learn continuously, collaborate with intelligent systems, interpret complex data and make decisions under conditions of uncertainty. Education can no longer be viewed as a phase ending with graduation. It extends throughout professional life, often in ways less orderly than institutions might prefer to acknowledge.
The lesson emerging from China does not simply consist in having closed thousands of degree programs. Its most significant aspect is the recognition, clear and even severe, that contemporary economic competition increasingly depends on the speed with which a society can transform knowledge into productive capability. Resources still matter, of course. Yet they matter ever more when organized by skills capable of giving them direction, efficiency and practical power.
Artificial intelligence, in this sense, is not merely a new technology. It is the environment within which many economic decisions, industrial strategies and professional identities will be formed. It changes the nature of work, the distribution of decision-making power and the relationship between data and responsibility. Those who rethink their educational systems today are not working only on classrooms, courses or curricula. They are helping determine the cognitive tools with which the next phase of global competition, founded upon knowledge, will be confronted.