Beyond the Comfort Zone: Rethinking Higher Education in the Age of AI

This piece offers a personal reflection on the relevance of today’s university system—often characterized by high costs and structural inefficiencies—in the context of AI’s growing influence on how knowledge is delivered and how research is conducted. While many of these issues have already been widely discussed, the aim here is not to revisit familiar arguments. Instead, the focus is on concerns that are less frequently addressed, particularly the inefficiencies that built up in higher education between 2000 and 2020—developments that, from some perspectives, have made university education feel increasingly ineffective, or even unnecessary.

To begin, it may be helpful to consider a parallel in the world of Go (baduk). Before AlphaGo, Go education followed a traditional model: aspiring players trained in academies under the close guidance of veteran instructors. These teachers shaped their students’ progress, corrected their form, and provided psychological support during losing streaks. Go was not just a game—it was an apprenticeship. But that changed after AlphaGo defeated Lee Sedol in 2016. Since then, AI tools like KataGo and Leela Zero have revolutionized how young players learn. Instead of leaning primarily on teachers, students now study AI-generated moves that often defy conventional wisdom—yet prove more effective. One Korean prodigy reportedly locked himself in his room for a full year, training solely with AI and emerging with a style no human teacher would have encouraged—but which soon led him to the top ranks. Human mentors still exist, but their role has shifted. They are now interpreters of AI logic, offering emotional support and situational judgment, rather than authoritative sources of knowledge.

A similar transformation appears to be unfolding in university education. As AI takes the lead in delivering knowledge, professors are increasingly expected to transition from being primary lecturers to serving as mentors and facilitators of learning.

In response to these shifts, many argue that for graduate education to retain its value, it must focus on areas that AI cannot easily replicate—such as problem formulation, experimental design, ethical reasoning, and creative exploration.

There also remains a perspective that values the style of graduate education from before the 1990s. In my own experience during graduate study in the U.S. in the 1980s, the PhD process was far more than academic training. Under the guidance of a strict Jewish advisor, the education demanded emotional endurance, deep intellectual engagement, and days of sleepless intensity. It was not simply hardship for its own sake—it was a rite of passage. Many who went through such a system recall it as a defining experience that forged resilience and enabled real growth.

Today, however, the institutional priorities have shifted. Student rights, emotional safety, and legal risk management dominate the landscape. As a result, high-intensity training of this sort has all but disappeared.  Professors are increasingly cautious, even hesitant, to push students beyond their comfort zones. After all, no one wants to risk a formal complaint, a negative teaching evaluation, or worse, legal action. Given these constraints, many faculty members find themselves facing a difficult dilemma: how to nurture resilience and independence in students without exposing them—or themselves—to emotional or legal risk. In such an environment, it may seem more reasonable to let students encounter real challenges only after graduation, when they must navigate the demands of the real world on their own. While care and respect are undoubtedly essential, one might still ask: can we truly call it education without discomfort, challenge, or risk? As with a bird’s first flight, real growth often demands fear, uncertainty, and a leap into the unknown. AI may teach content with speed and precision—but it cannot prepare students for that moment of decision, when knowledge must become action.

This is especially true in advanced, professional-level education, where the stakes are high and controlled environments can only go so far. Consider film director Steven Spielberg, who, at just 27 years old, faced countless unexpected problems during the production of Jaws in 1974. Despite meticulous planning, technical failures, unpredictable weather, and a malfunctioning mechanical shark pushed the production into chaos. Yet by responding decisively to each crisis and adapting under pressure, Spielberg transformed the experience into a defining moment in his career—a quantum leap that shaped him not just as a filmmaker, but as a leader. His growth didn’t come from executing a perfect plan, but from surviving and learning through high-stakes unpredictability. That kind of transformation is what higher education—especially at the doctoral level—should be able to foster. And it’s precisely the kind of growth no AI system can simulate or substitute.

At the same time, there is a growing body of criticism directed at today’s research-centered universities, particularly those focused on theoretical disciplines. Some critics contend that these fields have become increasingly detached from practical relevance, evolving into self-reinforcing systems that prioritize institutional prestige over societal contribution. According to this view, academic insularity is not the only issue—what’s more troubling is the emergence of a self-replicating structure devoted to maintaining its own authority.

In many theoretical areas, scholars are said to write in language only their peers can decipher, present their work at insular conferences, and exchange citations and honors within tight-knit academic circles. These practices, some argue, amount to intellectual self-indulgence and academic wordplay cloaked in the language of scholarship. Eric Weinstein, a Harvard-trained mathematician and economist, summed up a frustration that resonates with many: “Much of the research young scholars conduct to become professors amounts to little more than servicing the prestige of their elders.” It’s a personal view—but one that can no longer be easily dismissed.

Meanwhile, funding continues to pour into high-cost conferences and workshops that often yield little measurable impact on real-world industries or communities. This has led to concerns that academia has become a high-cost, low-output system sustained more by inertia than innovation. 

Of course, such critiques are not meant to dismiss the value of all academic work. Rather, they raise urgent questions about the evolving purpose of higher education. In an era increasingly shaped by AI and rapid technological change, perhaps the most pressing question is not how we teach or what we teach—but why we teach. What is higher education ultimately meant to do?

In fast-moving industries, the ability to adapt under pressure has become more valuable than mastering any fixed body of knowledge. Yet today’s university environment, with its focus on structured programs and risk avoidance, too often trains students for stability in a world defined by change.

Structurally, universities and research institutes that find it difficult to break out of their comfort zones can serve as starting points for building the foundations of new technologies, but they are often ill-equipped to deal with the complexity and uncertainty of the real world. For such groups, turning these foundations into tangible economic outcomes is rarely easy. When they fail to recognize this limitation, they often resort to invoking “national security,” “technological supremacy,” or “we are falling behind” as a crisis frame to secure funding for their own fields. The media amplifies these messages, encouraging exaggerated or biased interpretations to pressure governments, ultimately leading to the allocation of R&D budgets. This tendency is even stronger in “mysterious” fields that are difficult for the general public to fully understand. (For example, in areas such as quantum computing or nuclear fusion power, most people may not be fully aware of the ongoing maintenance costs, technical challenges, and years of work still needed before these technologies can be realized, nor that some issues remain even after decades of research. Media coverage often emphasizes their exciting potential, while giving less attention to the hurdles that must still be overcome, which can lead to an overly optimistic impression.)

Creativity is born from the pressure of deadlines. If a field is truly important, a clear research time frame—6 to 12 years—should be set, after which companies should take over. Endless public funding only worsens the problem; without change, universities will continue to produce papers while graduates face unemployment, and research labs will keep their lights on even as they grow increasingly disconnected from society.

!!!Needs revision!!!