Notes Toward a Critique of University God-Making

A central problem today is that the machinery of “god-making” within universities has become remarkably severe. Stories such as an entire dormitory securing graduate-school recommendations, students copying code by hand, or yet another undergraduate publishing in a top conference or journal are quickly packaged into narratives fit for veneration. Around the age of twenty, one is already unusually susceptible to the production of myths and idols; immediately afterward comes the most plastic decade of one’s life. The resulting atmosphere is therefore molded into an almost coercive consensus: if you do not secure graduate admission, pursue a PhD, or enter a major tech firm, your life has somehow already failed.

Nor do we ordinarily encourage the Kantian spirit of “dare to know.” In certain contexts, the pursuit of knowledge itself has even come to resemble a kind of transgression. Everything tends to be justified through seniority, experience, and title. Universities also rarely encourage the Socratic form of education that, through dialogue and questioning, “guides people to discover the knowledge within themselves.” Instead, the supervisor is installed as the subject of education, while the student is positioned as the object of reception and indoctrination. Moreover, many people formed within STEM are accustomed to objects that are quantifiable and falsifiable, yet rarely extend the same reflective pressure to the question of meaning.

The more decisive issue is that we all understand the supply-and-demand relation in talent recruitment. With undergraduate and graduate credentials increasingly inverted in value, and with master’s programs being extended, the number of available positions becomes the determining factor under conditions of general oversupply. In other words, under the present macro environment, demand determines the price of your labor. A typical supply-side inference then follows: one should increase the scarcity of one’s credentials; or, for those who think a little further, increase the scarcity of one’s abilities.

It is precisely here that the value of first-principles thinking becomes visible. One can in fact push the question further: does demand exist only in the positions offered by major tech companies? Only in research institutes and faculty posts? Ultimately, these are merely explicit forms of demand. What, then, of the demands that have not yet been adequately named? We are accustomed to the narrative of acquiring degrees, polishing skills, and then entering one organization or another as an employee; yet we rarely ask in earnest why, in an era supposedly driven by demand rather than technology, the scarcity of knowledge, skills, and even symbolic capital such as degrees is placed above a cultivated taste for demand itself. Just as academic taste requires long cultivation, the discernment of demand also contains a great deal of tacit knowledge, and it too must be trained. Yet this is precisely what the narrative sustaining the university refuses to provide: it teaches you how to “solve problems,” but not how to “find problems.” And the ability to “find problems”—to discover and define valuable questions—will be far more precious in the future than the ability to “solve problems.” This also explains the root of the claim that “a degree does not equal ability”: today, your price is no longer primarily governed by your own scarcity, but by the demand of those who might employ you.

A participant in Adventure X once lamented on their blog that universities are lifeless. The judgment is not exaggerated. In an environment that privileges indoctrination over discussion, reproduction over creation, and publication over implementation, one should hardly expect anything genuinely vital to grow. Even the practical value of many papers in top conferences and journals remains suspended in a rather ambiguous state of meaninglessness. All this is unfolding at a moment when AI continues to rise and compress the value of human proficiency: when humans cannot outperform AI on algorithm problems, and basic code has become nearly abundant, this fortress-like order of evaluation appears increasingly anachronistic.