Takis Konstantopoulos, University of Liverpool, contributes a short meditation on how we became data points:

Over the years I have written in these pages about teaching, learning, and the curious ways in which mathematics and statistics evolve inside universities. I would like to return to that theme, because something rather disturbing has been happening—in many countries—and it has been accelerating noticeably over the last ten to 15 years.

I have spent my life wandering through probability and mathematics, meeting the same familiar characters everywhere: curious, stubborn, mathematically honest people who simply want to understand something. But everywhere I look now, I find the same apparatus: dashboards, metrics, alignment rituals—an administrative ecosystem that appears to reproduce without external input. The late David Graeber would have had a field day with the contemporary university. His book Bullshit Jobs (2018) describes bureaucracies that expand for their own sake; the resemblance to academic life is uncanny.

But the machinery does more than expand; it influences and often distorts the way we work. It behaves in the classic McLuhan manner [see McLuhan, M., 1964. Understanding Media: The Extensions of Man. McGraw-Hill]: the medium shapes the message (content is decorative), usually without announcing its intentions. This applies beautifully to education. When teaching is mediated through administratively designed, content-vacuous pedagogy, learning-management systems, regulations, and reports, the content becomes optional; what truly matters is that the boxes are ticked and the dashboard turns green. Mathematics, which thrives on thinking, understanding, conceptual leaps, and the occasional productive confusion, is squeezed into learning outcomes, rubrics, and aligned assessments. The medium rewards neatness and penalizes depth. A proof does not fit easily into a rubric—although one suspects someone, somewhere, is working on it.

This is not a uniquely British phenomenon. Colleagues in the US, Australia, and Europe report the same pressures: recruitment targets, student-satisfaction scores, “value for money,” and the general idea that education should be smooth, predictable, and measurable. Mathematics, unfortunately, is none of these things. It’s lumpy, uneven, surprising, and occasionally difficult. It requires time and, above all, maturity.

Terence Tao recently spoke about AI and mathematical maturity (https://teorth.github.io/tao-web/ai-views.html [Part IV]). I agree with him. AI can be a useful tool—but only if the user already has a sense of what is true, what is plausible, and what is nonsense. Without that maturity, AI becomes a shortcut to nowhere. In many universities, students never quite reach the point of maturity we hope for, because the curriculum has been thinned to the point of vocational training. Courses with depth are quietly retired in favor of courses with “throughput.” In certain universities, faculty are even required to certify—before the exam is sat—that the average will lie between 55% and 68%. It is a curious inversion of probability: the distribution is fixed, and the teaching must adjust itself accordingly.

Many of us have observed the following structural issues. These are not complaints; they are simply features of the landscape.

First, metric-driven teaching. Departments are evaluated on satisfaction, progression, recruitment, and value for money. These incentives naturally push toward trivial vocational courses, without surprises, and with assessments that are simply performative. The solution for many—perfectly acceptable to administrators—is that they end up teaching about mathematics rather than teaching mathematics itself. A few years ago, while teaching calculus, I was asked—by a mathematician with an administrative title—to justify the course in terms of its employability benefits. It is a remarkable way to think about a subject that has shaped the entire scientific world.

Second, divide and conquer. Instead of cultivating a mathematical community—colloquia, reading groups, informal discussions, the occasional argument over coffee—the institution creates a steady stream of meetings between the head administrator and a rotating subset of subordinates. Everyone is kept separately busy, absorbed in reporting cycles, compliance tasks, and the endless preparation of documents that no-one will ever read. Interaction is encouraged only in meetings whose purpose is to determine the metrics of research output, usually without any understanding of the research itself. The communal life of mathematics becomes thinner; people teach, mark, and go home. Yet once a year a university might invite a Nobel laureate or a Fields Medalist, as if to demonstrate its commitment to scholarship.

Third, underprepared students. This is a global issue. Students arrive in “advanced” courses without solid foundations in analysis, combinatorics, algebra, probability, or statistics. Many are bright and capable, but the pipeline has changed. When we maintain appropriate standards, we are sometimes told we are expecting too much. But mathematics—pure, applied, computational, statistical—has its own internal logic; it does not bend easily. The subject is unforgiving in the best possible way: it demands clarity, structure, and genuine understanding, none of which can be supplied by policy.

Fourth, administrative dominance. Curriculum, assessment, and even the “tone” of feedback are increasingly dictated by people with no disciplinary background—a pattern of administrative control rather than academic judgment. This is not malice; it is simply the structure of the modern university. But it creates tension, because mathematics and statistics require disciplinary judgment. Uniformity across departments—mathematics and theology treated as pedagogical twins—is a charming idea, but a very silly one. It denies the intellectual structure of the disciplines themselves.

Fifth, manufactured outcomes. Universities increasingly expect exam results to fall within predetermined bands—a form of pre-certified performance that treats assessment as an exercise in quality assurance rather than a test of understanding. In some institutions, staff are even asked to certify in advance the “expected average,” as though learning were a controlled industrial process. This pressure produces grade inflation, softened exams, and a quiet insistence that failure is a sign of poor teaching rather than a natural part of intellectual life. It is an approach rooted in managerial metrics rather than the realities of mathematical reasoning.

Finally, erosion of research time. Mathematicians need long, uninterrupted stretches of thought. Yet the modern university demands constant engagement: meetings, training modules, and endless forms. Research becomes something done in the margins.

What, then, should younger mathematicians and statisticians do? My advice is simple.

Understand that things have not always been this way. Maintain your integrity. Protect your research time. Resist the temptation to compromise standards. Seek out real intellectual community—inside or outside your institution. And remember that mathematics is larger than any system that houses it.

I am not pessimistic. Mathematics and statistics have survived far worse than dashboards. They survive because people care about them, because they are beautiful, and because they are useful in ways that no metric can capture. The administrative university will continue to evolve, but the discipline will endure. As always, I welcome your thoughts. Our community is broad, international, and full of people who care deeply about teaching, research, and how each informs the other. If we keep talking to each other—honestly, without jargon or fear—then mathematics will be just fine. And perhaps we should also look upwards, not downwards: toward excellence rather than throughput. University leaders sometimes say that the problems are “political” and beyond their control. But part of their job, surely, is to explain to politicians why theoretical science matters. Abraham Flexner made this case beautifully in The Usefulness of Useless Knowledge (1939). It is a message worth repeating.