By Ts. Lukas J. Tan
Written on 6 September 2026 | Penang, Malaysia

Technology has always changed the value of human labour. Machines reduced the need for physical labour, software reduced the need for certain administrative work, and artificial intelligence is now beginning to reduce the amount of human effort required for many forms of knowledge work. I do not believe this means that human beings will become unnecessary. But I do believe that many things we once considered difficult, specialised or expensive will become easier to accomplish with fewer people. When that happens, we should not only ask what AI will do to jobs. We should also ask what it will do to the institutions that were built to prepare people for those jobs.

This brings me to universities.

The Degree Became the Product

For a long time, the role of a university was relatively clear. Universities educated people, developed specialised knowledge and issued recognised qualifications. Students went to university because knowledge was concentrated there, employers valued degrees, and a certificate provided evidence that a person had completed a certain level of education. For many families, particularly in countries such as Malaysia, the degree also represented something larger. It was associated with social mobility, professional status and the hope of a more secure future.

Over time, however, something gradually changed. Education remained important, but the certificate itself became increasingly important. Students did not necessarily enter university only because they wanted knowledge. They needed the qualification because the labour market asked for it. Employers used degrees as filters, parents regarded them as a form of security, and young people naturally followed the pathway society had created for them. A degree became both an educational experience and a ticket into parts of the employment market.

There is nothing inherently wrong with this system. Universities have contributed enormously to human development, and I would never argue that education is unnecessary. But the assumptions supporting this model deserve to be reconsidered because the environment around universities is changing very quickly.

When Industry Becomes the University

One change I have been observing in Malaysia is the growing involvement of industry in education. More companies and industrial groups are establishing their own academies, training centres and education programmes. Some have gone further into colleges and university-level education. I cannot speak for the motivation of every organisation, and there are certainly different reasons behind each institution. But from an industry perspective, one reason is easy to understand. Companies need talent that can actually function in their environment. When graduates do not arrive with the exact capabilities an industry requires, companies naturally begin to train people themselves.

In some ways, this is a sensible development. A manufacturer understands its machinery, processes and workforce requirements better than an outsider. A technology company understands the systems and tools its people need. An industry can therefore design training around actual problems instead of waiting for a general curriculum to catch up.

But when more industries begin becoming education providers themselves, another question appears. How many universities, colleges and academies does a country ultimately need?

Malaysia's Numbers Tell Two Stories at Once

Malaysia already has a substantial higher-education ecosystem. According to the Malaysia Higher Education Blueprint 2026–2035, using 2024 higher-education statistics, Malaysia had 544 higher-education institutions and approximately 1.35 million enrolled students. The private higher-education landscape alone included 64 universities, 36 university colleges, 271 colleges and 11 foreign university branch campuses.

At the same time, Malaysia is moving through an important demographic transition. According to the Department of Statistics Malaysia (DOSM), Malaysia’s total fertility rate stood at 1.6 children per woman in 2024, below the replacement level.

I believe those two developments deserve to be considered together.

If the supply of education continues expanding while future generations become smaller, competition for students will inevitably intensify. AI could add another layer of pressure by changing how young people think about learning itself.

The Competition for Students Is Already Visible

We can already see signs that higher education has become a much more competitive market. Twenty years ago, I do not remember universities competing for attention in quite the same way they do today. Now university advertisements are everywhere. We see them along roads, on social media, through digital advertising, at education fairs and through increasingly sophisticated student recruitment campaigns.

There is nothing wrong with a university advertising. Every institution needs to communicate with its market. But the intensity of recruitment tells us something important. Universities are increasingly competing for student numbers.

That competition may become much more difficult in the coming years, because AI is changing one of the fundamental reasons people historically needed educational institutions: access to knowledge.

What AI Changed for Me

This is where my view is influenced strongly by my own recent experience.

I have managed a technology company for more than a decade. Yet I have never considered myself the strongest technical person in my company. I understand business logic, system architecture, processes, how different components connect and what a system ultimately needs to achieve. But there have always been technical areas where my programmers knew considerably more than I did. That was normal. They spent years developing those skills, while my role required me to focus on a different level of the business.

Then AI changed something for me.

After spending an intensive period working deeply with AI, I found myself understanding areas that had remained outside my practical capability for years. In roughly a month, I was able to explore programming concepts, infrastructure, deployment, automation and technical workflows at a speed I had never experienced before. By September 2026, I found myself able to oversee multiple client projects while using several computers and AI systems to assist different parts of the work.

The Distance Between Not Knowing and Knowing Enough

I want to be careful about what this experience means. One month with AI did not magically give me the depth of an engineer who has spent ten or twenty years mastering a discipline. Experience still matters. Deep technical judgement still matters. Security, architecture, reliability and understanding what happens when things go wrong still require expertise.

But something significant did change.

The distance between “I don’t know how to do this” and “I can understand this well enough to make it work” became dramatically shorter.

That is the part that should interest universities.

For centuries, knowledge was scarce. If you wanted to understand an advanced subject, you needed to find the right books, the right teacher, the right institution or the right expert. Today, a person can begin with almost no knowledge of a subject and have an AI system explain it, simplify it, challenge them, answer questions, generate examples and help them apply what they have learned immediately.

The AI can be wrong, of course. That is precisely why judgement and critical thinking become even more important. But the cost and speed of acquiring practical knowledge have nevertheless changed.

Four Years, Measured in Time, Not Just Money

This leads to a question that I believe students and parents will increasingly ask: if I spend three or four years obtaining a degree, what exactly am I receiving in exchange for those years?

We normally calculate the cost of university in money. Perhaps we should also calculate it in time.

Four years is a significant portion of a young person’s life. Imagine an 18-year-old who enters university and graduates at 22. Now imagine another 18-year-old who spends the same four years intensely studying one field using AI, working with industry, building products, doing research, creating a company, failing, learning from those failures and producing real evidence of what he or she can do.

At 22, who is ahead?

The answer is not automatically the second person. A strong university student may have developed deeper theoretical understanding, powerful friendships, professional networks, exposure to research and intellectual discipline. Certain professions also cannot responsibly be reduced to self-learning. I would certainly not want doctors, civil engineers or other safety-critical professionals qualifying themselves simply because an AI told them they understood the subject.

But for many other areas, the comparison is becoming legitimate.

If one person can show a certificate while another can show four years of actual work, products, research, clients, experiments and results, employers will increasingly have more than one way to judge capability.

The degree is no longer the only signal.

What University Still Gives You

This is why I do not believe the correct conclusion is that university will become useless. My own experience tells me otherwise. University provides things that are difficult to measure on a transcript. It gives people an environment in which to grow. It creates friendships and networks. It exposes young adults to people from different backgrounds. It provides structure, discipline, mentors and intellectual challenges.

Most importantly, good education develops the ability to think.

I can often see the difference between someone who has learned how to analyse a problem and someone who simply knows how to repeat information. The ability to structure an argument, question assumptions, evaluate evidence and make a judgement remains extremely valuable.

People Who Know How to Learn vs. People Waiting to Be Taught

But here again AI creates an interesting challenge.

A person who knows how to use AI properly can also develop these capabilities outside a traditional classroom. AI can challenge an argument. It can present opposing positions. It can become a tutor. It can explain a subject at different levels. It can help someone experiment and immediately turn an idea into something tangible.

The important distinction in the future may therefore not be between people who went to university and people who did not. It may be between people who know how to learn and people who are waiting to be taught.

The Risk of a Higher-Education Bubble

This is why I believe there is a risk of a higher-education bubble.

By “bubble,” I do not mean that universities will suddenly collapse or that degrees will have no value. I mean that we may be creating more educational capacity based on assumptions about student demand and the value of traditional qualifications that could change faster than institutions expect.

Several forces are moving at the same time. Birth rates are declining. Industries are developing their own talent pipelines. Online education continues improving. Alternative credentials are becoming more accepted. People can work and earn money across borders without physically moving. AI is dramatically reducing the friction involved in self-learning and creation. Meanwhile, more institutions are competing for the same young people.

If these trends continue, something eventually has to adjust.

Why Government Still Has a Role

This is also why government has a role to play. Higher education cannot be treated purely as an ordinary commercial market where unlimited supply is always assumed to create healthy competition. When an educational institution fails, the consequences affect more than shareholders. Students may lose years. Parents may lose savings. Staff lose careers. Qualifications can become uncertain. Communities can be affected.

The question for government should therefore not simply be how many universities a country has. The more important questions are what outcomes those universities produce, whether graduates genuinely develop useful capabilities, whether programmes respond to changing economic realities, and whether institutions are financially and academically sustainable.

Access to Knowledge Is Not Access to Opportunity

At the same time, we need to be socially conscious about the alternatives we promote.

It is easy for someone with a laptop, reliable internet access, business experience and confidence to say that everyone can simply learn with AI. Reality is more complicated. Not every young person has the same environment. Some need the structure of university. Some need access to laboratories and equipment. Some need teachers because they have never learned how to teach themselves. Some need university networks because their families have no professional networks of their own.

For these students, university can still be an extraordinary engine of social mobility.

AI may make knowledge more accessible, but access to knowledge is not the same as access to opportunity.

How Universities Could Evolve

That is why the solution cannot simply be to close universities or tell young people not to study. The more constructive question is how universities should evolve.

Perhaps universities should stop seeing themselves primarily as places that deliver information and qualifications. Information is becoming abundant. Instead, universities could become environments that transform information into judgement, experience, relationships and capability.

Students could spend less time reproducing answers in examinations and more time solving real problems. Industry projects could become part of education rather than something students encounter only after graduation. AI could provide personalised tutoring while professors spend more time challenging assumptions, mentoring students and developing deeper thinking. Students could graduate not only with transcripts, but with portfolios demonstrating what they have actually built, researched, solved and contributed.

The university might also become less of a one-time four-year destination and more of a lifelong platform. People could move between employment and education repeatedly, learning what they need when they need it instead of attempting to predict at 18 everything they will need for the next 40 years.

Industry academies could also have an important role, but they should complement rather than simply duplicate universities. Industry understands immediate workforce needs; universities should retain the ability to think beyond the immediate needs of a particular employer. Society needs both.

This balance is important because education has a responsibility beyond employability. Universities also develop researchers, preserve knowledge, challenge society, study problems that have no immediate commercial return and provide spaces where ideas can be explored independently. If education becomes only training for today’s jobs, we may prepare people perfectly for industries that disappear tomorrow.

The Real Question for 2026

The deeper issue, therefore, is not whether AI will kill universities.

I do not think it will.

What AI may destroy is the assumption that universities have a monopoly over advanced learning.

That monopoly is already weakening.

Knowledge can come from almost anywhere. Skills can increasingly be demonstrated directly. Companies can train their own people. A teenager with the right tools can create something that once required an entire technical team. An experienced businessperson can use AI to enter technical areas that previously seemed inaccessible. A person with curiosity, discipline and judgement has access to learning capabilities that previous generations could hardly imagine.

Universities still have enormous value, but increasingly that value must be demonstrated rather than assumed.

Perhaps the most important question for a university in 2026 is therefore no longer, “What courses should we offer?”

The more difficult question is:

“What can a young person become after spending four years with us that he or she could not become after spending four years learning, building and creating with AI?”

If universities can answer that question convincingly, they have a powerful future.

If they cannot, the greatest threat may not be another university opening across the road.

It may be an 18-year-old sitting at home with a laptop, an AI system, four years of time and a very clear idea of what he or she wants to build.

And that is why, writing this in September 2026, I believe the discussion about the future of universities should begin now. Not because education is becoming less important, but because learning is becoming more accessible than at any other point in human history.

The institution that once controlled access to knowledge now has to compete in a world where knowledge is everywhere.

Its future will depend on what it can offer beyond knowledge.