From Scarcity to Abundance: Will Universities Survive the Age of AI?

By Richard Sebaggala

For centuries, higher education benefited from the scarcity of knowledge. Universities held the key to specialised information, and society paid a high price for the degrees and expertise that only these institutions could provide. Professors were the guardians of wisdom, lecture theatres the places where it was passed on, and libraries the guarded vaults of human progress. From an economic perspective, this was a textbook case of supply and demand: the supply of advanced knowledge was low, the demand from individuals and employers was high, and universities could command both prestige and price. Degrees acted as economic signals for scarce intellectual capital. This monopoly has disappeared. Artificial intelligence now produces literature reviews in seconds, explains complex theories on demand, and even designs experiments or business strategies that used to be hidden in the minds of experts. The supply curve of knowledge has shifted dramatically outwards, reducing scarcity and lowering the “price” of access to information to almost zero. Knowledge is no longer scarce. What is scarce is the ability to integrate, apply, and scrutinise AI-produced knowledge. In economic terms, the new scarce commodity is interpretability;  the human ability to assess, contextualise and create value from a wealth of data. The survival of universities will depend not on guarding data, but on how well they manage to integrate AI into teaching, research, and public engagement;  and that means faculty must lead the way.

 

Globally, the gap between student adoption and institutional readiness is widening. Nearly 80% of students are already using generative AI, but most have no structured support from their universities. Every semester without faculty readiness compounds what education strategist Dr Aviva Legatt calls “pedagogical infrastructure debt.” In economics, this resembles a rising cost curve: the longer an institution delays investing in AI capabilities, the higher the future cost of catching up, both financially and in terms of lost market share. We've seen this before. Learning management systems were universally used, but were mainly for administration rather than changing pedagogy. MOOCs promised democratic access but often delivered little more than repackaged lectures with low completion rates. In both cases, the opportunity costs were high, as universities gained efficiency but lost innovation and competitive differentiation to external platforms. There is much more at stake with AI. This is not just about content delivery, but also about how the next generation thinks, decides, and solves problems, and whether universities can maintain their comparative advantage in training graduates who offer unique value in a labour market transformed by automation.

 

While many leaders in higher education remain cautious or indifferent, it's a different story at some universities in Uganda. At Victoria University, Vice-Chancellor Dr Lawrence Muganga urges students to embrace AI rather than fear it. He warns that by 2030, many tasks that humans are trained to do today will be replaced by machines, and the most foolish advice would be to advise students to avoid AI. Under his leadership, the university has made AI literacy mandatory, set up a state-of-the-art AI lab, and started developing localised AI tools for the African context. Muganga’s approach treats AI not as a threat, but as a foundation for employability, entrepreneurship, and innovation—a practical example of the faculty-driven integration that Legatt believes is essential. In economic terms, this is a case of strategic first-mover advantage: by investing early in AI capabilities, Victoria University sets its 'product' (the graduates) apart from the competition in a competitive education market and potentially increases its value in the labour market.

 

The economic significance could not be clearer. McKinsey estimates that AI could add up to $23 trillion a year to the global economy by 2040, with the biggest gains going to countries and sectors that can reskill quickly. For Africa, where youth unemployment is high, integrating AI under the guidance of educators is not an option, but a competitive strategy. From a labour economics perspective, AI skills represent a form of human capital that yields high returns in terms of productivity and employability. From a macroeconomic perspective, widespread AI skills could shift a country’s production capabilities outwards so that more can be produced with the same input. It can bridge the employability gap, stimulate local innovation, and ensure that AI tools reflect local languages, cultures, and realities, rather than importing solutions that don't fit.

 

The era of knowledge scarcity is over, and universities that cling to their old role as gatekeepers will be left behind by alternative providers and self-taught, AI-powered learners. Classical economics teaches that scarcity determines value. Higher education once had a price because it controlled access to a limited resource. Now that AI has flattened the supply curve of information, the equilibrium point has shifted. The price, in this case, the willingness to pay for traditional knowledge transfer, will fall unless universities offer something that the market still values. That “something” is the ability to produce graduates who can create and apply new knowledge in a way that AI cannot. The advantage no longer lies in possessing the knowledge, but in the ability to interpret, apply, and gain insights that AI alone cannot deliver. In other words, the comparative advantage of universities must now lie in fostering the scarce capacity of human judgement in abundance. Faculties are the critical link that enables universities to move from monopolists in a scarce market to innovators in an abundant market. Globally, the warning signs are clear; locally, leaders like Muganga are proving what is possible. The question is whether others will follow before the opportunity passes.