When Intelligence Stops Mattering: The Economics of Attention in the AI Era

 By Richard Sebaggala (PhD)

 

 If you spend enough time teaching university students or supervising research in Uganda, you begin to notice something that contradicts the story we were all raised on. The brightest people do not always win. Some of the most intellectually gifted students drift into ordinary outcomes, while those labelled as average quietly build remarkable lives. It is one of those puzzles that economists enjoy because it challenges the assumption that intelligence is destiny. The truth is more uncomfortable: beyond a certain level, intelligence stops being the thing that separates people.

 

This idea is not new. In 1921, psychologist Lewis Terman selected more than 1,500 exceptionally intelligent children, convinced they would become the Einsteins, Picassos, and Da Vincis of modern America. Today, his famous “Termites” study reads almost like a cautionary tale. These children had extraordinary IQ scores, superior schooling, and strong early promise. Yet most lived ordinary, respectable lives. A few became professionals, but none went on to reshape the world in the way their intelligence suggested they might. The outcome was not what Terman expected. It revealed a principle that economists immediately recognise: a factor that is no longer scarce loses its power to generate outsized results. In this case, intelligence reached a point of diminishing marginal returns. After a moderate threshold, more IQ did not produce more achievement.

 

Threshold Theory emerged from this insight. It suggests that once someone has “enough” intelligence to understand and navigate the world, their long-term success depends far more on consistency, deliberate practice, and attention to detail. In other words, it is the boring habits that win, not the brilliance. You can see this in the lives of people like Isaac Asimov, who published more than 500 books not because he had superhuman intelligence but because he wrote every day. Picasso, often celebrated as a natural genius, produced an estimated 20,000 works, and that relentless productivity was responsible for his influence far more than any single stroke of innate talent.

 

These patterns appear clearly in our context as well. In my teaching and supervision, the student who simply shows up, writes a little every day, reflects regularly, and keeps refining their work eventually surpasses the student who delivers occasional bursts of brilliance but lacks rhythm. It is the slow, steady accumulation of effort that compounds over time. It is difficult to accept this truth because dedication feels less glamorous than talent, yet it explains far more about real outcomes.

 

This brings us to the present era where artificial intelligence has rewritten the economics of human capability. A century after Terman, we live in a world where tools like ChatGPT and Claude have made cognitive ability widely accessible. An undergraduate in Gulu can now generate summaries, explanations, models, and arguments that once required years of academic experience. AI has lifted almost everyone above the old intelligence threshold. The scarcity has shifted. Intelligence is no longer the differentiator. The new constraint is attention.

 

Attention is fast becoming a rare commodity. While knowledge is infinite, the real challenge isn't access, but sustained focus. In my online classes, students are often managing dozens of tabs, buzzing phones, and multiple background conversations, leading to fragmented concentration. They skim rather than read, and jump between tasks without reflection. The deepest poverty of our generation is no longer information poverty, but attentional poverty. In economic terms, focus is emerging as the new source of comparative advantage.

 

This phenomenon matters even more for African learners and institutions. The continent does not suffer from a shortage of intelligent people. What we struggle with are the habits that make intelligence useful: sustained concentration, deliberate practice, refinement, and a culture that values slow thinking as much as quick recall. Our education systems often reward memorisation, not reasoning. Our learners tend to fear discomfort instead of embracing it as part of growth. And when AI enters such an environment, it does not fix these gaps. It magnifies them. A distracted student given AI becomes even more distracted, because the illusion of shortcuts becomes stronger. But a focused student who pairs AI with discipline suddenly becomes incredibly productive.

 

This is where Threshold Theory becomes deeply relevant for the AI age. If intelligence is widespread and cheap, and AI has lifted everyone above the threshold, then the difference between people will increasingly come from their habits. The human work now is to protect attention, practise something meaningful every day, use AI to expand thinking rather than avoid effort, build routines that compound, and stay curious long after others settle into laziness. AI can assist with reasoning, but it cannot replace judgment, contextual understanding, ethical interpretation, or the capacity to sustain deep effort. These remain profoundly human strengths.

 

In the end, genius is slowly shifting from something you are born with to something you practice. AI gives everyone the same starting point. Discipline and attention determine the destination. The real question for each of us, especially in Africa where the opportunity is enormous but unevenly captured, is simple: what will you do with your attention?