AI’s Misallocation Paradox: High Adoption, Low Impact

 By Richard Sebaggala (PhD)

 

When the Washington Post recently analysed 47,000 publicly shared ChatGPT conversations, the findings revealed something both intriguing and troubling from an economic standpoint. Despite hundreds of millions of weekly users, most interactions with AI remain small, superficial, and low stakes. People turn to it for casual fact-checking, emotional support, relationship advice, minor drafting tasks, and personal reflections. What stands out is how little of this activity involves the kinds of tasks where AI is genuinely transformative—research, modelling, academic writing, teaching preparation, data analysis, supervision, and professional decision-making.

 

For economists, this pattern immediately recalls the familiar distinction between having technology and using it productively. The issue is not that AI lacks capability; it is that society is allocating this new form of cognitive capital to low-return activities. In classic economic terms, this is a misallocation problem. A technology designed to augment reasoning, accelerate knowledge production, and expand human capability is being deployed primarily for conversations and conveniences that generate almost no measurable productivity gains.

 

This conclusion is not only supported by the Washington Post’s dataset; it is something I encounter repeatedly in practice. Over the past two years, as I have conducted AI-literacy workshops, supervised research, and written about AI’s role in higher education, I am often struck by the kinds of questions people ask. They tend to revolve around the most basic aspects of AI: What is AI? Will it replace teachers? How can eliminate AI in my work? These questions do not reflect curiosity about using AI for complex professional or analytical work; instead, they reveal uncertainty about where to even begin. Many participants—professionals and academics included—have never attempted to use AI for deep reasoning, data analysis, literature synthesis, curriculum design, or research supervision. When I think about how transformative AI can be in teaching, research, and analytical work, I am often frustrated because it feels as though we are sitting on an intellectual gold mine, yet many people do not realise that the gold is there.

 

This personal experience is fully consistent with the Washington Post findings. Fewer than one in ten of the sampled conversations involved anything resembling technical reasoning or serious academic engagement. Data analysis was almost entirely absent. Interactions that could have strengthened research, teaching, policymaking, or organisational performance were overshadowed by uses that, while understandable on a human level, contribute little to economic or educational transformation. The bottleneck here is not technological capacity but human imagination and institutional readiness.

 

Several factors help explain why this misallocation persists. Many users simply lack the literacy to see AI as anything more than a conversational tool. Habits shaped in a pre-AI world also remain dominant, students still search manually, write from scratch, and labour through tasks that AI could meaningfully accelerate. Institutions are even slower to adapt. Universities, schools, government agencies, and workplaces continue to operate with old structures, old workflows, and outdated expectations, even as they claim to “adopt” AI. When technology evolves faster than institutional culture, capability inevitably sits idle.

 

Economists have long demonstrated that new technology produces productivity gains only when complementary capabilities are in place. Skills must evolve, organisational routines must adapt, and incentives must shift. Without these complements, even the most powerful general-purpose technologies generate only modest results. AI today fits this pattern almost perfectly. It has been adopted widely but absorbed shallowly.

 

This gap between potential and practice is especially relevant for Africa. The continent stands to benefit enormously from disciplined, high-value use of AI—particularly in strengthening research output, expanding supervision capacity, enhancing data-driven policymaking, improving public-sector performance, and enriching teaching and curriculum design. Many of Africa’s longstanding constraints—limited supervision capacity, slow research processes, weak analytical infrastructure—are precisely the areas where AI can make the most difference. Yet the prevailing pattern mirrors global trends: high adoption for low-value tasks and minimal use in areas that matter most for development.

 

Ultimately, the impact of AI will depend less on the technology itself and more on how societies choose to integrate it into their high-value activities. The real opportunity lies in shifting AI from consumption to production—from a tool of conversation to a tool of analysis, reasoning, modelling, and knowledge creation. This requires deliberate investment in AI literacy, institutional redesign, and a cultural shift in how we think about teaching, research, and professional work.

 

The paradox is clear: adoption is high, yet impact remains low because the technology is misallocated. The task ahead is not to wait for “more advanced” AI, but to use the AI we already have for the work that truly matters. Only then will its economic and educational potential be realised.