Seeing the Whole System: How Economists Should Think About AI 

By Richard Sebaggala (PhD)

Recently, while reading an article from The Economist debating whether economists or technologists are right about artificial intelligence, I found myself uneasy with how both camps framed the issue. Economists, true to their discipline, approached AI with caution. Erik Brynjolfsson of Stanford has long argued that “technology alone is never enough,” reminding us that productivity gains arise only when organizations redesign workflows, invest in skills, and realign incentives. Daron Acemoglu at MIT makes a similar point when he notes that “there is nothing automatic about new technologies bringing shared prosperity.” These warnings echo a familiar historical pattern: earlier general-purpose technologies, whether electricity, computers, or the internet, took decades before their full impact on productivity materialized.

Technologists, on the other hand, describe AI as a decisive break from that past. Sam Altman, CEO of OpenAI, has called AI “the most important technology humanity has ever developed,” emphasizing the speed and magnitude of socioeconomic disruption that may follow. Jensen Huang of NVIDIA goes even further, claiming we are “at the beginning of a new industrial revolution” driven by accelerated computing and machine intelligence. For thinkers in this camp, AI is not merely another digital tool, but a system endowed with reasoning capabilities that can automate cognitive functions once reserved for humans.

 

Both perspectives carry important truths, yet each misses a critical dimension. The debate often assumes AI is a self-contained phenomenon, detached from the digital infrastructure on which it actually operates. In reality, AI does not replace the computer or the internet; it builds on them. It exists because of them. The more productive question, therefore, is not how powerful AI is in isolation, but what happens when the billions of people who already use computers and the internet begin to work, learn, and think with AI assistance embedded in their daily routines.

From a pragmatic perspective, this framing changes everything. Pragmatism, unlike optimism or scepticism, asks what works, for whom, and under what conditions. It is concerned less with prediction and more with functionality. A pragmatic economist sees technology as capital whose productivity depends on how it is organized and incentivized within an institutional system. A pragmatic technologist, in turn, recognises that adoption depends on human adaptation—habits, trust, and training. The convergence of these two sensibilities produces a more grounded understanding of AI: not as a revolutionary force that will automatically transform society, but as an evolutionary layer that extends the power of existing digital infrastructures.

In my own thinking, the most useful way to understand AI is to see it as “computer plus internet plus intelligence.” This perspective recognizes that every technological breakthrough builds on the foundations laid by earlier digital layers. Computers automated calculation and data processing. The internet automated connectivity and access. AI now automates reasoning, prediction, and creation. Seen this way, AI is not an isolated revolution but the next evolutionary layer in a long digital continuum. The computer and internet revolution required complementary investments in education, governance, and organizational design before their full economic effects could materialize. When computers became widespread, firms had to reorganize workflows and hire IT specialists. When the internet emerged, they had to create digital marketing, cybersecurity, and logistics functions. The same will hold for AI: productivity gains will depend not merely on access to algorithms but on how societies redesign work, education, and decision-making to make intelligent tools genuinely useful.

 

This logic holds particular relevance for Africa. The continent’s technological progress has always been characterised by pragmatic adaptation rather than linear imitation. The success of mobile money, for instance, emerged not from cutting-edge infrastructure but from creatively reconfiguring existing resources to solve pressing coordination problems. In the same way, the potential of AI in African contexts may depend less on hardware and more on cognitive integration—how intelligently people and institutions use the tools already within reach. A university lecturer with a laptop, stable internet, and access to ChatGPT represents a new kind of productivity unit: a human–AI partnership capable of reimagining teaching, research, and supervision. But this transformation will not occur automatically; it requires investment in AI literacy, ethical awareness, and institutional readiness.

The economists are correct that such transformations take time. Every general-purpose technology has exhibited a lag between invention and impact, as economies struggle through a reorganization phase before productivity surges. But the technologists are equally right about the scope of change. Unlike earlier digital tools that mechanized physical or transactional processes, AI extends automation into the cognitive realm. It can assist in writing, designing, diagnosing, predicting, and problem-solving. It changes not only the speed of work but its very composition. The synthesis of both positions yields a pragmatic insight: AI’s short-term effects are often overestimated, but its long-term restructuring power is profoundly underestimated. The path to productivity follows a J-curve, with initial disruption followed by enduring dividends.

 

To think like an economist in the age of AI is to resist both technological euphoria and excessive caution. It is to examine incentives, complementarities, and institutional conditions rather than merely forecasting growth or disruption. The central question is not whether AI will replace human labour but how humans will reorganize around intelligence. The transformative potential of AI lies not in replacing human reasoning but in amplifying it, turning disciplined thought into augmented creativity.

This perspective is especially vital for developing regions where digital infrastructure already exists but underperforms. The challenge is to build the human and institutional complements that convert computational power into social and economic value. As teachers, researchers, and policymakers, the task is not to wait for AI to be perfected elsewhere but to make it work within our realities—to make AI ready for us. That is what it means to think pragmatically and, indeed, to think like an economist.