Head in the Sand vs. Pragmatism Economics: Which Way Should We Face the AI Storm?

By Richard Sebaggala (PhD)

When societies encounter uncertainty, two habitual responses emerge. One is to deny or downplay change, hoping the future will resemble the past. This is what we might call the head-in-the-sand approach. The other is to accept uncertainty as inevitable and act: experiment, adapt, and build resilience. With Artificial Intelligence advancing rapidly, we once again stand at that crossroads.

AI is no longer speculative; it is already reshaping research, education, healthcare, industry, and governance. Yet its long-term impact remains ambiguous. Some predict modest disruption; others foresee transformation on the scale of the industrial revolution. What is certain is that AI is progressing faster than educational systems, regulatory frameworks, and labour markets can adapt. That widening gap is precisely where choice matters.

The head-in-the-sand approach treats AI as if it were just another incremental upgrade. We see this in universities that ban ChatGPT instead of teaching students to use it critically and responsibly. The message is: ignore it, hope it goes away. Graduates then enter the workforce without AI literacy, unprepared for an economy where such skills are increasingly essential. Governments that adopt this posture often relegate AI to ICT departments, focusing on broadband rollouts or cloud adoption while avoiding tougher economic questions: Who benefits when cognitive labour becomes abundant? How do we tax new forms of value? How do we prevent data monopolies? Countries that take this route risk becoming passive importers of AI technologies, unable to influence their trajectory or capture their benefits. When shocks come, they will feel them most acutely.

Pragmatism looks very different. It does not claim to know exactly how AI will unfold, but it acts as if preparation matters. Singapore, for instance, has committed more than S$1 billion (about US$778 million) over five years to AI compute, talent, and industrial development. Its AI research spending, relative to GDP, is estimated to be eighteen times higher than comparable US public investments. Nearly a third of Singaporean businesses now allocate more than US$1 million annually to AI initiatives, higher than the share in the UK or US. Yet even there, progress is uneven: only about 14% of firms have managed to scale AI enterprise-wide. The lesson is clear: investment is essential, but assimilation, governance, and skills are equally critical.

South Korea offers another example of pragmatism. The AI boom there has fuelled record semiconductor exports, with chip sales rising 22% year-on-year in September 2025, driven in part by global demand for AI infrastructure. This underscores how embedding in the AI supply chain allows a country not merely to consume imported systems but to capture significant value from their production.

Africa presents a contrasting picture. A Cisco–Carnegie Mellon white paper stresses the importance of building lifelong learning ecosystems that embed AI into vocational training, promote micro-credentials, and offer offline access in local languages. The World Economic Forum’s Future of Jobs 2025 report similarly highlights AI and ICT as major drivers of labour-market change, making reskilling strategies urgent. Yet most governments on the continent are still moving slowly. The danger of head-in-the-sand thinking is stark: Africa could remain a peripheral consumer of AI, locked out of influence and value capture. But the opportunity is also real: with pragmatic strategies, such as integrating AI into education, governance, health, agriculture, and finance, African economies could leapfrog, turning disruption into transformation.

Organisations face similar choices. Aon finds that 75% of firms now demand AI-related skills in their workforce, yet only 31% have adopted a coherent company-wide AI strategy. Meanwhile, Salesforce reports that more than four in five HR leaders are already planning or implementing AI reskilling programmes. The private sector feels the pressure: denial is no longer an option.

The difference between denial and pragmatism can be illustrated with a simple thought experiment. Imagine two countries facing the same AI storm. Country A bans AI in schools, neglects retraining, and ignores data governance. Five years later, its graduates are unemployable in AI-augmented sectors, its industries depend entirely on foreign systems, and inequality deepens. Country B, by contrast, integrates AI literacy into curricula, retrains workers, and builds regulatory frameworks. Five years on, its workforce is more adaptable, its firms capture value from AI, and it helps shape global rules. Both faced uncertainty, but only one built resilience.

The stakes are high. Economists Erik Brynjolfsson, Anton Korinek, and Ajay Agrawal have identified nine “grand challenges” for transformative AI: growth, innovation, income distribution, power concentration, geoeconomics, knowledge and information, safety and alignment, well-being, and transition dynamics. None of these challenges can be addressed by denial. Each requires pragmatic experimentation in policy, governance, and institutional adaptation.

The AI storm is already here. We do not know if it will hit like a hurricane or come slowly like steady rain, but we do know that failing to prepare is dangerous. Hiding from change may feel safe for a while, but it leaves us weak. A practical approach takes effort, patience, and resources, yet it gives us the strength to adjust, to find new chances, and to survive shocks. Think of two farmers who see dark clouds. One covers his eyes and hopes the rain will pass. The other repairs the roof and stores extra food. When the storm arrives, only the prepared farmer is left standing.

In the age of AI, pragmatism, not denial, is the path that leads to survival, and perhaps to thriving. History will not be kind to the ostrich. Time and again, the head-in-the-sand approach has proven disastrous. Industrial revolutions have always punished the complacent. Nations that dismissed early mechanisation in the nineteenth century fell behind those that industrialised. Companies that ignored the digital revolution of the 1990s,Kodak is the famous example, lost their dominance when they refused to adapt to digital photography. Even at the national level, countries that underestimated globalisation or financial innovation found themselves playing catch-up after crises had already swept through. In each of these cases, denial did not slow the storm; it only increased the damage when inevitable change arrived.

That is why I have personally chosen the pragmatic path in facing AI. As a researcher, AI has already transformed my work by accelerating data analysis, enabling new forms of literature synthesis, and freeing time for deeper conceptual thinking. Rather than fearing it, I experiment with it daily, testing its strengths and identifying its limits. As a teacher, I refuse to banish AI from the classroom. Instead, I encourage students to engage with it critically, to learn how to use it responsibly, and to see it not as a substitute for human thought but as a tool for augmenting it. My conviction is simple: by embracing AI pragmatically, I can prepare my students not just to survive in an AI-shaped economy, but to lead within it.

The ostrich buries its head when danger approaches. The builder, by contrast, looks at the storm clouds and reinforces the roof. History has shown which one endures. The choice before us is no different today.