Countries' Preparedness for AI Dominance

Should the gap in AI utilization between the most advanced and least developed nations remain unaddressed, the scenario could mirror the film ‘The Gods Must Be Crazy’, written and directed by South African filmmaker Jamie Uys. The 1980 sequel portrays cultural upheaval that appears comical to contemporary urban societies, yet encapsulates the core narrative: the clash between modern and traditional civilisations. The story begins when Xi and several San tribe children in the Kalahari Desert receive a thrown empty Coca-Cola bottle. Having never encountered such an object, they perceive it as a divine miracle. Falling from the sky with no clear purpose, it sparks confusion among the tribe members. To them, it may seem a response to prayers for change in their otherwise stagnant desert lives. The bottle actually came from a civilian aircraft flying over the desert. The pilot, thirsty, drank the Coca-Cola and discarded the empty bottle out the window. The San tribe, unaware of its origin, had no idea what the object truly was. Interpretations of its use varied: cracking nuts, storing water, grinding roots, crafting tools, or even as a musical instrument when struck against hard surfaces. Tragically, for a tribe without private ownership concepts—where everything is shared—these differing interpretations sparked disputes over the bottle’s use. Minor disagreements escalated into anger and conflict, shattering the San tribe’s former peace. Xi decided to return the ‘miracle’ bottle to the gods, embarking on a journey to the world’s end to find its giver. This opening narrative could become reality if the AI utilisation gap remains unaddressed. It mirrors the contrast between the United States and the Kalahari Desert—one leveraging intensive AI technology, the other relying on natural means. Not merely lagging behind, but existing in fundamentally different realities, creating a visible divide. But how prepared are nations for AI adoption? How wide is the divide? Giovanni Melina’s 2024 article, ‘Mapping the World’s Readiness for Artificial Intelligence Shows Prospects Diverge’, outlines this. His analysis is based on research using variables such as digital infrastructure, human resources, labour policies, innovation, integration, and regulation. The study covered 174 countries in the context of AI development, resulting in the AI Preparedness Index (AIPI) Dashboard. The latest AIPI dashboard appears in Wayan Wota’s 2026 article, ‘AI Preparedness Index Shows Global South Is Not Ready for Artificial Intelligence Solutions’. The data shows developed nations average 0.68 on the AIPI, developing countries 0.46, and low-income nations around 0.32. These structural capacity differences help project which countries can harness AI for development and which risk being left behind. Wota identifies four structural capacity aspects: Digital Infrastructure Challenges, including connectivity. Gaps in connectivity affect AI adoption and lead to outdated models. In sub-Saharan Africa, this is termed ‘digital apartheid’—algorithmic discrimination. Next, a Human Capital Deficit. Shortages of digital talent cannot be resolved by training, even intensive programmes. Systemic causes include insufficient supply of digital talent, low basic computer skills among youth, and low STEM graduate output. Gender imbalance further compounds this, with few women entering STEM fields globally. Next, Innovation Ecosystem Gaps. Low-income nations face funding barriers for innovation ecosystems. On average, R&D expenditure in developing markets is far below the 2-3% of GDP typical in developed nations. This 2-3% benchmark is standard in advanced economies. Funding constraints mean 43.1% of firms are less likely to innovate compared to those without such hurdles. This creates a vicious cycle: low-income nations become consumers of AI technology from developed countries, yet these solutions often fail to address local needs. Finally, Governance Gaps. The largest weakness in AI implementation is the lack of regulation and ethics. Data shows 48% of countries have zero national AI policy scores, while 49% lack responsible AI ethics guidelines. This poses severe risks when AI applications are deployed on vulnerable populations, including children, women, and other groups.