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View all search resultsThe hidden footprint and opportunity of AI’s economic promise
If artificial intelligence remains concentrated in a small number of advanced economies, firms and platforms, it risks reinforcing global inequality rather than narrowing it.
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Words reading “Artificial intelligence AI“, a miniature robot and toy hand are pictured in this illustration created on Dec. 14, 2023. (Reuters/Dado Ruvic)
T
his year’s World Economic Forum (WEF) was undoubtedly momentous. Alongside the complex geopolitical backdrop, artificial intelligence emerged as a central theme both in formal and informal sessions. The attention shifted away from novelty to execution: scaling AI adoption, delivering measurable economic impact and ensuring its benefits are shared across countries and sectors.
Underlying many of these discussions was a shared concern: if AI remains concentrated in a small number of advanced economies, firms and platforms, it risks reinforcing global inequality rather than narrowing it. Without widespread and affordable adoption that benefits the real economy and democratizes breakthroughs, the “social license” to feed AI’s insatiable appetite for energy, water and infrastructure may quickly fade.
This challenge of widespread adoption and affordability is closely linked to the shifting geopolitical landscape. Trade frictions, export controls, industrial policy and security considerations are reshaping supply chains for semiconductors, data-center equipment and critical minerals. Diverging approaches to standards, data governance and technology regulation add a further layer of complexity. In this context, access to AI is shaped not only by innovation capacity, but also by access to compute, data sovereignty, reliable clean power, connectivity, financing and partnerships.
For developing economies, these considerations are immediate and consequential. The infrastructure investment needed to advance AI is already a magnitude more than the current SDG financing gap. This is why widespread AI adoption must be treated as a central development objective. In terms of energy - and very practically - AI adoption offers the opportunity for a “twin” sustainable technology and energy transition.
AI “diffusion” also requires affordable access to models, deployment in local languages and integration into small and medium-sized enterprises (SMEs), and public services. It is the difference between AI as a demonstration technology and AI as a driver of productivity, inclusion and resilience.
The projected benefits are even greater. Globally, AI-driven efficiency gains in industry, logistics, agriculture, energy and public services are projected to add US$15 trillion to gross domestic product by 2030. Regionally, ASEAN nations could see GDP contributions topping 3 percent, or about $1 trillion, with Indonesia adding about $360 billion to its economy. But these gains require deliberate policy choices, appropriate financing, and human capacity. In Indonesia, roughly US$3 billion is needed in computing infrastructure by 2030.
