This year's World Economic Forum demonstrated that, against a complex geopolitical backdrop, artificial intelligence (AI) has become a central topic in both formal and informal discussions. Participants' focus shifted from technological innovation to practical application: scaling up AI adoption, delivering measurable economic impact, and ensuring that its benefits are shared across countries and industries.
First, underlying many of these discussions was a shared concern: if AI remains concentrated in a few developed economies, companies, and platforms, it will not only fail to narrow global inequalities but may even exacerbate them. If AI is not widely and affordably accessible, benefiting the real economy and bringing breakthroughs in areas like healthcare and education to the masses, the "social license" to meet AI's insatiable demands for energy, water, and infrastructure may quickly disappear.
Furthermore, the challenges of AI accessibility and affordability are closely intertwined with the evolving geopolitical landscape. The global economy no longer revolves around a single technological hub. Trade frictions, export controls, industrial policies, and security considerations are reshaping supply chains for semiconductors, data center equipment, and critical minerals. Divergences in standards, data governance, and technology regulation further add to the complexity. In this context, AI adoption depends not only on innovation capabilities but also on computing power, data sovereignty, reliable clean energy, network connectivity, financing, and partnerships.
Second, these considerations are particularly pressing and far-reaching for developing economies facing limited fiscal space and competing development priorities. The infrastructure investments required to advance AI already far exceed the current funding gap for sustainable development goals. Therefore, widespread AI adoption must be considered a core development objective. In terms of energy, AI applications, in particular, offer a practical opportunity to achieve sustainable technology and energy transitions.
Moreover, the expected benefits are significant. Globally, AI-driven efficiency gains in industry, logistics, agriculture, energy, and public services are projected to contribute $15 trillion to GDP by 2030. At the regional level, ASEAN countries could see a GDP contribution exceeding 3%, or approximately $1 trillion, with Indonesia contributing around $360 billion to its economy. However, these gains require prudent policy choices, sufficient funding, and human resources. In Indonesia, approximately $3 billion will be needed for computing infrastructure by 2030.
Ultimately, blended finance is crucial for translating the vision of AI into reality. Through platforms such as the G20 Bali Global Blended Finance Alliance, concessional capital, guarantees, and risk-sharing mechanisms can mobilize and attract private investment for building infrastructure that supports sustainable and autonomous artificial intelligence, while also directing investment flows to areas that yield significant public and economic returns. In this regard, this year's "Blue Davos" theme provides a sobering perspective on how artificial intelligence will impact the physical environment and the "value" of nature. There is a growing recognition that oceans, coastal systems, and natural resources are crucial for sustainable economic growth, resilience, and food and energy security.
Currently, in aquaculture, AI-based sensing and feeding systems can increase yields while reducing waste, water consumption, and disease risk. Coastal nations are also applying AI technologies to map and monitor mangroves, coral reefs, and seagrass beds, thereby strengthening the valuation and protection of natural assets that underpin food security, tourism, and climate resilience. Furthermore, the ocean offers new solutions to address AI resource challenges; for example, some "floating" data centers utilize seawater for cooling and generate electricity from the ocean's wave energy.
Overall, while AI is reshaping economic growth engines, giving rise to new industries, and increasing total factor productivity, its profound impact lies in its potential to restructure the logic of value distribution, challenge traditional economic statistics and policy frameworks, and drive a paradigm shift in economic forms towards a new model fundamentally characterized by data and intelligence.
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