Artificial intelligence is pushing Asia-Pacific’s digital infrastructure harder than ever, and the region now has a choice: build fast, or let the gap widen between the AI haves and have-nots.
- ESCAP warns AI will strain APAC’s digital infrastructure
- The digital divide is shifting from internet access to compute and capability
- Governments are being pushed toward regional cooperation, not solo fixes
- AI is also being directed at climate, agriculture, and conservation work
The United Nations Economic and Social Commission for Asia and the Pacific, or ESCAP, says AI is creating fresh demands on the region’s digital backbone. That means more computing power, stronger connectivity, and greater data capacity. In plain English, it needs infrastructure that does not appear just because a minister said “innovation” three times in a speech.
ESCAP’s warning is simple: Asia-Pacific economies are not equally prepared for those demands. Some are already deep into cloud, semiconductor, and AI buildouts. Others are still trying to shore up the basics. If policy and investment do not keep pace, AI could widen the region’s digital divide instead of closing it.
That divide is no longer just about who can get online. It is about who can build, host, and use advanced systems, including model training, inference, cloud access, data storage, and the energy and cooling needed to keep all of it running. Access to a smartphone is one thing. Access to the compute stack behind modern AI is another beast entirely.
UN Under-Secretary-General and ESCAP Executive Secretary Armida Salsiah Alisjahbana put it bluntly, saying:
“Ensuring that AI-native network infrastructure benefits all economies of the region, rather than deepening existing inequalities, will require deliberate and coordinated policy action by Governments individually and through regional cooperation”
ESCAP also said regional cooperation will play a bigger role in strengthening policy dialogue, sharing good practices, building capacity, and coordinating multistakeholder partnerships to speed up AI-native network infrastructure across Asia and the Pacific.
AI-native network infrastructure is jargon, but the idea is clear enough: systems built from the ground up to handle AI-era workloads, not legacy networks patched together like they were assembled during a weekend panic attack. That means more data centers, faster networks, stronger cloud access, and enough power behind the whole thing to keep large models from grinding the system to a halt.
The political and economic stakes are obvious. Countries with better chips, stronger grids, more skilled workers, and tighter institutions will move first. Everyone else risks becoming a customer base for tools, standards, and platforms built elsewhere. That is not digital transformation. That is dependence with a glossy deck.
That is why Malaysian Prime Minister Anwar Ibrahim’s warning landed so sharply. Speaking at a ceremony at Markazu Saquafathi Sunniyya in Karanthur, India, after receiving the inaugural Excellence Award instituted by the Sheikh Abubakr Foundation, he said countries that do not understand AI risk being controlled by those who do. He was in India for the BRICS Leaders’ Summit.
“Without knowledge, we will fail. Without understanding artificial intelligence, we will be colonized”
“We need the knowledge. We need the expertise.”
“Colonized” here is not a literal historical claim. It is a warning about technological dependence: foreign systems, foreign standards, foreign cloud infrastructure, and foreign expertise setting the rules while everyone else pays the bill. If a country cannot understand AI well enough to govern it, audit it, or build on top of it, it will struggle to shape its own digital future.
There is also a practical point beneath the rhetoric. AI sovereignty is not just about national pride. It is about bargaining power, data control, deployment speed, and the ability to keep critical systems from becoming permanently dependent on outsiders.
At the same time, AI is not only a source of strain. It is also being aimed at real development problems, and Google’s new DeepMind Accelerator: AI for the Planet in Asia-Pacific is a good example of that push.
The three-month regional accelerator, which kicked off in Singapore on September 7, includes 16 organizations from Australia, India, Indonesia, Japan, New Zealand, Singapore, South Korea, and Thailand. According to ESG News, six participants are working on conservation and climate resilience, five on agriculture and agronomic services, and five on emissions reduction, carbon removal, and urban energy systems.
Google said the program offers “a unique opportunity that will help participants navigate technical complexities, and scale the next generation of solutions for our planet.” That is standard corporate language, sure, but the basic idea is not nonsense. AI can help track deforestation, improve farm advice, optimize energy use, and model climate risks faster than older tools can.
Still, AI-for-good programs deserve a hard stare, not a halo. A 16-participant accelerator over three months can be useful, but it is not proof that the broader AI boom is green, equitable, or harmless. One climate-focused initiative does not cancel the massive compute, energy, and data-center demands of the rest of the industry. You do not get to call the whole machine “sustainable” just because one corner of it has a leaf on it.
That tension runs through the entire APAC AI debate. On one side is the promise: better productivity, stronger services, and real tools for climate, agriculture, and conservation. On the other is the risk: a deeper divide between economies that can fund and control the infrastructure, and those that cannot.
What is the digital divide becoming?
It is moving beyond internet access. The newer divide is about computing power, connectivity quality, data systems, skilled talent, and the ability to deploy advanced technologies without relying entirely on outsiders.
Why does regional cooperation matter?
AI infrastructure is expensive, cross-border, and policy-heavy. Regional cooperation can help with standards, shared learning, talent development, and investment coordination so the benefits do not stay trapped in a few richer economies.
Can AI help with climate and food security?
Yes, potentially. AI tools can support conservation, farm advice, emissions reduction, carbon removal, and energy management, but only if they are affordable, locally usable, and deployed at a scale that matters.
Is AI growth in APAC mostly good news?
Not automatically. It brings real opportunity, but it can also concentrate power, increase dependence, and put more pressure on digital and energy infrastructure. The upside is real; so is the bill.
What does AI sovereignty actually require?
It requires more than slogans. Countries need infrastructure, technical talent, policy capacity, access to data, and enough domestic expertise to understand and govern the systems they use.
The basic message from APAC is not complicated: AI is no longer a side project. It is now a test of infrastructure, strategy, and political will. The countries that move early on compute, connectivity, data systems, and talent will shape the terms. The ones that wait will be stuck renting the future from someone else.
That is the real choice here: build regional capacity now, or let AI infrastructure consolidate power in a few external hands. Hype will not fix that. Investment, coordination, and sober planning will.
Further reading
For a bit more context on how AI, infrastructure, and power politics are colliding across the region:
- Malaysia’s BRICS profile and bilateral talks
- UNESCAP’s Asia-Pacific Digital Transformation Report 2026 on AI-native infrastructure
- Summary of the Asia-Pacific digital transformation report
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