Putting the AI into Asia

All eyes are on tariffs and trade wars. But another big problem is brewing for Asia’s middle powers. The productivity growth that comes from AI will reshape the world economy and the power that comes with it. Unless Asian economies start working together on AI infrastructure, they could quickly find themselves at the mercy of the United States and China as gatekeepers. Given both have already shown a willingness to weaponise trade and technology, Asian nations had best move quickly.

What happens when productivity growth booms in one country and not in others?

Put simply, the country which experiences the boom in productivity sees sharp increases in economic growth, wages and living standards. The countries that do not experience the productivity boom see their capital and labour shift into the country that does. The non-booming countries still benefit from this, but make no mistake: the centre of economic gravity and power shifts towards the country with higher productivity growth.

This is why AI is so important, and why it will shape a great deal of global power in the coming years. ‘Economic power is the foundation of military and political power’, as US political scientist Joseph Nye likes to say. And economic power in the rest of the 21st century will, to a significant extent, depend on who gets the productivity boom from AI and who does not.

This is the challenge that Asia’s middle powers face. Asian countries are by no means slow adopters of new technologies. They were rapid adopters of fintech, mobile and digital technologies. Asian countries used these technologies to leapfrog the developed world and dodge the trillions of dollars that developed countries had invested in hard infrastructure that is now largely obsolete.

Asian countries have already revealed themselves to be rapid adopters of AI, too. The problem is not AI adoption. The problem is who controls the AI stack.

The AI stack is roughly analogous to a traditional supply chain that connects the manufacturers of a product to its final customers. The ‘model development layer’ in the AI stack collects, stores and prepares data which it uses to design, train and fine-tune AI models. It’s a bit like the ‘manufacturer’ in a traditional supply chain.

These models are then integrated into real world systems in the ‘application layer’ — which is a bit like the customer-facing retailer in a traditional supply chain. Underpinning all this, however, is the ‘infrastructure layer’. Much like the ports, roads and fulfilment centres that connect manufacturers to customers, the infrastructure layer in the AI stack provides the computational power, physical storage and tools needed for AI systems to be built and deployed.

This is where Asia’s middle powers come in. Many countries in Asia — including Australia, South Korea, India, Indonesia and Singapore — have strong capabilities in the AI application layer. But they are entirely dependent on either the United States or China for the infrastructure layer.

This puts them in a tricky spot. It not only means their innovation will be limited to the application layer, it also means they are vulnerable to shifting geopolitical winds. The United States or China — both of which have already shown a willingness to weaponise access to technology — could cut off their access to vital AI infrastructure. In a world in which the future power and living standards of countries will be shaped by their adoption of AI, this is a precarious position to be in.

What should they do to manage this risk?

In this week’s lead essay, Jacob Taylor and Joshua Tan point to a ‘third way’. The idea of a ‘third way’ on AI was originally floated by French President Emmanuel Macron. It refers to a multilateral path for middle powers to cooperate on AI development and infrastructure, rather than defaulting to systems built in Silicon Valley or Shenzhen.

‘Recent breakthroughs in open-source technologies by AI companies like China’s DeepSeek and France’s Mistral highlight the technical potential for alternatives’ Taylor and Tan argue, ‘but these successes depend heavily on preexisting open-source ecosystems — and still face limitations in long-term product scaling and distribution without supportive infrastructure’.

Taylor and Tan outline efforts across India, Indonesia, Singapore and Australia, but warn that ‘these national efforts cannot individually match the scale of compute, talent or high-quality data of US or Chinese AI ecosystems’. ‘Worse still’, they note ‘they risk reinforcing a dependency cycle in which public sector investments in open infrastructure are captured by private hyperscalers with the capacity to productise and monetise faster’.

What would a ‘third way’ involve? One proposal that has traction is to create an ‘Airbus for AI’, modelled on the European response to US aviation dominance. ‘The idea is to create a public–private consortium of middle powers’ national AI laboratories to build scalable AI products and sell them under a public utility model’ Taylor and Tan argue. ‘The proposal is a go-to-market strategy for middle powers’ existing public AI investments, designed to serve public needs while anchoring technological sovereignty’.

Just as Airbus gave Europe a seat at the table in commercial aviation, an AI consortium could offer Asian nations an opportunity to establish industrial capacity and strategic autonomy.

The consortium would aggregate compute power from participating national labs and co-develop applications built on shared open-source or jointly trained foundation models. A dedicated team sourced from participating nations — combining engineering, policy and product expertise — would scale these applications across jurisdictions, ensuring commercial viability, responsiveness to local needs and alignment with public interests.

None of this comes without commitment, though. ‘Beyond the need to pool compute’, Taylor and Tan note, ‘the consortium’s success will rely on three enablers — operational coherence, a coordinated regional data policy framework and talent’.

Airbus succeeded because Europe had the political will to build strategic capability in a field dominated by others. If Asia’s middle powers are serious about forging a third path in AI, they must move beyond national scale strategies and take regional action through joint engineering, shared infrastructure and coordinated product development.

The US–China rivalry has expanded into multiple areas. Without proactive action from Asia’s middle-powers, AI will be next.

This article was published by the East Asia Forum.