Asia-Pacific’s AI scaling problem is bigger than compute

• 28 Sep 2026
• TM Team
• 0 Comments

Asia-Pacific has no shortage of ambition when it comes to artificial intelligence. Governments are announcing AI strategies, hyperscalers are expanding infrastructure, and enterprises are moving generative AI from experimentation into customer service, software development, finance and operations. But ambition is increasingly colliding with reality.

The region’s AI challenge is no longer simply whether organisations can access powerful models or GPUs. It is whether the infrastructure, energy systems, data foundations and operating models can support AI at scale.

McKinsey estimates that APAC could account for roughly 34 percent of global data-centre demand by 2030, up from its current position as a major growth market. More than 70 percent of current APAC data-centre demand comes from traditional compute, storage and cloud workloads, while AI training and inference account for around 30 percent. By 2030, McKinsey expects the mix to move closer to 50/50.

The scale of investment reflects that trajectory. AWS, Google, Microsoft and Oracle have committed more than US$160 billion between January 2024 and May 2026 to AI infrastructure in APAC, according to McKinsey.

Alibaba has announced at least US$52 billion in global cloud and AI infrastructure investment over three years, while ByteDance could spend around US$30 billion on AI infrastructure in 2026 alone.

China is expected to remain the region’s largest market, potentially accounting for more than 70 per cent of APAC data-centre demand by 2030. Outside China, new data-centre corridors are emerging across East and Southeast Asia, including Johor in Malaysia, Chonburi in Thailand, Jakarta in Indonesia and Osaka in Japan.

Yet building capacity is proving harder than expected.

AI infrastructure is ultimately constrained by physical infrastructure. Data centres require enormous amounts of electricity, cooling capacity and grid connectivity.

The International Energy Agency estimates that global data-centre electricity consumption increased 17 percent last year, while AI-focused data centres grew even faster. Data-centre electricity use is expected to double by 2030, with AI-focused facilities potentially tripling their electricity consumption. At the same time, shortages of transformers, gas turbines, advanced chips and other components, alongside delays in grid connections and approvals, are creating new bottlenecks.

For enterprises, this changes the equation. Adding more compute is not necessarily the solution when the constraint is electricity, cooling, network capacity or the availability of suitable facilities.