MAS says AI investment boom complicates monetary policy as Asia demand stays resilient

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AI boom complicates Asia policy

Highlights

AI-driven electronics contribute over 70% of Asia's export growth in 2025, mitigating the impact of trade friction and energy shocks, according to MAS.
Global merchandise trade growth is estimated at 2.4% in 2025, reflecting resilient supply chains despite overlapping shocks and costs from continual adaptation.
MAS highlights that monetary policy calibration is complicated by strong immediate AI-driven demand without broad-based productivity gains, requiring evidence-based, state-contingent approaches.

AI demand surge and supply uncertainty

As reported by Monetary Authority of Singapore, Deputy Managing Director and Chief Economist Edward S. Robinson says the AI boom is unfolding as a race between strong current demand and still-uncertain future supply gains. In remarks at the 2026 Beijing Financial Street Forum on 20 October, he says upbeat expectations have driven an unprecedented wave of AI investment and expansion, while verified productivity gains from the technology remain limited so far.

Robinson says the global economy enters 2026 still adjusting to trade measures introduced over the preceding year, while the Middle East conflict that begins at the end of February quickly develops into a global energy shock. He describes the current period as an era of frequent and overlapping shocks, but says the global economy remains resilient as firms reroute supply chains, diversify sourcing and reconfigure production.

He points to estimated world merchandise trade growth of 2.4% in 2025 as evidence that supply networks can also cushion disruption. At the same time, he warns that resilience is not unlimited because buffers can be depleted and repeated adaptation carries costs.

For Asia, Robinson says the emergence of AI is becoming a major force sustaining demand and offsetting some of the drag from trade tensions and higher energy costs. He says AI-driven electronics account for more than 70% of Asia's export growth this year, and adds that the region, including China, is also advancing in core AI technologies such as advanced chips and large language models.

Policy calibration and regional productivity effects

Robinson says current strength in AI-producing sectors appears to reflect capital accumulation and higher capital utilisation more than a broad rise in total factor productivity. New data centres are being built, advanced equipment is being used more intensively, and workers and machines are effectively working harder, rather than yet delivering sustained economy-wide efficiency gains.

On the adoption side, he says AI is spreading through the wider economy faster than the personal computer and the internet did, but it is still too early to identify a decisive productivity step-up across entire economies. He argues that, as with electrification, meaningful gains may depend on broader adoption, complementary investment in skills and infrastructure, and organisational redesign.

According to OECD estimates cited in the remarks, AI could raise total factor productivity growth by up to 0.6 percentage points a year if adoption becomes sufficiently broad and investment is appropriate. Robinson says that would help offset demographic ageing and other structural headwinds in Asia, especially if AI lifts productivity in domestic services and allows more workers to perform higher-value tasks.

For central banks, he says the main challenge is balancing AI's potential as a favourable long-run supply shock against its immediate demand effects. He says monetary policy must remain evidence-based and state-contingent, warning that it may be premature to assume AI has already shifted measures such as potential output, the natural unemployment rate, the neutral interest rate and, in Singapore's case, the neutral exchange rate path.

In our earlier coverage of U.S. AI data center buildouts, we examined how soaring demand is colliding with real-world limits on supply expansion. The piece highlighted rising financing costs, construction delays and cancellations, and power-grid and community constraints as key obstacles that could slow the rollout of new computing capacity and test investor confidence.

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