AI Supply Chains: The Economic Calculus of Cooperation Versus Competition for Global Growth

The global race for artificial intelligence (AI) leadership has spurred national strategies centered on securing critical supply chains. The United States, for instance, has undertaken initiatives to bolster its position in essential components like semiconductors and rare earths, aiming to enhance technological resilience and maintain primacy in innovative sectors.

However, this approach, which seeks to restrict access to specific markets or technologies, is viewed by some analysts as potentially counterproductive. The core argument suggests that fragmenting global value chains could escalate costs, diminish efficiency, and decelerate innovation, thereby hindering worldwide technological advancement. The intricate interdependence within a complex ecosystem implies that excluding major players might lead to significant diseconomies of scale and impede progress.

An alternative perspective proposes that collaboration between major economies, particularly in high-tech sectors like AI, would yield greater benefits. Rather than focusing on restrictions and exclusive alliances, the suggestion is to concentrate joint efforts on stimulating global economic development. This cooperation would generate the external demand crucial for sustaining long-term growth for all involved parties, potentially surpassing the gains from strict competition.

From a macroeconomic standpoint, the choice between containment and cooperative strategies carries profound implications. Cooperation could foster a more robust innovation environment and more efficient resource allocation, whereas intense competition might lead to duplicated efforts, elevated costs for consumers and businesses, and a deceleration of global technological progress. The pursuit of supply chain security must be balanced with the imperative for open and efficient markets to optimize global growth and mutual prosperity. Original source: South China Morning Post