Discussions of the global artificial intelligence ( AI ) race typically focus on the contest between the two innovation leaders – the United States and China – with the rest of the world positioned effectively as spectators. But while the US and China are pushing the frontiers of AI development, from foundation models to advanced semiconductor design, they will not dictate the technology’s economic impact. Those who apply it will.

While the invention of general-purpose technologies like AI creates new opportunities, it is the diffusion of these technologies across industries and economies that leads to transformation. Electricity, the internal combustion engine, and the internet took decades to generate large productivity gains because firms had to reorganize production, invest in infrastructure, and cultivate new skills.

Early evidence suggests that AI is likely to follow a similar trajectory. Last year, roughly 16% of the world’s working-age population used generative AI tools on a monthly basis. But adoption rates varied widely across countries, and the fastest adopters were not those on the leading edge of AI innovation. In fact, while the US and China race to develop the most powerful algorithms, a much larger set of countries is competing to achieve the broadest and fastest integration of AI into their economies.

According to the 2025 Government AI Readiness Index – which evaluates nearly 200 countries by policy capacity, governance, infrastructure and public sector adoption – the US ranks first for AI preparedness, followed by France, the United Kingdom, the Netherlands and South Korea. Germany, Singapore, China, Australia and Norway round out the top 10. The International Monetary Fund’s AI Preparedness Index, based on indicators of digital infrastructure, human capital, innovation and regulation, ranks Singapore, the US, the Netherlands, Finland, New Zealand and Germany among the world’s most prepared economies.

The multipolar nature of the AI adoption race partly reflects the fact that technologically capable economies across Europe and Asia have launched ambitious national strategies to accelerate AI diffusion. In Europe, policymakers are seeking to combine industrial AI adoption with regulatory frameworks that promote trust and responsible innovation.

In Asia, Singapore is investing heavily in digital infrastructure, workforce training, and public-sector experimentation. Japan has adopted an “innovation-first” approach to AI governance, encouraging experimentation and close collaboration between government and industry. And South Korea has set the ambitious goal of becoming one of the world’s top three AI powers. Initiatives like the Manufacturing AI Transformation strategy integrate AI into key industries—including semiconductors, automobiles, robotics and shipbuilding – while expanding computing infrastructure and nurturing AI talent.

Beyond these global leaders, other countries are also working to accelerate AI adoption. India, which has few frontier AI firms, is seeking to establish itself as a leader in AI applications and services. Malaysia has introduced a National Action Plan aimed at establishing a fully AI-driven economy, so that the country becomes a regional AI hub by 2030.

These efforts partly reflect an awareness among Asian and European policymakers that major productivity gains are vital to offset rapid population aging, shrinking labour forces and rising fiscal pressures. Under these conditions, AI offers one of the few realistic pathways to achieving such gains and sustaining long-term growth.

Ultimately, the most effective strategies will be those that focus on three mutually reinforcing pillars. The first is human capital. Equipping workers with stronger digital and problem-solving skills is vital to enable them to collaborate effectively with AI systems. This will require significant upgrades in education and lifelong learning.

The second pillar comprises technological capabilities. Countries must pursue sustained investment in computing infrastructure, data systems and research networks that support experimentation and innovation.

The final pillar is industrial transformation, including reorganization of production processes, introduction of new workflows and adoption of complementary technologies. Recent simulations of AI’s macroeconomic impact suggest that economies combining strong digital infrastructure with advanced manufacturing or data-intensive business services are particularly well positioned to benefit from the technology.

In this sense, countries like Finland, Germany, Singapore, South Korea and the Netherlands, as well as the US and China, have a head start. Europe’s large industrial base, from advanced machinery to energy systems, may provide a particularly significant advantage, facilitating broad AI adoption that helps to reinvigorate lagging productivity growth. But some analysts caution that Europe’s ambitions will remain constrained unless it reduces its dependence on foreign digital infrastructure and cloud platforms.

The success of such strategies will depend significantly on institutions and governance frameworks that encourage technological progress while ensuring the responsible use of AI. Priorities must include preserving competition, attracting global talent, supporting start-ups and implementing regulations that address risks such as data misuse, algorithmic bias and excessive market concentration.

Whether AI becomes a transformative engine of global productivity growth will depend not only on the pace and direction of innovation, but also on how effectively countries around the world adapt their institutions, labour markets and production systems to seize the opportunities the technology creates. The “AI race” will be won not just by those delivering breakthroughs in Silicon Valley or Hangzhou, but also—and perhaps even more so—by those applying them most effectively.

Lee Jong-Wha is a professor of economics at Korea University and a former chief economist at the Asian Development Bank and senior adviser for international economic affairs to the president of South Korea.

Copyright: Project Syndicate