Artificial intelligence is rapidly reshaping the global investment landscape, but leadership in the sector is far from concentrated in a single market. While the United States continues to dominate capital investment and model development, the next phase of AI growth is becoming increasingly distributed across regions, sectors and infrastructure layers.
In the following piece, Dina Ting, Head of Global Index Portfolio Management at Franklin Templeton ETFs, explores how value creation is evolving across the AI stack, and what that means for global investors.
US companies still dominate artificial intelligence (AI) capital and model production, but having an AI advantage is not a single leaderboard. In 2024, US private AI investment reached US$109 billion, dwarfing activity in China and the United Kingdom, which ranked as distant runners-up. Yet as AI investment moves from model creation to physical buildout and enterprise deployment, value creation increasingly extends beyond the United States.
China: infrastructure, manufacturing AI and platform-led deployment
China’s position in the AI stack is arguably less about dominance in proprietary frontier models and more about scale, infrastructure investment, and deployment across digital platforms and manufacturing. While US firms continue to lead in AI capital at the research frontier, China is advancing a different model centered on cost-efficient, open-source and application-driven AI, exemplified by DeepSeek, which highlighted the country’s ability to develop competitive large language models with far lower training costs and compute intensity.
This approach has lowered barriers to adoption and accelerated diffusion across enterprises. At the same time, China’s largest internet and digital services firms are entering a renewed multi-year capital expenditure cycle, collectively set to invest more than US$78 billion through 2027, with spending increasingly concentrated on AI infrastructure, data centers and cloud capacity.
China’s AI strategy is tightly integrated with its manufacturing upgrade roadmap. National policies emphasize embedding AI into smart factories, robotics and supply-chain optimization to improve productivity, reduce labor intensity and enhance global competitiveness. As a result, industrials, materials and hardware-oriented technology sectors play a central role in China’s AI exposure.
Beyond the United States: Global AI Vibrancy Leaders
The Top 10 government investment commitments highlight that AI leadership is geographically diverse. The United Kingdom, Germany and India each reflect distinct strategic approaches—from public services integration and industrial automation to digital public infrastructure and computing expansion—while China’s cumulative commitment underscores the scale of its long-term industrial AI ambitions.
Taiwan sits at a critical chokepoint in the AI compute stack, producing roughly 90% of the world’s most advanced logic chips. The concentration is often described as Taiwan’s “silicon shield” for its strategic global importance.
While Taiwanese manufacturers have expanded production footprints in the United States and other regions, replicating the full level of sophistication found in Taiwan’s semiconductor ecosystem is expected to take many years. Advanced chip manufacturing depends not only on fabrication facilities, but also on dense networks of specialized suppliers, experienced engineering talent, advanced packaging capabilities, and rapid production learning cycles. This ecosystem depth remains difficult to reproduce quickly, reinforcing Taiwan’s competitive advantage as AI-driven demand for high-performance computing accelerates.
South Korea’s memory-chip segment plays a central role in the AI hardware stack, with global leadership in dynamic random-access memory, including high-bandwidth memory, and a top position in NAND flash storage. South Korea also illustrates how AI advantage does not require leading the race to build the largest global models.
South Korea’s AI opportunity is increasingly visible in heavy industry, not just software. In late 2025, a major Korean industrial group touted that AI and big-data systems are being integrated across shipyard design, planning, and production workflows, with the goal of creating fully connected “smart yards” capable of predicting and optimizing output.
Japan: industrial AI and productivity capture
Japan’s position in the AI stack is defined less by capital intensity and more by industrial deployment, execution and policy alignment. Gauging by strong year-to-date net inflows into Japan-focused exchange-traded funds, early investor response has reflected optimism that clearer leadership and policy continuity could accelerate implementation in areas such as AI, advanced manufacturing and semiconductors.
Under Takaichi’s leadership, the government approved Japan’s first national AI plan, committing roughly US$6.3 billion over five years, to strengthen foundational AI capabilities, robotics integration and industrial deployment.
Brazil and Saudi Arabia: Energy abundance as an AI infrastructure advantage
Brazil’s AI exposure extends beyond commodities. The country’s energy mix is among the cleanest globally, with renewables accounting for roughly 90% of electricity generation. This clean energy advantage is increasingly relevant for enterprise-scale AI infrastructure, where power cost and stability directly affect data center economics.
Saudi Arabia is pursuing one of the most ambitious state-led AI infrastructure strategies outside the United States and China. Under Vision 2030, Riyadh is leveraging abundant low-cost energy, capital and land to position itself as a regional hub for AI infrastructure and computing capacity.
More broadly, AI should be viewed as a geographically dispersed, layered buildout across semiconductors, power, automation and digital infrastructure. As spending shifts from model development to deployment, the set of beneficiaries broadens—and so does the corresponding investment map.
