Summary of Key Points
This article discusses who will become the economic winner in the AI era, challenging the traditional notion that the country with the most advanced large models will prevail. It emphasizes that the benefits of the AI economy depend not only on technological research and development but also on "soft factors" such as labor market structure, fiscal capacity, social support, and control over supply chains. Both China and the United States, as leaders in AI, have their strengths and weaknesses: the U.S. leads in capital and cutting-edge models, but faces public opposition and supply chain dependencies; China has advantages in talent, government support, and energy resources, yet is constrained by a shortage of advanced chips. Additionally, countries that control key supply chain nodes (such as TSMC and ASML), mid-power nations (like Europe and India), and the global AI development gap significantly influence the economic landscape of this era.
I. The AI Economic Winner Depends on More Than Just Large Models; “Soft Factors” Are Crucial
Many believe that the winner of the AI race is the country that develops the most powerful models, but the article argues that there are several other unseen factors that determine success:
- Labor Market Structure: The U.S. labor market is highly flexible, allowing companies to lay off employees and adjust wages at will. This means that as AI becomes more widespread, companies may be more inclined to use it to reduce costs, which could lead to widespread unemployment and public opposition to AI (for example, 70% of Americans do not want AI data centers to be built in their regions). In contrast, countries like Europe, which prioritize job stability, experience less impact from the adoption of AI.
- Fiscal and Capital Capacity: AI is costly, and while private investment in the U.S. is high (109 billion dollars in 2024, 12 times that of China), the Chinese government has invested heavily over the past decade (900 billion yuan) to support long-term development.
- Social Support: The Chinese public is more receptive to AI, with the government promoting it through events and robotics competitions. In the U.S., however, concerns about unemployment and environmental issues (such as the energy consumption of data centers) have led companies to build data centers overseas.
- Supply Chain Cooperation: AI relies on components like chips, rare earths, and energy; without these, even the best models cannot function effectively. For instance, the U.S. depends on Chinese rare earths, and China relies on U.S. chips, making mutual dependence a critical factor.
II. The AI Race Between China and the U.S.: Both Have Advantages and Disadvantages
China and the U.S. are the two dominant players in AI, but each has its challenges:
- U.S. Advantages and Disadvantages:
- Advantages: Advanced models (such as ChatGPT), ample capital, chip design (NVIDIA), and leading software and cloud infrastructure.
- Disadvantages: Public opposition to AI implementation due to concerns about unemployment and environmental issues; dependence on Chinese rare earths; a flexible labor market that can lead to high layoffs.
- China's Advantages and Disadvantages:
- Advantages: Abundant talent (35% of global AI papers, 70% of patents, and a growing number of top researchers); government and public support; sufficient energy resources (twice the U.S.'s electricity production, with half coming from renewable sources).
- Disadvantages: Shortage of advanced chips, which hinders the development of cutting-edge models.
- Deployment Differences: The Chinese government is driving AI initiatives with the goal of doubling GDP per capita by 2035; the U.S. relies on private companies, but their focus on efficiency may lead to increased unemployment and further erosion of public support.
III. Can Countries Without AI Research Still Win? Opportunities for Those Controlling Supply Chains
Some countries do not engage in AI research but still profit significantly by controlling key supply chain links:
- TSMC: The world's leading chip manufacturer, essential for AI development.
- ASML (Netherlands): The only company capable of producing extreme ultraviolet lithography machines, which are crucial for high-end chip production, giving the Netherlands geopolitical influence.
- Japan: Produces 90% of the photolithography resists and fluorinated polyimides needed for chip manufacturing, thus controlling a significant portion of the global supply chain.
- Middle East: Countries like Saudi Arabia and the UAE have cheap energy and land, making them attractive for data centers; Qatar produces helium for cooling chips. The blockade of the Strait of Hormuz by Iran could impact the global AI supply chain.
These countries' advantages also pose risks, as they are vulnerable to geopolitical conflicts.
IV. The Path for Mid-Power Nations: Europe as a “Smart Latecomer” and India Relying on Digital Infrastructure
Europe and India, though not as powerful as China and the U.S., have their own strategies:
- Europe: Does not aim for leadership but focuses on AI applications (such as robotics and medical automation) and uses strict labor laws to protect jobs. It also seeks to establish global AI standards that both China and the U.S. must follow.
- India: Leverages its digital infrastructure, large amount of data, and young talent to promote AI quickly. However, it faces challenges such as a lack of data centers and insufficient power supply, while also balancing relations with China and the U.S.
V. The Global AI Gap: Rich Countries Monopolize Resources, Leaving Poor Countries Behind
AI development does not lead to shared prosperity; instead, it widens the gap between rich and poor:
- High-income countries (17% of the global population) own 87% of AI models, 86% of AI startups, 91% of venture capital, and 77% of data centers.
- Low-income countries have less than 0.1% of data centers and lack internet access, electricity, and skills. However, they can use small-scale AI applications (e.g., helping doctors analyze data or small businesses find customers) to catch up gradually.
The future of AI will depend on whether wealthy countries are willing to help poor nations and whether these countries can seize the opportunities offered by small-scale AI solutions.
Conclusion
In the AI era, economic success is not determined solely by technology but by a combination of factors—both hard technologies and soft environments, as well as the ability to leverage one's strengths and collaborate with supply chain partners. The competition between China and the U.S. is not the only story; countries controlling supply chain nodes, mid-power nations, and even small countries can find their place in the AI landscape. Ultimately, the value of AI lies in whether it benefits ordinary people, rather than being monopolized by a few companies.