A new lens for understanding the AI race focuses less on algorithms and more on “tokenomics”—the economics of generating and consuming tokens, the basic units of AI model output. The South China Morning Post article argues that China could gain a structural advantage in this emerging framework by combining vast energy capacity with low-cost AI models.
Tokens are increasingly treated as a commodity in AI systems, representing the cost of computation and output. As demand for large language models surges, the ability to produce tokens cheaply and at scale becomes a defining competitive factor. China’s advantage lies in its massive and expanding power grid, which supplies the electricity needed to run energy-intensive data centers. Since AI computation is fundamentally tied to energy consumption, abundant and affordable electricity directly translates into lower operating costs.
This energy advantage is paired with a different strategic approach to AI development. Chinese firms have emphasized efficiency and cost optimization, producing models that may not always match the absolute cutting-edge performance of Western counterparts but are significantly cheaper to run. Lower pricing attracts developers and businesses, increasing usage and generating more data, which in turn improves model performance over time.
The combination of scale and affordability creates a feedback loop. Greater usage drives more token consumption, which justifies further investment in infrastructure and model development. This dynamic could allow China to dominate global AI usage even without leading in every technical benchmark.
However, the article also suggests that this advantage is not guaranteed. Challenges remain, including reliance on imported semiconductor technology, uneven distribution of computing resources, and the need to integrate power generation with data center demand effectively.
The broader implication is a shift in what defines leadership in AI. Instead of focusing solely on model sophistication, the competition may increasingly hinge on cost, scalability, and infrastructure. In that context, control over energy and efficient token production could prove just as important as advances in algorithms.