AI Supremacy: Will China Win The Race?
For much of the last decade, the story of artificial intelligence supremacy has sounded straightforward: the United States leads, the rest follows. With companies like OpenAI, Google, and Anthropic setting the pace in large language models and frontier research, America has appeared comfortably ahead in the global AI race.
But a growing body of analysis suggests that this narrative may be too simplistic and possibly short-sighted.
In a recent column titled “China Will Win the AI Competition,” the Financial Times urged readers to rethink how AI dominance should be measured. According to FT economic columnist Tej Parikh, the competition is not a sprint defined by who releases the most impressive model first, but a marathon shaped by national strategy, industrial deployment, and long-term resilience.
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A Marathon, Not a Sprint
Parikh argues that while U.S. firms currently dominate in raw technological performance, China may ultimately gain the upper hand by excelling where it matters most: large-scale application.
“Ultimately,” he writes, “a national strategy encompassing not only AI technological performance but also energy, resources, and industrial deployment will determine success or failure.”
This is where China’s centralized governance model becomes a strategic advantage. While the U.S. leads in innovation at the frontier, China may be better positioned to integrate AI deeply and rapidly into manufacturing, logistics, healthcare, surveillance, and public administration, turning models into measurable economic and social impact.
Doing More With Less: The DeepSeek Lesson
A key example cited in the FT analysis is DeepSeek, a Chinese AI model that, while trailing leading U.S. models on some performance benchmarks, has gained attention for its efficiency.
China, Parikh notes, has demonstrated an ability to build advanced models using significantly less computational power than U.S. counterparts. Through algorithmic innovation, which involves smarter ways of solving problems rather than brute-force scaling, Chinese developers have compensated for limitations in access to cutting-edge chips and mass production capacity.
This approach challenges a core assumption in Silicon Valley: that AI progress is primarily a function of bigger models, more data, and more compute. If efficiency-driven innovation proves scalable, it could significantly reshape the economics of AI development.
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Investment, Energy, and Political Headwinds
Another factor narrowing the perceived gap is capital. Despite the U.S. tech ecosystem’s dominance, Parikh suggests that the difference in AI investment between the U.S. and China is not as wide as commonly assumed, especially when accounting for sustained Chinese government backing.
At the same time, political dynamics in the U.S. may introduce friction. Parikh points to former President Donald Trump’s pressure on the renewable energy sector as a potential liability for American AI competitiveness. Since AI development is energy-intensive, long-term leadership may depend as much on clean, abundant power as on algorithms and talent.
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Demis Hassabis: “The Gap Is Only Months”
Perhaps the most striking confirmation of this narrowing divide comes from within the U.S. AI establishment itself.
Demis Hassabis, CEO of Google DeepMind and a Nobel laureate in chemistry, recently told CNBC that the technological gap between the U.S. and China is far smaller than many believe. According to Hassabis, China’s AI capabilities have advanced rapidly and are now comparable to those of the U.S. and other Western nations, far more so than just one or two years ago.
“The current technological gap is likely only a few months,” he said.
However, Hassabis was careful to add a crucial caveat: matching existing technologies is not the same as redefining the frontier.
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“What matters,” he emphasized, “is achieving new innovations that go beyond cutting-edge technologies.” On this front, he argued, China has not yet clearly demonstrated its ability to lead.
The Real Question
So who will win the AI race?
If leadership is defined by who builds the most powerful model today, the U.S. remains ahead. But if the measure shifts to who can deploy AI most effectively across an entire economy, sustain innovation under constraints, and align technology with energy and industrial policy, the answer becomes far less certain.
The AI race, it seems, is no longer about speed alone. It is about endurance, strategy, and the ability to turn intelligence into impact.
And in that marathon, China is no longer chasing from behind; it is running shoulder to shoulder with the leaders.
