This article first appeared in The Edge Malaysia Weekly on November 24, 2025 - November 30, 2025

IN one of Aesop’s fables, an arrogant and fast hare challenges a slow but determined tortoise to a race. Confident that sheer speed will ensure victory, the hare naps while the tortoise steadily plods on to win the race. In the tech world, the buzzword until recently has been “move fast and break things” — the motto of Mark Zuckerberg who founded the social media giant now known as Meta Platforms in his Harvard dorm room at the ripe old age of 19. Yet rapid deployment of technology and addressing problems only as they arise may not be the way to go in the age of artificial intelligence (AI).

Now a race for tech supremacy is underway between the US and the world’s second-largest economy, China. So, who will win the AI race? Will it be the US, which by all accounts is still way ahead, with its access to high-end parallel processing chips like Blackwell Ultra designed by Nvidia as well as access to the 2nm chip-making prowess of Taiwan Semiconductor Manufacturing backed by the tools shipped by Dutch chip equipment giant ASML? Or will it be China — currently seen as the tortoise in the AI race, with access only to far inferior 7nm chip equipment — plodding along steadily and eventually pipping past America?

Until a year ago, the consensus was that the US was at least four to five years ahead of China in AI with access to top-of-the-line chip design, chip equipment and manufacturing. Moreover, America’s large language AI model pioneers — such as ChatGPT creator OpenAI, Gemini maker Google, Claude producer Anthropic and Grok maker Elon Musk’s startup xAI, which are expected to spend US$400 billion (RM1.66 trillion) on AI infrastructure this year and nearly US$600 billion next year — were reportedly far ahead of Chinese startups as well as established tech giants like Alibaba Holdings and Baidu, who are among the AI leaders.

That changed in January when a small Chinese AI startup DeepSeek released its R1 large language model, specifically tailored for reasoning and structured problem-solving, particularly for tasks like math and coding. What made DeepSeek so special was that, while its capabilities were close to OpenAI’s ChatGPT models, it was developed using far fewer resources — reportedly US$5.58 million compared with over half a billion spent on ChatGPT’s O1 model.

DeepSeek’s breakthrough notwithstanding, western analysts still believe US large language models like ChatGPT, Gemini, Claude and Grok are still far superior to anything that Alibaba, Baidu, Tencent and others like Shanghai-based MiniMax AI are working on. Still, Jefferies & Co tech analyst Edison Lee believes that China’s AI model performance gap with its American peers continues to narrow. The way Lee sees it, the performance of MiniMax M2, the best China AI model, may only be 10% lower than that of the best US AI model, OpenAI’s GPT-5. Lee cites tests done by Artificial Analysis, an independent AI research firm. Beijing-based AI startup Moonshot AI released its Kimi 2 Thinking model with a performance score of 67 that even exceeded MiniMax. Only Google’s Gemini 3 Pro with a score of 73 and OpenAI’s GPT-5.1 with 71 are better, though xAI’s Grok 4 and Anthropic’s 4.5 are not too far behind.

Moonshot AI trained its model on Nvidia’s H800 chip, a downgraded version of the NVDA H100 specially designed for China. The H800 chip is no longer in production. Nvidia has designed a newer, slightly downgraded H20 chip specially for China. Beijing had urged its top tech companies not to buy H20 and look for other alternatives. In recent months, Nvidia has not been able to export any chips to China even though there is no official ban on H20 chips. In a recent report, Lee notes that it is difficult to extrapolate the US-China AI model gap, given that American players will continue to have access to the next generation of AI chips from Nvidia Advance Micro Devices and Broadcom while other AI players will update and improve their own models, thereby widening the gap with Chinese upstarts. So, a more significant focus for investors is whether these Chinese models could be monetised by developing applications that will be adopted by consumers and enterprises, Lee notes.

Alibaba, Baidu, Tencent and startups like MiniMax and Moonshot AI want whatever AI chips they can get hold of from Nvidia and AMD but Beijing wants China to make its own AI chips and drastically cut its dependence on US chips. Ahead of his meeting with President Xi Jinping in Seoul late last month, US President Donald Trump had said he was willing to allow China to buy a slightly degraded version of Nvidia’s Blackwell chips but the matter wasn’t even brought up at the summit. Trump believes that the export controls on chips introduced by the Biden administration in October 2022 have proved to be a massive “own goal” for the US. That is because they have triggered an overwhelming incentive for China to set up its own semiconductor value chain while depriving US tech companies of a major customer.

Beijing and Washington have not yet agreed on AI chip sales to China. Xi reportedly didn’t want to be seen as weak by asking for degraded AI chips. The Chinese president believes that his country’s export control on rare earth elements gives it sufficient leverage with far-reaching, long-term impact on the chip supply chain including defence, technology and automotive industries. He would rather have Chinese tech giants buy lower-end domestic chips from Huawei Technologies Co, which for the first time publicly outlined detailed long-term plans for its Ascend AI & Kunpeng server chips. Beijing is using Nvidia’s market access as leverage in trade negotiations to push Washington for wider access to advanced semiconductors. What China really wants is access to advanced chip-making tools from ASML which will allow Huawei and others to make state-of-the-art chips in China.

While American proprietary or closed-source AI models like GPT, Gemini, Claude and Grok dominate in overall performance, China leads in performance for open-source models. US-based closed-source models have higher intelligence scores than Chinese open-source models. But that is a reflection of their technological edge, driven by access to superior data, larger compute resources, more advanced training and alignment pipelines, and proprietary inference techniques. Open-source models, on the other hand, offer flexibility, transparency, lower costs and ecosystem benefits. They can be fine-tuned and deployed locally without vendor lock-in while thriving communities and toolchains accelerate innovation and broaden adoption. Over the longer term, analysts expect Chinese AI models to slowly catch up with the US because some Chinese AI players will continue to conduct pre-training in data centres outside China, thereby avoiding chip restrictions. Already, US companies are using Chinese AI models. Airbnb’s CEO Brian Chesky recently revealed that his firm was relying on Alibaba’s Qwen model instead of OpenAI’s GPT because the connective tools were not “quite ready”. Chesky, by the way, is a long-time close friend of OpenAI’s CEO Sam Altman.

US AI infrastructure spending is expected to exceed US$400 billion this year. For their part, Chinese AI firms will spend less than one-fifth of that on AI infrastructure in 2025. Yet that’s not just because Beijing has told its AI players not to buy any more Nvidia chips but use their existing inventory of American AI chips or Chinese-made chips. China’s low AI capex is also due to higher model efficiency, rather than limited access to expensive advanced AI chips from the US. While US players like OpenAI and Meta are focused on model performance and artificial general intelligence (AGI) or AI that can perform any intellectual task a human can, Chinese open-source models are more focused on efficiency which helps drive down token cost and in turn will help improve application development and user adoption.

American AI giants are still raking in huge profits. On Nov 19, AI chip giant Nvidia, whose stock had been under pressure for the past two weeks over valuation concerns, reported blowout earnings, sending its stock soaring again. Nvidia reported 62% higher revenue of US$57.01 billion for its fiscal third quarter ending October, compared with analysts’ estimates of US$54.92 billion for the quarter. Nvidia’s net income in the quarter rose 65% to US$31.91 billion, from US$19.31 billion. Nvidia growth should be seen in the context of its exports of advanced AI chips to China completely halted. Excluding China, Nvidia’s sales to the rest of the world nearly doubled over the same quarter last year. CEO Jensen Huang said in late October that Nvidia has US$500 billion in orders, for 2025 and 2026 combined, for its AI chips. “The number will grow,” Chief Financial Officer Colette Kress said on an earnings call with analysts.

“China is going to win the AI race,” Huang told the Financial Times’ Future of AI Summit on Nov 5. Hours later, he attempted to walk back a bit. ”As I have long said, China is nanoseconds behind America in AI,” Huang said in a statement posted on X. “It’s vital that America wins by racing ahead and winning developers worldwide.” That was a subtle hint that the US should allow Nvidia to sell whatever chip it wants to sell to China.

For AI Compute, US needs power hungry chips in the massive data centres that being built at home and abroad. Nvidia’s CEO highlighted lower energy costs in China, citing Beijing’s energy subsidies that made power more affordable for local tech firms, allowing them to run Chinese alternatives to Nvidia’s AI chips cheaply. Compared to rising energy costs in the US, power is ridiculously cheap in China and that gives China a leg up in its competition with the US on AI supremacy. One reason why energy costs are low in China is its superior battery storage technology. Falling costs of battery storage and plummeting prices of photovoltaic cells have made solar more competitive than coal in China. Solar and wind power have long been thought of as intermittent energy but when combined with cheaper storage, they are now seen as a regular source of energy like coal, LNG or indeed nuclear power.

Increasingly, US firms are pressing the government to address the country’s growing energy deficit. “Whoever wins the energy race likely will win  the AI race,” says William Thompson, an analyst at Barclays in New York. “This is increasing emphasis on energy needs and risks to AI spending, economic growth and American exceptionalism,” he notes. OpenAI last month wrote an open letter to the White House, calling for closing the “electron gap” by setting up an ambitious national target of building 100GW a year of new energy capacity to “fuel American AI dominance”.

The largest US AI startup noted that last year alone China added 429GW of new power capacity or one-third of the entire US grid and over half of all global electricity growth. Indeed, China added 240GW of solar generation capacity in the first nine months of 2025 — more than the entire installed solar capacity in the US of about 178GW. While China’s total power demand increased by 170TWh (or terawatt hours) in the first half of this year, power generation from solar and wind sources increased by 250TWh.

Here is why China’s cheap energy costs matter. Electricity required to generate the same amount of “tokens” from Chinese AI chips is between 30 and 50% higher than Nvidia’s H20 chips. Little wonder, then, that Beijing has begun encouraging local governments to incentivise Chinese tech giants with data centres by offering subsidies that slash their electricity bills by as much as 50% provided they are powered by domestic chips like those made by Huawei. Like the tortoise in Aesop’s Fables, China is slowly gaining on the American hare in the AI race. Not moving on energy needs now would be akin to the hare napping as the tortoise plods on.

Assif Shameen is a technology and business writer based in North America 

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