Another week has passed, and uncertainty continues regarding the export of Nvidia’s advanced AI chips to China. Proponents of maintaining the controls argue that these chips would help build Chinese military systems that threaten the US and its allies. They also argue that controls are necessary to maintain and expand American leadership in the AI services market.
But this is wrong. These arguments assume that China would not be able to advance in AI without access to these advanced chips — which is not true.
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Advanced AI chips simply reduce the cost of artificial intelligence. Current cutting-edge models require a large number of chips to train and operate. An advanced chip has superior performance; therefore, fewer chips are needed to achieve the same level of performance.
But AI costs can be reduced in other ways. As DeepSeek has shown, intelligent software and algorithm design can drastically reduce the number of chips needed.
China’s decision to make its AI models open source, in particular, allows it to take advantage of the best in software and algorithms to cut costs. Furthermore, AI chips represent only a part of the total cost.
AI-based systems involve numerous other expenses — engineering, data, software and licenses, regulation, energy and infrastructure — in which China has significant advantages.
Another point is that hardware performance depends heavily on packaging and interconnection — how chips are grouped and connected. China can use its world-class expertise on these two points to achieve high performance.
Huawei’s newly announced SuperClusters are more powerful than any Nvidia system, even without using the most advanced AI chips.
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Advanced chips also reduce the energy cost of AI. These chips are made using the latest technology from TSMC (and sometimes Samsung) — each new generation is more efficient than the last.
High energy consumption increases the cost and slows down the pace of deployment, as it is difficult to guarantee rapid access to large amounts of energy, especially in the US.
However, China is expanding its energy supply much faster than the US and, therefore, has a better chance of meeting the energy demands of its AI data centers, even though they consume more energy because they do not have access to advanced chips.
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High consumption also increases the carbon footprint, but this should not limit Chinese ambitions in technologies it considers strategic.
Furthermore, many AI applications do not need advanced chips. Various applications in network security, facial recognition, medical image analysis, advanced driver assistance systems, logistics and robotics can be operated with much simpler models than the cutting-edge ones.
These models can be trained and run on chips that China can produce itself. The country intends to dominate these areas. Even in more complex applications, recent research suggests that cutting-edge models can be replaced with a much simpler set — and that set doesn’t require advanced chips to build or operate. Therefore, it is unclear whether China will fall behind in these areas as well.
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It’s also unclear whether the development and future use of high-end models will actually require advanced chips. There are signs that the gains from these models are reaching a limit.
Given the large investments required, it is possible that future models will be different and use fewer resources, including chips. This would help even the playing field even more, even with export controls.
There is also the possibility that China will learn to produce its own advanced chips — after all, the country has already invested heavily in technologies with the potential to surpass the current state of the art.
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Overall, China is able to significantly mitigate the disadvantages of not having access to advanced AI chips. Furthermore, it will be willing to absorb higher upfront costs, especially in AI technologies for military and strategic purposes, because it knows it can reduce downstream costs thanks to the scale and strength of its manufacturing.
It is not surprising that China continues to produce competitive cutting-edge models and dominate AI-based applications such as robotics and autonomous vehicles, despite controls imposed in recent years.
The argument in favor of controls may even seem reasonable — why not take advantage of the chance to raise AI development costs in China, even if only a little, if it would have no impact on the US? But the costs are significant.
China could have been one of the biggest markets for American advanced chip companies. The US lost that market. Furthermore, the controls turned the issue into a matter of national pride for the Chinese and triggered a wave of investment in a domestic AI chip ecosystem.
It’s unclear whether the U.S. will be able to regain market share even if it rolls back controls. China also retaliated in several ways — measures that further damaged the U.S. economy and geopolitics.
If the US wants to lead in AI, chip controls are not the answer. It is necessary to focus on improving innovation, investment, energy and regulatory ecosystems. We need to make life easier for the best AI scientists in the world so they can live and work in the country.
Diversify, strengthen and secure AI supply chains. Work with allies to lead the development of international standards and practices. Reduce the cost of AI (through selective open source initiatives or public-private partnerships, for example) to ensure that American AI — and its values — are the most prevalent in the world.
And prioritize highly complex applications aimed at companies, where the US competitive advantage is greater compared to a fast competitor with a lot of talent, resources and cost and speed advantages.
The value of AI chip controls is hugely overstated. They barely slowed China’s advance and caused major economic and geopolitical damage to the US. It’s time to abandon them and focus fully on maintaining and expanding AI leadership through innovation.
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