But they are wrong. These arguments assume that China cannot succeed in AI without access to these advanced AI chips, which is not the case.

Advanced AI chips simply reduce the cost of AI. Today’s state-of-the-art AI models require a large number of AI chips to build and run. An advanced chip has higher performance; therefore, fewer are needed to achieve the same AI performance.  

But AI costs can be reduced in other ways. As DeepSeek showed, clever software and algorithm design can dramatically reduce the number of AI chips needed. China’s decision to open-source its AI models particularly allows it to leverage the best software and algorithms to reduce AI costs. Second, AI chips constitute only part of the overall costs. AI-based systems incur several other costs – engineering, data, software and licensing, regulations, energy, and infrastructure – where China has considerable cost advantages. Third, AI hardware performance depends greatly on packaging and interconnection – how AI chips are put together and connected. China can leverage its world-class strengths in both to achieve high performance. Recently announced Huawei SuperClusters are more powerful than any Nvidia system, despite not using the most advanced AI chips.

Advanced chips also reduce the power cost of AI. These chips are manufactured using the latest technology from TSMC (and sometimes Samsung) – each new technology is more energy efficient than the last. High power consumption of an AI system worsens monetary cost and the speed of deployment since fast access to a large amount of power is challenging, especially in the U.S.  However, China is growing its power supply much faster than the U.S. and is much more likely to successfully serve the power demands of its AI data centers, even if they consume more power due to lack of access to advanced AI chips.  High power also leads to greater carbon footprint, but it should not limit Chinese ambitions in any technology it considers important.

Besides, many AI applications do not need advanced chips. Several applications in network security, facial recognition, medical image analysis, advanced driver assistance systems (ADAS), logistics, and robotics can be handled using AI models much simpler than state-of-the-art models. These models can be built and run on chips that China can produce itself. China aims to dominate these applications. Even for more complex applications, recent work suggests that state-of-the-art models can be replaced by a collection of much simpler models. This collection does not need advanced AI chips to build and run. So, it is unclear if China will be left behind for these applications either.

It is also not clear whether future development and use of state-of-the-art models will require advanced chips. There are signs that the benefits of state-of-the-art models are plateauing. Given the large investments these models require, future models may look different and use fewer resources, including chips.  It will further level the playing field, even if access to advanced AI chips is controlled.  There is also a possibility that China may learn how to produce advanced AI chips itself – it has certainly invested in several technologies with the potential to leapfrog past the state-of-the-art.

Overall, China can significantly mitigate the disadvantages of not having access to advanced AI chips. Besides, China will be willing to absorb any higher upfront costs, especially for AI-based military and strategic technologies, since they know that they can reduce the downstream costs through scale and manufacturing strengths. Unsurprisingly, China continues to produce competitive state-of-the-art models and dominate AI-based applications such as robotics and autonomous vehicles despite the AI chip controls implemented over the last several years.

The argument for AI chip controls may still make some sense – why not get the advantage of increasing AI development costs for China, however small, if there were no cost to it. But the costs are significant. China could have been one of the largest markets for U.S. advanced AI chip companies. The U.S. has lost the market. Second, AI chip controls have made this an issue of national pride and led to a wave of investments into a domestic AI chip ecosystem within China. It is unclear if the U.S. will ever regain market share even if chip controls are reversed. China has also retaliated in many ways – those measures have further hurt the U.S. economy and geopolitics.

If the U.S. wants to lead in AI, chip controls are not the answer. Instead, it should focus on improving innovation, investment, energy, and regulatory ecosystems. It should make it easier for the best AI scientists in the world to live and work here. It should diversify, strengthen, and secure AI supply chains. It should work with allies to lead the development of international AI standards and practices. It should reduce the cost of AI (through selective open sourcing or public-private partnerships, for example) to ensure that American AI (alongside its values) is most pervasive. It should prioritize high-end and enterprise applications where the moat is wider against a talent and resource-rich fast follower that has cost and speed advantages.

The value of AI chip controls is vastly exaggerated. These controls have barely slowed China down and caused significant economic and geopolitical damage to the U.S. It is time to abandon them and focus fully on maintaining and growing AI lead through innovation instead. 

The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.