US vs. China: The Race to AI Supremacy

Intro: What is AI?

AI stands for “Artificial Intelligence” and has become a sought-after tool since the launch of Large Language models like the more commonly known ChatGPT and Claude. AI is a mixture of hardware, software, memory, and machine training using open-source data. Governments and businesses have become invested in AI for its computational power and problem-solving capabilities. Technology, science, and medical industries have experienced exponential growth since the introduction of AI and will continue to for as long as production and resources allow. According to Ernestas Naprys of Cyber News, “AI is expected to become a crucial component of economic and military power in the near future” (1). History reflects that military innovation is always a key ingredient for establishing power. This was proven during the emergence of gunpowder, automatic weapons, chemical warfare, and new technology.

The Key Components of AI: Hardware and Software

AI consists of many key components that allow it full functionality. The key components include silicon wafers, semiconductor materials, deposition and etching chemicals, CAD software for chip design, rare earth minerals, memory, power supply (capacitors), and cooling systems. Ernestas Naprys states, “The US clearly dominates the semiconductor design market, having an 85% global share, with 5% left to Asian countries, according to the Department of Defense” (1). Although this is only the hardware, this is a vital part of the production process. The software utilizes GPUs and many different types of proprietary software that are trained and programmed using open-source data and processes such as Reinforcement Learning from Human Feedback. According to Dave, a senior writer at IBM, “Reinforcement learning from human feedback (RLHF) is a machine learning technique in which a ‘reward model’ is trained with direct human feedback, then used to optimize the performance of an artificial intelligence agent through reinforcement learning” (2). There are many other tools utilized to train AI, such as finetuning and supervised pretraining, but these are the most common.

AI Manufacturing Mania: Who can produce the most?

Lithography machines are a key component in manufacturing semiconductors that power Artificial Intelligence models. Ibtisam Abbasi of AZO Materials writes, “The method of transmitting geometric forms to the base of a silicon wafer is known as photolithography” (3). During this process, lithography machines utilize deep ultraviolet light and intense ultraviolet light to create abstract patterns on microchips. China has shown a decline in outsourcing semiconductor manufacturers year over year. According to Ernestas Napyrs of Cyber News, “China, which set a target to reach 70% self-sufficiency in chips by 2025, appears to be working on manufacturing its lithography machines, even if they will be dwarfed by ASML’s” (1). The US maintains a strategy of controlling the bear share of lithography production sources. Although this seems like a promising strategy, Ernestas writes, “The US strategy could, in fact, strengthen Beijing’s determination to achieve tech self-reliance and could backfire on its own (US) IC companies” (1).

Game-Changing Innovations

The US and China are both progressing in leaps and bounds by means of scalability, application and technological advances, and algorithmic breakthroughs with the help of AI. Companies like OpenAI, Google, and Nvidia are all US-based companies that may have multinational shareholders, but all primarily operate to contribute to the advancement of the American AI initiative to leverage computing power. China has shown a large focus on manufacturing hardware, AI-powered surveillance, and large-scale deployment. Chinese companies such as Baidu, Huawei, and Tencent are gradually integrating into the nation’s infrastructure. We see the same happening in the US. Technology and AI have not only become a commodity, but they now also drive the economy, protect national security, and become the future of ethical governance.

Conclusion: The Next Chapter of AI

Quantum computing is on the horizon, and the application of this type of computing power is yet to be determined. Quantum computing marks a new age in technological computing power. With AI and quantum computing combined, manufacturers will have a limitless opportunity to forward the initiative to better human lives and heighten security measures. In an ever-evolving industry, AI and its security, science, and military applications show no signs of slowing. China has taken a self-sufficient approach to creating proprietary technology and manufacturing capabilities. The US continues its international collaborative effort to pump out some of the most impressive innovations we have seen in such a short amount of time. Two very different and impressive strategies. Only time will tell which strategic approach is most advantageous in the race to acquire AI supremacy.  

References

Ernestas Naprys. November 28 2023. China vs US: who’s winning the race for AI supremacy. https://cybernews.com/tech/chi...

Dave Bergmann. November 10 2023. What is Reinforcement Learning from Human Feedback. https://www.ibm.com/think/topi...

Ibtisam Abbasi. Jan 6 2025. Lithography Machines and the Chip Making Process. https://www.azom.com/article.a...

Forbes Technology Council.Danny Jenkins.The Growing Risk of AI-Generated Cyber Attacks https://www.forbes.com/council...

IBM. Security Team. What is an Intrusion Detection System (IDS). https://www.ibm.com/think/topi...

APELID: Enhancing real-time intrusion detection with augmented WGAN and parallel ensemble learning. Hoang V. Vo. Hanh P. Du. Hoa N. Nguyen. https://www.sciencedirect.com/...

CyberSecurityNews.Priya Naveen. 25 Best Intrusion Detection & Prevention Systems (IDS & IPS) in 2024. https://cybersecuritynews.com/...