AI Competition USA China Explained

Imagine a world where every decision, from choosing whether to cross the street to allocating funding for scientific research, could be influenced by artificial intelligence. This intriguing possibility is playing out on the grand stage of AI competition between the United States and China, as narrated in “AI Competition explained in 10 minutes” by Caleb Writes Code. Published on August 24, 2025, this video dives into the intricate layers powering AI’s evolution – from large language models (LLMs) to the infrastructure supporting them. A core argument here is that technological dominance isn’t merely about who has the best ideas but also who possesses the resources – notably advanced chips necessary to make those ideas a reality.

The battle between the US and China starkly highlights how access to cutting-edge resources like Nvidia’s coveted H100 GPUs marks a significant competitive edge. On paper, the numbers tell a compelling story. For instance, to train Meta’s Lama 3.1 model, an impressive feat requiring around 38 septillion floating-point operations per second (flops), Meta reportedly utilized 16,000 H100 units at an expense of $400 to $640 million dollars, condensing years of computation into mere months. This highlights how intensive resource utilization accelerates innovation, pushing the boundaries of AI technology possibilities.

But while the US giants like Meta and OpenAI can flex their technological muscle, Chinese companies face steeper challenges. American-imposed bans and restrictions have made chip access precarious for companies like Deepseek or Alibaba. Yet, China continues to innovate, as exemplified by Deepseek V3’s remarkable performance despite using less sophisticated H800 GPUs. Deepseek’s model, leveraging only 248 H800 GPUs, achieved impressive results with significantly fewer flops. This serves as a testament to Chinese resilience and tactical innovation amid constraints.

While this video argues effectively for the high stakes of the AI chip race, the resource disparity remains glaring. Caleb Writes Code outlines how barriers like US sanctions and scarcity of cutting-edge tools such as the EUV lithography from ASML impose significant operational challenges. However, it leaves some questions open about the future sustainability of such competition—how long can either country drive progress under these conditions remains an open question.

When reflecting on China’s efforts to mirror US capabilities, like its aim for self-sufficiency in chip production by 2025 or rival facilities like the ambitious Stargate by OpenAI, one can’t ignore the evident gaps in infrastructure and manufacturing. Yet, these challenges complement their resolve, as evidenced by even modest successes shaping AI’s global narrative.

Despite the competitive underpinnings, the video leaves viewers contemplating the ultimate layer – application. Under such intense global competition, AI’s value is judged by its practical benefits across varied sectors like healthcare and defense. At the ground level, innovations in application can have ripple effects, catalyzing widespread adoption and furthering national interests.

The battle isn’t just between nations but among numerous players, maker garages, and startups, as they experiment within the AI landscape’s rich, shifting tapestry. Caleb Writes Code’s overview is both a testament to AI’s transformative prospects and a reminder of the complex machinations behind the scene that define global tech supremacy.