DeepSeek’s inception is as strategic as its technology. Before U.S. regulators banned the export of Nvidia’s A100 AI chips to China, Liang secured a vast stockpile, which became the foundation for his groundbreaking platform. Despite relying on equipment considered less advanced than industry norms, DeepSeek has developed a low-cost, highly efficient way to train artificial intelligence models. Its core advantage lies in its model’s use of “inference-time computing,” a mechanism that activates only the relevant parts of the AI architecture during each query. This targeted approach minimizes both expense and processing strain while maintaining impressive computational capabilities.

What truly sets DeepSeek apart is its commitment to open-source principles. By allowing developers worldwide to access and improve its platform, DeepSeek fosters collaboration that could spur significant technological advancements. The platform is already drawing comparisons to industry heavyweight OpenAI, with reports suggesting that DeepSeek matches OpenAI’s performance—at a staggering 95% lower cost. Estimates place DeepSeek’s development costs at around $6 million, a stark contrast to the projected $1 trillion U.S. companies like OpenAI and Google are expected to spend on AI development over the coming years. Even government investments, such as the $500 billion pledged by the Trump administration for AI initiatives, reflect an approach that seems extravagant compared to DeepSeek’s lean innovation.