According to Tetranode, the efficiency of AI models is inversely related to the demand for silicon, as increased efficiency may lead to more tasks being assigned, thereby increasing hardware demand. Tetranode suggests that larger GPU arrays remain advantageous regardless of algorithmic improvements, highlighting a potential oversight in understanding the relationship between AI model efficiency and hardware requirements.