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.

According to Paolo Ardoino, the future of model training in AI will require fewer GPUs, reducing costs significantly. This development will likely influence cryptocurrency trading by decreasing operational expenses, facilitating more efficient data processing. Ardoino emphasizes that access to data remains crucial, suggesting that trading platforms should prioritize data acquisition to maintain a competitive edge. The transition to local or edge inference could lead to faster decision-making processes in trading environments, enhancing real-time trading capabilities.