According to God of Prompt on Twitter, a new research paper has demonstrated that it is possible to match the performance of Transformer models without computing any attention weights. This finding challenges the foundational mechanism behind widely used AI models such as GPT4 and BERT, suggesting alternative architectures could achieve comparable results with potentially lower computational costs. The breakthrough opens new avenues for AI research and development, allowing companies and researchers to explore more efficient deep learning models without relying on traditional attention mechanisms, as reported by God of Prompt.