According to Greg Brockman (@gdb), recent advancements in AI are heavily powered by optimized matrix multiplications (matmuls), which serve as the computational foundation for deep learning models and neural networks (source: Twitter, August 28, 2025). By leveraging efficient matmuls, AI models such as large language models (LLMs) and generative AI systems achieve faster training times and improved inference capabilities. This trend is opening new business opportunities in AI hardware acceleration, cloud computing, and enterprise AI adoption, as companies seek to optimize large-scale deployments for competitive advantage (source: Twitter, @gdb).

According to @OpenAI, a new AI model has achieved state-of-the-art results compared to other models without tool use, excelling in LiveCodeBench V6—a benchmark that rigorously tests competitive code generation—and Humanity’s Last Exam, which assesses model expertise across challenging domains such as science and mathematics. This performance demonstrates significant advancements in AI’s ability to solve complex, real-world problems without external tool assistance, highlighting new opportunities for deploying AI in enterprise coding, education, and technical domains (source: OpenAI, 2024).