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 DeepLearning.AI, Google researchers have developed AlphaEvolve, an innovative AI agent that leverages Gemini 2.0 Flash and Pro models to autonomously run, assess, and iteratively edit code until unit test performance improves. Starting with basic placeholder functions, AlphaEvolve's feedback loop generated optimized routines for complex 4x4 matrix multiplication, achieving results that matched or surpassed existing solutions. This advancement demonstrates the practical business impact of AI in automated code optimization, reducing development time and enhancing code reliability for software engineering teams (Source: DeepLearning.AI on Twitter, August 3, 2025).