This post discusses CUDA programming targeted at Python developers, exploring the necessity of understanding the mathematical foundations of AI/ML for effective utilization of CUDA in applications. Readers express doubts about whether they can prepare for AI/ML roles without a strong math background, while also discussing the readability and efficiency of CUDA implementations compared to frameworks like PyTorch. Moreover, there is a curiosity about how some companies can enhance efficiency beyond traditional CUDA capabilities, hinting at ongoing improvements in the CUDA library despite its long developmental history. The conversation touches on job market requirements in MLE/AI Data Engineering without a thorough knowledge of AI/ML, pointing to a trend where familiarity with such technologies is heavily prioritized in job descriptions.