In the evolution of AI hardware, early GPU coding was manageable but became untenable as deep learning models grew in complexity and size, necessitating the development of AI compilers like TVM and OpenXLA to automate and optimize GPU code generation. TVM, originating from an academic project, aimed to optimize AI models across various hardware by applying techniques like kernel fusion, but struggled to keep pace with modern hardware advancements and evolving AI needs, leading to fragmentation and underperformance. Similarly, Google's XLA was developed to enhance TPU performance but faced challenges with flexibility and hardware integration, limiting its broader adoption despite its success within Google. Both projects highlighted the difficulty in balancing extensibility and control over hardware with dynamic AI development needs. Meanwhile, new approaches like Triton are emerging, trying to bridge the gap between CUDA's capabilities and user-friendly programming, yet the dominance of CUDA persists, underscoring the complexities of advancing AI compiler technology.