AI compilers face a challenge in balancing usability with the need for programmability and hardware control, particularly for modern GenAI workloads. CUDA C++ offers control but is difficult to use, while Python, the preferred language for AI development, cannot run on GPUs directly. To address this, Embedded Domain-Specific Languages (eDSLs) like Triton have been developed, offering Python-based abstractions that compile into efficient GPU code, providing a more accessible alternative to CUDA. However, eDSLs trade off some performance for ease of use, often lacking the full capabilities of CUDA and facing challenges in debugging and tooling. Triton, developed by OpenAI, is notable for its integration with PyTorch and its focus on simplifying GPU programming, but it struggles with governance, limited hardware support, and is not widely adopted for AI inference tasks. Other Python eDSLs, such as Google's Pallas and NVIDIA's CUTLASS Python and cuTile, explore varied trade-offs, but the fragmented ecosystem indicates a need for more unified solutions like the MLIR compiler framework to address scalability and flexibility in AI development.