AI software development faces significant challenges due to the complexity and fragmentation of current infrastructure, with platforms like TensorFlow, PyTorch, and CUDA being less modular and harder to scale. This has resulted in a fragmented AI deployment industry, dominated by large tech companies with proprietary toolchains that complicate innovation and accessibility. The article draws a parallel to the 1990s software industry, which overcame similar fragmentation through the rise of GCC and later, the modular design of LLVM, which enabled broad innovation. The future of AI demands a similar shift towards modular, composable infrastructure that is multi-framework, multi-cloud, and multi-hardware, allowing for a more accessible, scalable, and efficient system. This new approach should integrate existing technologies without requiring complete rewrites, aiming to democratize AI development and maximize its potential impact across various fields. The vision is to create a world where AI is more usable and accessible, enabling developers to focus on solving real-world problems rather than grappling with fragmented tools and systems.