CUDA's dominance in the GPU computing landscape is attributed to NVIDIA's strategic, long-term platform thinking, which integrated technical excellence with ecosystem lock-in and deep strategic investments. By keeping GPUs compatible across generations, NVIDIA allowed developers to build on existing hardware, lowering barriers to entry and creating a network effect that expanded CUDA's reach beyond gaming into fields like scientific computing, AI, and high-performance computing. The explosion of deep learning further cemented CUDA's position as the default compute backend, with frameworks like PyTorch and TensorFlow optimized for NVIDIA's hardware, reinforcing its lock-in. The surge in demand for AI compute, driven by generative AI breakthroughs, has further solidified NVIDIA's position, as companies are compelled to optimize for CUDA to remain competitive. However, while CUDA's grip on AI compute tightens, questions arise about whether this dominance truly benefits the AI research community or primarily serves NVIDIA's interests.