Python did not become the dominant AI language because it was the fastest or the most powerful. It became dominant because it was the most practical. That distinction matters a lot if you work in this space. Today, almost every major AI framework - TensorFlow, PyTorch, scikit-learn, Hugging Face Transformers, ships Python as its primary interface. When researchers publish new models, the code is in Python. When companies build AI pipelines, they reach for Python. When developers want to run a model locally in five minutes, Python makes that possible. This didn't happen overnight. It's worth understanding how it happened, and more importantly, what it means for professionals building with AI today. How Python Got Here Python has been around since 1991. For most of its early life, it was a general-purpose scripting language used for automation, web development, and system tasks. It was never designed for scientific computing. That changed in the mid-2000s when the scientific community started adopting it.…