[D] It’s 2026. Can we finally admit TensorFlow is the "COBOL of Machine Learning"?

We keep telling students to learn both, but let’s look at the actual landscape:

• Research: 95%+ of HuggingFace and arXiv is PyTorch.
• Innovation: Even Google's own researchers are using JAX more than TF.
• DX: Debugging a custom layer in TF still feels like a fever dream compared to PyTorch’s native Pythonic flow.

TF has the "legacy enterprise" crown, but for anything moving at the speed of SOTA, it’s not even a contest anymore. Is there any technical reason to start a greenfield project in TF today, or are we just clinging to it for the TFX pipeline?