TensorFlow isn't dead. It’s just becoming the COBOL of Machine Learning.

I keep seeing "Should I learn TensorFlow in 2026?" posts, and the answers are always "No, PyTorch won." But looking at the actual enterprise landscape, I think we're missing the point. 1. Research is over: If you look at , PyTorch has essentially flatlined TensorFlow in academia. If you are writing a paper in TF today, you are actively hurting your citation count. 2. The "Zombie" Enterprise: Despite this, 40% of the Fortune 500 job listings I see still demand TensorFlow. Why? Because banks and insurance giants built massive TFX pipelines in 2019 that they refuse to rewrite. My theory: TensorFlow is no longer a tool for innovation; it’s a tool for maintenance. If you want to build cool generative AI, learn PyTorch. If you want a stable, boring paycheck maintaining legacy fraud detection models, learn TensorFlow. If anyone’s trying to make sense of this choice from a practical, enterprise point of view, this breakdown is genuinely helpful: **PyTorch vs TensorFlow** Am I wrong? Is anyone actually starting a greenfield GenAI project in raw TensorFlow today?