If you take a look at some of the popular machine learning models written in the last few years (YOLOv5, Stable Diffusion), they've been written in PyTorch, not TensorFlow.

I remember when TensorFlow was released in 2015. Kubernetes was released around the same time (part of Google's reasoning for open-sourcing both was to not make the same mistakes they did with Hadoop/Map Reduce – see Diseconomies of Scale at Google). It was a time when many of the deep learning models (Inception, ResNet, other CNNs, and DNNs) were built with TensorFlow, and the industry rallied around the framework. Facebook released PyTorch a year later.

Since then, PyTorch seems to be growing faster than TensorFlow.  

Why did PyTorch seem to win?

Why might TensorFlow still win?