It's somewhat disappointing that research is the primary motivator for the switch. PyTorch still has a ways to go in tooling for toy usage of models and deployment of models to production compared to TensorFlow (incidentally, GPT-2, the most public of OpenAI's released models, uses TensorFlow 1.X as a base). For AI newbies, I've seen people recommend PyTorch over TensorFlow just because "all the big players are using it," without listing the caveats.
The future of AI research will likely be interoperability between multiple frameworks to support both needs (e.g. HuggingFace Transformers which started as PyTorch-only but now also supports TF 2.X with relative feature parity).
The future of AI research will likely be interoperability between multiple frameworks to support both needs (e.g. HuggingFace Transformers which started as PyTorch-only but now also supports TF 2.X with relative feature parity).