TensorFlow was an overengineered Google-style mess and they constantly made breaking changes.

All the graph building and session running was way too complex, with too much global state and variable sharing was complicated and based on naming and variable scopes and name scopes and so on.

It was an okay try, but that design simply didn't work so well for quick prototyping, iterating, debugging that's crucial in research.

PyTorch was much closer to just writing straightforward numpy code. TensorFlow 2 then tried to catch up with "eager mode", but in the background it was still a graph and tracing often broke and you had to write the code very carefully and with limitations.

In the end, Pytorch also developed proper production and serving tools as well as graph compilation, so now there's basically no reason to go to TensorFlow. Not even Google researchers use it (they use jax). I guess some industries still use it but at some point I expect Google to shut down TF and focus on the JAX ecosystem with some kind of conversion tools for TF.

> But then again, TensorFlow's got its enterprise backing, and I can't help but think about the implications of that. How long can PyTorch ride this wave before it runs into pressure from industry demands?

PyTorch has a huge collection of companies, organizations and other entities backing it, it's not gonna suddenly disappear soon, that much is clear. Take a look at https://pytorch.org/foundation/ for a sample

The thing about Tensorflow in 2017 is that everyone acknowledged how difficult it was to use. While it was almost the only game in town, no one was happy. Those are probably the areas where an upstart can come in and disrupt.