>I would argue that this is not the fault of Tensorflow, but rather the hazard of being the first implementation in an extremely complex space. Seems like usually there needs to be some sacrificial lamb in software domains.

I'd agree if Google didn't have a history of building things with (arguably) unnecessarily complex APIs, like Angular1. I remember when Angular and React were new, seeing an Angular "cheatsheat" that was around 14 pages; the equivalent React cheatsheet was only 2 pages. Now, I do love the idea of Tensorflow 1, essentially a functional DSL to explicitly construct computation graphs, but Google's implementation of that idea was suboptimal: hard-to-follow error messages, not intuitive, multiple APIs to do the same thing (and continual API breakage as new APIs are introduced), difficult to debug. And even if the graph "compiled" correctly, it could still fail at execution time.

It's like they were building a programming language but lacked anyone with language design or PL theory background. Which makes sense given that anyone passionate about language design might prefer to work for somewhere closer to the cutting edge like Microsoft (C#, F#, Typescript, F*...), Facebook (Bucklescript, Hack), Apple (Swift) or Mozilla (Rust). Google does have its own languages, Dart and Go, but they're notable for ignoring and rejecting respectively cutting-edge PL theory (e.g. not disallowing null pointers, and in Go's case not even supporting parametric polymorphism). The day-to-day languages used at Google are also not particularly appealing to a PL enthusiast: Python, Java and non-modern C++.

Google software often also seems to care more about enforcing their idea of "best practices" on the user than about user experience. Tensorflow's C++ support is an example of this: it requires using Babel. As Babel doesn't easily support integration into an existing C++ project, this essentially means you have to change your whole project over to Babel just to use Tensorflow C++, which is a huge amount of effort to go to just to use a library. Especially when it's probably quicker to just rewrite the model in PyTorch, which provides a simple header file and static library for linking, the standard way of distributing C++ libraries. PyTorch also provides a nicer C++ API, because it's not confined to the ancient C++ standards that Google enforces, so can provide a modern API that's not full of macros (macros in modern C++ are considered bad practice; should only be used when there's absolutely no alternative).

To be fair, Google is really good at engineering language runtimes. Dart, Go and Tensorflow are all impressive works of engineering. They just seem to lack the organisational DNA for making them really nice to use, which maybe makes sense given the main source of their revenues is search/AdWords, the success of which is primarily driven by superior science/engineering. Compared to e.g. Facebook and Microsoft, that were/are in the business of making pretty things that users like to use (operating system, word processor, website). Or even comparing to Apple: people pay a huge premium for Apple phones over Android phones, in spite of their worse hardware, because of their more appealing design.