This isn't surprising to me at all, since these systems are only "developed independently" in a very narrow sense. Each company may have it's own implementation (Tensorflow vs. Torch, etc) but they are implementations of nearly identical underlying models, namely deep convolutional neural networks. We shouldn't be surprised that they all agree since they are all the same under the hood, probably even down to the level of choice of hyperparameters (stride length, filter size, etc) since there is plenty of research on what kind of deep net works best on particular applications.

That would be convenient, but even when the machine learning models are implemented differently, as long as they accomplish the same task, the attacks often transfer from one to another. Papernot et al.'s latest research suggests that it is the space of the embeddings within a network, rather than the weights and nodes themselves, which define the effectiveness of adversarial examples. Models which accomplish the same tasks often have spaces that overlap, and that overlap multiplies by dimensions, if I read it right.

Everyone is using the imagenet database, so this outcome is less surprising than it may seem.

Google and Facebook are also using their own huge datasets