This is very true, but the mention of data science is really significant here.
If you're trying to productionize a neural network or something similar, you ideally want to be able to pin down every source of noise to make sure your results are reproducible, so you can evaluate whether a change in your results reflects a change in the data, a change in the code, or just luck from your initial weights - with a big enough network, for all you know it decided that the 10th decimal place of some feature was predictive. I wouldn't be surprised if the author brings up tanh as an example specifically because it's a common activation function.
If you're pinning all your Javascript dependencies, but the native code it dynamically links to gets ripped out from under you, it could completely mislead you about what produces good results. Similarly, if you want to be able to ship a model to run client-side, it would be nice if you could just test it on Node with your Javascript dependencies pinned and be reasonably confident it'll run the same on the browser.
Of course, if you can't do that it's not the end of the world, since you can compensate by doing enough runs, and tests on the target platform, to convince yourself that it's consistent, but it's a lot nicer if things are just as deterministic as possible.