The techniques they used in the post actually try to overcome the lack of labeled data. (by using semi-supervised techniques/label propagation)

You are right that Google has a lot more raw data, but that is much cheaper to obtain, and depending on your problem domain, there could be a bunch of public datasets that are available for you to cobble together.

Some of these big tech companies don't necessarily have the right training data for their applications either. For example, Adam Coates from Baidu outlined some of the ways that they generated a better training set for their speech recognition use case here: https://youtu.be/g-sndkf7mCs?t=3817