I don't think it's typical. For example, JFT has 350e6 images, and it probably cost ~$35M to hand-label, but Google has paid people far in excess of that to work on image classification.
From Google:
>Of course, the elephant in the room is where can we obtain a dataset that is 300x larger than ImageNet? At Google, we have been continuously working on building such datasets automatically to improve computer vision algorithms. Specifically, we have built an internal dataset of 300M images that are labeled with 18291 categories, which we call JFT-300M. The images are labeled using an algorithm that uses complex mixture of raw web signals, connections between web-pages and user feedback. This results in over one billion labels for the 300M images (a single image can have multiple labels). Of the billion image labels, approximately 375M are selected via an algorithm that aims to maximize label precision of selected images. However, there is still considerable noise in the labels: approximately 20% of the labels for selected images are noisy. Since there is no exhaustive annotation, we have no way to estimate the recall of the labels.
https://ai.googleblog.com/2017/07/revisiting-unreasonable-ef...
Companies nowadays have really nothing to protect to, the higher up the tech chain they are.
TSMC can easily dump all and every of their "family jewels" onto the Internet, and I guarantee that no mainland fab will ever manage to extract any value from that.
P.S. Stay away from the whole M.L. space, it is filled with plain frauds, pump and dumpers, and people seeking to sell their companies upon first opportunity. If you are an established professional, you do yourself a disservice working in that.