The decision to prewarm is ultimately a product decision to give users a better experience across the many surfaces where they encounter file previews. There are limitations to how fast previews can be generated on the fly, even under optimal conditions (unlimited top-of-the-line hardware, max of 1 request at a time). For instance, optimal on-the-fly preview generation for more expensive files (say, a video that is an hour long or a gigapixel image) can add 10s of seconds to tti -- not good from a user perspective!
Given this constraint, we wanted to optimize where we spend on the preview generation infrastructure without negatively impacting the user experience. We chose to do this with a combination of heuristics and the ML solution described in the article.
No, often it's black. You have to do scene-detection if you want meaningful previews/thumbnails, can't always go random about it.
And the original files are stored in HDD and not SSD/RAM.
Source: I don't work there.