When running a batched migration it is important to batch using a strictly monotonic field so that new rows wont get inserted in already processed range
For me the bigger thing is the randomness. A uid being random for a given row means the opposite is true; any given index entry points to a completely random heap entry.
When backfilling this leads to massive write amplification. Consider a table with rows taking up 40 bytes, so roughly 200 entries per page. If I backfill 1k rows sorted by the id then under normal circumstances I'd expect to update 6-7 pages which is ~50kiB of heap writes.
Whereas if I do that sort of backfill with a uid then I'd expect to encounter each page on a separate row. That means 1k rows backfilled is going to be around 8MB of writes to the heap.
We do use a custom uuid generator that uses the timestamp as a prefix that rotates on a medium term scale. That ensures we get some degree of clustering for records based on insertion time, but you can't go backwards to figure out the actual time. It's still a problem when backfilling and is more about helping with live reads.
This is problematic if you try to depend on the ordering. Nothing is stopping some batch process that started an hour ago from committing a value 100k lower than where you thought the sequence was at. That's an extreme example but the consideration is the same when dealing with millisecond timeframes.