User data integrity definitely should be a concern. It's also known that regulations is being outpaced, so the cost of being/using frontier products is a double-edged sword for sure.
Map-reduce as a pattern might be on its way back. Hear me out. High localization wins even when coverage is not super great -- just map shards of the corpus and reduce the learnings. Rinse and repeat, do as many rounds of map and reduce to traverse the corpus until converge. This can also work well when the cluster is combined with different agents, they are tasks equally by prompts anyway.