Eh, when we have trillion dollar companies whose core product is AI commonly used for question/answer tasks, and query accuracy directly impacts the bottom line

Eh, when we have trillion dollar companies whose core product is AI commonly used for question/answer tasks, and query accuracy directly impacts the bottom line, I don't think spending 10 billion a year on annotation is a big ask. It might not be perfect, but as long as your query responses are significantly more accurate than the competition you'll end up as Google to their Altavista. Additionally, you don't have to manually curate the entire data set, only a large enough chunk to predict accuracy annotations for the stuff you haven't curated yet. Facts that change over time shouldn't be hard to identify if you periodically do cross validation on existing annotations to pick out ones where the predicted accuracy differs significantly from the annotation. There are a lot of process and engineering questions still to be answered in terms of building high quality AI products, but I don't think there are hidden dragons that are going to lead to another AI winter. There will be hiccups and snafus, but AI is the real deal.