E.g. a GiST equivalent (for e.g. spatial indexes) would be a hassle to maintain due to its nature of having no precise knowledge about the location of each index tuple, GIN (e.g. FTS indexing) could be extremely bulky due to a lack of compressibility in posting trees, and I can't imagine how they'd implement an equivalent to BRIN (which allows for quickly eliminating huge portions of a physical table from a query result if they contain no interesting data), given their use of index-organized tables. Sure, you can partition on PK ranges instead of block ranges, but value density in a primary key can vary wildly over both time and value range.
Does the author have any info on how they plan to implement these more complex (but extremely useful) index methods?
This doesn't even consider the issues that might appear if the ordering rules (collation) change. Postgres' heap and vacuuming is ordering-unaware, meaning you can often fix corruption caused by collation changes by removing and reinserting the rows that are in the wrong location after the collation changed, with vacuum eventually getting rid of the broken tuples. I'm not sure Oriole can do that, as it won't be able to find the original tuple that it needed to remove with point lookup queries, thus probably requiring a full index rebuild to fix known corruption cases in the index, which sounds like a lot of additional maintenance.