This is definitely true, and marks a strong detachment of asymptotic analysis and theoretical computation models from real-world computers, which are just too complex to reason about them abstractly.
In theoretical computer science, data structures like this are mostly theorems, proving what you can do in a given computation model. This doesn't mean that they have desirable properties in real-world implementations, compared to classical algorithms such as hash-tables, binary trees, tries, ...
However, they are not completely useless in practice, as they usually bring up new ideas that, if properly engineered, can improve on "classical" algorithms. I've been working in this field for the last couple of years and I've observed that in certain niche applications advanced algorithms can really be game-changers (think computational molecular biology).
For example I recently used succinct data structures to engineer a compressed trie that is fairly fast and has very good compression. For example if you have a big set of URLs you can compress it to a size around 12% of the original data and still do searches and retrievals in matter of microseconds. (paper http://siam.omnibooksonline.com/2012ALENEX/data/papers/018.p... , code https://github.com/ot/path_decomposed_tries ) [/shameless plug]