Even if you solely consider moore's law (even if it has slowed down slightly)the post's title will hold true
http://agtb.wordpress.com/2010/12/23/progress-in-algorithms-...
PS: Not to mention architecture specific improvements such as ever increasing L3 cache sizes that make may algorithms a lot faster on today's hardware without showing similar speedups on hardware that's 10 years old.
The assumption here is that at any given time, you're reading the latest published papers and implementing the best known algorithms, in a field that uses complicated algorithms on big problems.
It's not just because of faster chips that Google has a self-driving car now.
(As for sorting, it's been proven that the best you can do is O(n log n), and John von Neumann achieved that with mergesort in 1945.)
PS: Also Radix sorting is O(n) operation under conditions where mergesort is O (n log n). (AKA in practice merge sorting distinct strings is 0 (n * (log n) * (log n) as you need to look at an ever increasing number of symbols, or if your strings have a fixed max length or if your theoretical computer can compare infinitely long strings in a single step then Radix sorting is also O(n).)
You're right that it's difficult to measure algorithm progress, but using problem sizes that you're interested in running in practice is probably a decent heuristic. "I wanted to run this problem but it would have taken 80 years. But then I caught up on recent published algorithms and look, I can do it in an hour on the same machine."
I don't think I claimed the progress was steady. Then again, Moore's Law might get more jumpy as silicon wafers reach their limits, and we transition to memristors, spin devices, or whatever else they come up with.