I'm using it in production, and speedups tend to be on the order of 4-5x for my app (the compute-intensive part involves hierarchical agglomerative clustering of documents by text similarity, so it's data/numbers-heavy). Obviously it'll depend on your individual application (and non-CPU-bound tasks won't benefit much), but we switched to PyPy because it showed major improvements in profiling of our app on production data (and we switched around PyPy's 1.9 release, so it's even better now). It's not like everyone's just imagining the speed improvements...
I've just finished writing "High Performance Python" for O'Reilly (due August), we have a chapter on Lessons from the Field and one chap talks about his successful many-machine roll out of a complex production system using PyPy for a 2* overall speed gain. We also cover Numba, Cython, profiling, numpy etc - all the topics you'd expect.
Not disagreeing, but they implied that this benchmark only showed a speed improvement because it's a toy, and that real workloads with real data are usually slower. That hasn't been the case in my experience.
