Yup - long running and very repetitive processes are the best fit for PyPy. If you have a slow but short-lived process then PyPy is not going to improve things for you.
This is exactly why PyPy blew both Cannoli and CPython away in the microbenchmarks used for analysis. As I've said elsewhere, the focus was on comparing Cannoli (unoptimized) to Cannoli (optimized) and not a direct comparison to CPython or PyPy. However, the microbenchmarks were running iterations of 1-10 million, giving the JIT plenty of time to find beneficial traces in the PyPy interpreter.
BTW, the test I did where PyPy was slower than CPython ran for a minute or so (IIRC). It wasn't that long lived, but it wasn't like the "instant" invocation you often see with shell scripts either.
I don't think the JIT warmup was the main issue there; I think it was PyPy's lack of ability to optimize certain kinds of code combined with increased memory usage.