An example of where the GIL gets in Python's way:
In the finance world, companies need to tie their systems into news/data feeds. And I'm not talking just one feed. More like 20-50 different feeds, all with differing formats (various dialects of FIX, etc). Python is nearly ideal for this application: you can build a nice OO framework, but also hack in little ad-hoc chunks as necessary.
Python could *own* this space, save for two little problems.
Problem #1: those 20-50 feeds flow continuously. Traffic is very bursty, and often you have only a small window of time to process a given message.
Problem #2: finance companies are not very tolerant of application failure. Apps & servers need to be (triply) overbuilt to withstand Murphy's Law.
SMP hardware is cheap insurance for those days when messages get delayed upstream for hours on end, then come blasting down the pipe right before market close. And you do want that insurance, or you will be out of a job when Murphy strikes.
It's all about providing a palette of options for scaling. If you need to scale up, you typically need to do it pronto. And if you need to completely re-architect your application, you're out of the game.
FYI, I know a company that was using CPython for exactly this application. They have since switched to a Java core for the app plus JPython for scripting, specifically to get away from the GIL.