Randal Schwartz recently gave a talk called “Dynamic Returns” dealing with misconceptions about dynamically typed languages (published as [episode 135](http://www.cincomsmalltalk.com/blog/blogView?showComments=true&printTitle=Industry_Misinterpretations_135:_Dynamic_Returns&entry=3419405760) of the Industry Misinterpretations podcast). His audience for the talk is composed of Smalltalk developers, but the points made are general and equally valid for other dynamic languages like IDL, Python, etc (at least in concept). The six myths are that statically typed languages like Java, C++, and C:
1. reduce development cost
2. increase speed of development
3. eliminate need for some tests
4. improve run-time stability
5. scale better
6. are faster
I agree that the first five points are indeed myths, but I’m not sure IDL has the tools for making IDL as fast or faster than a statically typed language. I know Python has a lot more tools in this area: Pyrex, Psyco, ctypes, f2py, and a bunch more. Of course, there are some things in IDL’s favor: the thread pool automatically uses multiple processors for array operations, there are libraries to make [GPU computing](http://www.txcorp.com/products/GPULib/) and [cluster computing](http://www.txcorp.com/products/FastDL/) easier, and a fairly straight-forward way to extend IDL using C when needed.