Code that has lots of attention is different, certainly, but it's also the exception rather than the rule; the last figure I saw was that 90% of code is internal business applications that are never even made publicly available in any form, much less subject to outside code review or contributions.

> As time spent on the project increases, I suspect that any gain an interpreted language has over an (efficient) compiled one not only gets smaller, but eventually reverses in most cases.

In terms of the limit of an efficient implementation (which certainly something like Python is nowhere near), I've seen it argued both ways; with something like K the argument is that a tiny interpreter that sits in L1 and takes its instructions in a very compact form ends up saving you more memory bandwidth (compared to what you'd have to compile those tiny interpreter instructions into if you wanted them to execute "directly") than it costs.

Plenty of line-of-business systems I've seen, but systems big enough to matter tend not to be public. Bitbucket's cloud and on-prem version are the only case I can think of where you can directly compare something substantial between an implementation known to be written in Python and an implementation that's known to be written in C/C++ (and even then I'm not 100% that that's what they use).