Peter Norvig, the author of one of the standard textbooks on AI, and now at Google, has this to say
I came to Python not because I thought it was a better/acceptable/pragmatic Lisp, but because it was better pseudocode. Several students claimed that they had a hard time mapping from the pseudocode in my AI textbook to the Lisp code that Russell and I had online. So I looked for the language that was most like our pseudocode, and found that Python was the best match. Then I had to teach myself enough Python to implement the examples from the textbook. I found that Python was very nice for certain types of small problems, and had the libraries I needed to integrate with lots of other stuff, at Google and elsewhere on the net.
I think Lisp still has an edge for larger projects and for applications where the speed of the compiled code is important. But Python has the edge (with a large number of students) when the main goal is communication, not programming per se.
In terms of programming-in-the-large, at Google and elsewhere, I think that language choice is not as important as all the other choices: if you have the right overall architecture, the right team of programmers, the right development process that allows for rapid development with continuous improvement, then many languages will work for you; if you don't have those things you're in trouble regardless of your language choice.