The reason that Perl hasn't become a bigger contender in the field of AI programming is because there's no one in the ring willing to give it a chance.

I'd go further, and say that the reason AI hasn't become a bigger contender in any field is that there are no compelling AI applications. Sure, machine learning has been making some advances in recent years (SVMs and boosting come to mind). But:

1. The advances are precisely in the "non-AI" fields -- the more "statistical" the classification problem, the better the performance of the methods.
2. Humans do much better, except when the sheer volume of data overwhelms them. (So the chief advantage of modern ML is in simple data processing -- not in displaying any intelligence).
3. ML seems to work initially due to the "80%-20%" problem: it is quite easy to solve 80% of any given classification problem (this is one of the reasons why so many classification techniques give such similar results). But this does nothing to help solve the remaining 20% of the problem.

   For instance, classifying 80% of webpages is easy (even if you discard the 97% p0rn, 80% of the remaining 3% is easy to achieve). Google does a fair job, and on smaller collections researchers can do much better. But all these systems make huge, embarrassing mistakes.

After around 50 years, I think AI people could at least show applications rather than just "ideas".