Current theorem provers don't have a "sense of direction".

From the description of Polya's Mathematics and Plausible Reasoning: Vol. II: Patterns of Plausible Inference:

This is a guide to the practical art of plausible reasoning, particularly in mathematics but also in every field of human activity. Using mathematics as the example par excellence, Professor Polya shows how even that most rigorous deductive discipline is heavily dependent on techniques of guessing, inductive reasoning, and reasoning by analogy. In solving a problem, the answer must be guessed at before a proof can even begin, and guesses are usually made from a knowledge of facts, experience, and hunches. The truly creative mathematician must be a good guesser first and a good prover afterward; many important theorems have been guessed but not proved until much later. In the same way, solutions to problems can be guessed, and a good guesser is much more likely to find a correct solution...

Current theorem provers search for a proof, and even contain some ad hoc heuristics for exploring their search trees. (For example, Paulson's book ML for the working programmer ends with a chapter on theorem proving and offers "We have a general heuristic: never apply universal:left or existential:right to a goal if a different rule can usefully be applied.) However, Polya's notion of being a good guesser has been formalised as Bayesian reasoning. If a theorem prover, facing a fork in its search tree, uses Bayes Theorem to pick the most likely branch first, it may find much deeper results.

I don't think there is currently much overlap between those working on theorem proving and those working on Bayesian statistics. There is perhaps even a clash of temperaments between those who seek absolute certainty and those who seek an edge in the messy process of muddling through. Nevertheless I foresee great possibilities of using Bayesian reasoning to guide the internal search of theorem provers, thus giving them a sense of direction.

Reasoning "X hasn't been progressing recently, therefore X is unlikely to progress in the near future." is good reasoning if that is really all that we know. But it is also weak reasoning which we should be quick to discard if we see a relevant change looming. The next ten or twenty years might see the incorporation of Bayesian reasoning as a guiding principle for tree search heuristics inside theorem provers and lead to large advances.