Over at an online publication called AI Game Dev, there is an elucidating post on how to do multithreading of game AI code (posted in June 2007). Basically, the conclusion is that most of the CPU time in an AI system is spent doing collision detection, path finding, and animation. This focus of time in a few domain-given hot spots turns the problem of parallelizing the AI into one of parallelizing some core supporting algorithms, rather than trying to parallelize the actual decision making itself. The key to achieving this is to make the decision-making part able to work asynchronously with the other algorithms, which is not trivial but still much easier than threading the decision making itself. The threading of the most time-consuming parts turns into classic algorithm parallelization, which is more familiar and easier to do than threading general-purpose large code bases. A good read, basically, that taught me some more about parallelization in the games world.