The benefits of parallelization are highly depended on the task, but there are quite a lot of tasks that are very amenable to it. It’s difficult to rewrite a system from the ground up to take advantage of parallelization, but systems are designed with it in mind from the beginning, they can simply be scaled up as a larger number of processors becomes economically feasible. For quite a few algorithms, setting up parallelization is quite easy. Creating a new bitcoin block, for instance, is already a highly parallel task. As far as society-changing applications, there’s a wide variety of tasks that are very susceptible to parallelization. Certainly, human-level intelligence does not appear to require huge serial power; human neurons have a firing rate in at most a few hundred hertz. Self-driving cars, wearable computers, drones, database integration … I don’t see a need for super-fast processors for any of these.
The benefits of parallelization are highly depended on the task, but there are quite a lot of tasks that are very amenable to it. It’s difficult to rewrite a system from the ground up to take advantage of parallelization, but systems are designed with it in mind from the beginning, they can simply be scaled up as a larger number of processors becomes economically feasible. For quite a few algorithms, setting up parallelization is quite easy. Creating a new bitcoin block, for instance, is already a highly parallel task. As far as society-changing applications, there’s a wide variety of tasks that are very susceptible to parallelization. Certainly, human-level intelligence does not appear to require huge serial power; human neurons have a firing rate in at most a few hundred hertz. Self-driving cars, wearable computers, drones, database integration … I don’t see a need for super-fast processors for any of these.