Linear compute is the ideal solution. Parallelization is a useful tool when we run up against the limitations of linear compute, but it is not ideal. Parallelization is simply not an option for some tasks. Nine mothers cannot make a baby in a month. It also adds overhead, regardless of the context.
Take businesses for example. Businesses don't want to hire employees to get the job done. They want as few workers as possible because each one comes with overhead. There's a good reason why startups can accomplish many tasks at a fraction of what it would cost a megacorp. Hiring, management, training, HR, etc... they are all costs a business has to swallow in order to hire more employees (ie parallelize).
This is not to say parallelization is bad. Given our current technological limitations, adding more cores and embracing parallelization where possible is the most economical solution. That doesn't faster linear compute is a "nice to have".
Take businesses for example. Businesses don't want to hire employees to get the job done. They want as few workers as possible because each one comes with overhead. There's a good reason why startups can accomplish many tasks at a fraction of what it would cost a megacorp. Hiring, management, training, HR, etc... they are all costs a business has to swallow in order to hire more employees (ie parallelize).
This is not to say parallelization is bad. Given our current technological limitations, adding more cores and embracing parallelization where possible is the most economical solution. That doesn't faster linear compute is a "nice to have".