Numerical computing is important for science in general, and really hard. But if you're perfectly fine with the current state of software, then sure, there's no need to change anything. Future CPUs will gain an increasing amount of their performance from parallelism - both coarse- (more cores) and fine-grained (better vector units), but there is no reason we have to exploit it. I suspect many people will want to, however, and they will likely find current programming models somewhat awkward.

It depends what you're optimizing for. If it's for programmer time / inexpensive programmers, sure. But remember the (probably apocryphal) story of Jobs complaining that the Mac took too long to boot, costing human lifetimes worth of waiting per year. Look at all the slow web pages, how slow Word or Excel are, etc -- that's a lot of human lifetimes.

More relevant is power. The more wasted cycles the shorter your battery life. The hotter that device implanted in you becomes. The worse the cooling problem in the datacenter.

It's naive to think that "computers are fast enough".

Progress on Moore's law has always created new markets that weren't predicted, or seemed unlikely/silly.

There are a lot of computational tasks that can not run on a smartphone today. It'd be great to run an IDE, a few baclground classification tasks, and a high quality augmented reality VR headset app on a device with an 8 hour battery under that load and fits in my pocket.

That's not happening without lots of hardware customization.