They are teaching us how to solve problems with hardware instead with algorithms, and we, as individuals, will never have access to their computing power.
I work in the field, and the way it works is like this, in this order:
1) Someone works out how to do something
2) Someone works out how to improve the accuracy
3) The accuracy maxes out
4) People improve training efficiency.
We see this over and over again. Take a look at the FastAI results on ImageNet training speed for example.
All of this is within reach of the average developer. If there's any monopoly, it's over training data, which Google and Amazon happen to specialize in. But even large datasets exist as open source.
From what I can tell, machine learning, the precursor to AI, is here, and both knowlege and implementation are fully accessable to the general populace.
First we built awesome high power single core chips, then multi core chips and continued improving performance per dollar.
A single GPU in 10 years might be able to smoke these 512 GPUs.
Firstly, it will not, because there are physical limitations to silicon-based computing and I don't think we will get a different kind in the next 10-20 years. Secondly, unlike commodity programming, AI is an area where improving algorithm/learning efficiency is infinitely more valuable than figuring out how to throw more hardware at the problem. For starters, any algorithmic improvement makes all further research and experimentation easier for everybody as opposed to a handful of agents who have access to ridiculously powerful hardware. I can list many other reasons, like global energy consumption and the need for learning in embedded devices.
I find it highly annoying that deep learning enthusiasts immediately turn into uber-skeptics whenever the conversation touches on other ML approaches. Makes me wonder how difficult it is to get funding for fundamentally new AI research in this climate.
Improving algorithm/learning efficiency gets much easier when you can iterate faster.
The physical limitations may be reached, but we'll just throw more cores unto the PCB to compensate.