My recollection from PC CPUs is that we've gotten many more operations per second, and many more operations per second per dollar, but that the power and corresponding cooling requirements for the CPUs have tended to go up as well. I don't really know what power per operation has looked like there. (I guess it's clearly improved, though, because it seems like the power consumption of a desktop PC has only increased by a single order of magnitude, while the computational capacity has increased by more than that.)
A reason that I wonder about this in this context is that people are saying that the power and cooling requirements for these devices are currently enormous (by individual or hobbyist standards, not by data center standards!). If we imagine a Moore's Law-style improvement where the hardware itself becomes 1/10 or 1/100 of its current price, would we expect the overall power consumption to be similarly reduced, or to remain closer to its current levels?
More seriously, even 16GB was essentially the 'norm' in consumer PCs about 15 years ago.
Cost reduction for cutting-edge products in the semiconductor industry has historically been driven by 1) reducing transistor size (by following the Dennard scaling laws), and 2) a variety of techniques (e.g. high-k dielectrics and strained silicon, or FinFETs and now GAAFETs) to improve transistor performance further. These techniques added more steps during manufacturing, but they were inexpensive enough that they allowed to reduce $/transistor still. In the last few years, we've had to pull off ever more expensive tricks which stopped the $/transistor progress. This is why the phrase "Moore's law is dead" has been circulating for a while.
In any case, higher performance transistors means that you can get the same functionality for less power and a smaller area, meaning that iso-functionality chips are cheaper to build in bulk. This is especially true for older nodes, e.g. look at the absurdly low price of most microcontrollers.
On the other hand, $/wafer is mostly a volume-related metric based on less scalable technology and more conventional manufacturing (relatively speaking). Cerebra's innovation was in making a wafer-scale chip possible, which is conventionally hard due to unavoidable manufacturing defects. But crucially, such a product (by definition) cannot scale like any other circuit produced so far.
It may for sure drop in price in the future, especially once it gets obsolete. But I don't expect it to ever reach consumer level prices.
The main reason why Cerebras has succeeded and the previous attempts have failed is not technical, but the existence of market demand.
Before ML/AI training and inference, there has been no application where wafer-scale chips could provide enough additional performance to make their high cost worthwhile.
By the way, I am a software developer, so you will not see me challenging their patent. I am just curious.