Well, maybe they are right this time, who knows, but I'm way more skeptical.
nVidia couldn't get their economics at 28nm. RAM cells are now more expensive in 20nm than 28nm. There are lots of other examples.
Remember: the strong form of Moore's law was that transistors get cheaper ever 12-18 months. The original formulation of Moore's law was always about economics, not technology.
The problem is: nobody knows what to do.
You now need clever circuit design and humans who can squeeze another 15-20% out of existing technologies. Unfortunately, all the humans who knew how to do custom VLSI design are dead, retired, or doing something else since custom VLSI was fool's errand for so long (see DEC, IBM, Silicon Graphics, etc.).
Broadwell is a custom-designed part, and it's scaled extremely well from 22nm to 14nm. There have been yield issues going to 14nm, but the biggest hurdles have been solved, with a lot of help from the design team. I guess Intel must have hired all the people you think are dead or retired.
I for one doubt Broadwell is hand-built, hand-routed, full-custom. Which is what your parent was probably talking about, as they mention DEC.
Well, sort of. The ol' DEC guys are still out there, many at semiconductor companies.
Anyway, design is not the biggest lever right now, and it stabilizes last. When architecture & process slow down, design will probably see increased focus as you finally have more time for squeezing out 20% extra speed- and without everything being tossed the next go-round.
Our brain is awesome with a merely few 10's of billions of elements. We sure can do better than what we have with cpus with a few billions of transistors -- the problem must be we don't have a clue of what to do with them. A hint to that is even the most powerful supercomputers with 100's of trillions of transistors still can't do better than our brain in some tasks.
A modern CPU has some 100000 times less transistors than we have synapses, but it's effective frequency is at at least 1000000 faster than synapse firing rates. So on "volume" ratio our CPUs are already more powerful than our brains, we just don't know how to wire those transistors properly.
It may well be that the power of such a neural network is nonlinear to its size, and a tenfold smaller/tenfold faster network is much weaker. But we do already have supercomputers that have a similar number of transistors than our brains have synapses, while still having the millionfold speed advantage.
Our limit is in our [lack of] ability to build brains. If we knew how, then our current silicon tech would be enough to run ubiquitous stronger-than-human AIs - but we don't; and "bruteforcing" a brain emulation without a better understanding is so inefficient that it requires currently unfeasible resources.
None of the possible future processes has any of these constraints (with the exception of perhaps E-beam lithography, but it's not a hard constraint).
If you can build a continuous fabrication line with a process, then no matter how much it costs, the capital-investment eventually diminishes to a tiny fraction of the cost of each chip, compared to consumables.
Current lithography processes were "too expensive" previously. But they've been running continuously since they were built, and so are again a tiny fraction of the cost of an actual processor today.