Kevin Kelly at The Technium posted one of his trademark book length blog posts going into extraordinary detail on the nature of Moore’s Law, and what sort of factors behind it are its driving force.

He starts the post with a fascinating graphic originally created in 1953 as a part of a research report by Air Force Office of Scientific Research plotting the progress of top speed.

The general explanation is that Moore’s Law isn’t naturally occurring physical law, but one of economics and human behavior. It happens, he argues, because Moore’s Law has become a self-fulfilling prophecy.

Here’s where I think Kevin’s otherwise wonderfully written post comes off the rails a little:

Whether Moore’s Law — as the count of transistor density — has one, two, or three decades left to zoom and drive our economy, we can be sure it will peter out as other past trends have by being sublimated into another rising trend. When we reach the limits of miniaturization, and can no longer cram more circuits on one chip, we can just make the chip bigger (that’s Moore’s suggestion!). Carl Anderson of IBM cites three next-generation technologies that are candidates for the next round of exponential growth: piling transistors on top of each other (known as 3-D chips), optical computers, or making existing circuits work faster (accelerators). And then there is parallel processing using many core processors at once, lots of chips connected in parallel.

In other words, maybe we don’t need more and more transistors on one chip. Maybe we need re-arrangements of the bits we have. We may consider ourselves to be a million times cleverer than a monkey, but we don’t have a million times as many genes, or a million times as many neurons. Our gene and neuron count is almost identical with all apes.

It’s an interesting thought, but it disregards the nature of progress and severely limits the observed effects of Moore’s Law strictly to the number of processors that are contained in a chip.  Certainly, when looked at in such precise terms, there is a general limit to what is physically possible with the known scope of what technology allows, but when looked at through the somewhat broader scope of Kurzweil’s Law of Accelerating Returns, it becomes a much different picture.

 
It’s fascinating that Kelly didn’t make the connection beyond the transistor count to successor technologies, since that’s exactly what’s being graphed in the Air Force research – disparate successor technologies.

Ray addressed this very topic in a piece he wrote for his site back in 2001:

We can organize these observations into what I call the law of accelerating returns as follows:

• Evolution applies positive feedback in that the more capablemethods resulting from one stage of evolutionary progress are used to create the next stage. As a result, the
• rate of progress of an evolutionary process increases exponentially over time. Over time, the "order" of the information embedded in theevolutionary process (i.e., the measure of how well the informationfits a purpose, which in evolution is survival) increases.
• A correlate of the above observation is that the "returns" of anevolutionary process (e.g., the speed, cost-effectiveness, or overall "power" of a process) increase exponentially over time.
• In another positive feedback loop, as a particular evolutionary process (e.g., computation) becomes more effective (e.g., cost effective), greater resources are deployed toward the furtherprogress of that process. This results in a second level ofexponential growth (i.e., the rate of exponential growth itself grows exponentially).
• Biological evolution is one such evolutionary process.
• Technological evolution is another such evolutionary process. Indeed, the emergence of the first technology creating speciesresulted in the new evolutionary process of technology. Therefore, technological evolution is an outgrowth of–and a continuation of–biological evolution.
• A specific paradigm (a method or approach to solving a problem, e.g., shrinking transistors on an integrated circuit as an approach to making more powerful computers) provides exponential growth until the method exhausts its potential. When this happens, a paradigm shift (i.e., a fundamental change in the approach) occurs, which enables exponential growth to continue.

Kevin acknowledges Kurzweil later in the post, but only from within the scope of processor speed and transistor growth.

Again, backing out with a bit of a wider view of things, as Kurzweil does, it doesn’t become necessary for us to imagine that we or our machines are better in non quantifiable ways because the machines will continue to become better in quantifiable ones, even if the limits of the nanometer process have been reached.

Advances in quantum computing, cloud computing and parallel processing, power management, DNA computing and nanotech are all providing forked paths for the possible successors to the silicon transistor.

Much of this sounds like a bit of science fiction – we don’t see the latest cup of coffee or sleekest brand of DNA showing up in the pages of Gizmodo these days.

Rest assured, though, that these solutions or something like them will be in our future to keep Moore’s Law on it’s path.