The Death of Moore's Law Will Not Kill off Computational Disruption

Exponential increases in computational power generate most of the rapid social change in our time. Some of the changes are largely good. The increase in amount and speed of information promotes the availability of more diverse and expert views on policy and politics. The rise of genomics and personalized medicine can lead to longer and  healthier lives. Even energy production, both of fossil fuels and the greener variety, is boosted by computational power. But computation is also the cause of domestic turbulence, as automation replaces some kinds of jobs, and of danger abroad, as it empowers the organization of non-state terrorist actors.

Moore’s law is thought to encapsulate ongoing computational improvements.  This law, named after Gordon Moore, one of the founders of Intel, is in a reality a prediction of a regularity, i.e. that the number of transistors that can be fitted onto a silicon computer chip doubles every eighteen months to two years.  This week Moore’s law reached  the age of fifty and there are widespread predictions and fears that it will die before sixty, because of the physical impossibility of shrinking transistors further and the expense of  trying to do so.

But the computational revolution has deeper and broader roots than Moore’s law and thus the rate of computational and social change will continue even after its demise and may indeed accelerate.   The technologist Ray Kurzweil shows that Moore’s law is actually part of a more general exponential growth in computation that has been gaining force for over a hundred years. Integrated circuits replaced transistors, which previously replaced vacuum tubes, which in their time had replaced electromechanical methods of computation. Thus, there is reason to suspect that new forms of computation, such as optical computing or carbon nanotube computing, will replace silicon as the paradigm for exponential growth in hardware.

Focusing only on the exponential increase in hardware capability also substantially understates the acceleration of computational capacity. Computational capacity advances with progress in software as well as progress in hardware. A study showed that one kind of computer task also been increased by approximately forty-three thousand times through improvements in software algorithms in the last fifteen years. Like many creative human endeavors, progress in software alternates between breakthroughs and periods of consolidation where gains are less spectacular. But in general it is a force multiplier for the gains in computational hardware.

Gains in connectivity also increase the effective power of computation. The results of the faster and greater collaboration made possible across long distances are reflected in exponential growth in the volume of scientific knowledge which powers innovation.

Thus, I am quite confident that the exponential change in computation will continue. As I have described elsewhere, computation is enveloping more and more spheres of life—from law to education to medicine, increasing the rate of technological and social change. Social change will continue to accelerate and democratic politics may thus have an even bumpier ride.