SFI researchers have studied the pace of technology improvements over the last century and found that, rather than improving at a (merely) exponential rate as some have theorized, information technology has improved superexponentially -- which is to say, its progress accelerates.

Their work appeared in Technological Forecasting & Social Change.

Former SFI postdoctoral fellow Bela Nagy, Professor Doyne Farmer, Omidyar Fellow Alumnus Jessika Trancik, and SFI student researcher John Paul Gonzales based their results on a comprehensive Performance Curve Database they created, which collects performance measures of technologies dating to the 19th century.

Using these curves they tracked information processing improvements in three areas: information storage per unit volume, communications bandwidth, and computation speed. In every performance curve the team plotted, a single exponential could be definitively rejected in favor of a superexponential curve.

Gonzales says this research suggests an accelerating rate of improvement in technology, noting that history has shown that when a technology reaches its limit, a new technology that serves the same purpose, but better, often comes along to replace it. Mechanical calculators, for example, were replaced by vacuum tube mainframes, and it is reasonable to assume that some new technology will take the place of the integrated circuits we now use to compute.

If these trends continue, he says, “in some ways things will continue to get better. In that sense it is a hopeful paper.”

The Performance Curve Database, which tracks performance for many and widely disparate technologies, is open and available to the public here

Read the paper in Technological Forecasting & Social Change (October 2011, subscription required)

Read the SFI working paper (2011)

Read more SFI research news in the SFI Update newsletter (January-February issue)

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