Law of Accelerating Returns

 

In today's world, there is a sense that the life we are experiencing appears to be moving at a faster pace as each subsequent year passes.  The acceleration of one's life is actually occurring; it is not a mirage and has been predicted.  The predictor of the acceleration of life and the world as we know it is Raymond Kurzweil.  Raymond Kurzweil, in his 1999 book The Age of Spiritual Machines, predicted/postulated "The Law of Accelerating Returns."  He wrote in 2001 an essay titled "The Law of Accelerating Returns," in which Raymond Kurzweil further elaborated on his prediction through the example/lens of Moore's law.  In a nutshell, the law of accelerating returns is where progress speeds up over time in an exponential manner because of the common force of evolution driving it forward, especially concerning information technology (Berman & Dorrier, Technology Feels Like It’s Accelerating – Because It Actually Is, 2016).  The specifics of Raymond Kurzweil's "Law of Accelerating Returns" is as follows:

Evolution applies positive feedback in that the more capable methods 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 the evolutionary process (i.e., the measure of how well the information fits a purpose, which in evolution is survival) increases.

A correlate of the above observation is that the "returns" of an evolutionary 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 further progress of that process. This results in a second level of exponential 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 species resulted 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 (Kurzweil, 2004, p. 3).

The forecasting and prediction of the "Law of Accelerating Returns" in an innovation context is the already mentioned example of Moore's law, which will be some more, human genome and nanotechnology. Moore's law only refers to integrated circuits' exponential price-performance improvements (Diamandis, 2016). Moore's law also only describes the latest period of computational exponential growth (5^th paradigm).  Exponential growth in computation has traversed over a century with five different paradigms:  1^st paradigm was electromechanical computers, 2^nd paradigm was relay-based computers, 3^rd paradigm was vacuum-tube based computers, 4^th paradigm was transistor-based computers, and the 5^th paradigm is integrated circuits (Moore's Law) (Diamandis, 2016).  Raymond Kurzweil states that the sixth paradigm is already upon us, which employs three dimensions (Kurzweil, 2004).  The fifth paradigm, integrated circuits (Moore's Law), consists of flat chips, moving beyond the fifth paradigm, shifting to three dimensions like how the brain is organized.  The use of nanotubes where circuits are built from pentagonal arrays of carbon atoms is in place.  The law of accelerating returns is shown in computing – optical computing, crystalline computing, DNA computing, quantum computing, and three-dimensional silicon chips, and all will allow the continual progression of the law to move into the future (Kurzweil, 2004). 

Computing trends in an exponential fashion and is composed of an array of sequential S-shaped technological life cycles, called S-curves (Berman, Dorrier, & Hill, How to Think Exponentially and Better Predict the Future, 2016).  The S-curves form is because of the three growth phases each curve depicts; slow growth initially leads to explosive growth and leveling off as technology matures.  The S-curves overlap each other; when one technology levels off, another takes over and speeds up, resulting in each successive S-curve achieving higher degrees of performance in a shorter amount of time (Berman, Dorrier, & Hill, How to Think Exponentially and Better Predict the Future, 2016).  At this time, people typically have thought locally and linearly that the world evolves in an iterate fashion like climbing stairs.  Linear growth results from repeatedly adding a constant, whereas exponential growth is the repeated multiplication of a constant (Berman, Dorrier, & Hill, How to Think Exponentially and Better Predict the Future, 2016).  When viewing an exponential curve for a short time period, it approximates a straight line; however, the exponential curve reveals itself when the time period is expansive (Kurzweil, 2004).  With people's propensity (social forces) to see the future, they intuitively assume that the current rate of progress will continue into the future, which is a misnomer.  Raymond Kurzweil (2004) calls this line of thinking an "intuitive linear" view, whereas a "historical exponential view" is more appropriate with the Law of Accelerating Returns that he predicted now becoming a reality.

The propensity for advances to feed on themselves, augmenting the pace of further advancement, and exceeding what would be calculated for a typical project linearly is demonstrated by the human genome project.  The human genome project initially took many years to complete 1% of it, resulting in decades' expectations to complete. Instead, it took only seven years to complete due to improved methods (Buchanan, 2008).

Watts & Porter (1997) postulate that innovation forecasting and prediction rely on a multitude of variables.  Characteristics of the subject under research, fit between the subject and firm/persons, the firm/person knowledge of the market and infrastructure of the subject under study, market forces, economic climate and resource obligations, other socio-economic factors, and corporate actions or interactions. 

 

 

References

Berman, A., & Dorrier, J. (2016, March 22). Technology Feels Like It’s Accelerating – Because It Actually Is. Retrieved from SingularityHub: https://singularityhub.com/2016/03/22/technology-feels-like-its-accelerating-because-it-actually-is/

Berman, A., Dorrier, J., & Hill, D. (2016, April 5). How to Think Exponentially and Better Predict the Future. Retrieved from SingularityHub: https://singularityhub.com/2016/04/05/how-to-think-exponentially-and-better-predict-the-future/

Buchanan, M. (2008). The law of accelerating returns. Nature Physics, 4(7), 507-507. doi:10.1038/nphys1010

Diamandis, P. (2016, January 1). WHY TECH IS ACCELERATING. Retrieved from Diamandis: https://www.diamandis.com/blog/why-tech-is-accelerating

Kurzweil, R. (2004). The law of accelerating returns. In Alan Turing: Life and legacy of a great thinker (pp. 381-416): Springer.

Watts, R. J., & Porter, A. L. (1997). Innovation forecasting. Technological Forecasting and Social Change, 56(1), 25-47.