Platforms and Artificial Intelligence by Unknown

Author:Unknown
Language: eng
Format: epub
ISBN: 9783030901929
Publisher: Springer International Publishing

2 Artificial Intelligence and the Speed of Technological Progress

A lively debate has arisen in recent years as to whether technological change has accelerated or not. Some observers have stressed the fact that new products and services seem to arrive every year, requiring constant change and adaptation. Means of interaction and communication have shifted significantly and certain relicts from not too long ago (a stamp, a letter, a post office) seem hopelessly old-fashioned. Business leaders, in particular, are concerned that change is coming very fast, requiring them to radically change means of production and lines of business within a few years. More importantly, some believe that we are fast approaching a “singularity”, a moment when technological progress will have reached a stage where it becomes autonomous, evolving independently from human intervention (Berman & Dorrier, 2016). Others have questioned this view, arguing that current technological progress—if anything—is only marginally improving peoples’ lives (Gordon, 2016). Major transformations, such as the introduction of electricity or sewage systems, that transformed mobility, communication and production systems, have all been implemented long time ago and their productivity gains have been spreading out among advanced economies.

Settling the debate has proven difficult, not alone because of the lack of a generally agreed metric of how to measure “technological progress”. How is technological change being assessed? By the number of patents filed? The number of (university) researchers? Or rather by measuring the growth of output in an economy? And what about the labour market? Can the number of jobs destroyed be an indication of how fast new technologies are spreading? The following brief tries to shed some light on this discussion by listing different indicators that potentially could reflect the speed of technological change. It argues that depending on where to look at, support for either view—the optimistic and the pessimistic one—can be found in the data. Looking at these from a unifying perspective, linking innovation and diffusion can help to provide some answers on how these seemingly contradictory pieces of evidence can fit together.

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