<p>A general purpose technology (GPT) is an exceptional link in a complex chain of innovations connected through space and time. Common examples include the steam engine, electrification, and the automobile (Bresnahan & Trajtenberg, 1995; Bresnahan & Trajtenberg, 1995; & Bekar, et. al., 2018). A GPT is set apart from other innovations because of the massive breadth, depth, and duration of their impact on our world and, therefore, worthy of special consideration. (Trajtenberg, 2019; Bekar, et. al., 2018; Strohmaier & Rainer, 2016; Ott, et. al., 2009; & Bresnahan & Trajtenberg, 1995). Yet, the changes GPTs bring can take decades and even centuries to manifest fully and much of GPT research to date is post hoc (Bresnahan & Trajtenberg, 1995; Lipsey, Bekar, & Carlaw, 2005; & Jovanovic & Rousseau, 2005). Yet executives, entrepreneurs, researchers, policymakers, and investors would all benefit from foresight anticipating the next GPT. This research explores this prospect empirically by tracing the ‘pervasiveness dimensions’ of a candidate GPT before its ultimate economic and societal impact is observed. Our candidate GPT is Artificial Intelligence (AI) and a key enabling technology, Deep Learning (DL). We trace the evolution of AI and DL from their birth in the latter half of the 20th century to the present, with a focus on the diffusion of large-scale applications of AI/DL from 2000 to 2020 (Goldfarb, et. al, 2021). We are aided by a rich base of prior academic research across various disciplines as well as industry analyses and reports. We hope to add a forward-looking tool to the decision-makers toolkit, while also increasing our understanding of AI not only as an innovation but as an extraordinary technological, economic, social, and even political phenomenon.</p>
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<p>Executive Doctorate of Business Adminsitration, Temple University</p>