There has been a great deal of speculation that machine learning might be a general-purpose technology; however, the commercial application of machine learning is relatively new and general-purpose technologies are typically identified with the benefit of many years of hindsight. It is useful to have an early sense of whether machine learning is a general-purpose technology in order to understand whether distinct complementary business practices and the help of third- party service providers will be needed in order to benefit from the technology. In this paper, we provide an approach to identifying a general-purpose technology before it has widely diffused. Using data from online job postings, we compare machine learning to eight other emerging technologies in terms of breadth of industries with job postings, the importance and breadth of research roles, and the costs of innovation in organizational practices. Our results show that ML is particularly likely to be a general-purpose technology, suggesting that firms adopting ML should be patient, should expect to implement changes to organizational processes, and should recognize that their industries are likely to change as a result. In contrast, firms adopting other technologies should look for more direct and tangible benefits.

Submitted by crowston on