Digital computers have transformed work in almost every sector of the economy over the past several decades. We are now at the beginning of an even larger and more rapid transformation due to recent advances in machine learning (ML), which is capable of accelerating the pace of automation itself. However, although it is clear that ML is a general purpose technology, like the steam engine and electricity, which spawns a plethora of additional innovations and capabilities, there is no widely shared agreement on the tasks where ML systems excel, and thus little agreement on the specific expected impacts on the workforce and on the economy more broadly. We discuss what we see to be key implications for the workforce, drawing on our rubric of what the current generation of ML systems can and cannot do [see the supplementary materials (SM)]. Although parts of many jobs may be suitable for ML (SML), other tasks within these same jobs do not fit the criteria for ML well; hence, effects on employment are more complex than the simple replacement and substitution story emphasized by some. Although economic effects of ML are relatively limited today, and we are not facing the imminent end of work as is sometimes proclaimed, the implications for the economy and the workforce going forward are profound.
A wave of business innovation is driving the productivity resurgence in the U.S. economy. In Wired for Innovation, Erik Brynjolfsson and Adam Saunders describe how information technology directly or indirectly created this productivity explosion, reversing decades of slow growth. They argue that the companies with the highest level of returns to their technology investment are doing more than just buying technology; they are inventing new forms of organizational capital to become digital organizations. These innovations include a cluster of organizational and business-process changes, including broader sharing of information, decentralized decision-making, linking pay and promotions to performance, pruning of non-core products and processes, and greater investments in training and education. Brynjolfsson and Saunders go on to examine the real sources of value in the emerging information economy, including intangible inputs and outputs that have defied traditional metrics. For instance, intangible organizational capital is not directly observable on a balance sheet yet amounts to trillions of dollars of value. Similarly, such nonmarket transactions of information goods as Google searches or views of Wikipedia articles are an increasingly large share of the economy yet virtually invisible in the GDP statistics. Drawing on work done at the MIT Center for Digital Business and elsewhere, Brynjolfsson and Saunders explain how to better measure the value of technology in the economy. They treat technology as not just another type of ordinary capital investment by also focusing on complementary investments–including process redesign, training, and strategic changes–and ton he value of product quality, timeliness, variety, convenience, and new products. Innovation continues through booms and busts. This book provides an essential guide for policy makers and economists who need to understand how information technology is transforming the economy and how it will create value in the coming decade.