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This chapter analyses the relationship between the diffusion of general purpose technologies (GPTs) and surges in the growth of productivity. It first explores the dynamics of GPT diffusion by considering the generic and differentiating aspects of the US experience with industrial electrification and in comparison with that of the UK and Japan. It then discusses the analogies and contrasts between the historical case of a socio-economic regime transition involving the electric dynamo and the modern experience of the information and communications technology (ICT).

Several important questions bear on the impact of military and defense-related research, development, and procurement on future technology development in the U.S. One is whether changes in the structure of the American economy and of the defense industrial base preclude military procurement from playing a role in the development of advanced technology comparable to that it played in the past. Another is whether the military and defense-related industries have become so small relative to the size of the U.S. industrial sector that they no longer exert significant leverage on the rate and direction of technical change. A more disturbing question is whether a war, or threat of war, will be necessary to induce the mobilization of the scientific, technical, and financial resources to generate new general-purpose technologies. It is argued that war or its threat will be a less powerful inducement to technical change in the first half of the 21st century than it was during the last half of the 20th century.

This chapter presents a comprehensive historical analysis of two general purpose technologies (GPTs) — the Internet and nanotechnology. A GPT is an enabling technology, one that when adopted and used is expected to change production and consumption activity and behaviour. Both of these technologies have benefited from public sector initiatives and public sector intervention. Such public sector influence is economically justifiable, in part, on the grounds that the technologies generate technological spillovers, thus the marginal social benefits of the technologies are greater than the marginal private benefits so firms will not invest in these technologies on their own to the level that is socially desirable.

This chapter focuses on the recombination and reuse of key general-purpose technologies (GPTs), which are defined as widely used discoveries capable of ongoing improvement that enable complementary innovations. A critical factor behind the creation of these key technologies is the extent to which the broad prospects for reuse can be anticipated. The chapter distinguishes between two kinds of knowledge: technical and entrepreneurial. Technical knowledge—the understanding of how a firm can transform a technology into a product—is relatively commonplace. An understanding of market demand and how an invention might be used in other sectors is a rarer and more valuable asset. Because of the scarcity of entrepreneurial knowledge, the returns from developing a GPT may be much lower than they would be otherwise. However, over time, through a process of innovations and product introductions, this scarce entrepreneurial knowledge may become much more widely known. This theory is illustrated with a number of cases from the information technology industry, where important GPTs were only developed after numerous false starts.

There has been an explosion in data availability under the rubric of "big data" and computer-based advances in capabilities to discover and process those data. We can view these technologies in part as "meta technologies"-technologies for the production of new knowledge. Innovation is often predicated on discovering useful new combinations of existing knowledge in highly complex knowledge spaces. These needle-in-a-haystack type problems are pervasive in fields like genomics, drug discovery, materials science, and particle physics. We develop a combinatorial-based knowledge production function and embed it in the classic Jones growth model (1995) to explore how breakthroughs in artificial intelligence (AI) that dramatically improve prediction accuracy about which combinations have the highest potential could enhance discovery rates and consequently economic growth. This production function is a generalization (and reinterpretation) of the Romer/Jones knowledge production function. Separate parameters control the extent of individual-researcher knowledge access, the effects of fishing out/complexity, and the ease of forming research teams.

This chapter offers an empirical case study of the Internet architecture from an economic viewpoint. Data collected from the two main Internet standard setting organizations, which are the Internet Engineering Task Force (IETF) and the World Wide Web Consortium (W3C), demonstrate the modularity of the Internet architecture, as well as the specialized division of labor that produces it. An analysis of citations to Internet standards provides evidence on the diffusion and commercial application of new Internet protocols. The author ties these observations together by arguing that modularity helps the Internet, and perhaps digital technology more broadly, to avoid long-run decreasing returns to investments in innovation by facilitating low-cost adaptation of a shared general-purpose technology to the demands of heterogeneous applications.

We live in an age of paradox. Systems using artificial intelligence match or surpass human-level performance in more and more domains, leveraging rapid advances in other technologies and driving soaring stock prices. Yet measured productivity growth has declined by half over the past decade, and real income has stagnated since the late 1990s for most Americans. We describe four potential explanations for this clash of expectations and statistics: false hopes, mismeasurement, redistribution and implementation lags. While a case can be made for each explanation, we argue that lags have likely been the biggest contributor to the paradox. The most impressive capabilities of AI, particularly those based on machine learning, have not yet diffused widely. More importantly, like other general purpose technologies, their full effects won't be realized until waves of complementary innovations are developed and implemented. The adjustment costs, organizational changes, and new skills needed for successful AI can be modeled as a kind of intangible capital. Some of the value of this intangible capital is already reflected in the market value of firms. However, going forward, national statistics could fail to measure the full benefits of the new technologies and some may even have the wrong sign.

In September 2017, the National Bureau of Economic Research held its first conference on the Economics of Artificial Intelligence in Toronto. The purpose of the conference and associated volume is to set the research agenda for economists working on AI. This introductory chapter organizes and summarizes the key ideas. We categorize the papers into four broad themes. First, several papers emphasize the role of AI as a general purpose technology, building on the existing literature on general purpose technologies from the steam engine to the internet. Second, many papers highlight the impact of AI on growth, jobs, and inequality, focusing on research and tools from macro and labor economics. Third, five chapters discuss machine learning and economic regulation, with an emphasis on microeconomic consequences and industrial organization. The final set of chapters explores how AI will affect research in economics.

In the past decade there has been a sharp increase in the extent that companies use data to optimize their businesses. Variously called the `Big Data' or `Data Science' revolution, this has been characterized by massive amounts of data, including unstructured and nontraditional data like text and images, and the use of fast and flexible Machine Learning (ML) algorithms in analysis. With recent improvements in Deep Neural Networks (DNNs) and related methods, application of high-performance ML algorithms has become more automatic and robust to different data scenarios. That has led to the rapid rise of an Artificial Intelligence (AI) that works by combining many ML algorithms together-each targeting a straightforward prediction task-to solve complex problems. We will define a framework for thinking about the ingredients of this new ML-driven AI. Understanding the components of these systems and how they fit together is important for those who will be building businesses around this technology. Those studying the economics of AI can use these definitions to clarify the conversation on AI's projected productivity impacts and data requirements. Finally, this framework should help clarify the role for AI in the practice of modern business analytics and economic measurement.

Liability laws designed to compensate for harms that are caused by defective or dangerous products or that are the result of professional negligence may also affect innovation incentives. Advances in artificial intelligence and in robotics have generated lively debates over whether existing liability systems constrain technological progress and whether they present an opportunity to redesign liability rules. This chapter reviews empirical studies on the links between liability and innovation using a large sample of data. It aims to provide some insights into the potential impacts that liability laws and likely changes in the system may have on the rate and direction of innovation in robots and artificial intelligence, and to identify areas and questions for future research.

The global economy has enjoyed more than a half-century of unprecedented economic growth and a growth ideology is firmly entrenched in advanced and developing countries alike. It is the axis for policy making and is buttressed by a wealth of theorizing and empirical research on the sources of growth and how these can be mobilized by dint of policy making and institution building. But with policy-makers now demanding growth that is sustainable, green, and inclusive, growth economics must enlarge the menu of practical policy options so as to: enable countries to increase capital investment embodying advances in (green) technology; improve education delivery in order to enhance the quality of human capital, increase employability, and arrive at equitable outcomes; and implement vital institutional reforms to yoke and temper market forces. Growth economics is in great demand but arguably overdue for a “scientific revolution” to accommodate new demands.