3.9 AI or ML Software Is Used More Widely in High-Income Countries

FIGURE 3.9 AI or ML Software Is Used More Widely in High-Income Countries

Number of firms that purchased AI or ML software, top 15 countries, 2018

United States United Kingdom India Canada France Germany Australia Netherlands Spain Italy Switzerland China Singapore Brazil Ireland

0 2,000 4,000 6,000 8,000 10,000 12,000 Number of firms

Source: Calculations based on iDatalabs (now Enlyft, http://www.enlyft.com) data for the World Bank. Note: The dataset is generated using web scraping. It covers 17,000 formal firms in 107 countries across regions and income levels. AI = artificial intelligence; ML = machine learning.

skills, changes in organizational design and business models, legal constraints, and even cultural expectations (Brynjolfsson and Mitchell 2017).

The Rise of Intangible Capital

Chapter 2 showed that except in transportation, warehousing, and telecommunications, physical capital has typically played a much smaller role in the production process among services firms than among manufacturing firms. The physical capital per worker for low-skill services sectors such as retail or accommodation and food services is a third of that in manufacturing, and it was also lower in many global innovator services such as programming and IT.

The question is the extent to which the steady rise of firms’ investment in intangible capital over the past 20 years—often even exceeding growth in tangible capital (Corrado et al. 2018)—will affect the services sector. Evidence from high-income economies suggests that for every £1 of investment in tangible capital such as buildings or machinery, firms spent £1.10 on intangible capital in 2013 (Haskel and Westlake 2018).

Intangible capital can be classified into three broad categories: (a) computer-related software and data; (b) properties of innovation such as R&D, design, and artistic originals; and (c) company competencies such as marketing and branding,

firm-specific training, and business process engineering (Corrado, Hulten, and Sichel 2005). Investments in these intangible assets complement each other. For instance, the impact of R&D investment depends on the firm’s ability to invest in other intangibles such as managerial skills, network building, or organizational practices (Andrews, Nicoletti, and Timiliotis 2018; Bloom, Sadun, and Van Reenen 2012; McAfee and Brynjolfsson 2012). The success of the tech giant Apple illustrates how R&D and design in the iPhone, combined with the organizational design of the App Store as well as Apple’s branding, have created one of the world’s most profitable products.

The increase in intangible capital has been associated with the diffusion of information and communication technologies because realizing the potential of investment in computers and the internet also requires firms to create new business processes, develop managerial skills, train workers, patch software, and build a strong company brand (Basu, Fernald, and Kimball 2004; Bresnahan 2010; Bresnahan and Trajtenberg 1995). And the importance of intangible capital is only likely to increase as ICT becomes more sophisticated, such as with the advance of cloud computing and big-data analytics. In fact, the advent of AI and ML represents a new GPT that is likely to spawn complementary investments in intangible capital as it improves over time (Brynjolfsson, Rock, and Syverson 2021).

Investments in intangible capital are not readily measured in firms’ balance sheets, and therefore there is little traceable data in a standard accounting sense. Yet, available evidence from high-income economies suggests that the services sector accounts for 64 percent of total investment in intangible capital in the United States and for 61 percent in the EU-14.10 Services are also more intensive in the use of intangible capital relative to tangible capital. This higher propensity for investing in intangible capital in the services sector relative to manufacturing is seen in both the United States (1.25 versus 1.03) and the EU-14 countries (0.85 versus 0.79) (Corrado et al. 2018).

Investment in innovation. R&D intensity as an indicator of innovation was the highest in the manufacturing sector, on average, across OECD economies in 2017.11 Among services subsectors, firms in professional, scientific, and technical services; ICT services; and education services had the highest R&D intensities in 2017 (figure 3.10). These data reinforce evidence from US-listed firms, where, between 1990 and 2006, knowledge-based intangible assets in the services sector were sizable only in these global innovator services (Demmou, Stefanescu, and Arquié 2019).

Services firms tend to innovate less than manufacturing firms in LMICs too, even when innovation is defined more broadly to include the introduction of a new or significantly improved product or production method. Among services subsectors, the share of firms that engage in such product and process innovation is the highest in professional services (50 percent) and lowest in retail services (25 percent) (Cirera and Maloney 2017).

Investment in computer software and data. Innovation in much of the services sector relates less to formal R&D and technological (product and process) innovation than to