Intellectual monopoly capitalism

The emergence of intellectual monopoly capitalism

Introduction

Intellectual monopolies are corporations that base their accumulation on continuously monopolizing access to knowledge. They collect intellectual rents from their in-house innovations but also appropriate data, knowledge and value from other actors participating in their innovation and production networks and platforms. Some intellectual monopolies are data-driven. These are companies that innovate on the basis of processing havested data from individuals and other organizations with machine learning. These algorithms transform data into digital intelligence that is used to keep innovating.

The monopolistic condition of intellectual monopolies does not refer to their market positions, despite some of them may operate in highly concentrated markets. Their monopoly lies in increasingly transforming knowledge into intangible assets, which allows them to capture greater rents. These corporations build on their knowledge monopoly to outsource steps of their production processes, keeping most of the generated value. Although intellectual monopolies working on the same or close industries are rivals and compete for technology, they also engage in technological cooperation for steps of their innovation processes.

As was introduced in Chapter 1, empirical evidence for the United States (US) shows that R&D is being used to expand concentration. R&D efforts were shown to be positively and significantly correlated with corporate net profits and with performance in Standard & Poor’s stock market index (Lambert, 2019). Likewise, based on the distribution of cash holdings by US corporations, Schwartz (2016) suggests that an uneven distribution of intellectual property rights (IPRs) explains profits differentials. In the same vein, Orhangazi (2018) revealed that intangible-intensive industries’ rate of profit grew faster than their total assets. Beyond the United States, in every major economy the mark-ups of firms at the top of the mark-up distribution have grown and are higher in digital-intensive sectors (Calligaris et al., 2018).

This chapter starts by delving into the emergence of intellectual monopolies. Then, Section 3 analyses the effects of innovation as an accomplished result monopolized by a few intellectual monopolies. This section explores the subordination of complementers in digital platforms as well as of outsourced firms in global value chains (GVC) (Gereffi, 2014; Gereffi et al., 2005; Sturgeon, 2009). These are examples of how corporations monopolizing knowledge subordinate others. Section 4 elaborates on how intellectual monopolies plan and control innovation processes beyond their capital legal ownership by organizing multiple global innovation networks from which they predate and assetize knowledge results. Section 5 offers a typology of subordinate firms. Finally, Section 6 concludes by reflecting on intellectual monopolies’ sources of profits.

Intangible assets, rents and the emergence of intellectual monopoly capitalism

Intangible assets and intellectual rents

The OECD (2011, p. 1) defines intangible assets as: “computerized information (such as software and databases), innovative property (such as scientific and non-scientific R&D, copyrights, designs, trademarks) and economic competencies (including brand equity, firm-specific human capital, networks joining people and institutions, organisational know-how that increases enterprise efficiency, and aspects of advertising and marketing)”. Thus, intangible assets comprise knowledge that has been introduced to the economic sphere.

Historically, this integration of knowledge into the economic sphere was conceived as an innovation.1 In Chapter 1, we recalled Schumpeter’s (1934) innovation definition. It refers to the creation of a new production techniques or product. This definition includes the creation of a new market. For instance, the development of a brand could lead consumers to conceive a product as different from all the other equal ones in terms of their material properties. Therefore, if society values a Nike shoe as unique, then this shoe will be sold in a separate market of its own, and the creation of this market can be considered an innovation. Products are useful as far as they satisfy a twofold condition: they are useful due to their technical specificities and be socially recognized as useful.2 When a new market is created without a technical change, we draw on Marx’s (1867, Chapter 1) use-value concept to say that the innovation transformed the social dimension even if, in its materiality, the product serves for the same use as previously existing ones.

Innovations can be science-based or non-science based (Ernst, 2009). The latter generally refers to the use of existing capacities in a different (new) way. They are early technical adaptations. Moreover, technical changes are not only the result of engineering transformations but also new designs.3

Once knowledge is assetized, its owner can garner intellectual rents, also dubbed knowledge, or technoscientific rents (Birch, 2019; Durand & Milberg, 2020; Foley, 2013; Pagano, 2014; Rikap, 2018; Teixeira & Rotta, 2012). Given that intangible assets spring from the private appropriation of knowledge, they exclude others from accessing and mobilizing that knowledge for their and others’ benefit. In Baranes’s (2016) terms, they give the privilege to access, thus to use certain ideas while locking-out others. Copyright, trademarks or patents are legal locking-out mechanisms. The strengthening of IPRs is a key precondition of the emergence of intellectual monopoly capitalism (see Chapters 3 and 4). Knowledge is also monopolized w'hen it is kept as industrial secrets or tacit knowledge.4 Beyond legal monopoly rents, Durand and Milberg (2020) suggest three other types of intellectual rents: vertical natural monopoly, intangibles-differential rents and data-driven innovation rents. Chapter 7 analyses this taxonomy more in detail showing that Apple enjoys the four mentioned types of intangibles-driven rents.

Vertical natural monopoly rents refer to GVC leaders’ exclusive knowledge covering the w'hole chain and, thus, the capacity to integrate it assuring network complementarities. The different scale economies between intangible and tangible assets result, according to Durand and Milberg (2020), in intangibles-differential rents. In addition, under intellectual monopoly capitalism, (big) data are a source of knowledge. Since big data remain under the exclusive access of a certain firm, analysed data become intangible assets. As a result, Durand and Milberg (2020, p. 421) defined data-driven innovation rents as “benefits accruing from the enhancement of innovation capabilities derived from data centralization”.

Data rentiership is of utmost importance for expanding a companies’ degree of intellectual monopoly. Big data are processed with machinelearning algorithms producing digital intelligence (UNCTAD, 2019). Within machine-learning techniques, deep learning and neural networks are characterized by algorithms that learn and improve themselves as they process more data. This has led Cockburn et al. (2018) to consider this technology a general-purpose method of invention. Therefore, monopolizing big data results in monopolizing a source of potentially unlimited inventions. This innovation cum economic potential of analysed data is beneath data rentiers’ quest for owning and controlling new sources of big data. As more sources of data are monopolized by the same intellectual monopoly, more accurate the predictions of their algorithms will be, and they will be able to shape behaviours even more. Therefore, their business and innovations wall blossom at the expense of society at large.

Summing up, intangible assets are monetized knowledge, from the as- setization of the most basic forms of knowledge (centralized and processed data) to revolutionary science-based innovations. This appropriation of knowledge grants its owner monopoly pow'er that enables it to garner intellectual rents.

 
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