What is new with contemporary (global) leading corporations? If gigantic monopolies are a repeated phenomenon in capitalism’s history, why all the fuss we see every day regarding high concentration?
Leading corporations of the 21st century are intellectual monopolies. These are firms that rely on a permanent and expanding monopoly over portions of society’s knowledge. A recent joint OECD and European Union report shows that the top 2000 corporations in business expenditure in research and development (BERD) concentrated 60% of total IP5 patents between 2014 and 2016 (Dernis et al., 2019).
How did this happen if intellectual rents enjoyed by the innovator were supposed to disappear once the rest of the industry adopts the new technique? They disappeared if the secret was broken, the patent expired, or when another firm innovated, overcoming the innovating firm’s advantage. Knowledge is cumulative and those innovating have a greater absorptive capacity to keep innovating. Aided by a more stringent and global intellectual property regime, the continuous reinforcement of knowledge monopolies has led to a perpetuation of the core, maximizing rentiership over time.
Intellectual monopolies may not monopolize the markets they operate, which can even be competitive markets like Amazon’s marketplace, where Amazon sells its products with millions of other sellers. Their monopolistic condition relies on their capacity to significantly and systematically monopolize knowledge, which generally – but not always – contributes to market concentration.
What we are witnessing is the climax of a process that began almost half a century ago with the formation of global value chains (GVC) led by multinational corporations that retained the exclusive knowledge on how to integrate the supply chain. It was also in the 1970s that the big pharma blockbuster drug model emerged signalling a turning point in terms of intellectual property and rents. Furthermore, the initial policy transformations that paved the way for intellectual monopoly capitalism also date from the 1980s and continued during the 1990s (from the Bayh-Dole Act to the Trade Related Aspects of Intellectual Property Rights followed by free trade agreements, bilateral investment treaties, and regional pacts that installed a global intellectual property regime). Yet, it was not until this century, in particular after the advancements in deep learning and neural networks that unleashed big data based innovations that the consequences of intellectual monopoly capitalism became apparent.
To acknowledge them, I suggest thinking of innovation, as any process based on human labour, not only from its implications but also as a social relation of production. In other words, innovating always has a twofold meaning. One looks forward and starts when the innovation is achieved, thus studying the effects of innovation as an accomplished result. The subordination of complementors in digital platforms (such as third-party sellers in e-commerce) as well as of outsourced firms in GVC exemplify how innovations (or more broadly intangibles), once monopolized, are used to subordinate other organizations. The other approach looks backward and delves into the social relations of production that take place in order to innovate. This is innovation as a process. In contemporary capitalism, this process increasingly takes place as networks, organized and planned by intellectual monopolies.
Intellectual monopolies are not only – nor mainly – a result of giant corporations’ in-house R&D. Their knowledge monopoly is based on appropriating and monetizing knowledge results from their multiple innovation networks organized as modularized knowledge steps in charge of different organizations (from start-ups to public research organizations and universities). Intellectual monopolies also outsource innovation steps by actively engaging in open access or open science initiatives (including open-source software in the case of tech giants), monetizing knowledge commons.
In this context, what is the fate of the rest of the firms – which are the overwhelming majority of the industrial landscape – and how do they manage to remain profitable while subordinating to intellectual monopolies? How are science and technology transformed in this context? What are the implications for research universities and other public research organizations? What is the place of the peripheries as profits concentrate in a handful of corporations from core countries? What is the role played by those core countries’ states in the emergence and spread of intellectual monopoly?
To answer these questions, I elaborate on the concept of predation, initially defined by Veblen (1899) as a direct relation of spoliation. Predation contributes to explaining the higher concentration of intangible assets by intellectual monopolies. Intellectual monopolies predate knowledge from other organizations. Innovation in capitalism has thus grown as a power relationship. “Inventors” (those actually working on intellectual monopolies’ innovations) will at most receive a minor payment in comparison with the rents amassed by the intellectual monopoly that surveilled the whole process. As a result of this division of intellectual labour, the industrial landscape is split between corporations that control production, distribution, and consumption by controlling innovation processes and a myriad of organizations whose best alternative is to subordinate.
Meeting the world’s predators
In our epoch, intangibles assets’ rise cannot be understood without considering the -highly asymmetric- digital economy. Its five leading corporations represent over 25% of the S&P 500. The combined market capitalization of Google, Apple, Facebook, Amazon, and Microsoft (GAFAM) is even above Japan’s 2019 GDP. As well as their counterparts from China, GAFAM concentrate profits and (tangible and intangible) capital based on monetizing knowledge and data. Their continuous innovations rely on their exclusive access to big data sources, thus predating society by curtailing access to an input that was socially constructed. Furthermore, they analyse data with artificial intelligence (AI) algorithms that, more often than not, were developed by other organizations. They use that data to orient their business and innovate based on customized models that are capable of predicting and shaping each individual’s behaviours with the greatest existing accuracy.
Although at the forefront of this new stage, intellectual monopoly capitalism goes beyond digital industries. Big pharmaceuticals are another paradigmatic example. Furthermore, companies from the most diverse industries are becoming intellectual monopolies. From State Grid Corporation of China (SGCC), China’s state-owned utility company, to BlackRock’s financial data monopoly.
Depending on the diversity of knowledge management techniques and on the multiplicity of monopolized technologies, intellectual monopolies differ in scope. Some are focused on narrow niches – such as Siemens’ dominance of AI for life and medical sciences inventions or SGCC’s lead in AI-related inventions for energy management (World Intellectual Property Organization, 2019). Meanwhile, GAFAM and its Chinese counterparts expand their power, dominating general-purpose technologies. All in all, intellectual monopoly capitalism can be conceived as the stage in capitalism where capital accumulation (and distribution) is led by a core of intellectual monopolies that base their accumulation (and power) on their permanent and expanding monopoly (and assetization) of predated knowledge.
By synthesizing the common traits of these cases, I argue that intellectual monopoly capitalism capital accumulation is increasingly driven (and hampered) by rent-seeking and predation. Intellectual monopolies sabotage society by privately monetizing intangible goods; they are –simultaneously- capitalists, rentiers, and predators. The more their rents grow, the more the rest of the world will be deprived of access to knowledge and of a greater portion of the total value produced. Intellectual monopolies are also the corporations leading the rankings of offshored retained earnings and declare profits in tax havens, further favouring their shareholders by minimizing paid taxes. This points to the entangled connection between an accumulation strategy based on rentiership and predation that results in levels of earnings and financial strength that allow to further expand rents, this time by participating in financial markets.
Intellectual Monopolies maximize their appropriation of wealth ultimately at the expense of workers of all the subordinate firms but also including their own workers. Workers’ differentiation under intellectual monopoly capitalism results in a majority with earnings below what they need to reproduce themselves and their families. To tilt the scale, unions must regain the power they lost in Western countries and gain the power they never had in Asia. A global organization of labour is required. However, this is much easier said than done.
The effects of intellectual monopoly capitalism on the peripheries
The persistently uneven distribution of innovation in the world is a structural truth worsened by intellectual monopoly capitalism. Intellectual monopolies originate in core countries, in particular in the United States, but their effects are spread all over the world. In my book “Capitalism, Power and Innovation: Intellectual Monopoly Capitalism Uncovered” I study intellectual monopoly capitalism integrating three levels of analysis: global, national, and network. At the national level, it distinguishes between core and peripheral countries. At the network level, it focuses on how intellectual monopolies plan, organize and predate from their innovation networks, thus also including a broad set of subordinate firms, universities, and public research organizations from all over the world.
In the peripheries, intellectual monopolies systematically pass over – less powerful – states. Part 3 begins with an assessment of innovation and upgrading policies for development under intellectual monopoly capitalism. In this context, innovation studies (including the idea of an entrepreneurial state), as well as GVC and catching-up approaches, have shortcomings in providing viable policy recommendations. Peripheral countries’ specific traits result in a greater technological gap with (intellectual monopolies and their innovation networks from) core countries and reinforce underdevelopment. However, the relatively more developed countries within the peripheries exhibit an unbalanced knowledge and innovation structure with their leading research institutions integrated into global knowledge networks, thus risking being subordinated to intellectual monopolies, while local firms generally lag behind.
To account for specific effects of intellectual monopoly capitalism on the peripheries, I propose the concepts of knowledge and data extractivism, two specific forms of what can be dubbed intangibles extractivism.
Knowledge extractivism refers to science and technology from the peripheries that are monetized in core countries, usually by corporate intellectual monopolies and eventually by academic intellectual monopolies. This form of extractivism mainly affects leading universities and public research organizations from so-called emerging or middle-income countries.
Concerning data, a new layer in the international division of labour is emerging. It splits the world between raw data providers and a handful of data-driven intellectual monopolies. Raw data is valueless, but as continuous streams of data are centralized and processed with deep learning and neural network approaches, algorithms improve by themselves and learn faster. Peripheral countries (and even Europe) are net providers of raw data and pay for digital intelligence.
Intangibles extractivism results in a higher concentration of intangible assets in the hands of a few corporations from the core, which expands their rents at the expense of knowledge and data produced in the peripheries. Some authors conceive this unequal exchange of knowledge and data as a new form of colonialism, defined as data or digital colonialism (Couldry & Mejias, 2019a, 2019b; Kwet, 2019). A vicious cycle is established between intangibles extractivism and the lack of technological autonomy in the peripheries.
My book provides evidence knowledge extractivism from the peripheries by global (but born in core countries) intellectual monopolies. Chapter 12 focuses on the distribution of innovation rents from Singapore’s innovation hub, showing that it is multinational corporations that appropriate most of them. Chapter 13 presents evidence of pharmaceutical knowledge extractivism from the University of Buenos Aires, in Argentina.
Hence, what can we do to tilt the scale against intellectual monopoly capitalism?
We must first acknowledge that this question should be answered through community debates and collective and democratic decision-making processes. So, I only anticipate here some ideas attempting at contributing to such discussions.
Beyond counterbalancing trends, such as knowledge commons and open access initiatives, the current private knowledge regime needs to be overcome. A new commons knowledge regime should simultaneously consider knowledge access and use. In this respect, sci-hub -a website that grants access to academic publications- works as a democratizing development policy. This reform becomes all the more important if we consider the Covid-19 pandemic. Democratizing access to knowledge also requires public (free) education to guarantee that local populations can critically understand it and further elaborate on it.
In a similar vein, data privacy acts aimed at limiting tech giants’ power, further contribute to knowledge privatization by fostering individual property over data. On the contrary, I think that digital global public goods should be fostered. Every Google search, every Amazon purchase, every Facebook or YouTube post, and so on contribute to improve the algorithms used, thus, to improve the services we all receive. Digital platforms are globally produced by society at large and since digital services -in particular those in the hands of big tech companies- tend to be natural monopolies, why not envisioning them as global public goods?
Moreover, peripheral countries must set their own agenda to battle against intellectual monopolies, which should include limiting all forms of extractivism (data, knowledge, and also natural goods, some of them essential for digital value chains).
The accomplishments of these measures and others -such as global agreements to strengthen labour market regulation forbidding new and old forms of informality that the digital (gig) economy takes to unprecedented levels within capitalism-, will only take place if grassroots social movements and workers unions fight for them. As social scientists, we should engage in these fights and this includes retargeting our research agendas. We need to be bold and ask challenging questions, bridge the gap between general trends and specific, in-depth analyses. Our research priorities should consider social and environmental impacts, which require integrating other social actors in the definition of science and technology agendas. Uncovering new and structural trends of capital accumulation, thus identifying the roots of poverty, inequality, and underdevelopment cannot wait. My research on intellectual monopoly capitalism pursues this aim.
Couldry, N., & Mejias, U. A. (2019a). Data colonialism: Rethinking big data’s relation to the contemporary subject. Television & New Media, 20(4), 336–349.
Couldry, N., & Mejias, U. A. (2019b). The costs of connection: How data is colonizing human life and appropriating it for capitalism. Stanford University Press.
Dernis, H., Gkotsis, P., Grassano, N., Nakazato, S., Squicciarini, M., van Beuzekom, B., & Vezzani, A. (2019). World Corporate Top R&D investors: Shaping the Future of Technologies and of AI (EUR 29831). Joint Research Centre and OECD report.
Kwet, M. (2019). Digital colonialism: US empire and the new imperialism in the Global South. Race & Class, 60(4), 3–26.
Veblen, T. (1899). The theory of the leisure class: An economic theory of institutions. Macmillen.
World Intellectual Property Organization. (2019). WIPO Technology Trends 2019. Artificial Intelligence. WIPO.
 Patents in the 5 largest patent offices: European Patent Office (EPO), Japan Patent Office (JPO), Korean Intellectual Property Office (KIPO), National Intellectual Property Administration of the People’s Republic of China (CNIPA) and the United States Patent and Trademark Office (USPTO).
Cecilia Rikap is a tenure researcher of the CONICET (Argentina’s National research council), associate researcher of the Centre de Population et Développent (CEPED), IRD/Université de Paris and of the COSTECH lab, Université de Technologie de Compiègne. She tweets at @CeciliaRikap.