Rethinking of Marxist Perspectives on Big Data, Artificial Intelligence (AI) and Capitalist Economic Development

Dr Nigel Walton

Assistant Professor in Business Strategy

Coventry Business School, Coventry University, UK.

Email: ad0006@coventry.ac.uk

Dr Bhabani Shankar Nayak

Reader in Business Management

University for the Creative Arts, UK.

Email:Bhabani.nayak@uca.ac.uk

Abstract

AI and big data are not ideologically neutral scientific knowledge that drives economic development and social change. AI is a tool of capitalism which transforms our societies within an environment of technological singularity that helps in the expansion of the capitalist model of economic development. Such a development process ensures the precarity of labour. This article highlights the limits of traditional Marxist conceptualisation of labour, value, property and production relations. It argues for the rethinking of Marxist perspectives on AI led economic development by focusing on conceptual new interpretation of bourgeois and proletariat in the information driven data-based society. This is a conceptual paper which critically outlines different debates and challenges around AI driven big data and its implications. It particularly focuses on the theoretical challenges faced by labour theory of value and its social and economic implications from a critical perspective. It also offers alternatives by analysing future trends and developments for the sustainable use of AI. It argues for developing policies on the use of AI and big data to protect labour, advance human development and enhance social welfare by reducing risks.

Keywords: Platforms, Big Data, artificial intelligence, Internet-of-Things, disintermediation, sharing economy, bourgeoisie, proletariat, labour power.

Introduction

The twenty first century has seen a new “industrial” landscape emerge due to the growth of platform-Internet companies and new technologies such as Big Data and artificial intelligence (AI). This is in stark contrast to the industrial era in which Karl Marx wrote about the labour theory of value and the demarcation between the bourgeoise owners of the means of production and the proletariat.

Over the last decade, due to the rise of platform-Internet companies, this relationship has changed as data and information have become the new factors of production and owning access to the Internet (the gateway to data) has become the new source of power with data becoming the new form of capital (data capital).

The move from physical to digital products and services; the non-rivalrous nature of the products; the emergence of prosumers and user generated content and free products; plus changes in the ownership of resources through the sharing economy; have all resulted in the emergence of a `neo-bourgeois` of platform Internet companies and a ‘neo-proletariat` workforce of data providers challenging Marx`s original labour power theory.

This paper analyses the implications of these developments and how the resulting technologies such as Big Data and artificial intelligence (AI) are changing the nature of capitalism.

A New Industrial Landscape

Big data is continuously produced, collected, managed, monitored and used by major corporations in such a way that has changed how economic, political and social systems function in the world today. Major advances in Artificial Intelligence (AI) help in accelerating such a process in which machine learning and automation increases human capabilities to produce more for economic growth and development. The reasoning power and abilities of AI also make human labour obsolete (Gordon, 2016) in the process of production, distribution, exchange and consumption; four central pillars of all economic and social systems. In this way, AI creates this complex and disturbing trajectory where capitalism remains the ultimate system in which AI helps in creating the inhuman power of big data which is a weapon of capital that controls the everyday lives of labour (Dyer-Witheford, Kjosen and Steinhoff, 2019). Such control mechanisms are central to the survival of capitalism as an economic, political, social and cultural system where profit rules over people.

Alternatively, the increased availability of data and information has enhanced the power of labour to challenge the inhuman laws of capitalism. The Internet has resulted in greater transparency regarding workplace practices. Organisations are pressured into becoming more socially responsible (Burke and Logsdon, 1996) due to the power of workers to share workplace information and experiences over social media thereby naming and shaming exploitative employers. The growth of artificial intelligence (AI) also significantly promotes technological innovation, knowledge and investment (Liu, et al., 2020) which helps in economic growth and development. Big data plays a significant role in shaping modern life, smart cities, society and economy (Iqbal, et al., 2020).

However, the profit driven technological singularity of AI creates social challenges (Totschnig, 2017) and the economic impacts of AI are potentially fatal under a neoliberal economic system (Whyte, 2019). The massive growth of a data driven neoliberal market led by AI controls the capitalist production processes where “the object of accumulation consists mainly of knowledge, which becomes the basic source of value, as well as the principal location of the process of valorisation (Boutang, 2012: 57)”. The valorisation of labour is central to capitalist knowledge production which is driven by big data and ignores knowledge about other social, cultural, religious, economic and political environments that shape production processes and conditions the labour theory of value. It is a process where the capitalist model of economic development escapes from the petty controls of the working conditions and its challenges (Barbrook, 1998). However, the theoretical frameworks of cognitive capitalism and its data driven capitalist knowledge economy in information society does not help us to understand the ideological foundations of “cinderella of capitalism” (Boutang, 2012:46).

AI is influenced by a host of underlying ideologies and values (Impett, 2015) which are essentialist and empiricist in nature. As the ideological and philosophical foundation of AI is based on cognitive science where neomechanical and casual explanations owe their origins to casual structure (Miłkowski, 2013: 70); it does not engage with the complexities of social, economic, cultural, political and religious structures in which production, distribution, consumption and exchange for economic development takes place. The deterministic technological singularity of AI also challenges and dominates nature (Lyra, 2015) which is central to every economic, social and political system. Therefore, AI led technologies and algorithms create significant strategic disruptions and changes to current processes of economic and business systems (Davenport and Mahidir, 2018; Faraj et al. 2018).

AI and its technological singularity creates a digital divide based on availability and accessibility to technological knowledge and its infrastructure. Therefore, it has the potential to further expand existing societal rifts and class conflicts (Gevel and Noussair, 2013). In such an ever changing and challenging environment, it is important to understand, explain and use emerging technologies for human development and social welfare (Korinek and Stiglitz, 2018). This is only possible by developing policies to protect labour, privacy, trade and liability and reduce the consequences of AI in terms of its impact on employment, inequality and competition (Agrawal, Gans, and Goldfarb, 2019) under capitalism.

Although the European Union (EU) is currently pioneering a technology doctrine that aims to give individuals control over their own information and the profits that flow from this (O`Hara and Hall, 2018), this is still in its early stages. As new technologies such as the Internet-of-Things and edge-computing gain momentum and as data is processed on hardware devices (such as smartphones, home appliances and cars etc.) instead of being transferred directly back to the servers of the large platform companies, this may create an opportunity for individuals to monetise their personal data for their own direct benefit thereby changing the balance of power and potentially transferring control and ownership to the actual data producers. Looking further ahead, new decentralised autonomous organisations (DAOs) using Blockchain technologies would remove the need for platform intermediaries and create an Internet-of-People (IOP) where individuals would also have control of their personal data (Conti et al., 2017).

Big Data and Major Changes in Capitalism

Major changes have occurred during the last few decades as a new economic landscape has emerged (Hitt et al., 2003) that is fundamentally different to the industrial capitalist era in which Karl Marx wrote his most important seminal work. The era of the mid to late 1800s was undergoing its own industrial revolution as steam power, factories, canals and railways became the new innovations driving social and economic change (Schwab, 2017). In the twenty first century, however, new Internet technologies, personal and mobile computing, digitisation, GPS and smartphones have created a completely new “industrial” landscape where one of the greatest innovations has been the creation of value through business model innovation using data and information (Voelpel et al., 2004; Wirtz et al., 2016: 41). This value creation process is in stark contrast to Marx`s (1996) labour theory of production and his delineation between the owners of the means of production (the bourgeoisie capitalist class) and the proletariat (working class).

The main driver of this new competitive landscape (Hitt et al., 2003) has been the changes that have occurred in the factors or means of production. Factors of production were defined by Marx in Das Kapital (1996) as labour, subjects of labour and instruments of labour. The term is equivalent to the means of production (plus labour) and it is therefore very similar to the factors of production concept derived from the classical school of economics, otherwise referred to as `land, labour and capital`. What both Marx (2013) and the neoclassical economists` definitions of the means or factors of production did not include, however, were two new variables commonly referred to today as data and information (information representing processed data). However, when the classical economists identified the key factors of production (Smith, 2010) it wasn`t possible to capture and store data so this wasn’t quantifiable and it was not considered to be a relevant factor.

In the twenty first century information age this is no longer the case. According to Brynjolffson (2016), data has now become a form of capital (data capital) which is capable of generating new digital products and services. The emergence of data and information as new factors of production were made possible by technological breakthroughs in information and communications technology (ICT) brought about by the commoditisation of computer hardware and software and other related technologies. This is what the Austrian economist Schumpeter (1943) referred to as `competing down` following a period of `swarming` and competitive imitation (Schumpeter, 1934; 1939). The cost of three core digital technology capabilities - computing power, data storage and bandwidth - relative to their performance, has decreased exponentially and at a faster rate than that of previous technological advances such as electricity and telephones. The increasing affordability of digital technology, coupled with wireless networks and powerful mobile devices such as smartphones and tablets, has created an increasingly broad platform for users to connect and communicate anywhere and at any time (Voelpel et al., 2004; Brynjolfsson and McAfee, 2014).

The cost of computing power has also decreased significantly, from $222 per million transistors in 1992 to $0.06 per million transistors in 2012. Similarly, the cost of data storage has decreased considerably, from $569 per gigabyte of storage in 1992 to $0.03 per gigabyte in 2012. The decreasing cost-performance of digital storage enables the creation of increasingly rich digital information. The cost of Internet bandwidth has also steadily decreased, from $1,245 per 1000 megabits per second (Mbps) in 1999 to $23 per 1000 Mbps in 2012. The declining cost performance of bandwidth has enabled faster collection and transfer of data, facilitating richer connections and interactions (Deloitte Centre for the Edge: 2013).

These developments have been the key drivers behind the data deluge and have resulted in new phenomenon such as Big Data analytics (McAfee and Brynjolffson, 2012). This new data-rich `industrial` landscape has also given rise to a new form of `factory` known as the Internet platform company. These companies include the large US and Chinese firms such as Google, Amazon, Facebook, Netflix, Alibaba and Tencent plus Internet platforms in the sharing economy such as Uber and Airbnb etc.

The new means and factors of production (data and information) have created a wide range of benefits. These include the democratisation of information and knowledge (Nowotny, 2003). Where information and knowledge were previously controlled by governments and institutions, the bargaining power of ‘proletariat’ workers and consumers have increased because of the Internet. They now have access to price comparison sites and they have become more active as stakeholders as was illustrated during the Arab spring of 2010 when large numbers of disaffected citizens were able to mobilise using social media. The democratisation of information and knowledge has also enabled the ‘neo proletariat’ to evolve to a new level through access to online education and learning particularly during the Covid-19 pandemic. The recent emergence and popularity of video conferencing, telemedicine and remote diagnosis and consultation are also examples of the evolutionary benefits of the data deluge. This has made it possible for the global economy to continue to operate during the pandemic albeit at a lower level of capacity.

The new means and factors of production (data and information) have also impacted upon Marxist philosophy in two areas closely related to value creation. First, Marx`s (1996) labour theory of value that all goods and services in a capitalist system were sold at prices that reflected their true value and all values were measured in labour hours. Secondly, Marx`s philosophy was almost entirely predicated upon the existence of tension between the bourgeois capitalist owners of the means of production and the labour power of the proletariat working class. Marx was drawn to the labour theory because of his belief that human labour was the only common characteristic shared by all goods and services exchanged on the market. The paper will now analyse the relevance of these two philosophies in the light of recent technological developments.

Theoretically speaking, the traditional Marxist conceptualisations of capital, labour and value help us to understand contemporary capitalism. The earlier materialist perspectives of Marxist philosophy offer a limited framework to understand and analyse the transitions within contemporary capitalism, and its new modes of production, distribution, and exchange relations led by complexities of big data, communication technologies and information driven economic system dominated by capitalism. The nature of capitalism has changed with the growth of AI and practices of data driven economic system-based programming of collective cognitive power of labour. The commodification of cognitive labour is the foundation of new capitalist system in which modes of control over production, consumption, distribution and exchanges are very different for earlier forms of capitalism in history. Boutang (2012) has conceptualised this new political economy of capitalist transformation as ‘cognitive capitalism’, where the power of capital is expressed through data and information. It fundamentally changes the nature and traditional Marxist concepts of labour, value, property and materiality of social and economic life.

Value of Products and Services are Measured in Labour Hours

First, the belief that the value of all products and services were measured in labour hours has now been undermined by the dematerialisation of physical products into digital formats and the advent of free services on the Internet. This process of digitisation is referred to as `datafication` (Lycett, 2013). According to Norman (2001), datafication can be conceptualised via three innovative concepts that allow the logic of value creation to be rethought. These comprise: dematerialisation, liquification, and density. Dematerialisation is the ability to separate the informational aspect of an asset/resource and its use in context from the physical world. Liquification is the point at which, once dematerialised, information can be easily manipulated and moved around (given a suitable infrastructure). This allows resources and activity sets that were closely linked physically to be unbundled and `rebundled` in ways that would have traditionally been difficult, overly time-consuming or expensive. Finally, density involves the best (re)combination of resources, mobilised for a particular context at a given time and place and it is the outcome of the value creation process (Norman, 2001). The driving force in this value creation process is the platform Internet company that provides the necessary infrastructure.

A good anecdotal example of the process of datafication is provided by Netflix. Netflix is currently the leading provider of on-demand Internet media streaming. However, historically Netflix`s operating model was physical in nature with the core business being a mail order-based disk rental (DVD) company. Dematerialisation of the product occurred when Netflix decided to move to its present streaming model. This represented the unbundling of physical content from the brick-and-mortar shops. Under the old system of hiring physical disks this would result in delays and queues in the supply chain. However, liquification removed these delays by providing an on-demand service 24/7. This was enhanced by the development of a data-relationship between the provider and subscriber through the manipulation of the data gathered online.

Moreover, with streaming, subscribers could sample videos before selecting a particular title as well as being able to consume several videos in one sitting. This enabled Netflix to observe viewing statistics at a more granular level and in real-time. Therefore much more data was dematerialised using the streaming model. This meant that a richer and more varied set of data sources evolved including the catalogue data (more than 1,000 facets are now associated with a title), search terms, stream queues and plays, interactions and external sources such as film reviews and social data. Liquification is also greater due to the increasing pervasiveness of recommendations in the streaming model. The removal of time and distance from the business model also increased the potential for interaction between the provider and the subscriber via dynamic personalisation, the explanation of content to promote trust, ratings, rankings and reviews and the social influence stemming from what connected friends may have watched or rated.

Netflix`s dematerialisation has resulted in over 180 million subscribers signing up for the service. This combined dematerialisation and liquidity resulted in a high level of density as Netflix had moved from streaming content to actually producing it as well. The statistical analysis of many years` worth of user behaviour was utilised to inform the choice of content, presenting Netflix with successful and unique programming.

This highly automated process using Big Data analytics (Chen et al., 2012) has removed labour hours from the value creation process. This is further exacerbated by the fact that a digital product, unlike a physical product, can be replicated and disseminated multiple times at near-zero marginal cost (Rifkin, 2016). This value creation process therefore requires even fewer labour hours as does the storage of digital inventories instead of physical product inventories. This enables platform companies such as Amazon and Alibaba to stock and sell a broader range of products often in niche` segments. This is a phenomenon that Chris Anderson (2006) referred to as the long-tail (Brynjolfsson et al., 2006; Elberse, 2008). This is where the company can sell small quantities of a very large range of products rather than large quantities of a small range of items (Enders et al., 2008).

Another characteristic of digital products is that they are non-rivalrous and non-excludable (Brynjolffson, 2016). In economics, a good that is both non-excludable and non-rivalrous means that use by one individual will not reduce its availability to others. For example, a physical newspaper, video/DVD or CD can only be purchased or rented by a single individual at a time excluding others from hiring or buying the same product (i.e. it is rivalrous) whereas digitised movies or music can be streamed multiple times and watched by millions of people simultaneously. This on-demand business model also enhances value through higher availability and lower cost and requires fewer labour hours if any at all.

The use of labour hours to value a product or service is also undermined as a theory when considering the availability of free digital services and products via the Internet. Google utilises a `Hidden Revenue` business model where it provides a broad range of free services including search, maps, email, mobile phone software and videos but monetises these services through advertising (McGrath, 2010; Teece, 2010). Other business model innovations include the `freemium` pricing model which is applied to online computer games and the discounting of computer gaming consoles (cost-price) to attract software games developers. These are designed to direct traffic onto the respective websites and encourage product adoption through network effects (Enders et al., 2008). This has no direct relation to the value of labour inputs whatsoever.

Marx`s Labour Theory of Value (Baumaol, 1984) is also challenged by the concept of the `Prosumer` (Toffler, 1981). As most online content uploaded onto technology platforms today is actually produced by the consumer, free of charge, this means that the traditional value creation process is rendered obsolete. The value of the commodity being uploaded is no longer objectively measured by the average labour hours required to produce the commodity. This value is also ignored when calculating the gross domestic product figures of a country. This topic is discussed further in the next section of the paper which explores the relationship between the ownership of the means of production (the bourgeoisie) and the traditional role of the proletariat working classes.

Bourgeoisie Owners of the Means of Production vs. the Proletariat Working Classes

The second area of contention is concerned with Marx`s (2013) philosophy that there was a clear delineation and tension between the bourgeoisie owners of the means of production and the proletariat working class who produced the goods and services.

Modern platform Internet companies do not operate in the same way as traditional industrial era manufacturing firms and use different business models to create value (Gawer, 2009). These platform companies do not make products but provide an Internet based infrastructure and act as intermediaries in matching buyers and sellers (or users) through the leveraging of large data resources that they collect from online `traffic`. Instead of these firms owning the means of production they own the means of connection to the Internet (Moazed and Johnson, 2016). The successful platform companies control the flow of `traffic` through the Internet gateway (search engine) and ensure that they attract a critical mass of users whom they can then monetise. This business model is therefore based on leveraging customer data by building a large ecosystem of external followers (Moore 1993, 1996; Iansiti and Levien, 2004). The leading exponents of this Big Data business model are Amazon, Google, Facebook, Alibaba and Tencent etc.

Marx`s (2013) ownership of the means of production philosophy (based on the traditional manufacturing firm) is therefore completely inverted (Parker et al., 2017). Platform Internet companies invert the traditional firm because the bulk of a modern Internet firm`s value is created by its external community of users whose data is harvested and monetised by the platform (Van Alstyne and Parker, 2018). This spawns a massive portfolio of digital products that are provided free or at a very low-cost including media entertainment, maps, mobile services, apps, financial services and newsfeeds etc. (Moazed and Johnson, 2016).

The role of the platform owners (Amazon, Google, Facebook, Alibaba and Tencent etc.) is not to control the means of production but to control the gateway to the Internet and curate and nurture the production of content by their external network communities. Modern consumers of Internet products and services have, as mentioned earlier in the paper, become producers through the development of user-generated content. The content they produce and upload includes photographs, videos, text messages, apps, articles and reviews (etc.) on major Internet platforms such as Facebook, Instagram, Snap, You Tube, Wikipedia, Trip Advisor, Google Play and the Apple App Store etc. This means that the former Marxian proletariat (Marx et al., 2015) have now become `prosumers` (Toffler, 1980) who own the content production process (which they provide free of charge) as well as enjoying the outputs from the process as final consumers. This is a radical change to the former Marxian value model and the delineation of owners and workers.

This platform business model that leverages data and enhances the value of goods and services by democratising the availability and dissemination of information was not a characteristic of the Marxian philosophy where the owners would deliberately restrict information flows in order to retain power (Holbert, 2002). This democratisation of information and the availability of information goods have also increased efficiencies by aggregating unorganised markets (Parker et al., 2016). Market aggregation is the process whereby platforms provide centralized markets to service widely dispersed individuals and organisations. Market aggregation provides power and information to platform users (not just the owners) who formerly engaged in interactions in a haphazard fashion often without access to reliable or up-to-date market data. Amazon Marketplace, Alibaba and Etsy provide online sites where vendors of products from around the world can offer their wares to consumers illustrating the shift in power from the former bourgeois owners to individuals and entrepreneurs – the new proletariat. This two-sided, marketplace model facilitates interaction and value enhancement between two complementary groups for mutual benefit via the intermediary/Internet platform (Eisenmann et al., 2006).

The shift from physical to digital products has also seen a major change in the relationships and value creation processes between manufacturers/producers (owners of the means of production) and the former working class proletariat due to disintermediation (Choudary, 2015). Internet-driven disruption has been responsible for the re-engineering and reinvention of industry-wide value chains through the elimination of middlemen and intermediaries by establishing direct connections between producers and consumers resulting in the decline of traditional businesses and value creation models. In the pure information goods sector such as news and media (newspapers, movies and music), the disintermediation not only includes the intermediaries but also the producers (Clemons and Lang, 2003). Where content is dematerialised into a digital format the need to manufacture video tapes, DVDs and CDs and print physical newspapers, magazines and books is removed as is the requirement for a physical distribution and retail network. This does not only impact upon Marx’s labour theory of value by removing labour from the value creation process but also changes the relationship between supplier and consumer.

This means that the previous owners of the means of production and distribution (large and powerful music labels, newspaper groups, media companies and publishers) no longer dictate terms and conditions over a subservient proletariat class but have experienced a major shift in power and profitability to new platform companies (Netflix, Spotify, Amazon and Google) that deliver services and new on-demand (24/7) value propositions directly to consumers. The use of Internet and digital technologies has reduced the cost and lowered the barriers to entry for writers, musicians, journalists and actors/directors by circumnavigating the former gatekeepers (Choudary, 2015) who controlled the means of production and distribution resulting in a more egalitarian value creation process.

Disintermediation from digital disruption by the platform companies has also seen a parallel trend towards re-intermediation. Reintermediation involves replacing non-scalable and inefficient agent intermediaries with online tools and systems (often automated) that offer valuable new goods and services to participants on both sides of the platform. Networked platforms serve as more efficient intermediaries owing to their ability to use market mediating mechanisms that scale. Whereas traditional intermediaries relied on manual efforts, platform intermediaries rely on algorithms and social feedback which scale quickly and efficiently. Moreover, their ability to gather data over time and use it to make the system more intelligent allows platforms to scale their intermediation in the market in a manner that was impossible for traditional middlemen (Parker et al., 2016). Examples of re-intermediation platforms include Yelp, Angie`s List and Trip Advisor which provide reputational information based on social feedback about producers by certifying the quality of products and services. Meanwhile, the emergence of the app ecosystems of Apple and Google following the launch of the iPhone is another example of re-intermediation.

This serves to further illustrate how the traditional relationship between the bourgeois owners of the means of production and the proletariat has broken down and been replaced by a new value-creating mechanism driven by external ecosystem communities that reside outside the boundaries of the traditional firm.

Another significant trend driven by new digital technologies that has upended the traditional relationship between the bourgeoisie capitalist class and the proletariat working class has been the emergence of the sharing economy (Rifkin, 2016) and the de-linking of assets from value (Parker et al., 2016). The sharing economy is a very important development because it illustrates how the digital platform business model is now affecting physical goods and fixed assets and not just pure information goods (Clemons and Lang, 2003). The most famous and successful examples of platform companies that have been able to de-link assets from value are Uber and Airbnb. By de-linking the ownership of the physical asset (cars and properties) from the value it creates allows the use of the asset to be independently traded and applied to its best use. This is the use that creates the greatest economic value rather than being restricted to uses specific to the owner resulting in under-utilisation. As a result, efficiency and value rise dramatically.

In this scenario, the owners of the means of production are the drivers (owners) of the cars and the real estate hosts (owners) plus the smartphone handset owners (the customers). The only thing that Uber and Airbnb own is the means of connection to the buyers and sellers via their Internet enabled websites and apps supported by rented computing power from the cloud and free satellite navigation (GPS). This completely inverts the traditional value creation process (Parker et al., 2017) based on bourgeois capitalists and proletariat workers.

In fact, the sharing economy model can be extended to most physical assets in the business-to-consumer (B2C) context. It has also been extended into the business-to-business arena and includes co-working/shared office space (We Work), Crowdfunding (Funding Circle) to start and expand a business and cloud computing (Amazon, Microsoft and Google) for the rental of computing infrastructure. This means that modern day companies rarely own their means of production but utilise an external ecosystem of resources in order to create value.

As we can see from Table 1.0, the differences between the bourgeoisie and the neo-bourgeoisie and the proletariat and the neo-proletariat far outweigh the commonalities, particularly in the case of the former.

Differences	Differences
Bourgeoisie	Neo Bourgeoisie
Ownership of the means of production.	Ownership of the means of connection (the gateway to the Internet).
High fixed costs (plant and equipment).	Low fixed costs (IT infrastructure).
Factors of production - land, labour and capital.	Factors of production - data and information.
Control the flow of products.	Control the flow of data and ‘traffic’.
Value created and controlled internally within the firm (hierarchy).	Value nurtured and curated externally within the ecosystem and business community (marketplace).
Configuration is linear and sequential.	Configuration is a multi-sided network.
Vertically integrated manufacturers.	Market aggregators.
Long, integrated supply chains.	Short, direct-to-consumer, disintermediated supply chains.
Direct autocratic control.	No direct control, relationships based on nurturing the co-creation of content and products/services.
Mass production of standard products only.	Mass customisation of services through micro-targeting.
Differences	Differences
Proletariat	Neo Proletariat
Direct remuneration based on labour hours.	No direct remuneration. Free services in exchange for personal data.
Producers of value only	Prosumers - producers and consumers of value
High barriers to accessing resources i.e. capital.	Low barriers to accessing resources i.e. data and information.
Ownership of labour as a resource and capability only.	Ownership of a wide range of resources and capabilities including content creation capabilities, goods, cars and properties etc.
Commonalities	Commonalities
Bourgeoisie	Neo Bourgeoisie
Monopoly power.	Monopoly power.
High scale.	High scale.
Ownership of a critical value-creating resource: ownership of the means of production.	Ownership of a critical value-creating resource: ownership of the means of connection.
High economic rents.	High economic rents.
Commonalities	Commonalities
Proletariat	Neo Proletariat
Low power.	Low power.
Provide a critical and essential input (labour).	Provide a critical and essential input (data).

Table 1.0: The differences and commonalities between the bourgeoisie and the neo-bourgeoisie and the proletariat and the neo proletariat.

It is therefore unsurprising that the Internet companies have had such a disruptive impact due to the inability of traditional incumbents to replicate their platform business models as highlighted in the table.

Future Trends and Developments

As Schumpeter`s (1943) `gale of creative destruction` unfolds and current and emerging technologies evolve, changes in the value creation process are predicted to continue. Technologies such as robotics, sensors, the Internet-of-Things (IOT), Blockchain, crypto currencies, autonomous vehicles, connected homes/workplaces and artificial intelligence (AI), will further transform how producers, employees and consumers relate to one-another and how value is ultimately created and retained (Brynjolfsson and McAfee, 2016; Schwab, 2018)

Through the Internet-of-Things, products will evolve into smart services because of the data that they generate and as this phenomenon is extended to transportation and the home, through sensors and artificial intelligence (AI), the ability to remove the low class proletariat jobs using robotics will increase. Meanwhile, the new owners of the means of production in a world of advanced machine learning and artificial intelligence (AI) will be the data-rich platform companies who control and own the information gathered from the ecosystems where they curate and disseminate value.

A key risk, however, is the extent to which the large Internet companies become data monopolies and this is allowed to distort the information and knowledge available. Since the large Internet firms sell or use their data for advertising and marketing purposes, focusing on data and information that is populist and more readily commercialised could impact upon the availability of reliable knowledge.

The battle to become the leader in artificial intelligence (AI) and the new digital superpower is currently unfolding between the USA and China. The core ingredient for the development of successful artificial intelligence capabilities is access to limitless troves of data (The Economist, 2017). Both countries are well endowed in this respect with the world`s largest Internet platform companies being based in North America and China. China has been referred to as the Saudi Arabia of data because it has more Internet users than any other country at 730 million and almost all of them go online using smartphones and mobile devices that generate more data than desktop computers because of the sensors and GPS that are installed in them and their extensive use as payment devices (The Economist, 2017). Meanwhile, privacy is also not a concern with Chinese citizens who are regularly monitored using smart CCTV cameras and facial recognition technologies. In the US, adherence to the First Amendment has meant that freedom of speech and information has been a major priority and the country is currently the leader in artificial intelligence research.

The two nations` pathways to digital supremacy are therefore based on different perspectives. China`s approach is highly totalitarian in comparison to the USA. It still retains its Great [Internet] Firewall and it is regulating all virtual private networks (VPNs) within the country. Meanwhile the powerful role of the Chinese Communist party has been further strengthened owing to the detailed digital records of Chinese citizens that it now has at its disposal. America, on the other hand, is still pursuing a libertarian Silicon Valley egalitarian-style model where freedom of information is still prioritised over privacy and security (at least for the time being).

This may well result in a capitalist vs. communist battle to be the global leader in artificial intelligence in a digital cold war between two ideologies. America is already banning the sale of key strategic technological assets to the Chinese such as silicon chip companies and AI firms in a bid to slow-down China`s progress. So we may be about to see a capitalist vs. communist rivalry although this will be subtly different to the one portrayed by Marx and Engels (2015). The owners of the means of production in the future are more likely to be the large Internet firms (rather than the former bourgeois factory owners) who control the means of connection and hence the data and information generated by their users. The proletariat, meanwhile, may well be the ordinary citizens whose data the new owners of production/ connection are harvesting. What is certain, however, is that the new factors of production and the new source of power, competitive advantage and value creation will be data, information and artificial intelligence capabilities not labour hours.

Policy Implications of the New “Industrial” Landscape Built on Big Data, AI & the New Factors of Production (Data & Information).

There is very little that policymakers in government and regulatory bodies can do to reverse the changes inflicted by technology through the creation of the new “industrial landscape”.

The dematerialisation of physical products into digital formats and the advent of free services on the Internet has meant that consumers are now receiving better products and better service value propositions than previously provided by the old industrial economy. Moreover, the role of consumers as producers or `prosumers` (Toffler, 1980) and the removal of labour hours from the value creation process does not breach any employment or competition rules.

Meanwhile, disintermediation of incumbent manufacturers and retailers and their supply chains by digital platform companies (Parker et al., 2016) is no different to the substitution of the cottage industry and home working by the factories in the industrial era (Yeah, 2018) and doesn’t constitute anti-competitive behaviour.

Nevertheless, there are now sections of society and members of the political and regulatory establishment who view the relationships between the new elite, consisting of the `neo-bourgeois` platform Internet companies and the ` neo-proletariat` workforce of data providers, as one of exploitation - not dissimilar to Marx`s original labour power theory. In certain areas of the sharing economy such as ride hailing and food delivery, there is now regulatory pressure to reclassify drivers and delivery operatives as employees of the platforms rather than contractors so that they can receive a minimum wage, statutory holidays and sick pay plus pension rights. This has serious implications for the business models of these platform companies since the driver represents approximately half the cost of an Uber ride. This has resulted in the ride hailing companies and food delivery firms increasing their investments in autonomous vehicles and delivery robots designed to remove the drivers and delivery operatives completely (`robotaxis`). In the long term, this will lead to further disintermediation and the removal of labour hours as a factor of production.

Where a platform company operates as a marketplace as well as retailing its own products in parallel (Google, Apple and Amazon), regulators have also started imposing large fines after accusing the Internet firms of favouring their own products over third party merchants during the search process. However, these fines have not led to any changes in behaviour nor any break-up of the platform companies.

Privacy issues and offensive social media content are also areas being targeted by regulators. To overcome the privacy issues, end-to-end encryption has been introduced by Facebook and other Internet companies. However, governments are now complaining that this prevents them from accessing social media feeds to combat crime.

The uploading of what is deemed illegal or offensive material is also gaining considerable attention. However, the need to defend freedom of speech and the problems of defining what is offensive and illegal (which is a very judgemental and subjective issue) are proving to be stumbling blocks for the regulators. If regulators cannot come up with a definition that clearly states what is offensive and/or illegal then the social media companies cannot be held liable for its non-removal.

Due to the huge volumes of data being uploaded onto the platforms each second, this has been difficult to `police`. This is why Google and Facebook are investing heavily in artificial intelligence (AI) which, when fully developed, will be able to automatically manage and curate the quality of data uploads and authorise their removal. This automated process will further remove labour hours from the production process.

Political and regulatory concern over the power of the platform companies has given rise to requests for the firms to be broken up. However, because these platforms cross industry boundaries and do not have a traditional monopoly as defined by existing competition law, this makes such as task very difficult. The only way this is likely to happen is if there is a route-and-branch re-write of competition law which is no longer appropriate for the regulation of Internet companies since they do not belong to any single Standard Industrial Classification (SIC) but are industry agnostic (Moazed and Johnson, 2016). Consumers also benefit hugely from the `free` services and products being offered and this cannot be considered as being to their detriment financially.

Finally, the proposed introduction of the Libracoin by Facebook was also likely to have result in the disintermediation of the global banking system. The use of a `stablecoin` tethered to a fiat currency supported by Blockchain technology would revolutionise the payments industry and money supply. Facebook have since decided to launch Libracoin as a digital currency since governments were threatening to ban the currency. Cryptocurrencies therefore have the potential to further reduce the labour hours required to deliver banking services as this would represent the democratisation or `denationalisation of money` (Hayek, 1972; Yeah, 2018).

In summary, the policy implications for the new age economy and the new “industrial” landscape are; as policy makers step up their regulatory fight, the technology companies will seek to address the issues with more technological innovation (encryption, robotics and artificial intelligence); and this will ultimately result in a further reduction of labour hours from the production process over the longer term; not more.

However, as the Industrial Internet of Things (IIoT) and Industry 4.0 start to build traction, Big Data and artificial intelligence (AI) can now play a key role in enhancing the evolution of work and the ‘neo proletariat’. Not only has the Internet, through services such as video conferencing and home working and cloud computing, ensured business continuity during the pandemic, it also provides a range of additional workplace employee benefits. These include improved productivity of the workforce by reducing costs through automation, the ability to make faster decisions, the reduction of errors, better targeted marketing campaigns and more integrated supply chain and operations functions (Clifton et al., 2020). These AI-driven processes have made work less repetitive and arduous with the use of robotics, thereby allowing the worker to upskill and carry out more rewarding and better paid tasks. Rather than displacing workers, this has resulted in the evolution of work by helping people to do their work to a higher standard and to perform more meaningful tasks (Clifton et al., 2020). AI is also being increasingly used in the health sector to scan x-rays for patterns relating to alzheimer’s disease and cancer which can be performed faster, more effectively and at a lower cost leaving personnel free to undertake other high value activities.

Conclusion

The `Neo-Bourgeois` Internet Firms and the `Neo-Proletariat` Data Providers

In summary, as the platform Internet companies continue their inexorable and exponential growth trajectories (Ismail et al., 2014) and as their power continues to rise through greater levels of data ownership and artificial intelligence capabilities (AI), there are now sections of society and members of the political and regulatory establishment (particularly in Europe) who view the relationships between the new elite form of `neo-bourgeois` platform Internet company and the ` neo-proletariat` workforce of data providers as one of exploitation not dissimilar to Marx`s original labour power theory.

Although, until recently, the relationship between Internet firms and their users has been a healthy and successful one (particularly between the technology firms and the millennial and Generation Z population), the `neo-bourgeois` technology companies are now facing a potential backlash, at least in Europe, regarding the issue of data ownership and rewards. This has partly been triggered by the failure of leading platform companies such as Facebook to protect user data and the abuse of data-ownership by firms such as Google and Amazon in the form of anti-competitive behaviour.

Nevertheless, the level automation of domestic and workplace tasks and the machine learning capabilities of the large platform companies will ensure that the `neo-bourgeoisie` will still wield power and influence despite any attempts by the regulators to reconfigure their value networks.

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Technological singularity refers to irreversible and unforeseeable growth of technological super intelligence, which can surpass all human intelligence causing unpredictable changes to modes of human living and economic development (Eden et al., 2012). Simolin (2020) has outlined the dangers, challenges and benefits of technological singularity. Therefore, Aschoff and Mahta (2020) have argued for a ‘people’s AI’ which can minimise negative consequences of technological singularity.

Cinderella of capitalism indicates cognitive transformations within capitalist economic system, which promotes unachievable desire-based society within its complex modes of production, distribution and exchange relationships established by collective programming of cognitive power of labour power expressed with the help of new information and communication technologies (Boutang, 2012).