Inhuman Power

Artificial Intelligence and the Future of Capitalism

Nick Dyer-Witheford, Atle Mikkola Kjøsen, James Steinhoff

• Publisher: Pluto Press
• Year: 2019
• ISBN:
  9781786803962
• Edition: 1
• Topic: Ciencia de la computación
• Subtopic: Inteligencia artificial (IA) y semántica

Artificial Intelligence (AI) has seen major advances in recent years. While machines were always central to the Marxist analysis of capitalism, AI is a new kind of machine that Marx could not have anticipated. Contemporary machine-learning AI allows machines to increasingly approach human capacities for perception and reasoning in narrow domains. This book explores the relationship between Marxist theory and AI through the lenses of different theoretical concepts, including surplus-value, labour, the general conditions of production, class composition and surplus population. It argues against left accelerationism and post-Operaismo thinkers, asserting that a deeper analysis of AI produces a more complex and disturbing picture of capitalism's future than has previously been identified. Inhuman Power argues that on its current trajectory, AI represents an ultimate weapon for capital. It will render humanity obsolete or turn it into a species of transhumans working for a wage until the heat death of the universe; a fate that is only avoidable by communist revolution.

1

Means of Cognition

[T]he Microsoft view is that AI needs to be included – or in Microsoft speak, ‘infused’ – in everything, from a simple word processor to a quantum computer.

James Thompson (2018)

THE NEW ELECTRICITY

In 2016, Andrew Ng, Stanford professor, entrepreneur, former Chief Scientist at Baidu and former head of Google Brain, pronounced AI ‘the new electricity’ and argued: ‘Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don’t think AI will transform in the next several years’ (Lynch 2017). Ng is not the only one to espouse the notion of AI as a basic utility leading to a new industrial revolution – the idea is implicit in proclamations of a ‘fourth industrial revolution’ issued by capitalist institutions such as the World Economic Forum (Schwab 2017). It has also been explicitly advanced by tech guru Kevin Kelly (2014), who predicts that in the near future we will have a ‘common utility’ of ‘cheap, reliable, industrial-grade digital smartness running behind everything … Like all utilities, AI will be supremely boring, even as it transforms the Internet, the global economy, and civilization. It will enliven inert objects, much as electricity did more than a century ago. Everything that we formerly electrified we will now cognitize.’ Companies such as Viv (n.d.) deploy this idea in their business plans, asserting that with their AI platform ‘intelligence becomes a utility’.

Predictions such as those of Ng and Kelly suggest that AI could become part of what Marx referred to as the ‘general conditions of production’ (Marx 1990: 506; 1993: 530), i.e. the technologies, institutions and practices which form the environment for capitalist production in a given place and time. Marx spoke of infrastructure, which includes the means of communication and transport, as a significant component of the general conditions of production. If AI becomes the new electricity, it will be applied not only as an intensified form of workplace automation, but also as a basis for a deep and extensive infrastructural reorganization of the capitalist economy as such. This ubiquity of AI would mean that it would not take the form of particular tools deployed by individual capitalists, but, like electricity and telecommunications are today, it would be infrastructure – the means of cognition – presupposed by the production processes of any and all capitalist enterprises. As such, it would be a general condition of production. We propose the term ‘means of cognition’ – the AI-equivalent to Marx’s means of communication and transport – but insist that it not be conflated with the post-operaismo notion of ‘cognitive capitalism’ (Moulier-Boutang 2011), for reasons we discuss in the conclusion of this chapter.

To make this argument first requires a review of the history of capitalism’s adoption of AI, a survey of some existing and anticipated commercial applications of AI founded on the ML approach, and analysis of the contemporary AI industry. While the basis for accumulation in this industry is a highly advanced techno-scientific commodity, it is, like all capitalist enterprises, governed by compulsions to produce surplus-value, i.e. seek profit, compete, attract investment, control markets and defeat rivals through the formation of oligopolies and monopolies. We draw attention, however, to two structural features of this industry that could contribute to AI becoming a part of the general conditions of production: its lavish support and subsidization by neoliberal nation states eager to foster AI development for economic, administrative and military purposes; and the seemingly anomalous presence of a large and vigorous open-source component to AI research, in which tools and templates are distributed for free and worked on cooperatively, but are nevertheless channelled towards the platforms and priorities of AI oligopolists.

We speculate on how, in the near future, ML-enabled functions of cognition and perception could become ubiquitous via applications ranging from simple chatbots up to smart cities and the Internet of Things (IoT). These examples demonstrate some ways AI could be positioned as a general condition of production. This analysis paints a picture which runs counter to post-operaismo’s humanist reconfiguration of the notion of the ‘general intellect’ (Marx 1993: 706) as referring to the novel capacities of a networked multitude. Contrarily, the possible future of AI as part of the general conditions of production supports Marx’s original formulation of the general intellect as capital’s accumulated machinic capacities, excised from social human labour. While AI development does, for the moment, depend largely on the mining and processing of data drawn from a networked multitude, the aim of such development is to attain a whole new level of automation giving capital unprecedented independence from labour.

THE AI INDUSTRY AND THE OLIGOPOLISTS OF MACHINE INTELLIGENCE

While corporate interest in the actual and potential uses of the new AI are manifold, ranging from retail sales to entertainment and industrial production, the actual production of AI systems is a central concern for a more limited circle of high-tech companies. We refer to this complex as ‘the AI industry’, distinct from the broader field of commercial AI applications. While business-oriented publications continually remind us that AI will ‘revolutionize’ capitalist production (Columbus 2016), our analysis suggests that such a transformation, if it occurs, is still in its earliest phases. Instead, we see AI as one emerging industry whose influence is tied up with that of other emerging technologies and is as yet difficult to ascertain with certainty. Although business interest in AI is high, outside the AI industry this does not entail high levels of actual investment in the technology. A 2017 survey of attendees at an applied artificial intelligence conference concluded that ‘AI adoption … remains low with the majority of major success stories coming only from the largest tech players in the industry’ (Rayo 2018).

AI development first appeared as a distinct industrial sector in the 1980s. This first era of the AI industry was based around GOFAI expert systems. During this era, the AI industry consisted of a few small companies which produced systems as means of production for, and typically in cooperation with, their corporate customers. In some cases, large firms established internal AI departments to develop proprietary expert systems. Such systems required a considerable degree of specialization, had extremely narrow fields of application and required a lot of labour to produce and update. While attempts were made to develop ‘generic’ expert systems which could be applied to any field, they ultimately failed (Roland and Shiman 2002: 205). The commercial craze for these systems subsided in the 1990s, but around the same time the ML approach gained traction in academia and, during the 2010s, returned AI to the commercial realm, propelled by advances in computing power and improved learning algorithms. By 2017, The Economist (2017a) was proposing a shortlist of domains in which ML’s power to ‘sift through data to recognize patterns and make predictions without being explicitly programmed to do so’ was becoming commercially important. It is worth surveying a few.

The ML-based AI industry is much more diverse than the first era of expert systems; this is one reason why advanced capitalism has recently contracted a serious bout of AI fever. The Economist (2017a) has been enthusiastic about the prospects of targeting online advertisements and product recommendations; the creation of virtual personal assistants and of augmented reality systems; and autonomous vehicles. As of early 2019, some of these were already highly advanced, while others only incipient. Algorithmic targeting of advertisements and recommendations has been a foundation of digital Web 2.0 enterprises for over a decade. Digital personal assistants, such as Apple’s Siri, Amazon’s Alexa or Microsoft’s Cortana, are gradually becoming commonplace. Augmented reality (AR) products, overlaying physical reality with a mesh of virtual images and information, are only beginning to be sold as commodities or distributed as free vehicles for in-app purchases and data mining. Games such as Pokémon Go and other mobile apps are testing the AR waters, while further frontiers, such as medical applications, are being actively researched by companies such as Google, Apple and Microsoft. Perhaps the biggest prize for the commercial use of ML, but also its most daunting challenge, is the creation of self-driving cars and trucks, a ‘moonshot’ that has attracted leading information companies such as Google and Baidu, established auto-industry giants such as Ford, General Motors and Daimler, and upstart entrants such as Uber and Tesla, all racing to transform capitalism’s entire transportation sector.¹

AI industry enterprises build ML technologies, often initially for use in their own business operations, but also as commodities for sale or rent, or as a ‘free’ service. They produce commodities for both of the major ‘departments’ into which Marx divided society’s total product and its total production process: (Department 1) means of production, i.e. commodities intended for productive consumption; (Department 2) means of subsistence, i.e. commodities destined for individual consumption (1992: 471). Some commentators on ML have suggested that, neatly corresponding with these two departments, there will be ‘two AIs’: one for business applications, the other for consumer devices (Economist 2017a). In Department 1 we find examples like SAP’s HANA, a ML-powered cloud database platform that enables behemoths like Walmart to monitor their entire organization’s functioning in fine-grained, real-time detail (Ruth 2017), and Andrew Ng’s start-up Landing.ai (founded in 2017) which aims to totally overhaul industrial manufacturing by providing ‘AI-powered adaptive manufacturing, automated quality control, predictive maintenance, and more’ (Landing n.d.). In Department 2, examples include various consumer commodities like Amazon Home and similar devices. Marketed as a ‘smart speaker’, Home is a user voice interface to the Alexa digital personal assistant that enables a variety of home automation and organizational tasks. AI is also found in other smart devices like phones and TVs and is also ‘given away’ as a component of free product-services such as Facebook, Twitter or YouTube where ML-based recommender systems curate timelines and give users suggestions on what to watch or listen to next. In turn, these systems gather customer data to fuel advertising revenues. However, as we will see, production of both Department 1 and Department 2 AI is often dominated by the same oligopolistic corporations, and may also be interconnected in a variety of ways, including the use of shared cloud computing facilities.

From 2015 on there has been a rapid escalation of corporate investment in AI research, venture funding of ML start-ups, and competitive hiring of AI talent as well as lots of acquisition and merger activity. Measuring the scale of this activity is difficult. According to one analysis, the AI industry had a revenue of $126 billion in 2015 and is projected to grow to $3,061 billion by 2024 (Statista 2016: 9), but another reckons worldwide spending on AI stood at only $19.1 billion in 2018, an increase of 54.2 per cent over 2017, and predicts it will reach a mere $52.2 billion by 2021 (International Data Corporation 2018). The Economist (2017a) calculates that in 2017 companies globally spent around $21.3 billion in mergers and acquisitions related to AI – 26 times more than in 2015. While such conflicting estimates (often manifestly driven by the self-interest of AI vendors and business consultancies) are confusing, it is clear that AI has seized the imagination of advanced capital’s representatives (see also Press 2018). As The Economist (2017a) puts it, ‘Fueled by rivalry, high hopes and hype, the AI boom can feel like the first California gold rush.’

Corporate competition for ML experts is ferocious. One study, based on LinkedIn profile data, puts the number of PhD educated people ‘capable of working in AI research and applications’ at 22,000, with only 3,074 currently looking for work (Gagné 2018). Demand far exceeds supply (Economist 2017b). US information capitalists are in competition both with new contenders – such as major auto companies with autonomous vehicle projects – but also now with China’s tech companies, some of which have set up subsidiaries in Silicon Valley. As hiring top talent is seen as crucial for the success of AI-capital, this competition has ‘set off a trend of firms plundering academic departments to hire professors and graduate students before they finish their degrees’ and created an atmosphere in which job fairs resemble frantic ‘Thanksgiving Black Friday sales at Walmart’ (Economist 2017b). This competition for ML talent also means that wages are high.

A recent New York Times article reports that ‘Typical A.I. specialists, including both Ph.Ds fresh out of school and people with less education and just a few years of experience, can be paid from $300,000 to $500,000 a year or more in salary and company stock, according to nine people who work for major tech companies or have entertained job offers from them’ (Metz 2017b). When Google acquired DeepMind in 2014, it paid $650 million for a company of 50 employees; in 2016, the lab’s ‘staff costs’ alone, as it expanded to 400 employees, totalled $138 million, an average of $345,000 an employee. In the light of such figures, it has been suggested that ML experts are ‘the new investment bankers’ (Shead 2017). The rewards are even higher, of course, for executives with experience managing AI projects. In a court case against Uber over ownership of autonomous car technologies, Google revealed that one of the leaders of its self-driving car division took home over $120 million in incentives before jumping ship to join their competitor. However, even fresh graduates with skills in ML may make ‘in excess of £100,000 and sometimes up to £1 million’ while still in their mid-twenties (Shead 2017).

The AI industry is international in scope. Between 2016 and 2018 it became widely recognized as a critical axis of technological competition between the United States and China, particularly given its potential for military application in an era of growing tensions. Important Chinese AI developers include its largest search engine corporation, Baidu, and ecommerce giant Alibaba (K-F. Lee 2018). Other important national sites for the AI industry include Canada, Israel and the United Kingdom. However, nearly all assessments suggest that the United States is the leading location (Jang 2017; Rapp and O’Keefe 2018; Fabian 2018). By one estimate, which surveyed over 3,000 companies around the world involved in aspects of AI development, 40 per cent are in the US (Fabian 2018). Six, however, are preeminent: Alphabet (Google’s parent company), Amazon, Apple, Facebook, IBM and Microsoft. These companies all exemplify what Tarleton Gillespie (2010) and Nick Srnicek (2016) respectively describe as ‘platforms’ or ‘platform capitalism’, a key feature of which is the digital gathering of big data generated by customers, be they users of search-engines, social media networks and video or music streaming services, or computer software or retail consumers. Access to such troves of data makes platform firms favourable sites for training ML systems.

IBM

Amongst these, IBM is in many ways an outlier, even though ‘Big Blue’ has a long record of interest in AI, stretching from its researchers’ involvement in the famous 1956 Dartmouth workshop to the triumph of its chess-playing Deep Blue over world champion Garry Kasparov in 1997 and its AI Watson, whose 2011 victory over human competitors in the television quiz show Jeopardy made it briefly the public face of the new generation of AI. Yet despite IBM’s $15 million investment in the system, Watson has subsequently had only limited commercial success. While it has been described as ‘one of the most complete cognitive platforms available’ (Kisner, Wishnow and Ivannikov 2017: 1), and has been applied eclectically to commercial ventures in fields from fashion to telecommunications, IBM’s major emphasis was on potential uses in the highly profitable medical and health insurance sectors. In 2018, however, the company laid off many of the staff in this key division and announced it would be seeking new areas of focus. It is uncertain how far this setback was the result of technological failures and how much was due to the rigidities of IBM’s organizational practices (Strickland 2018). IBM is likely hampered in its AI efforts due to not possessing the large proprietary pools of big data necessary for training ML systems; instead IBM has to acquire it, expensively, by buying up smaller firms engaged in medical research and data collection (Kisner, Wishnow and Ivannikov 2017: 19–20). The other major US AI producers, however, do not suffer from this problem.

Alphabet (Google)

Alphabet has been harvesting user data and applying it to advance their AI projects for years, first by algorithmically improving search patterns and matching them with ad placements, and then using similar methods for categorizing, filtering and recommending video content on YouTube or predicting which apps users of its Android mobile phone operating system would purchase. Alphabet’s Google Brain unit is widely seen as the leading corporate ML research group. Between 2014 and 2018 Google bought up no less than 12 AI-related companies (Patrizio 2018), the most notable being DeepMind, which made the ML system AlphaGo that in 2016 scored an uncanny victory over the reigning human Go world champion, thus supplanting Watson as the poster-boy for AI. Such research connects not only with Google’s algorithmic online services and its Google Home devices but also with its Waymo autonomous vehicles unit and suite of robotics-related companies it acquired in the early 2000s. The development of AI is an endeavour fervently advocated by Google’s owners Sergey Brin and Larry Page as well as the transhumanist thinker Ray Kurzweil who is their ‘director of engineering’ (Simonite 2017); the combination of vast funds, deep expertise and ideological commitment places Google in an exceptional position in commercial AI research. Other US platform capitalists are, however, following similar paths.

Facebook and Amazon

Algorithmic analysis and prediction have been central to the success of Facebook in plotting the ‘social graph’ of users’ interests and interrelations which drives its massive online advertising reve...