Grammatization, we argue, should be understood as an extension of the real subsumption of labour to what Marx in the 1844 Manuscripts termed ‘life-activity’ (2007: 75). What ML subsumes is not just labour but more general human behaviour, communication, knowledge and skills that relate to how we live – including purchasing and consumption, but also the social relations of friendship, conviviality and love that are, from capital’s point of view, engines to be harnessed for its accelerated circulation. As Stiegler (2010: 45) puts it, using three useful French phrases, savoir-faire (knowing how to make and do), savoir-vivre (knowing how to live) and savoir-être (knowing how to be) are stripped from humans across the entire span of the social or planetary factory. In a recent work he explicitly relates this to AI and big data, suggesting that in their current forms these create disaffected, confused and incapacitated subjectivities, in a profound sense ‘stupefied’ (Stiegler 2015). For Stiegler, machine learning ‘calculate[s] correlations … in order to automatically anticipate individual and collective behaviour, which they also provoke and “auto-realize” by short-cutting and bypassing any deliberation’ (Stiegler 2017: 231). In our terms, they create an intensified state of decomposition.
It is through social media that capital is exercising some of its most powerful AI interventions, which at once restructure everyday social relations and have major implications for class composition. Google’s search-ranking algorithm and Facebook’s social graph of networked personal relationships have for over a decade been transforming the activities of these platforms’ users into digital profiles for targeting content and sale to advertisers. Where this crosses the boundary into the realm of AI is debatable; algorithmic processing is subject to the AI effect. Nevertheless, across two significant frontiers – the capacity to improve their own performance, and the escalating degree of predictive confidence that results – both corporations claim to be taking giant strides.
Facebook in 2016 unveiled its ‘self-improving, artificial intelligencepowered prediction engine’, dubbed ‘FBLearner Flow (FBL)’ (Biddle 2018). This uses ‘machine learning expertise’ to draw on ‘location, device information, Wi-Fi network details, video usage, affinities, and details of friendships, including how similar a user is to their friends’, to address corporate ‘core business challenges’. FBL is not merely offering advertisers the ability to target people based on demographics and consumer preferences, it also provides a ‘loyalty prediction’ service that searches a user base of over 2 billion individuals to detect millions ‘at risk’ of jumping ship from one brand to a competitor, subjects primed to be ‘targeted aggressively with advertising that could pre-empt and change their decision entirely’.
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inhuman power