through lifelong learning.
In other words, Schwab’s approach seeks to capture both the stunning opportunities and threatening risks stemming from AI. Pressed to an extreme, however, his analytic approach is never free from a certain degree of ambivalence, as every social change associated with the digital revolution appears mediated through this both/and logic. This might be said to be the conceptual equivalent of wanting to have your cake and eat it too. Towards the latter sections of The Fourth Industrial Revolution, Schwab’s analytic reserve – where his lack of a conclusion on the consequences of AI becomes a conclusion all of its own – gives way to a more robust transformationalist sensibility. As he concludes:
The digital mindset, capable of institutionalizing cross-functional collaboration, flattening hierarchies, and building environments that encourage a generation of new ideas, is profoundly dependent on emotional intelligence . . . The world is fast changing, hyper-connected, even more complex and becoming more fragmented but we can still shape our future in a way that benefits all. The window of opportunity for doing so is now.12
In the end, AI for Schwab is an exhilaratingly progressive affair. He argues that AI has the potential to be institutionalized as a global, cosmopolitan form of life, one to be celebrated rather than castigated.
In contrast to this business-school approach to understanding AI, radical French theory informs Bernard Stiegler’s Automatic Society. Like Schwab, Stiegler holds that the AI revolution is already upon us. AI for Stiegler inaugurates a new social order of ‘total autonomization’, in which production and manufacturing are controlled by software and big data. But unlike Schwab with his stab at analytic even-handedness, Stiegler is out to develop a more full-blooded critique of the destructive aspects of AI for economy and society. He writes, for example, of today’s ‘immense transformation’ whereby ‘capitalism becomes purely computational’, of ‘generalized autonomization and autonomisms’, and of ‘algorithmic governmentality’. Taking his cue from the post-structuralist analysis of ‘control societies’ developed by Gilles Deleuze, Stiegler seeks to lay bare the short-circuiting of minds and spirits – the ‘shock and stupefaction’ inflicted on contemporary women and men – arising from full automatization. Drawing upon quantum physics, Stiegler argues that automatized societies are increasingly locked in a contradictory relationship between entropy (where life-energy dissipates) and negative entropy (the reversal, or undoing, of such decomposition). ‘Automation’, writes Stiegler, ‘has given rise to an immense amount of entropy, on such a scale that today, throughout the entire world, humanity fundamentally doubts its future – and in young people especially so.’13 Google Translate, as Stiegler remarks, is a good example of the immense linguistic entropy occurring throughout the world today, as split-second machine translation of the world’s diverse languages into English results in a radical impoverishment of vocabulary. Google’s algorithms simply flatten both the individual and collective use of language. What is at stake, as Stiegler shrewdly points out, is human knowledge in the broadest sense; knowing how to think, reflect, talk, communicate and act in the world.
If for Stiegler Google Translate represents destructive linguistic entropy, the algorithmic automation of society signals massive economic entropy. AI makes it possible not just to economize upon labour, but to fully automate tasks and thus render employees redundant. This is a redundancy of the worker’s expertise, as advanced automation for Stiegler produces a generalized (economic as well as environmental) ‘disorder of hyper-standardization’ – where work, and the value of employees, are determined by calculating probabilities based upon averages. Today’s industrial capitalism, writes Stiegler, is ‘an era in which calculation prevails over every other criteria of decision-making, and where algorithmic and mechanical becoming is concretized and materialized as logical automation and automatism . . . as computational society becomes a society that is automated and remotely controlled’.14 We are at the beginning of a process of technological transformation that will have a massive impact upon the nature of work, expertise and knowledge – the algorithmic governmentality of 24/7 capitalism, according to Stiegler, will precipitate ‘entropic catastrophe’.
The new technological landscape, however, results not only in doom and gloom. Stiegler also seeks to discern a hidden trend in economic entropy for reversing the devastating impacts of algorithmic capitalism. Emancipation for Stiegler is linked to the primacy of meaningful work, which he sharply differentiates from employment. In this perspective, work is fundamentally meaningful and creative, whereas the bureaucratized terrain of employment is increasingly automated and dependent upon computational software. His argument, broadly speaking, is that the production and transformation of automation prepare the way, paradoxically, for the ‘dis-automatization of society’. In a striking irony, the kind of employment which is bound up with automated entropy also consists in de-automating routines, which can liberate most of the population from exploitative domination. If employment is increasingly the terrain of advanced automation, complex algorithms and computational software on the one hand, work produces value and the creation of something new to society on the other hand. From this angle, Stiegler emphasizes that work consists of practical know-how (savoir-faire), formal knowledge (savoirs formels) and life skills (savoir-vivre). The ‘data economy’ is therefore not the inevitable destiny of automated society; a range of other possible systems can be envisaged. This scenario, Stiegler proposes, is already practicable. We have reached a stage, in algorithmic capitalism, in which the automated forces of production are overdeveloped and new economic models based on the social economy and cultural solidarity – especially through associations, cooperatives and public services, as well as new industries – will create novel, intermittent forms of work and new professions. A non-repressive automated society, Stiegler argues, would become an ‘economy of contribution’.
How is it possible that there should be such significant differences in assessment between two authors associated with the transformationalist position? To begin with, Stiegler’s writings serve as an apt counterbalance to Schwab’s emphases, particularly the former’s penetrating analysis of the very large decline in jobs worldwide resulting from advanced automation. Schwab’s work is explicitly concentrated on how organizations create value, and he repeatedly emphasizes that technological transformation today creates new opportunities and dilemmas – the results of which can lead to positive, shared benefits for all of society. Stiegler on the other hand clearly does intend his analysis to have a very broad application: not just economics and the market, but society and the politics of life itself. Whilst some have dismissed Stiegler’s work as excessively influenced by the jargon of radical French theory, his critique of the production of automation in contemporary social life, as AI displaces labour, remains highly significant. In demonstrating that advanced automation produces an entropic violence of hyper-standardization, Stiegler’s critique arguably confronts head-on the most painfully destructive and debilitating aspects of algorithmic capitalism. We can also see that fundamental lines of difference are to be found among voices advocating the transformationalist position. This is an important point. Contrasting the contributions of Schwab and Stiegler highlights that the transformationalist position is not cut of one cloth.
Box 2.2 Transformationalists
1 Rejecting the claim of business-as-usual for the global economy, transformationalists see AI as an expression of broader digital shifts occurring in institutional life and contemporary society. Industry 4.0, big data and supercomputers are key examples.
2 There is an emphasis upon a revolutionary transformation of manufacturing and services, which demands a radical rethinking of labour market strategies.
3 Transformationalists are concerned not just with intensified economic dynamism stemming from AI, but with changes in society, culture and political life. In other words, AI transforms not only how we work but also how we live.
4 Some assessments emphasize that AI promotes productivity and economic growth, which in turn fosters innovation. Other assessments position economic growth and social equality as out of alignment,
