When it was rapidly expanding in 2014 and 2015, Uber was adding up to 50,000 drivers every month, a number impossible for human managers to schedule and supervise, ‘so some system of virtual management had to be put in place’ (Caddell 2017). The company filled the gap with a variety of automated management tools providing a ‘rating system, performance targets and policies, algorithmic surge pricing [and] insistent messaging and behavioral nudges’, that ‘steer drivers to work at particular places and at particular times’ – a system that, as Alex Rosenblat (2016) remarks ‘complicate[s] claims that drivers are independent workers whose employment opportunities are made possible through a neutral, intermediary software platform’. From 2016 Uber consolidated these algorithmic management tools within its own internally produced ‘Michelangelo’ ML system, which was ‘designed to cover the end-to-end ML workflow: manage data, train, evaluate, and deploy models, make predictions, and monitor predictions’ (Hermann and Del Baso 2017). The logical culmination of Michelangelo’s trajectory seems to await the fulfilment of Uber’s ambitions to supply the software for self-driving cars, in which case the data culled from hundreds of thousands of precarious self-employed drivers would be absorbed by AI-directed autonomous vehicles.
A third current tendency AI is likely to amplify is the polarization between ‘high-end’ and ‘low-end’ jobs. This trajectory, apparent in the US since the 1950s, has intensified as digital automation and outsourcing hollowed out ‘middle-level routine’ jobs in favour of either high-paying cognitive labours or low-paying manual work, albeit with downward pressures on both (Autor and Dor 2013; Beaudry, Green and Sand 2013; OECD 2016; Elliott 2017). The US Department of Labour’s (2015) occupational projections to 2024 lists ‘computer and mathematical occupations’ as one of the fastest growing, and best paid, areas of employment. AI-capital will (at least until its self-automating capacities kick in) contribute to this growth, as it requires not just direct programming of ML, but also coding of the social media, apps and sensors that provide the Big Data on which it depends. The US national average salary for IT jobs is about \$81,000 (more than double the national average for all jobs), and the field is ‘set to expand by 12 percent from 2014 to 2024, faster than most other occupations’ (US Department of Labour 2015). But coding labour is dwarfed in absolute numbers and rate of growth by jobs in areas such as personal care work, nursing and medical assistance, retail sales and food service, many with wages in the \$20,000 a year range (US Department of Labour 2015; Henwood 2015). This suggests a future labour force divided between programmers coding other mid-level jobs out of existence, leaving tasks such as the care of an aging population to workers whose jobs remain at once too cheap and too humanly complex to automate – though this does not inhibit rampant fantasies about the imminent possibility of sex-bots and robot teachers. Such dreams and nightmares may be finding realization in the elder-care facilities of Japan, where the demographic problems of an aging population are particularly acute, and automata find relatively easier cultural acceptance. The development of ‘social robots’ as potential aides to caregivers has been a focus of government-supported industrial research (Kyung-Hoon 2018), and the Japanese public is exposed to the constant promotion of ‘exoskeletons, internet-of-things gizmos, humanoid or animal-shaped robots ... destined for a career of endlessly cheerful toil in the country’s growing network of nursing homes’ (Lewis 2017). Elsewhere, such trajectories will probably be slow-moving, at least while wages for personal care workers remain low.
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inhuman power