{"resolved":true,"match_type":"uuid","source_type":"original_book","book_id":"a2eab101-adfc-4a0d-8142-63a4d11cee02","title":"人工智能和资本主义的未来","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9780745338613","requested_by":"printed_page","book":{"source_type":"original_book","book_id":"a2eab101-adfc-4a0d-8142-63a4d11cee02","id":"a2eab101-adfc-4a0d-8142-63a4d11cee02","uuid":"a2eab101-adfc-4a0d-8142-63a4d11cee02","title":"人工智能和资本主义的未来","normalized_title":"人工智能和资本主义的未来","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9780745338613","isbns":["9780745338613","9780745338606","9781786803955","9781786803979","9781786803962"],"parsed":true,"ai_readable":true,"document_type":"book","page_count":225,"navigation_url":"https://workshop.closeclaw.top/ai/book/a2eab101-adfc-4a0d-8142-63a4d11cee02","title_url":"https://workshop.closeclaw.top/ai/title/%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD%E5%92%8C%E8%B5%84%E6%9C%AC%E4%B8%BB%E4%B9%89%E7%9A%84%E6%9C%AA%E6%9D%A5","fixed_page_url_template":"https://workshop.closeclaw.top/ai/book/a2eab101-adfc-4a0d-8142-63a4d11cee02/page/{printed_page}","path_api_url_template":"https://workshop.closeclaw.top/api/ai/book/a2eab101-adfc-4a0d-8142-63a4d11cee02/page/{printed_page}","read_url":"https://workshop.closeclaw.top/api/ai/read/a2eab101-adfc-4a0d-8142-63a4d11cee02","page_url":"https://workshop.closeclaw.top/api/ai/books/a2eab101-adfc-4a0d-8142-63a4d11cee02/page","search_url":"https://workshop.closeclaw.top/api/ai/books/a2eab101-adfc-4a0d-8142-63a4d11cee02/search"},"page":{"printed_page":90,"pdf_page":98,"physical_index":98,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"0f3603e4c7f4f4d4843997cdcc554135b89a6a935cc2c3ae4e54d501fd02be1d"},"chapter":"Digital Barricades: Interventions in Digital Culture and Politics","content":"In general, studies that focus on AI replacing entire jobs – such as that of Frey and Osborne (2013) – have been succeeded by more fine-grained analyses of tasks within any one job that are susceptible to automation. This allows for possibilities of AI complementing or intensifying human labour rather than entirely substituting for it. In the preferred business jargon: ‘augmenting’ rather than ‘automating’. Subsequent OECD studies have dialled back the percentage of employment at imminent risk of automation from the apocalyptic to the merely alarming: 14 per cent for all its member countries, equivalent to over 66 million jobs, with another 33 per cent ‘deeply restructured’ by automation. In the US a mere 9 per cent or some 13 million jobs are tagged as AI-vulnerable, although the authors remark ‘this would amount to several times the disruption in local economies caused by the 1950s decline of the car industry in Detroit’ (Nedelkoska and Quintini 2018). The picture is further revised when various estimates of employment opportunities created by AI are reckoned in. A few have gone so far as to promise overall employment increases consequent on technologies of the fourth industrial revolution. Yet they often still admit the possibility of a net job loss (Arntz, Gregory and Zierahn 2016; Economist 2016a; Nedelkoska and Quintini 2018; Acemoglu and Resteropo 2018).\n\nThere have also been reversals over the types of jobs most liable to replacement. One of the main claims of AI Apocalyptics (and, one might cynically suggest, a reason for the prominence given to the issue) is that not only manual but also mental ‘middle-class’ jobs are threatened. More recent studies nevertheless stress that it is routinized work most immediately at risk. It is generally agreed that transport drivers, administrative assistants, cashiers, counter and rental clerks, telemarketers and accountants are the most endangered, and of those middle-class jobs, the most likely to be automated are ‘paraprofessional’ jobs, such as ‘paralegals, payroll managers, and bookkeepers’ and ‘semi-administrative jobs’ that provided a way into professional industries for those without advanced educational qualifications (Vincent 2017).\n\nThe geographical distribution of AI-induced automation is similarly contested. Many studies assume that developed economies are likely to be hit hardest, as ‘higher average wages incentivizes automation’ (Manyika et al. 2017). Others suggest AI may have the most dramatic effects in less advanced areas of the planet factory, precisely because there are more routine industrial jobs there (Kinder 2018). A study by the International Labour Organization (Chang and Huynh 2016) estimates that more than 137 million workers in Cambodia, Indonesia, the Philippines, Thailand and Vietnam are at high risk of replacement by machines such as ‘sewbots’ that combine automated sewing with computer vision (Larson 2018). Such ‘premature deindustrialization’ (Rodrik 2015) jeopardizes models of capitalist development, threatening to ‘slash millions of jobs and create an upswing in trafficking and slavery across south-east Asia’ in a ‘race to the bottom’ between the costs of machines and of humans (Kelly 2018). The geographical distribution of job losses in the centres of advanced capitalism will also be uneven. A study by Frank et al. (2018), focusing on the US, argues that job losses to AI automation will affect small cities worst because large cities have a disproportionate number of occupations that are of a cognitive or analytical nature at less risk of automation, while small city jobs are disproportionately routine, such as clerical work in retail and food service.\n\n90\n\ninhuman power","previous_page":89,"next_page":91}