A few years ago, when ChatGPT and Claude were beginning to take off, some tech leaders seemed to develop a curious interest in oceanography. Consider, for instance, the Microsoft CEO Satya Nadella\u2019s suggestion in 2023 that AI ought to be compared to a \u2018tidal wave\u2019; or Mustafa Suleyman\u2019s book on AI, The Coming Wave (2024), in which the DeepMind co-founder talks urgently about an \u2018impending deluge\u2019 (while repeatedly warning us that the \u2018wave is coming\u2019, and, even more alarmingly, \u2018the coming wave really is coming\u2019).<\/p>\n
It didn\u2019t take long for the analogy to spread. The IMF\u2019s Kristalina Georgieva would liken the technology to a \u2018tsunami hitting the labour market\u2019. Not only do these watery and somewhat gloomy metaphors imply that technological change is natural and inevitable; they also suggest that the displacement caused by such change can only be prepared for and then mopped up afterwards (though preferably not by the companies who poured billions into building the wave machines).<\/p>\n
\u2018We think we\u2019re robotising our work, but what if we\u2019re actually robotising ourselves?\u2019<\/p>\n
Sarah O\u2019Connor\u2019s We Are Not Machines is a powerful and refreshing riposte to this sort of fatalistic thinking. Rather than making sweeping predictions about how technology will change the future of work, O\u2019Connor chooses, in her own words, to \u2018get [her] shoes dirty\u2019 and interview those whose jobs look to be most exposed to technological change. As a former self-proclaimed \u2018techno-optimist\u2019, who once believed that certain forms of labour should be automated away, she now understands the current situation \u2013 in which AI and robots are actually threatening to do this \u2013 to be a little bit more nuanced and complex than she\u2019d suggested in her column for the Financial Times a decade ago.<\/p>\n
The book is structured into three parts: \u2018Mind\u2019, \u2018Body\u2019 and \u2018Soul\u2019. In the first, O\u2019Connor takes us to EMA4, one of Amazon\u2019s newest UK warehouses, just outside Birmingham. There, she witnesses thousands of robots ferry shelves to human pickers. This reversal of the old logic of the warehouse (workers no longer walk to the goods; the goods come to them) might appear as an ingenious solution to the notoriously brutal physical work these jobs typically require. But the workers O\u2019Connor interviews are hardly thrilled as they stand at fixed stations where a light in one box tells them what to pick and another light in another box tells them where to put it. O\u2019Connor, invited to try, is stunned: she discovers she doesn\u2019t have to make \u2018a single decision\u2019 of her own. In attempting to eliminate the drudgery of warehouse work (while, of course, improving productivity gains), the new process has obliterated any need for human decision-making whatsoever.<\/p>\n
In the chapters on translation and software engineering, AI does not simply replace skilled workers, nor does it straightforwardly assist them. It often just changes the nature of the task (and, in most cases, makes it rather dull). Petr, a Czech subtitler, who found his dream job after years in a factory, relays what he used to love: how to faithfully convey jokes, certain registers, idioms and puns. But following the AI boom, Petr\u2019s work has changed dramatically. Most clients, he finds, use AI to create a first draft, which is then sent to the human translator for correction. The work remains, but the pleasure and judgment have been extracted from it. His job has now begun, grimly, to resemble the very factory work he was glad to leave behind.<\/p>\n
In the \u2018Body\u2019 section, O\u2019Connor turns to labour that is physically hazardous or monotonous, such as lorry driving and mining. The most hopeful parts involve machines doing exactly what one might want: removing people from danger, reducing bodily strain and making work generally safer. But even here the distinction between protection and control is unstable. Monitoring technologies can prevent accidents, but they can also intensify surveillance. Autonomous vehicles may spare drivers exhaustion, but they end up producing new forms of loneliness for workers now reduced to \u2018overseeing\u2019 the vehicles they once drove. O\u2019Connor is keen to point out that all tools arrive with assumptions built into them: about productivity, attention, fatigue, error, obedience. Getting rid of one set of problems usually only creates another.<\/p>\n
The final section, \u2018Soul\u2019, is the book\u2019s most moving. O\u2019Connor considers Holly- wood writers, students, AI companions and care workers: forms of labour in which what\u2019s at stake is the transmission of knowledge, trust, affection and human presence. The chapter on Dutch community nursing is an especially effective snapshot because it offers a vision of sensible business management without the need for unnecessary automation. At Buurtzorg, nurses work in self-managing teams, deciding among themselves how best to care for patients. Jennifer, one of the nurses O\u2019Connor follows, explains that care cannot be reduced to a list of automated tasks: you are there \u2018to see the people, to see the life\u2019. A sandwich left repeatedly in the bin might mean illness, depression or a dislike of cheese. The point is that you only notice if your work allows you the time and space to do so. All this plays out against a backdrop of certain nations (most notably, Japan) deciding how best to deploy robots to look after the elderly.<\/p>\n
It\u2019s worth pointing out that We Are Not Machines isn\u2019t against technology per se. In fact, what animates the book is a question more concerned with humanity than technology \u2013 one gestured to in the title. \u2018We think we\u2019re robotising our work,\u2019 O\u2019Connor observes at one point, \u2018but what if we\u2019re actually robotising ourselves?\u2019 Whether or not we believe in the wave, it\u2019s this question that feels most pertinent.<\/p>\n","protected":false},"excerpt":{"rendered":"A few years ago, when ChatGPT and Claude were beginning to take off, some tech leaders seemed to…\n","protected":false},"author":2,"featured_media":622061,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[554,2151,733,4308,14088,1921,184990,211114,5981,211115,26740,86,56,54,55],"class_list":["post-622060","post","type-post","status-publish","format-standard","has-post-thumbnail","category-artificial-intelligence","tag-ai","tag-amazon","tag-artificial-intelligence","tag-artificialintelligence","tag-autonomous-vehicles","tag-chatgpt","tag-economic-history","tag-human-decision-making","tag-robots","tag-social-history","tag-surveillance","tag-technology","tag-uk","tag-united-kingdom","tag-unitedkingdom"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/posts\/622060","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/comments?post=622060"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/posts\/622060\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/media\/622061"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/media?parent=622060"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/categories?post=622060"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/uk\/wp-json\/wp\/v2\/tags?post=622060"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}