Summarize this content to 2000 words in 6 paragraphs in Arabic Unlock the Editor\u2019s Digest for freeRoula Khalaf, Editor of the FT, selects her favourite stories in this weekly newsletter.There has been a lot of talk about how quickly \u2014 or not \u2014 artificial intelligence is going to replace humans in the workforce. Short version: the robots are learning fast but are not that smart yet.A lot less attention has been paid to where the impact of job displacement will eventually fall. Although the speed of the journey may be in doubt, the direction of travel is certain: AI will increasingly outperform humans in a widening range of cognitive tasks. And initial research suggests that this AI-driven automation may produce a geographic distribution of disruption unlike any seen before.It could well be that some of the beneficiaries of earlier waves of industrial automation, who have tended to be clustered in the richer east and west coast cities of the US, will be most exposed to the next big digital dislocation.\u00a0That could have potentially huge social, economic and political ramifications, with more rich, urban centres hit than poorer, rural regions, more blue Democrat states than red Republican states. While President Donald Trump obsesses about the trade in physical goods and slaps tariffs on \u201cforeign scavengers\u201d who dare to export to the US, he should be thinking far more about how AI is going to affect the domestic economy and the global exchange of services.\u00a0The story of automation in the US is that it has mostly impacted on manual workers in manufacturing. For example, factory employees \u2014 such as carmakers \u2014 performing routine tasks have lost their jobs to robots \u2014 or lower-cost Asian competitors. Industrial automation has tended to affect lower-skilled, blue-collar jobs in the \u201crustbelt\u201d heartlands and small-town, less-educated communities in the south and midwest.\u00a0But a recent study from the Brookings Institution suggests that the communities most exposed to AI-driven job dislocation will be white-collar information workers. The researchers studied the usage of OpenAI\u2019s generative AI tools across more than 1,000 occupations and mapped this against where those jobs were most commonly located.\u00a0Their analysis suggests that many coders, lawyers, financial analysts and bureaucrats in cities such as San Jose, San Francisco, Durham, New York and Washington DC might want to rethink their futures. But non-office-bound workers in places such as Las Vegas, Toledo, Ohio and Fort Wayne, Indiana may be less exposed to AI disruption.\u00a0\u00a0However, Mark Muro, senior fellow at Brookings Metro who led the research, suggests the picture is more complicated than the raw data and simple correlation suggest. Many of the biggest winners from the AI transformation, including top corporate managers, professional experts and shareholders in tech companies, live in the most exposed metropolitan areas, while poorer districts may lose out on the productivity gains that AI can bring. \u201cIt is both a potential benefit and a potential dislocation,\u201d Muro tells me.\u00a0Other studies of specific sectors paint an even more complex picture, especially when viewed in a global context. Take the case of translators, for instance, one of the most exposed professions to AI automation following the widespread adoption of tools such as Google Translate.\u00a0A recent paper by Pedro Llanos-Paredes and Carl Benedikt Frey of Oxford university found that for every 1 percentage point increase in machine translation usage across 695 local labour markets in the US, translator employment growth dropped by about 0.7 percentage points. That resulted in an estimated loss of 28,000 new translator positions that might have otherwise been created between 2010 and 2023.Although that may be bad news for anyone wanting to be a translator, the adoption of machine translation tools is a big boost to service companies in many other countries. Language is one of the biggest barriers to global trade, particularly in services. Machine translation can help lower those barriers as service sector workers in India, Vietnam or Nigeria, say, become even more proficient in the global language of the services trade: English.\u00a0\u201cAs manufacturing disappears as an escalator for economic growth, moving into services may be the only sustainable pathway for countries to catch up,\u201d Frey tells me. \u201cAnd I think these translation tools make that more feasible.\u201dIn fixating on hardware and ignoring software, Trump is in danger of trying to refight the last, lost economic war rather than anticipating the next one. The winners and losers of AI may not be where we expect.john.thornhill@ft.com<\/p>\n","protected":false},"excerpt":{"rendered":"
Summarize this content to 2000 words in 6 paragraphs in Arabic Unlock the Editor\u2019s Digest for freeRoula Khalaf, Editor of the FT, selects her favourite stories in this weekly newsletter.There has been a lot of talk about how quickly \u2014 or not \u2014 artificial intelligence is going to replace humans in the workforce. Short version:<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[63],"tags":[],"class_list":{"0":"post-262398","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-tech"},"_links":{"self":[{"href":"https:\/\/globetimeline.com\/ar\/wp-json\/wp\/v2\/posts\/262398","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/globetimeline.com\/ar\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/globetimeline.com\/ar\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/globetimeline.com\/ar\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/globetimeline.com\/ar\/wp-json\/wp\/v2\/comments?post=262398"}],"version-history":[{"count":0,"href":"https:\/\/globetimeline.com\/ar\/wp-json\/wp\/v2\/posts\/262398\/revisions"}],"wp:attachment":[{"href":"https:\/\/globetimeline.com\/ar\/wp-json\/wp\/v2\/media?parent=262398"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/globetimeline.com\/ar\/wp-json\/wp\/v2\/categories?post=262398"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/globetimeline.com\/ar\/wp-json\/wp\/v2\/tags?post=262398"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}