{"id":7970,"date":"2020-05-28T09:05:06","date_gmt":"2020-05-28T09:05:06","guid":{"rendered":"http:\/\/blog.experfy.com\/?p=7970"},"modified":"2023-12-11T10:46:36","modified_gmt":"2023-12-11T10:46:36","slug":"what-history-tells-us-about-the-accelerating-ai-revolution","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/what-history-tells-us-about-the-accelerating-ai-revolution\/","title":{"rendered":"What History Tells Us About the Accelerating AI Revolution"},"content":{"rendered":"\t\t
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A few weeks before our lives were turned upside down by Covid-19, I\u00a0read\u00a0Technology at Work v4.0<\/a>, the fourth report in the\u00a0Technology at Work Series<\/a>\u00a0developed\u00a0by\u00a0Citigroup<\/a>\u00a0in collaboration with\u00a0Oxford University<\/a>.\u00a0\u00a0The report includes an excellent chapter on What History Tells Us About the Coming AI Revolution by Oxford professor\u00a0Carl Benedikt Frey<\/a>\u00a0based on his 2019 book\u00a0The Technology Trap<\/a>.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Recent AI advances have \u201csparked much excitement\u2026\u00a0\u00a0yet despite this, most ordinary people don\u2019t feel particularly optimistic about the future,\u201d wrote Frey.\u00a0 For example, a\u00a02017 Pew Research survey<\/a>\u00a0found<\/a>\u00a0that three quarters of Americans expressed serious concerns about AI and automation, and just over a third believe that their children will be better off financially than they were.<\/p>\n\n\n\n

But, in fact,\u00a0serious concerns<\/a>\u00a0about the impact of technology are part of a historical pattern.\u00a0\u00a0\u201cMany of the trends we see today, such as the disappearance of middle-income jobs, stagnant wages and growing inequality were also features of the Industrial Revolution, which began in Britain around 1750.\u00a0\u00a0This was a period of unprecedented technological change but also one of turbulent social and political change.\u201d<\/p>\n\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\u201cWe are at the brink of a technological revolution that promises not just to fundamentally alter the structure of our economy, but also to reshape the social fabric more broadly.\u00a0\u00a0History tells us anxiety tends to accompany rapid technological change, especially when technology takes the form of capital which threatens people\u2019s jobs.\u201d\u00a0 Moreover, the Covid-19 pandemic\u00a0will likely accelerate<\/a>\u00a0the rate and pace of technological change<\/a>.<\/p>\n\n\n\n

What can we learn from the Industrial Revolution that can help us better face our emerging AI revolution?\u00a0\u00a0Let me summarize some of professor Frey\u2019s key points.\u00a0<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Over the past two centuries\u00a0we\u2019ve learned<\/a>\u00a0that there\u2019s a\u00a0significant time lag<\/a>, between the broad acceptance of major new transformative technologies and their long-term economic and productivity growth.\u00a0\u00a0This is particularly the case for General Purpose Technologies (GPTs), – like the steam engine, electricity and computers, – which have the potential to radically reshape entire economies and societal norms.<\/p>\n\n\n\n

As\u00a0explained<\/a>\u00a0in a\u00a0recent paper<\/a>, the life cycle of such historically transformative technologies consists of two phases,\u00a0investments<\/em>\u00a0and\u00a0harvesting<\/em>.\u00a0 In their initial phase, transformative technologies\u00a0require massive complementary\u00a0investments<\/em>, such as business process redesign, co-invention of new products and business models, and the re-skilling of the workforce.\u00a0\u00a0The more transformative the technologies, the longer it takes them to reach the\u00a0harvesting<\/em>\u00a0phase when they\u2019re widely embraced by companies and industries across the economy.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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The time lags between the investment and harvesting phases are typically quite long.\u00a0\u00a0Frey cites several historical examples.\u00a0\u00a0While\u00a0James Watt\u2019s<\/a>\u00a0steam engine ushered the Industrial Revolution in the 1780s, \u201cBritish factories were for the most part powered by water up until the 1840s, when the fuel consumption of steam engines had dropped sufficiently for them to find widespread use.\u201d\u00a0\u00a0Similarly, productivity growth did not increase until 40 years after the introduction of electric power in the early 1880s.\u00a0\u00a0\u201cDispensing with the apparatus for mechanically distributing power throughout the factory was essential in order to harness the flexibility of electricity, and reorganizing production took a long time: electricity only showed up in the productivity statistics by the 1920s.\u201d<\/p>\n\n\n\n

Computers followed a similar pattern.\u00a0\u00a0While first deployed in business in the \u201950s and \u201960s, computers were too bulky, expensive and difficult to program to have any meaningful impact on jobs, wages and productivity.\u00a0\u00a0As was the case with factory electrification, companies had to rethink their overall operations to take full advantage of the new digital technologies.\u00a0\u00a0It was only in the late1990s that the Internet, business process reengineering and other technical and organizational changes helped to spread productivity-enhancing innovations across the economy.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\u201cProductivity growth has slowed since 2005, but seen through the lens of history it is not all that puzzling,\u201d noted Frey.\u00a0\u00a0Despite the recent\u00a0hype<\/a>, we\u2019re still in the\u00a0early stages<\/a>\u00a0of AI\u2019s deployment.\u00a0\u00a0In their early stages, the extensive investments required to embrace a GPT like AI will generally reduce productivity growth.\u00a0\u00a0For example, a recent Brookings Institution\u00a0report<\/a>\u00a0estimated that between 2014 and 2017 investments in autonomous vehicles amounted to roughly $80 billion with almost no returns, lowering labor productivity by 0.1 percent per year over this period.<\/p>\n\n\n\n

Frey wrote that \u201cthe short run consequences of rapid technological change can be devastating for working people, especially when technology takes the form of capital which substitutes for labor.\u201d\u00a0\u00a0In the long run, the Industrial Revolution led to a rising standard of living, improved health, an many other benefits.\u00a0\u00a0\u201cYet in the short run, the lives of working people got nastier, more brutish, and shorter.\u00a0\u00a0And what economists regard as \u2018the short run\u2019 was a lifetime, for some,\u201d given that it generally takes decades to realize the benefits of transformative technologies.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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A 2017 McKinsey\u00a0study<\/a>\u00a0concluded<\/a>\u00a0that while a growing technology-based economy will create a significant number of new occupations, – as has been the case in the past, \u201cthe transitions will be very challenging – matching or even exceeding the scale of shifts out of agriculture and manufacturing we have seen in the past.\u201d\u00a0<\/p>\n\n\n\n

The US and other industrial economies have seen a remarkable rise in the\u00a0polarization<\/a>\u00a0of job opportunities and\u00a0wage inequality<\/a>\u00a0by educational attainment, with the earnings of the most-educated increasing, and the earnings of the least-educated falling in real terms.\u00a0\u00a0Since the 1980s, the earnings of those with a four year college degree have risen by 40% to 60%, while the earnings of those with a high school education or less have fallen among men and barely changed among women.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\u201c[O]ur age of automation, which began with the computer revolution of the 1980s, closely resembles the experience of the British Industrial Revolution in a number of ways: middle-income jobs have been taken over by machines, forcing many people into lower-paying jobs or out of the workforce; real wages have been stagnant or even falling for those with no more than a high school degree; the labor share of national income has declined; and income disparities have skyrocketed.\u00a0\u00a0In this light, it is hardly surprising technological change at times has brought fierce resistance.\u00a0\u00a0Indeed, resistance to technologies that threaten people\u2019s skills has been the historical norm rather than the exception.\u201d<\/p>\n\n\n\n

\u201cHistorically, the way people have adjusted to technological change is by acquiring new skills\u2026 During the twentieth century, the expansion of education was critical to helping people adjust to the accelerating pace of change\u2026\u00a0\u00a0Because the prime source of most people\u2019s income is their human capital rather than physical capital, their wealth is in their skills.\u00a0\u00a0And those skills must be regularly updated as technology progresses.\u00a0\u00a0When upskilling is lagging behind, entire social groups might end up being excluded from the growth engine.\u201d<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

We are at the brink of a technological revolution that promises not just to fundamentally alter the structure of our economy, but also to reshape the social fabric more broadly. Recent AI advances have \u201csparked much excitement\u2026 yet despite this, most ordinary people don\u2019t feel particularly optimistic about the future<\/p>\n","protected":false},"author":612,"featured_media":7971,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[183],"tags":[881,97],"ppma_author":[3320],"class_list":["post-7970","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-ml","tag-ai-revolution","tag-artificial-intelligence"],"authors":[{"term_id":3320,"user_id":612,"is_guest":0,"slug":"irving-wladawsky-berger","display_name":"Irving Wladawsky-Berger","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/?s=96&d=mm&r=g","user_url":"","last_name":"Wladawsky-Berger","first_name":"Irving","job_title":"","description":"Irving Wladawsky-Berger, a Guest Columnist at WSJ CIO Journal, is Research Affiliate at MIT Sloan School of Management, Adjunct Professor at Imperial College, London, and Chairman Advisory Board at r4 Technologies."}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/7970","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/612"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=7970"}],"version-history":[{"count":5,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/7970\/revisions"}],"predecessor-version":[{"id":34810,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/7970\/revisions\/34810"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/7971"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=7970"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=7970"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=7970"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=7970"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}