AI-driven unemployment

Extracted Attributes

• Type

  Key type of structural unemployment.

• Definition

  Loss of jobs caused by technological change, specifically advancements in artificial intelligence and automation.

• Keynes' View

  Only a temporary phase of maladjustment.

• Historical Context

  Debated since at least Aristotle's time.

• Keynes' Contribution

  Phrase 'technological unemployment' popularized by John Maynard Keynes in the 1930s.

• Debate Stance - Optimists

  Believe innovation may be disruptive in short term but ensures no long-term negative impact on jobs due to compensation effects.

• Nature of AI Displacement

  Unlike previous technological revolutions, generative AI can target cognitive tasks performed by knowledge workers.

• Debate Stance - Pessimists

  Contend new technologies can lead to a lasting decline in total employment.

• EIG 2022-Early 2025 Finding

  Unemployment rate for most AI-exposed workers rose by 0.30 percentage points; for least exposed, it climbed by 0.94 percentage points.

• World Bank 2019 Report Finding

  Technological innovation creates more new industries and jobs on balance than it displaces.

• Goldman Sachs Research Estimate

  Unemployment will increase by half a percentage point during the AI transition period.

• St. Louis Fed 2022-2025 Finding

  Occupations with higher AI exposure experienced larger unemployment rate increases (0.47 correlation coefficient).

• Goldman Sachs Historical Observation

  Historically, upheaval from technological innovation has proven to be temporary—after two years there is no noticeable impact.

Timeline

• The topic of machines displacing human labor was rarely a prominent concern, but when it arose, both the elite and common people generally took a pessimistic view. (Source: Wikipedia)

  Unknown (Pre-18th century)

• Fears over the impact of machinery on jobs intensified with the growth of mass unemployment, especially in Great Britain during the Industrial Revolution. (Source: Wikipedia)

  18th century

• Arguments against job fears were formalized by classical economists, claiming that overall innovation would not have negative effects on jobs. (Source: Wikipedia)

  Early 19th century

• John Maynard Keynes popularized the term 'technological unemployment', describing it as 'only a temporary phase of maladjustment'. (Source: Wikipedia)

  1930s

• Dozens of economists warned about technological unemployment during a brief intensification of the debate. (Source: Wikipedia)

  1930s

• A brief intensification of the debate about technological unemployment occurred. (Source: Wikipedia)

  1960s

• Further warnings about technological unemployment emerged in Europe, as commentators noted an enduring rise in unemployment suffered by many industrialized nations. (Source: Wikipedia)

  1970s-2000s

• A study examined the effect of artificial intelligence on unemployment in 24 high-tech developed countries, finding that AI decreases the level of unemployment and validating the 'displacement effect'. (Source: Web Search - ScienceDirect)

  2005-2021

• The World Bank's World Development Report argued that while automation displaces workers, technological innovation creates more new industries and jobs on balance. (Source: Wikipedia)

  2019

• A St. Louis Fed study found that occupations with higher AI exposure experienced larger unemployment rate increases, with a 0.47 correlation coefficient. (Source: Web Search)

  2022-2025

• An EIG study found that the unemployment rate for the most AI-exposed workers rose by 0.30 percentage points, while for the least exposed workers, it climbed by 0.94 percentage points. (Source: Web Search)

  2022-Early 2025

Technological unemployment

The term technological unemployment is used to describe the loss of jobs caused by technological change. It is a key type of structural unemployment. Technological change typically includes the introduction of labour-saving "mechanical-muscle" machines or more efficient "mechanical-mind" processes (automation), and humans' role in these processes are minimized. Just as horses were gradually made obsolete as transport by the automobile and as labourer by the tractor, humans' jobs have also been affected throughout modern history. Historical examples include artisan weavers reduced to poverty after the introduction of mechanized looms (See: Luddites). Thousands of man-years of work was performed in a matter of hours by the bombe codebreaking machine during World War II. A contemporary example of technological unemployment is the displacement of retail cashiers by self-service tills and cashierless stores. That technological change can cause short-term job losses is widely accepted. The view that it can lead to lasting increases in unemployment has long been controversial. Participants in the technological unemployment debates can be broadly divided into optimists and pessimists. Optimists agree that innovation may be disruptive to jobs in the short term, yet hold that various compensation effects ensure there is never a long-term negative impact on jobs, whereas pessimists contend that at least in some circumstances, new technologies can lead to a lasting decline in the total number of workers in employment. The phrase "technological unemployment" was popularised by John Maynard Keynes in the 1930s, who said it was "only a temporary phase of maladjustment". The issue of machines displacing human labour has been discussed since at least Aristotle's time. Prior to the 18th century, both the elite and common people would generally take the pessimistic view on technological unemployment, at least in cases where the issue arose. Due to generally low unemployment in much of pre-modern history, the topic was rarely a prominent concern. In the 18th century fears over the impact of machinery on jobs intensified with the growth of mass unemployment, especially in Great Britain which was then at the forefront of the Industrial Revolution. Yet some economic thinkers began to argue against these fears, claiming that overall innovation would not have negative effects on jobs. These arguments were formalised in the early 19th century by the classical economists. During the second half of the 19th century, it stayed apparent that technological progress was benefiting all sections of society, including the working class. Concerns over the negative impact of innovation diminished. The term "Luddite fallacy" was coined to describe the thinking that innovation would have lasting harmful effects on employment. The view that technology is unlikely to lead to long-term unemployment has been repeatedly challenged by a minority of economists. In the early 1800s these included David Ricardo. There were dozens of economists warning about technological unemployment during brief intensifications of the debate that spiked in the 1930s and 1960s. Especially in Europe, there were further warnings in the closing two decades of the twentieth century, as commentators noted an enduring rise in unemployment suffered by many industrialised nations since the 1970s. Yet a clear majority of both professional economists and the interested general public held the optimistic view through most of the 20th century. Advances in artificial intelligence (AI) have reignited debates about the possibility of mass unemployment, or even the end of employment altogether. Some experts, such as Geoffrey Hinton, believe that the development of artificial general intelligence and advanced robotics will eventually enable the automation of all intellectual and physical tasks, suggesting the need for a basic income for non-workers to subsist. Others, like Daron Acemoğlu, argue that humans will remain necessary for certain tasks, or complementary to AI, disrupting the labor market without necessarily causing mass unemployment. The World Bank's 2019 World Development Report argues that while automation displaces workers, technological innovation creates more new industries and jobs on balance.

Web Search Results

• How Will AI Affect the Global Workforce? - Goldman Sachs

  There are two ways in which AI could hypothetically lead to an increase in unemployment. The first is if AI capabilities advance to a point where human input becomes redundant for many types of production, leading to persistent structural unemployment. [...] Despite concerns about widespread job losses, AI adoption is expected to have only a modest and relatively temporary impact on employment levels. Goldman Sachs Research estimates that unemployment will increase by half a percentage point during the AI transition period as displaced workers seek new positions. [...] However there could also be a period of higher unemployment while AI-displaced workers are looking for new jobs. “Frictional unemployment is not unique to AI and occurs during most periods of rapid technological change,” Briggs and Dong write. Historically, upheaval from technological innovation has proven to be temporary—after two years there is no noticeable impact. Is AI already causing job losses?

• Is AI Contributing to Rising Unemployment? | St. Louis Fed

  The findings presented here represent correlation, not causation. Multiple factors could explain rising unemployment in AI-exposed occupations, including economic uncertainty, postpandemic monetary policy tightening and chance timing. Furthermore, this analysis is based on preliminary findings in a rapidly evolving field. However, the consistency across both theoretical exposure and actual adoption measures suggests the relationship may be more than coincidental. [...] The figure below shows that occupations with higher AI exposure experienced larger unemployment rate increases between 2022 and 2025, with a 0.47 correlation coefficient. We measured the percentage point change in unemployment using the three-month average unemployment rates for May through July in 2022 and 2025 to smooth out monthly volatility and control for seasonality in the data. Computer and mathematical occupations—predictably among the most AI-exposed, with a score around 80%—saw some [...] Our results suggest we may be witnessing the early stages of AI-driven job displacement. Unlike previous technological revolutions that primarily affected manufacturing or routine clerical work, generative AI can target cognitive tasks performed by knowledge workers—traditionally among the most secure employment categories. Limitations and Future Research

• Evaluating the Impact of AI on the Labor Market - Yale Budget Lab

  Even when specifically examining the unemployed population, there is no clear growth in exposure to generative AI. Figure 12 depicts the average percentage of tasks exposed amongst unemployed workers by duration of unemployment. AI-driven displacement might suggest a growth in the proportion of exposed tasks amongst recently unemployed workers. Irrespective of the duration of unemployment, however, unemployed workers were in occupations where about 25 to 35 percent of tasks, on average, could

• Artificial intelligence and unemployment in high-tech developed ...

  This study examines the effect of artificial intelligence on unemployment in high-tech developed countries. While artificial intelligence is more discussed in futuristic aspects, comprehensive empirical studies are limited in the literature. Therefore, this study uses the dataset, which includes 24 high-tech developed countries from 2005 to 2021 and examines the relationship between a country's Google Trend Index related to AI and the unemployment rate. In the empirical approach, we control the [...] dynamic effect of unemployment by using dynamic panel data and GMM-system estimation to determine the effect of AI on unemployment. The main results show that artificial intelligence decreases the level of unemployment, and the ‘displacement effect’ of AI is validated. [...] Skip to article My account Sign in View PDF ## Research in Globalization Volume 7, December 2023, 100140 # Artificial intelligence and unemployment in high-tech developed countries: New insights from dynamic panel data model Author links open overlay panel rights and content Under a Creative Commons license Open access ## Abstract

• AI and Jobs: The Final Word (Until the Next One)

  What it shows is that although the unemployment rate for the most AI-exposed workers is indeed rising, it is actually rising even faster for the least exposed workers. To be more precise, between 2022 and the beginning of 2025 the unemployment rate for the quintile of the most exposed workers rose by 0.30 percentage points. For the quintile of the least exposed workers, it climbed by 0.94 percentage points. The unemployment rate also climbed during this period for the other three quintiles. [...] Another response to AI-driven job displacement could be occupational switching. That is, maybe we don’t see rising unemployment or falling participation among highly exposed workers because they’re changing careers into less exposed occupations. Maybe computer programmers are becoming ballerinas. [...] By the most obvious measure, then, the effect of AI on jobs is invisible. But what about less obvious measures? ### II. Bailing on Work? Even though we see little evidence of AI’s impact on unemployment, maybe it’s because we are looking underneath the wrong lamppost. Rather than falling into unemployment, workers displaced by AI might be exiting the labor force entirely. Older workers, for example, might see the AI writing on the wall and decide to pack up and retire.