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240607 ||| eng |
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|a 9798400262548
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|a Cazzaniga, Mauro
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| 245 |
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|a Gen-AI
|b Artificial Intelligence and the Future of Work
|c Mauro Cazzaniga, Florence Jaumotte, Longji Li, Giovanni Melina, Augustus Panton, Carlo Pizzinelli, Emma Rockall, Marina Mendes Tavares
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| 260 |
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|a Washington, D.C.
|b International Monetary Fund
|c 2024
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| 300 |
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|a 41 pages
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| 653 |
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|a Income
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| 653 |
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|a Finance
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| 653 |
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|a Labour; income economics
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| 653 |
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|a Financial crises
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| 653 |
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|a Aggregate Labor Productivity
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| 653 |
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|a National accounts
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| 653 |
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|a Labor
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| 653 |
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|a Economics of specific sectors
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| 653 |
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|a Informal Economy
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| 653 |
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|a Foreign Exchange
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| 653 |
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|a Currency crises
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| 653 |
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|a Financial markets
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| 653 |
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|a Emerging and frontier financial markets
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| 653 |
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|a Artificial intelligence
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| 653 |
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|a Macroeconomics
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| 653 |
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|a Income inequality
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| 653 |
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|a Employment
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| 653 |
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|a Economic & financial crises & disasters
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| 653 |
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|a Technological Change: Choices and Consequences
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| 653 |
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|a Technology
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| 653 |
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|a Income distribution
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| 653 |
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|a Economics: General
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| 653 |
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|a Unemployment
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| 653 |
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|a Intelligence (AI) & Semantics
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| 653 |
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|a Diffusion Processes
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| 653 |
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|a Informal sector; Economics
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| 653 |
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|a Aggregate Factor Income Distribution
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| 653 |
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|a General Financial Markets: General (includes Measurement and Data)
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| 653 |
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|a Economic sectors
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| 653 |
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|a Aggregate Human Capital
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| 653 |
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|a Labor Economics: General
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| 653 |
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|a Financial services industry
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| 653 |
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|a Wages
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| 653 |
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|a Underground Econom
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| 653 |
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|a Intergenerational Income Distribution
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| 653 |
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|a Finance: General
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| 653 |
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|a Labor economics
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| 700 |
1 |
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|a Jaumotte, Florence
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| 700 |
1 |
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|a Li, Longji
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| 700 |
1 |
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|a Melina, Giovanni
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| 041 |
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|a eng
|2 ISO 639-2
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| 989 |
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|b IMF
|a International Monetary Fund
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| 490 |
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|a Staff Discussion Notes
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| 028 |
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|a 10.5089/9798400262548.006
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| 856 |
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|u https://elibrary.imf.org/view/journals/006/2024/001/006.2024.issue-001-en.xml?cid=542379-com-dsp-marc
|x Verlag
|3 Volltext
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|a 330
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|a Artificial Intelligence (AI) has the potential to reshape the global economy, especially in the realm of labor markets. Advanced economies will experience the benefits and pitfalls of AI sooner than emerging market and developing economies, largely due to their employment structure focused on cognitive-intensive roles. There are some consistent patterns concerning AI exposure, with women and college-educated individuals more exposed but also better poised to reap AI benefits, and older workers potentially less able to adapt to the new technology. Labor income inequality may increase if the complementarity between AI and high-income workers is strong, while capital returns will increase wealth inequality. However, if productivity gains are sufficiently large, income levels could surge for most workers. In this evolving landscape, advanced economies and more developed emerging markets need to focus on upgrading regulatory frameworks and supporting labor reallocation, while safeguarding those adversely affected. Emerging market and developing economies should prioritize developing digital infrastructure and digital skills
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