The recessionary pressures of generative AI: A threat to wellbeing
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arXiv
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| Format: | Preprint |
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2024
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| author | Occhipinti, Jo-An Prodan, Ante Hynes, William Green, Roy Burrow, Sharan Eyre, Harris A Skinner, Adam Ujdur, Goran Buchanan, John Hickie, Ian B Heffernan, Mark Song, Christine Tanner, Marcel |
| author_facet | Occhipinti, Jo-An Prodan, Ante Hynes, William Green, Roy Burrow, Sharan Eyre, Harris A Skinner, Adam Ujdur, Goran Buchanan, John Hickie, Ian B Heffernan, Mark Song, Christine Tanner, Marcel |
| contents | Generative Artificial Intelligence (AI) stands as a transformative force that presents a paradox; it offers unprecedented opportunities for productivity growth while potentially posing significant threats to economic stability and societal wellbeing. Many consider generative AI as akin to previous technological advancements, using historical precedent to argue that fears of widespread job displacement are unfounded, while others contend that generative AI`s unique capacity to undertake non-routine cognitive tasks sets it apart from other forms of automation capital and presents a threat to the quality and availability of work that underpin stable societies. This paper explores the conditions under which both may be true. We posit the existence of an AI-capital-to-labour ratio threshold beyond which a self-reinforcing cycle of recessionary pressures could be triggered, exacerbating social disparities, reducing social cohesion, heightening tensions, and requiring sustained government intervention to maintain stability. To prevent this, the paper underscores the urgent need for proactive policy responses, making recommendations to reduce these risks through robust regulatory frameworks and a new social contract characterised by progressive social and economic policies. This approach aims to ensure a sustainable, inclusive, and resilient economic future where human contribution to the economy is retained and integrated with generative AI to enhance the Mental Wealth of nations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_17405 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The recessionary pressures of generative AI: A threat to wellbeing Occhipinti, Jo-An Prodan, Ante Hynes, William Green, Roy Burrow, Sharan Eyre, Harris A Skinner, Adam Ujdur, Goran Buchanan, John Hickie, Ian B Heffernan, Mark Song, Christine Tanner, Marcel Computers and Society Generative Artificial Intelligence (AI) stands as a transformative force that presents a paradox; it offers unprecedented opportunities for productivity growth while potentially posing significant threats to economic stability and societal wellbeing. Many consider generative AI as akin to previous technological advancements, using historical precedent to argue that fears of widespread job displacement are unfounded, while others contend that generative AI`s unique capacity to undertake non-routine cognitive tasks sets it apart from other forms of automation capital and presents a threat to the quality and availability of work that underpin stable societies. This paper explores the conditions under which both may be true. We posit the existence of an AI-capital-to-labour ratio threshold beyond which a self-reinforcing cycle of recessionary pressures could be triggered, exacerbating social disparities, reducing social cohesion, heightening tensions, and requiring sustained government intervention to maintain stability. To prevent this, the paper underscores the urgent need for proactive policy responses, making recommendations to reduce these risks through robust regulatory frameworks and a new social contract characterised by progressive social and economic policies. This approach aims to ensure a sustainable, inclusive, and resilient economic future where human contribution to the economy is retained and integrated with generative AI to enhance the Mental Wealth of nations. |
| title | The recessionary pressures of generative AI: A threat to wellbeing |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2403.17405 |