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| Format: | Preprint |
| Published: |
2024
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| Online Access: | https://arxiv.org/abs/2407.19586 |
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| _version_ | 1866929439915376640 |
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| author | Li, Jiasun |
| author_facet | Li, Jiasun |
| contents | With the phenomenal rise of generative AI models (e.g., large language models such as GPT or large image models such as Diffusion), there are increasing concerns about human creatives' futures. Specifically, as generative models' power further increases, will they eventually replace all human creatives' jobs? We argue that the answer is "no," even if existing generative AI models' capabilities reach their theoretical limit. Our theory has a close analogy to a familiar insight in financial economics on the impossibility of an informationally efficient market [Grossman and Stiglitz (1980)]: If generative AI models can provide all the content humans need at low variable costs, then there is no incentive for humans to spend costly resources on content creation as they cannot profit from it. But if no human creates new content, then generative AI can only learn from stale information and be unable to generate up-to-date content that reflects new happenings in the physical world. This creates a paradox. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_19586 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Is Generative AI an Existential Threat to Human Creatives? Insights from Financial Economics Li, Jiasun Human-Computer Interaction Artificial Intelligence With the phenomenal rise of generative AI models (e.g., large language models such as GPT or large image models such as Diffusion), there are increasing concerns about human creatives' futures. Specifically, as generative models' power further increases, will they eventually replace all human creatives' jobs? We argue that the answer is "no," even if existing generative AI models' capabilities reach their theoretical limit. Our theory has a close analogy to a familiar insight in financial economics on the impossibility of an informationally efficient market [Grossman and Stiglitz (1980)]: If generative AI models can provide all the content humans need at low variable costs, then there is no incentive for humans to spend costly resources on content creation as they cannot profit from it. But if no human creates new content, then generative AI can only learn from stale information and be unable to generate up-to-date content that reflects new happenings in the physical world. This creates a paradox. |
| title | Is Generative AI an Existential Threat to Human Creatives? Insights from Financial Economics |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2407.19586 |