AI Cap-and-Trade: Efficiency Incentives for Accessibility and Sustainability
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| Format: | Preprint |
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2026
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| _version_ | 1866918487446781952 |
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| author | Bornstein, Marco Bedi, Amrit Singh |
| author_facet | Bornstein, Marco Bedi, Amrit Singh |
| contents | The race for artificial intelligence (AI) dominance often prioritizes scale over efficiency. Hyper-scaling is the common industry approach: larger models, more data, and as many computational resources as possible. Using more resources is a simpler path to improved AI performance. Thus, efficiency has been de-emphasized. Consequently, the need for costly computational resources has marginalized academics and smaller companies. Simultaneously, increased energy expenditure, due to growing AI use, has led to mounting environmental costs. In response to accessibility and sustainability concerns, we argue for research into, and implementation of, market-based methods that incentivize AI efficiency. We believe that incentivizing efficient operations and approaches will reduce emissions while opening new opportunities for academics and smaller companies. As a call to action, we propose a cap-and-trade system for AI. Our system provably reduces computations for AI deployment, thereby lowering emissions and monetizing efficiency to the benefit of academics and smaller companies. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_19886 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AI Cap-and-Trade: Efficiency Incentives for Accessibility and Sustainability Bornstein, Marco Bedi, Amrit Singh General Economics Economics Artificial Intelligence Computers and Society Computer Science and Game Theory The race for artificial intelligence (AI) dominance often prioritizes scale over efficiency. Hyper-scaling is the common industry approach: larger models, more data, and as many computational resources as possible. Using more resources is a simpler path to improved AI performance. Thus, efficiency has been de-emphasized. Consequently, the need for costly computational resources has marginalized academics and smaller companies. Simultaneously, increased energy expenditure, due to growing AI use, has led to mounting environmental costs. In response to accessibility and sustainability concerns, we argue for research into, and implementation of, market-based methods that incentivize AI efficiency. We believe that incentivizing efficient operations and approaches will reduce emissions while opening new opportunities for academics and smaller companies. As a call to action, we propose a cap-and-trade system for AI. Our system provably reduces computations for AI deployment, thereby lowering emissions and monetizing efficiency to the benefit of academics and smaller companies. |
| title | AI Cap-and-Trade: Efficiency Incentives for Accessibility and Sustainability |
| topic | General Economics Economics Artificial Intelligence Computers and Society Computer Science and Game Theory |
| url | https://arxiv.org/abs/2601.19886 |