用碳配额交易机制激励AI效率，降低能耗并让小机构也能参与。
AI Cap-and-Trade: Efficiency Incentives for Accessibility and Sustainability
- 提出AI领域碳配额交易制度，用市场机制鼓励节能。
- 理论上可减少部署计算量，降低碳排放。
- 适合关注可持续发展和资源公平的科研与中小企业。
人工智能领域的竞争常以规模为先，追求更大模型、更多数据和更多算力，导致效率被忽视。这种超大规模趋势加剧了计算资源成本，使学术界和小型企业难以参与；同时，日益增长的能源消耗也带来显著环境负担。为此，我们主张通过市场机制激励AI效率提升。本文提出一种针对AI的碳配额交易体系，该体系在理论上可减少模型部署所需的计算量，从而降低碳排放，并将效率转化为经济收益，惠及学术研究者和中小型企业。
原文摘要 · Abstract (English)
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.
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