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.

翻译：人工智能（AI）领域的领先竞赛往往优先考虑规模而非效率。超大规模化是业界通行的做法：更大的模型、更多的数据，以及尽可能多的计算资源。使用更多资源是提升AI性能的捷径。因此，效率被忽视。其后果是，对昂贵计算资源的需求使得学术界和小型企业被边缘化。同时，随着AI应用的增长，能源消耗增加导致环境成本不断上升。针对可及性和可持续性方面的担忧，我们主张研究并实施基于市场的方法来激励AI效率的提升。我们相信，激励高效运行和高效方法将减少碳排放，同时为学术界和小型企业开辟新的机遇。作为行动倡议，我们提出针对AI的限额交易系统。该系统可被证明能减少AI部署中的计算量，从而降低碳排放，并将效率货币化，使学术界和小型企业受益。