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 of academics and smaller companies.

翻译：当前人工智能（AI）主导地位的竞争往往将规模置于效率之上。超大规模化已成为行业普遍采用的方法：构建更庞大的模型、使用更多数据、并尽可能调用尽可能多的计算资源。通过增加资源投入是提升AI性能的更简单路径，因此效率问题长期未受重视。这导致对昂贵计算资源的需求使得学术界与小型企业逐渐边缘化。与此同时，随着AI应用的扩展，能源消耗的持续增长已引发日益严重的环境成本问题。针对可及性与可持续性挑战，本文主张研究并实施基于市场的激励机制以促进AI效率提升。我们相信，对高效运算与方法的激励既能减少碳排放，同时能为学术界与小型企业创造新的发展机遇。作为行动倡议，我们提出面向AI领域的总量控制与交易体系。该体系经证明可减少AI部署所需的计算量，从而降低碳排放，并通过效率货币化的方式使学术界与小型企业受益。