AI Cap-and-Trade: Efficiency Incentives for Accessibility and Sustainability
This addresses accessibility and sustainability issues in AI for researchers and smaller entities, but it is incremental as it applies an existing economic concept to a new domain.
The paper tackles the problem of AI's prioritization of scale over efficiency, which marginalizes academics and smaller companies and increases environmental costs, by proposing a cap-and-trade system that provably reduces computations for AI deployment, lowering emissions and monetizing efficiency.
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.