AI Cap-and-Trade: Efficiency Incentives for Accessibility and Sustainability

TL;DR

This paper advocates for a market-based cap-and-trade system to incentivize AI efficiency, aiming to reduce environmental impact and improve accessibility for smaller entities.

Contribution

It proposes a novel cap-and-trade framework for AI that incentivizes efficiency, reduces emissions, and benefits smaller organizations and academia.

Findings

Cap-and-trade system can effectively reduce AI computational emissions.

Efficiency incentives can democratize AI development.

Proposed system has provable benefits for sustainability.

Abstract

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.