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

Marco Bornstein, Amrit Singh Bedi

TL;DR

The paper tackles AI's dual challenge of accessibility and environmental impact under a growth-by-scaling regime. It proposes a cap-and-trade framework for AI with AI Allowances freely allocated to mandated firms and enabling trading to reward efficiency. The theoretical analysis derives a FLOP-equilibrium under cap-and-trade, with $x^* = ( rac{k}{a+b})^{\frac{1}{k+1}}$ and $y^* = F_i - x^*$, indicating reduced FLOP usage and potential utility gains for participants. If designed with benchmarking and leakage-mitigation, this market-based approach could lower AI-related emissions while expanding participation beyond incumbents.

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 of academics and smaller companies.

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

TL;DR

The paper tackles AI's dual challenge of accessibility and environmental impact under a growth-by-scaling regime. It proposes a cap-and-trade framework for AI with AI Allowances freely allocated to mandated firms and enabling trading to reward efficiency. The theoretical analysis derives a FLOP-equilibrium under cap-and-trade, with and , indicating reduced FLOP usage and potential utility gains for participants. If designed with benchmarking and leakage-mitigation, this market-based approach could lower AI-related emissions while expanding participation beyond incumbents.

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 of academics and smaller companies.
Paper Structure (18 sections, 11 equations, 2 figures)

This paper contains 18 sections, 11 equations, 2 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. AI Without Market-Based Incentives
  4. Market-Based Methods for Efficiency
  5. Charge Systems
  6. Pigouvian Taxes
  7. User Fees
  8. Credits and Subsidies
  9. Deposit Refund
  10. Tradable Permits Systems
  11. Cap-and-Trade
  12. Credit Programs
  13. Call to Action: Cap-and-Trade for AI
  14. AI Allowance Distribution
  15. Importance of AI Allowance Markets
  16. ...and 3 more sections

Figures (2)

  • Figure 1: AI Cap-and-Trade System Reduces FLOP usage. We verify that the number of FLOPs a company uses is always smaller in a cap-and-trade system. This is true for varying cost-per-FLOP values $a$ with fixed and scaled buy/sell price-per-FLOP $b$.
  • Figure 2: AI Cap-and-Trade Can Increase Utility. When the maximum number of allowable FLOPs $F_i$ for company $i$ is large enough, our AI cap-and-trade framework increases utility compared to the current AI setting. This holds across various cost-per-FLOP values.

Theorems & Definitions (2)

  • proof
  • proof