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The Economics of AI Supply Chain Regulation

Sihan Qian, Amit Mehra, Dengpan Liu

Abstract

The rise of foundation models has driven the emergence of AI supply chains, where upstream foundation model providers offer fine-tuning and inference services to downstream firms developing domain-specific applications. Downstream firms pay providers to use their computing infrastructure to fine-tune models with proprietary data, creating a co-creation dynamic that enhances model quality. Amid concerns that foundation model providers and downstream firms may capture excessive consumer surplus, along with increasing regulatory measures, this study employs a game-theoretic model involving a provider and two competing downstream firms to analyze how policy interventions affect consumer surplus in the AI supply chain. Our analysis shows that policies promoting price competition in downstream markets (i.e., pro-price-competitive policies) boost consumer surplus only when compute or data preprocessing costs are high, while compute subsidies are effective only when these costs are low, suggesting these policies complement each other. In contrast, policies promoting quality competition in downstream markets (i.e., pro-quality-competitive policies) always improve consumer surplus. We also find that under pro-price-competitive policies or compute subsidies, both the provider and downstream firms can achieve higher profits along with greater consumer surplus, creating a win-win-win outcome. However, pro-quality-competitive policies increase the provider's profits while reducing those of downstream firms. Finally, as compute costs decline, pro-price-competitive policies may lose their effectiveness, whereas compute subsidies may shift from ineffective to effective. These findings offer insights for policymakers seeking to foster AI supply chains that are economically efficient and socially beneficial.

The Economics of AI Supply Chain Regulation

Abstract

The rise of foundation models has driven the emergence of AI supply chains, where upstream foundation model providers offer fine-tuning and inference services to downstream firms developing domain-specific applications. Downstream firms pay providers to use their computing infrastructure to fine-tune models with proprietary data, creating a co-creation dynamic that enhances model quality. Amid concerns that foundation model providers and downstream firms may capture excessive consumer surplus, along with increasing regulatory measures, this study employs a game-theoretic model involving a provider and two competing downstream firms to analyze how policy interventions affect consumer surplus in the AI supply chain. Our analysis shows that policies promoting price competition in downstream markets (i.e., pro-price-competitive policies) boost consumer surplus only when compute or data preprocessing costs are high, while compute subsidies are effective only when these costs are low, suggesting these policies complement each other. In contrast, policies promoting quality competition in downstream markets (i.e., pro-quality-competitive policies) always improve consumer surplus. We also find that under pro-price-competitive policies or compute subsidies, both the provider and downstream firms can achieve higher profits along with greater consumer surplus, creating a win-win-win outcome. However, pro-quality-competitive policies increase the provider's profits while reducing those of downstream firms. Finally, as compute costs decline, pro-price-competitive policies may lose their effectiveness, whereas compute subsidies may shift from ineffective to effective. These findings offer insights for policymakers seeking to foster AI supply chains that are economically efficient and socially beneficial.
Paper Structure (26 sections, 15 theorems, 14 equations, 5 figures)

This paper contains 26 sections, 15 theorems, 14 equations, 5 figures.

Table of Contents

  1. Introduction
  2. Foundation Models
  3. Fine-tuning
  4. The AI Supply Chain
  5. Motivation and Research Questions
  6. Key Findings
  7. Literature Review
  8. AI Regulation
  9. SaaS
  10. Implications of Declining IT Costs
  11. Modeling Framework
  12. Foundation model provider
  13. Firms
  14. Model Timeline
  15. Equilibrium Analysis
  16. ...and 11 more sections

Key Result

lemma 1

Given the inference price $p_I$ and fine-tuning price $p_F$ set by the foundation model provider, the equilibrium data volumes and product prices for Firm $i$ ($i\in\{1,2\}$) in the subgame at Stage 1 are as follows:

Figures (5)

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Theorems & Definitions (15)

  • lemma 1: Equilibrium Decisions of the Firms in the Subgame
  • lemma 2: Equilibrium Pricing Decisions of the Provider
  • lemma 3: Effects of Downstream Competition on the Fine-Tuning Price
  • lemma 4: Effects of Downstream Competition on Inference Price
  • proposition 1: Impact of Pro-competitive Policies on Consumer Surplus
  • lemma 5: Impact of Compute Subsidies on Consumer Surplus
  • proposition 2: Cost-Effectiveness of Compute Subsidies
  • corollary 1: Relationship between Cost-Effectiveness and Subsidy Rate for Compute Costs
  • proposition 3: Impact of Pro-competitive Policies on the Foundation Model Provider’s Profits
  • corollary 2: Impact of Downstream Competition on Firms’ Profits
  • ...and 5 more