The Economics of AI Supply Chain Regulation
人工智能供应链监管的经济学分析AI 解读 ↓
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人工智能供应链监管的经济学分析
- 研究问题
- 在基础模型提供商与下游应用企业共同提升模型质量的供应链中，价格竞争、质量竞争及算力补贴如何影响消费者剩余与各方利润？
- 方法与思路
- 作者构建一个提供商与两家竞争性下游企业的博弈模型，分析算力和数据预处理成本如何改变不同政策的消费者剩余及利润效应。
- 材料陈述的贡献
- 摘要称价格竞争政策与算力补贴分别在高成本和低成本时有效，而质量竞争政策始终提高消费者剩余，但会降低下游企业利润。
- 阅读关注点
- 应核对价格与质量竞争政策的模型定义、均衡求解和成本分界条件，以及质量提升机制与利润分配结论对市场结构设定的依赖。
- 阅读边界
- 所给版本为 arXiv 预印本，同行评审及期刊接收未核实；摘要未说明需求函数、均衡条件或实证验证，政策结论的外推范围无法判断。
原文摘要
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.