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.

翻译：基础模型的兴起推动了人工智能供应链的出现，其中上游基础模型提供商为开发特定领域应用的下游企业提供微调与推理服务。下游企业通过支付费用使用提供方的计算基础设施，结合专有数据对模型进行微调，这种协同创造机制提升了模型质量。针对基础模型提供商与下游企业可能攫取超额消费者剩余的担忧，以及日益加强的监管措施，本研究构建包含一个提供商与两个竞争性下游企业的博弈论模型，以分析政策干预如何影响人工智能供应链中的消费者剩余。研究表明：促进下游市场价格竞争的政策（即价格竞争促进政策）仅在计算或数据预处理成本较高时能提升消费者剩余，而计算资源补贴政策仅在成本较低时有效，说明这两类政策具有互补性。相比之下，促进下游市场质量竞争的政策（即质量竞争促进政策）始终能提高消费者剩余。研究还发现，在价格竞争促进政策或计算补贴政策下，提供商与下游企业均可实现利润增长与消费者剩余提升，形成三方共赢格局。然而，质量竞争促进政策在增加提供商利润的同时会降低下游企业利润。最后，随着计算成本下降，价格竞争促进政策可能失效，而计算补贴政策可能从无效转为有效。这些发现为政策制定者构建兼具经济效率与社会效益的人工智能供应链提供了重要参考。