As generative AI commercializes, competitive advantage is shifting from one-time model training toward continuous inference, distribution, and routing. At the frontier, large-scale inference can function as cognitive infrastructure: a bottleneck input that downstream applications rely on to compete, controlled by firms that often compete downstream through integrated assistants, productivity suites, and developer tooling. Foreclosure risk is not limited to price. It can be executed through non-price discrimination (latency, throughput, error rates, context limits, feature gating) and, where models select tools and services, through steering and default routing that is difficult to observe and harder to litigate. This essay makes three moves. First, it defines cognitive infrastructure as a falsifiable concept built around measurable reliance, vertical incentives, and discrimination capacity, without assuming a clean market definition. Second, it frames theories of harm using raising-rivals'-costs logic for vertically related and platform markets, where foreclosure can be profitable without anticompetitive pricing. Third, it proposes Neutral Inference: a targeted, auditable conduct approach built around (i) quality-of-service parity, (ii) routing transparency, and (iii) FRAND-style non-discrimination for similarly situated buyers, applied only when observable evidence indicates functional gatekeeper status.

翻译：随着生成式人工智能的商业化，竞争优势正从一次性模型训练转向持续的推理、分发与路由。在前沿领域，大规模推理可作为认知基础设施运作：这是一种下游应用赖以竞争的关键瓶颈性投入，通常由那些通过集成助手、生产力套件和开发工具在下游竞争的企业所控制。市场封锁风险不仅限于价格。它可通过非价格歧视（延迟、吞吐量、错误率、上下文限制、功能门控）来实施，并且在模型选择工具和服务的场景中，还可通过难以观测且更难以诉讼的引导与默认路由来实现。本文提出三个核心论点。首先，它将认知基础设施定义为一个可证伪的概念，其构建基础是可量化的依赖性、纵向激励与歧视能力，而无需预设明确的市场界定。其次，它运用提高竞争对手成本的理论框架来分析纵向关联市场和平台市场的损害理论，指出即使没有反竞争定价，市场封锁行为也可能有利可图。第三，它提出"中立推理"原则：这是一种有针对性的、可审计的行为规范，其核心在于（i）服务质量对等，（ii）路由透明度，以及（iii）对处境相似的购买者采用FRAND式的非歧视待遇，且仅当可观测证据表明存在功能性守门人地位时才适用。