The Inference Bottleneck: Antitrust and Neutrality Duties in the Age of Cognitive Infrastructure

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.