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

• URL: http://arxiv.org/abs/2602.22750v1
• Date: Thu, 26 Feb 2026 08:37:32 GMT
• Title: The Inference Bottleneck: Antitrust and Neutrality Duties in the Age of Cognitive Infrastructure
• Abstract summary: As generative AI commercializes, competitive advantage is shifting from one-time model training toward continuous inference, distribution, and routing.<n>This essay defines cognitive infrastructure as a falsifiable concept built around measurable reliance, vertical incentives, and discrimination capacity.<n>It frames theories of harm using raising-rivals'-costs logic for vertically related and platform markets, where foreclosure can be profitable without anticompetitive pricing.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• PerceptionRubrics: Calibrating Multimodal Evaluation to Human Perception [75.70273182182397]
  PerceptionRubrics is a rubric-based evaluation framework.<n>It addresses the gap between saturated benchmark scores and real-world brittleness.<n>It pairs 1,038 information-dense images with over 10,000 instance-specific rubrics.
  arXiv  Detail & Related papers  (2026-06-26T17:59:15Z)
• Going Headless? On the Boundaries of Vertical AI Firms [0.0]
  General-purpose AI agents are now unbundling that package, prompting founders and investors to advocate "going headless"<n>This article argues that going headless is correct for some firms and destructive for others.<n>It proposes a three-position taxonomy determined not by sector but by task-accountability regime.
  arXiv  Detail & Related papers  (2026-05-18T03:38:29Z)
• FERA: Uncertainty-Aware Federated Reasoning for Large Language Models [60.52562148874846]
  We study federated reasoning, where a server improves multi-step reasoning by coordinating with heterogeneous clients holding private demonstrations.<n>The key challenge is that client reliability is query-dependent, while the server cannot inspect client data to determine which contributions are trustworthy.<n>We propose Uncertainty-Aware Federated Reasoning (FERA), a training-free framework based on iterative server-client co-refinement.
  arXiv  Detail & Related papers  (2026-05-11T07:04:51Z)
• Beyond Distributive Justice: Hermeneutical Fairness in Ad Delivery [7.857920011652655]
  We argue that online advertising can still produce harms arising from ads' content and from how recipients interpret and uptake them.<n>We model ad delivery as a mechanism that distributes interpretative resources and can fail in two ways.<n>We integrate them into a benchmark, utility-driven ad allocation framework that already enforces distributive justice.
  arXiv  Detail & Related papers  (2026-05-05T06:51:40Z)
• The Inference Bottleneck: A Formal Model of Vertical Foreclosure in AI Markets [0.0]
  As generative AI commercializes, competitive advantage is shifting from model training toward inference, distribution, and routing.<n>This paper develops a formal game-theoretic model of vertical foreclosure in inference markets, as the formal-model companion to Besanson and Celani (2026)<n>The model isolates two foreclosure mechanisms operating without predatory pricing: quality-of-service (QoS) discrimination against downstream rivals via latency, throughput, context limits, or feature access; and routing bias in assistant-layer interfaces.
  arXiv  Detail & Related papers  (2026-04-19T13:23:34Z)
• The AI Transformation Gap Index (AITG): An Empirical Framework for Measuring AI Transformation Opportunity, Disruption Risk, and Value Creation at the Industry and Firm Level [0.0]
  This paper introduces the AI Transformation Gap Index (AITG), a composite empirical framework that measures the distance between a firm's current AI deployment and a time varying, industry constrained capability frontier.<n>I calibrate the framework for 22 industry verticals and apply it to 14 public companies using public filings.<n>A counterintuitive result emerges: the largest AI transformation gaps do not produce the highest value density, because implementation friction, CES bottlenecks, and timing lags erode the theoretical upside of wide gaps.
  arXiv  Detail & Related papers  (2026-02-27T10:54:58Z)
• Fairness Dynamics in Digital Economy Platforms with Biased Ratings [50.29721091981893]
  We study how digital platforms can perpetuate or counteract rating-based discrimination.<n>Our results demonstrate a fundamental trade-off between user experience and fairness.<n>Our results also provide evidence that intervening by tuning the demographics of the search results is a highly effective way of reducing unfairness.
  arXiv  Detail & Related papers  (2026-02-18T18:41:16Z)
• From Sycophancy to Sensemaking: Premise Governance for Human-AI Decision Making [0.0]
  Low-friction assistants can become sycophantic, baking in implicit assumptions and pushing verification costs onto experts.<n>We argue reliable human-AI partnership requires a shift from answer generation to collaborative premise governance.
  arXiv  Detail & Related papers  (2026-02-02T17:42:54Z)
• Capabilities and Fundamental Limits of Latent Chain-of-Thought [9.586079816543178]
  Latent Chain-of-Thought (Latent CoT) models promise efficient reasoning via continuous representations, yet exhibit puzzling performance inconsistencies.<n>We reveal that this trade-off is governed by decisional certainty.
  arXiv  Detail & Related papers  (2026-02-01T10:46:00Z)
• Why Does the LLM Stop Computing: An Empirical Study of User-Reported Failures in Open-Source LLMs [50.075587392477935]
  We conduct the first large-scale empirical study of 705 real-world failures from the open-source DeepSeek, Llama, and Qwen ecosystems.<n>Our analysis reveals a paradigm shift: white-box orchestration relocates the reliability bottleneck from model algorithmic defects to the systemic fragility of the deployment stack.
  arXiv  Detail & Related papers  (2026-01-20T06:42:56Z)
• Making LLMs Reliable When It Matters Most: A Five-Layer Architecture for High-Stakes Decisions [51.56484100374058]
  Current large language models (LLMs) excel in verifiable domains where outputs can be checked before action but prove less reliable for high-stakes strategic decisions with uncertain outcomes.<n>This gap, driven by mutually cognitive biases in both humans and artificial intelligence (AI) systems, threatens the defensibility of valuations and sustainability of investments in the sector.<n>This report describes a framework emerging from systematic qualitative assessment across 7 frontier-grade LLMs and 3 market-facing venture vignettes under time pressure.
  arXiv  Detail & Related papers  (2025-11-10T22:24:21Z)
• Neither Consent nor Property: A Policy Lab for Data Law [4.588656982178105]
  This paper makes the opaque data market in the AI economy empirically legible for the first time.<n>The pipeline begins with multi-year fieldwork to extract the market's hidden logic.<n>It is validated against reality, reproducing observed trade patterns.
  arXiv  Detail & Related papers  (2025-10-30T17:27:03Z)
• HALO: Hindsight-Augmented Learning for Online Auto-Bidding [2.9058410231275014]
  Digital advertising platforms operate millisecond-level auctions through Real-Time Bidding (RTB) systems.<n>This dynamic mechanism enables precise audience targeting but introduces profound operational complexity.<n>We propose HALO: Hindsight-Augmented Learning for Online Auto-Bidding.
  arXiv  Detail & Related papers  (2025-08-05T09:47:26Z)
• TrustLoRA: Low-Rank Adaptation for Failure Detection under Out-of-distribution Data [62.22804234013273]
  We propose a simple failure detection framework to unify and facilitate classification with rejection under both covariate and semantic shifts.<n>Our key insight is that by separating and consolidating failure-specific reliability knowledge with low-rank adapters, we can enhance the failure detection ability effectively and flexibly.
  arXiv  Detail & Related papers  (2025-04-20T09:20:55Z)
• Are You Getting What You Pay For? Auditing Model Substitution in LLM APIs [71.7892165868749]
  Commercial Large Language Model (LLM) APIs create a fundamental trust problem.<n>Users pay for specific models but have no guarantee that providers deliver them faithfully.<n>We formalize this model substitution problem and evaluate detection methods under realistic adversarial conditions.<n>We propose and evaluate the use of Trusted Execution Environments (TEEs) as one practical and robust solution.
  arXiv  Detail & Related papers  (2025-04-07T03:57:41Z)
• Fairness-Accuracy Trade-Offs: A Causal Perspective [58.06306331390586]
  We analyze the tension between fairness and accuracy from a causal lens for the first time.<n>We show that enforcing a causal constraint often reduces the disparity between demographic groups.<n>We introduce a new neural approach for causally-constrained fair learning.
  arXiv  Detail & Related papers  (2024-05-24T11:19:52Z)
• Provable Unrestricted Adversarial Training without Compromise with Generalizability [44.02361569894942]
  Adversarial training (AT) is widely considered as the most promising strategy to defend against adversarial attacks. The existing AT methods often achieve adversarial robustness at the expense of standard generalizability. We propose a novel AT approach called Provable Unrestricted Adversarial Training (PUAT)
  arXiv  Detail & Related papers  (2023-01-22T07:45:51Z)
• Balancing Discriminability and Transferability for Source-Free Domain Adaptation [55.143687986324935]
  Conventional domain adaptation (DA) techniques aim to improve domain transferability by learning domain-invariant representations. The requirement of simultaneous access to labeled source and unlabeled target renders them unsuitable for the challenging source-free DA setting. We derive novel insights to show that a mixup between original and corresponding translated generic samples enhances the discriminability-transferability trade-off.
  arXiv  Detail & Related papers  (2022-06-16T09:06:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.