Beyond Private or Public: Large Language Models as Quasi-Public Goods in the AI Economy

• URL: http://arxiv.org/abs/2509.13265v1
• Date: Tue, 16 Sep 2025 17:22:00 GMT
• Title: Beyond Private or Public: Large Language Models as Quasi-Public Goods in the AI Economy
• Authors: Yukun Zhang, TianYang Zhang,
• Abstract summary: This paper conceptualizes Large Language Models (LLMs) as a form of mixed public goods within digital infrastructure.<n>We develop mathematical models to quantify the non-rivalry characteristics, partial excludability, and positive externalities of LLMs.
• Score: 4.887749221165767
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper conceptualizes Large Language Models (LLMs) as a form of mixed public goods within digital infrastructure, analyzing their economic properties through a comprehensive theoretical framework. We develop mathematical models to quantify the non-rivalry characteristics, partial excludability, and positive externalities of LLMs. Through comparative analysis of open-source and closed-source development paths, we identify systematic differences in resource allocation efficiency, innovation trajectories, and access equity. Our empirical research evaluates the spillover effects and network externalities of LLMs across different domains, including knowledge diffusion, innovation acceleration, and industry transformation. Based on these findings, we propose policy recommendations for balancing innovation incentives with equitable access, including public-private partnership mechanisms, computational resource democratization, and governance structures that optimize social welfare. This interdisciplinary approach contributes to understanding the economic nature of foundation AI models and provides policy guidance for their development as critical digital infrastructure

Related papers

• Bridging VLMs and Embodied Intelligence with Deliberate Practice Policy Optimization [72.20212909644017]
  Deliberate Practice Policy Optimization (DPPO) is a metacognitive Metaloop'' training framework.<n>DPPO alternates between supervised fine-tuning (competence expansion) and reinforcement learning (skill refinement)<n> Empirically, training a vision-language embodied model with DPPO, referred to as Pelican-VL 1.0, yields a 20.3% performance improvement over the base model.<n>We are open-sourcing both the models and code, providing the first systematic framework that alleviates the data and resource bottleneck.
  arXiv  Detail & Related papers  (2025-11-20T17:58:04Z)
• Toward a Public and Secure Generative AI: A Comparative Analysis of Open and Closed LLMs [0.0]
  This study aims to critically evaluate and compare the characteristics, opportunities, and challenges of open and closed generative AI models.<n>The proposed framework outlines key dimensions, openness, public governance, and security, as essential pillars for shaping the future of trustworthy and inclusive Gen AI.
  arXiv  Detail & Related papers  (2025-05-15T15:21:09Z)
• Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey [58.50944604905037]
  Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications.<n>Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems.<n>This survey provides a structured tutorial on fundamental architectures, enabling technologies, and emerging applications.
  arXiv  Detail & Related papers  (2025-05-03T13:55:38Z)
• The Role of Open-Source LLMs in Shaping the Future of GeoAI [11.083173173865491]
  Large Language Models (LLMs) are transforming geospatial artificial intelligence (GeoAI)<n>This paper examines the open-source paradigm's critical role in this transformation.
  arXiv  Detail & Related papers  (2025-04-24T13:20:17Z)
• A Survey on Post-training of Large Language Models [185.51013463503946]
  Large Language Models (LLMs) have fundamentally transformed natural language processing, making them indispensable across domains ranging from conversational systems to scientific exploration.<n>These challenges necessitate advanced post-training language models (PoLMs) to address shortcomings, such as restricted reasoning capacities, ethical uncertainties, and suboptimal domain-specific performance.<n>This paper presents the first comprehensive survey of PoLMs, systematically tracing their evolution across five core paradigms: Fine-tuning, which enhances task-specific accuracy; Alignment, which ensures ethical coherence and alignment with human preferences; Reasoning, which advances multi-step inference despite challenges in reward design; Integration and Adaptation, which
  arXiv  Detail & Related papers  (2025-03-08T05:41:42Z)
• An Overview of Large Language Models for Statisticians [109.38601458831545]
  Large Language Models (LLMs) have emerged as transformative tools in artificial intelligence (AI)<n>This paper explores potential areas where statisticians can make important contributions to the development of LLMs.<n>We focus on issues such as uncertainty quantification, interpretability, fairness, privacy, watermarking and model adaptation.
  arXiv  Detail & Related papers  (2025-02-25T03:40:36Z)
• A Multi-LLM-Agent-Based Framework for Economic and Public Policy Analysis [0.0]
  This paper pioneers a novel approach to economic and public policy analysis by leveraging multiple Large Language Models (LLMs) as heterogeneous artificial economic agents.<n>We first evaluate five LLMs' economic decision-making capabilities in solving two-period consumption allocation problems.<n>We construct a Multi-LLM-Agent-Based (MLAB) framework by mapping these LLMs to specific educational groups and corresponding income brackets.
  arXiv  Detail & Related papers  (2025-02-24T06:27:07Z)
• The Open Source Advantage in Large Language Models (LLMs) [0.0]
  Large language models (LLMs) have rapidly advanced natural language processing, driving significant breakthroughs in tasks such as text generation, machine translation, and domain-specific reasoning.<n>The field now faces a critical dilemma in its approach: closed-source models like GPT-4 deliver state-of-the-art performance but restrict accessibility, and external oversight.<n>Open-source frameworks like LLaMA and Mixtral democratize access, foster collaboration, and support diverse applications, achieving competitive results through techniques like instruction tuning and LoRA.
  arXiv  Detail & Related papers  (2024-12-16T17:32:11Z)
• Creating a Cooperative AI Policymaking Platform through Open Source Collaboration [14.120384828192067]
  Current incentive structures and regulatory delays may hinder responsible AI development and deployment.<n>To address these challenges, we propose developing a large multimodal text and economic-timeseries foundation model.
  arXiv  Detail & Related papers  (2024-12-09T19:25:29Z)
• SRAP-Agent: Simulating and Optimizing Scarce Resource Allocation Policy with LLM-based Agent [45.41401816514924]
  We propose an innovative framework, SRAP-Agent, which integrates Large Language Models (LLMs) into economic simulations. We conduct extensive policy simulation experiments to verify the feasibility and effectiveness of the SRAP-Agent.
  arXiv  Detail & Related papers  (2024-10-18T03:43:42Z)
• Edge-assisted Democratized Learning Towards Federated Analytics [67.44078999945722]
  We show the hierarchical learning structure of the proposed edge-assisted democratized learning mechanism, namely Edge-DemLearn. We also validate Edge-DemLearn as a flexible model training mechanism to build a distributed control and aggregation methodology in regions.
  arXiv  Detail & Related papers  (2020-12-01T11:46:03Z)

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.