# The AI Community Building the Future? A Quantitative Analysis of Development Activity on Hugging Face Hub

arXiv: https://arxiv.org/abs/2405.13058 · HTML: https://mlsanity.com/paper/2405.13058/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 146 references that are in the map.
- First posted on arXiv 20 May 2024.

## Builds on (most cited first, 25 of 25)

- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://mlsanity.com/paper/2204.06745/) (2022)
- [Release Strategies and the Social Impacts of Language Models](https://mlsanity.com/paper/1908.09203/) (2019)
- [Generative Language Models and Automated Influence Operations: Emerging Threats and Potential Mitigations](https://mlsanity.com/paper/2301.04246/) (2023)
- [Deep Learning for Deepfakes Creation and Detection: A Survey](https://mlsanity.com/paper/1909.11573/) (2019)
- [On the Societal Impact of Open Foundation Models](https://mlsanity.com/paper/2403.07918/) (2024)
- [The Gradient of Generative AI Release: Methods and Considerations](https://mlsanity.com/paper/2302.04844/) (2023)
- [Opening up ChatGPT: Tracking openness, transparency, and accountability in instruction-tuned text generators](https://mlsanity.com/paper/2307.05532/) (2023)
- [Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectives](https://mlsanity.com/paper/2311.09227/) (2023)
- [Typhoon: Thai Large Language Models](https://mlsanity.com/paper/2312.13951/) (2023)
- [Democratising AI: Multiple Meanings, Goals, and Methods](https://mlsanity.com/paper/2303.12642/) (2023)
- [Exploring the Carbon Footprint of Hugging Face's ML Models: A Repository Mining Study](https://mlsanity.com/paper/2305.11164/) (2023)
- [Analyzing the Evolution and Maintenance of ML Models on Hugging Face](https://mlsanity.com/paper/2311.13380/) (2023)
- [A Cost Analysis of Generative Language Models and Influence Operations](https://mlsanity.com/paper/2308.03740/) (2023)
- [Risks and Opportunities of Open-Source Generative AI](https://mlsanity.com/paper/2405.08597/) (2024)
- [BigScience: A Case Study in the Social Construction of a Multilingual Large Language Model](https://mlsanity.com/paper/2212.04960/) (2022)
- [Moderating Model Marketplaces: Platform Governance Puzzles for AI Intermediaries](https://mlsanity.com/paper/2311.12573/) (2023)
- [On the Suitability of Hugging Face Hub for Empirical Studies](https://mlsanity.com/paper/2307.14841/) (2023)
- [Towards Openness Beyond Open Access: User Journeys through 3 Open AI Collaboratives](https://mlsanity.com/paper/2301.08488/) (2023)
- [Public-private funding models in open source software development: A case study on scikit-learn](https://mlsanity.com/paper/2404.06484/) (2024)
- [Do software firms collaborate or compete? A model of coopetition in community-initiated OSS projects](https://mlsanity.com/paper/1808.06489/) (2018)
- [To Build Our Future, We Must Know Our Past: Contextualizing Paradigm Shifts in Natural Language Processing](https://mlsanity.com/paper/2310.07715/) (2023)
- [Collaboration in the open-source arena: The WebKit case](https://mlsanity.com/paper/1401.5996/) (2014)
- [How Firms Adapt and Interact in Open Source Ecosystems: Analyzing Stakeholder Influence and Collaboration Patterns](https://mlsanity.com/paper/2208.02628/) (2022)
- [The Cybersecurity Crisis of Artificial Intelligence: Unrestrained Adoption and Natural Language-Based Attacks](https://mlsanity.com/paper/2311.09224/) (2023)

## Built on by (most cited first, 18 of 18)

- [OptunaHub: A Platform for Black-Box Optimization](https://mlsanity.com/paper/2510.02798/) (2025)
- [The Future of Open Human Feedback](https://mlsanity.com/paper/2408.16961/) (2024)
- [Atlas: A Framework for ML Lifecycle Provenance & Transparency](https://mlsanity.com/paper/2502.19567/) (2025)
- [HuggingGraph: Understanding the Supply Chain of LLM Ecosystem](https://mlsanity.com/paper/2507.14240/) (2025)
- [Why Companies "Democratise" Artificial Intelligence: The Case of Open Source Software Donations](https://mlsanity.com/paper/2409.17876/) (2024)
- [Compliance Cards: Automated EU AI Act Compliance Analyses amidst a Complex AI Supply Chain](https://mlsanity.com/paper/2406.14758/) (2024)
- [Characterising Open Source Co-opetition in Company-hosted Open Source Software Projects: The Cases of PyTorch, TensorFlow, and Transformers](https://mlsanity.com/paper/2410.18241/) (2024)
- [FIGhost: Fluorescent Ink-based Stealthy and Flexible Backdoor Attacks on Physical Traffic Sign Recognition](https://mlsanity.com/paper/2505.12045/) (2025)
- [A Cartography of Open Collaboration in Open Source AI: Mapping Practices, Motivations, and Governance in 14 Open Large Language Model Projects](https://mlsanity.com/paper/2509.25397/) (2025)
- [Computational Compliance for AI Regulation: Blueprint for a New Research Domain](https://mlsanity.com/paper/2601.04474/) (2026)
- [Discovering Hidden Gems in Model Repositories](https://mlsanity.com/paper/2601.22157/) (2026)
- [The Shrinking Lifespan of LLMs in Science](https://mlsanity.com/paper/2604.07530/) (2026)
- [Responsible AI in Open Ecosystems: Reconciling Innovation with Risk Assessment and Disclosure](https://mlsanity.com/paper/2409.19104/) (2024)
- [A Practical Approach to Underwater Depth and Surface Normals Estimation](https://mlsanity.com/paper/2410.02072/) (2024)
- [Beyond Trusting Trust: Multi-Model Validation for Robust Code Generation](https://mlsanity.com/paper/2502.16279/) (2025)
- [Opening the Scope of Openness in AI](https://mlsanity.com/paper/2505.06464/) (2025)
- [MixtureKit: A General Framework for Composing, Training, and Visualizing Mixture-of-Experts Models](https://mlsanity.com/paper/2512.12121/) (2025)
- [Critically Engaged Pragmatism: Scientific Norm and Social, Pragmatist Epistemology for AI Science Evaluation Tools](https://mlsanity.com/paper/2601.09753/) (2026)

## Abstract (arXiv)

Open model developers have emerged as key actors in the political economy of artificial intelligence (AI), but we still have a limited understanding of collaborative practices in the open AI ecosystem. This paper responds to this gap with a three-part quantitative analysis of development activity on the Hugging Face (HF) Hub, a popular platform for building, sharing, and demonstrating models. First, various types of activity across 348,181 model, 65,761 dataset, and 156,642 space repositories exhibit right-skewed distributions. Activity is extremely imbalanced between repositories; for example, over 70% of models have 0 downloads, while 1% account for 99% of downloads. Furthermore, licenses matter: there are statistically significant differences in collaboration patterns in model repositories with permissive, restrictive, and no licenses. Second, we analyse a snapshot of the social network structure of collaboration in model repositories, finding that the community has a core-periphery structure, with a core of prolific developers and a majority of isolate developers (89%). Upon removing the isolate developers from the network, collaboration is characterised by high reciprocity regardless of developers' network positions. Third, we examine model adoption through the lens of model usage in spaces, finding that a minority of models, developed by a handful of companies, are widely used on the HF Hub. Overall, activity on the HF Hub is characterised by Pareto distributions, congruent with OSS development patterns on platforms like GitHub. We conclude with recommendations for researchers, companies, and policymakers to advance our understanding of open AI development.