Executive Summary
The release of Meta's LLaMA models in 2023 inaugurated a new era of open-source artificial intelligence. By early 2026, the open-source AI ecosystem has expanded dramatically: Hugging Face hosts over 800,000 models, the Mistral, Qwen, DeepSeek, and LLaMA families compete credibly with proprietary alternatives, and open-source models power an estimated 40% of enterprise AI deployments according to the Stanford HAI AI Index 2025. The cumulative investment in open-source foundation models — including training compute, researcher salaries, and infrastructure — exceeds $25 billion.
This paper analyses the open-source AI ecosystem through the lens of public goods theory and commons governance. Open-source AI models exhibit key characteristics of public goods: they are non-rivalrous (one organisation's use does not diminish another's) and, once released, effectively non-excludable. This creates the classic free-rider problem — organisations benefit from open-source models without contributing to their development or to the safety research that accompanies responsible deployment. We examine the sustainability challenges facing the open-source AI commons, the free-rider dynamics in AI safety research, and propose governance frameworks drawing on Elinor Ostrom's design principles for successful commons management.
The Economics of Open-Source AI: A Public Goods Analysis
The decision to open-source a foundation model is economically paradoxical. Training a frontier model costs $50–500 million in compute alone (Epoch AI estimates), and the resulting model, once released, can be freely downloaded and deployed by competitors. Why would profit-maximising firms invest hundreds of millions of dollars in assets they give away?
The answer lies in indirect appropriability mechanisms. Meta's open-sourcing of LLaMA is strategically rational despite the direct cost, because it: commoditises the AI model layer where Meta's competitors (Google, OpenAI) have advantages; shifts competition to the application layer where Meta's social media platforms and user data provide advantages; builds an ecosystem of developers dependent on Meta's model architecture and tooling; and establishes Meta's models as de facto standards, creating switching costs.
This strategic logic mirrors the economics of platform markets: open-source models function as loss-leading complements that enhance the value of firms' proprietary assets. The OECD's AI Policy Observatory documents that 78% of major open-source model releases between 2023 and 2025 originated from companies with significant proprietary revenue streams that benefit from ecosystem growth.
However, this corporate-driven open-source model creates governance tensions. When open-source AI is primarily a strategic tool of large corporations, the "commons" is effectively managed by entities whose interests may diverge from those of the broader community. Licence restrictions (such as Meta's LLaMA Community Licence, which prohibits use by companies with over 700 million users) reveal the limits of corporate open-source as a genuine public good.
The Free-Rider Problem in AI Safety Research
AI safety research — encompassing alignment, interpretability, robustness, and misuse prevention — exhibits even stronger public goods characteristics than the models themselves. Safety research produces knowledge that benefits all AI developers and users, yet the costs are borne by the researchers and organisations conducting it. The incentive to free-ride is powerful: any individual firm's safety investments benefit its competitors equally, while the costs reduce competitive resources available for capability development.
The empirical evidence confirms systematic underinvestment. The Stanford HAI AI Index 2025 estimates that AI safety research constitutes approximately 2.3% of total AI research spending, despite expert surveys consistently identifying safety as critical to the technology's long-term viability. Among open-source model developers, the ratio is even lower: an analysis of Hugging Face's model repository reveals that safety evaluation documentation (model cards with risk assessments, bias audits, and capability limitations) accompanies fewer than 15% of uploaded models.
The free-rider problem is compounded by the "dual-use dilemma" inherent in open-source AI. Models released for beneficial purposes can be fine-tuned for harmful applications — generating disinformation, synthesising dangerous materials instructions, or creating non-consensual intimate imagery. The costs of misuse are borne by society, while the benefits of open release accrue primarily to developers. This negative externality creates a gap between private and social returns to open-sourcing that standard market mechanisms do not address.
Biosecurity provides an instructive analogy. The dual-use research of concern (DURC) framework, developed in response to gain-of-function virology research, establishes governance mechanisms for research that generates both beneficial knowledge and misuse potential. Adapting DURC principles to open-source AI — including pre-release risk assessments, staged release protocols, and monitoring for misuse — could help address the safety free-rider problem without eliminating the benefits of openness.
Commons Governance: Applying Ostrom's Principles
Elinor Ostrom's research on successful commons governance identified eight design principles that enable communities to manage shared resources sustainably. Applied to the open-source AI commons, these principles suggest concrete governance innovations:
1. Clearly defined boundaries. The open-source AI commons needs clearer definitions of what constitutes membership and what obligations accompany access. Current open-source licences define usage rights but not contribution obligations. A "responsible AI licence" could condition access to powerful models on adherence to safety standards, creating a boundary between responsible and irresponsible users of the commons.
2. Proportional equivalence between benefits and costs. Organisations that derive commercial value from open-source models should contribute proportionally to their maintenance and safety. The Linux Foundation's model — where corporate members pay tiered fees based on company size — provides a template. An analogous "AI Commons Foundation" could fund safety research, infrastructure maintenance, and capability evaluation from contributions scaled to commercial deployment.
3. Collective-choice arrangements. Governance decisions about the commons — which models to release, what safety evaluations to require, how to handle misuse reports — should involve the community of stakeholders, not solely the releasing firm. Multi-stakeholder governance bodies, analogous to the Internet Engineering Task Force (IETF) for protocol standards, could establish community-driven norms for responsible open-source AI development.
4. Monitoring and graduated sanctions. Effective commons governance requires mechanisms to detect rule violations and impose proportionate consequences. For open-source AI, this implies investment in model deployment monitoring (detecting when models are used for prohibited purposes) and graduated responses ranging from warnings to licence revocation. Technical mechanisms such as model watermarking and inference-time monitoring could support enforcement.
The Compute Commons: Infrastructure as a Shared Resource
The concentration of AI training compute presents a distinct but related commons challenge. Epoch AI data shows that the compute required for frontier model training has doubled every six months since 2020, far outpacing Moore's Law. By early 2026, training a frontier model requires computational resources that only a handful of organisations can afford — creating a "compute oligopoly" that constrains who can participate in AI development.
Several initiatives have emerged to address compute concentration. The US National AI Research Resource (NAIRR), launched in pilot form in 2024, provides academic researchers with access to compute and datasets. The EU's EuroHPC Joint Undertaking allocates supercomputing resources for AI research. The OECD's recommendation on AI compute governance proposes principles for equitable compute access across nations. These initiatives represent early experiments in treating AI compute as a shared infrastructure resource — a digital equivalent of public research universities or national laboratories.
The network economics of compute sharing create positive feedback loops. As more researchers access shared compute, the volume and diversity of open-source models increases, which in turn increases the value of the shared ecosystem to each participant. This network externality suggests that compute commons programmes, if well governed, could become self-reinforcing — attracting contributions that exceed the initial public investment.
Governance Proposals: A Framework for the AI Commons
Drawing on the public goods analysis and commons governance principles articulated above, we propose a three-pillar governance framework for the open-source AI commons:
- Pillar 1 — Responsible Release Protocol: Establish graduated release procedures based on model capability. Models below defined capability thresholds are released under standard open-source licences. Models above thresholds undergo structured safety evaluations before release, with staged access (researchers, then developers, then public) and mandatory monitoring periods.
- Pillar 2 — AI Safety Commons Fund: Create a multilateral fund, financed by commercial deployers of open-source AI in proportion to their revenue, dedicated to safety research, red-teaming, and misuse monitoring. The fund would operate independently of any single corporate sponsor, with governance by an elected multi-stakeholder board.
- Pillar 3 — Compute Access Programme: Expand public compute infrastructure for AI research, with allocation governed by scientific merit and geographic diversity criteria. Embed compute access programmes within existing multilateral institutions (UNESCO, ITU) to ensure broad participation and legitimacy.
Implications for GDEF's Technology & Transformation Working Group
The open-source AI ecosystem represents one of the most significant experiments in digital commons governance since the development of the open-source software movement in the 1990s. The stakes, however, are vastly higher: foundation models are general-purpose technologies with transformative potential across every sector of the economy and society. GDEF's Technology & Transformation Working Group will advance the governance framework proposed in this paper, with particular focus on the AI Safety Commons Fund mechanism, in coordination with the OECD AI Policy Observatory and the Partnership on AI.
References & Sources
- Stanford HAI, Artificial Intelligence Index Report 2025. Stanford University Human-Centered Artificial Intelligence. aiindex.stanford.edu
- OECD, AI Policy Observatory: Open Source AI Governance. oecd.ai
- Epoch AI, Training Compute of Frontier AI Models, 2025 Database. epochai.org/data
- Ostrom, E. (1990). Governing the Commons: The Evolution of Institutions for Collective Action. Cambridge University Press. doi.org/10.1017/CBO9780511807763
- Samuelson, P.A. (1954). "The Pure Theory of Public Expenditure." Review of Economics and Statistics, 36(4), 387–389. doi.org/10.2307/1925895
- Linux Foundation Research, State of Open Source AI 2025. linuxfoundation.org/research
- Hugging Face, Open LLM Leaderboard and Model Hub Statistics, 2025. huggingface.co
- Kapoor, S. and Narayanan, A. (2023). "Leakage and the Reproducibility Crisis in Machine-Learning-based Science." Patterns, 4(9), 100804. doi.org/10.1016/j.patter.2023.100804
- National Academies of Sciences, Dual Use Research of Concern in the Life Sciences, 2024. nationalacademies.org