Intelligence as Commons: Shared AI Knowledge

Updated 1 December 2025

Intelligence as commons is a framework that defines collective reasoning and shared knowledge as a public good governed by diverse human and AI agents.
It adapts commons theory, employing formal models to balance open data contributions, AI extraction rates, and participatory incentives for equitable resource management.
Governance mechanisms like stewardship councils, data trusts, and open-source licenses are crucial for ensuring transparency, reciprocity, and sustainability in AI systems.

Intelligence as commons refers to the conceptualization and governance of collective reasoning capacity and knowledge-generating infrastructure as a shared, non-proprietary resource—analogous to environmental or digital commons—co-produced, maintained, and governed by diverse contributors, users, and stewards across human and artificial agent populations. This paradigm recognizes the technical, social, and normative challenges imposed by contemporary AI architectures and proposes principles, formal models, and institutional designs for sustaining intelligence as an equitable, non-excludable public good (Noroozian et al., 8 Aug 2025, Quillivic et al., 19 Mar 2024, Wright, 16 Jul 2025, Huang et al., 2023, Bingley et al., 8 Sep 2024).

1. Conceptual Foundations

Intelligence as commons is grounded in the adaptation of commons theory (notably Ostrom’s resource-governance framework) to informational and cognitive domains. Intelligence is defined not as a proprietary asset but as a “shared capacity co-produced, maintained, and governed by a plurality of actors—data contributors (e.g., Wikipedia volunteers), curators, infrastructure providers, open-source developers, and end-users” (Noroozian et al., 8 Aug 2025). The commons thus consists of both:

The stock of open data and knowledge artifacts: digital encyclopedias, code repositories, image libraries, forum archives, and similar shared resources (Noroozian et al., 8 Aug 2025, Huang et al., 2023).
The social practices of “commoning”: negotiation of norms, licenses, consent mechanisms, and curation workflows that maintain quality, access, and equitable governance.

In formal terms, the digital commons is often represented as $C = (R, U, G)$ , where $R$ is the resource stock (data, code, models, compute), $U$ the community of users/contributors, and $G$ the set of governance rules (licenses, roles, norms) (Quillivic et al., 19 Mar 2024).

Contemporary generative AI systems, particularly large foundation models, intensify the commons dynamic: they rely on large-scale public data and infrastructure for training, yet their deployment and extractive use threaten both the sustainability and the equity of the commons (Noroozian et al., 8 Aug 2025, Huang et al., 2023).

2. Formal Models and Metrics

Several formalisms extend Ostrom’s principles to model the dynamics of intelligence as commons. One key semi-formal model is provided by (Noroozian et al., 8 Aug 2025):

Let:

$C(t)$ : Stock of curated commons data at time $t$
$P(t)$ : Participation rate (new contributions per unit time)
$D(t)$ : Extraction demand by AI crawlers per unit time
$\delta$ : Natural decay rate of content relevance/quality
$\epsilon$ : Average data “cost” per crawler hit
$\eta$ : Conversion factor from contributor effort to usable data units

The dynamical system is:

$\;\; \frac{dC}{dt} = \eta P(t) - \delta C(t) - \epsilon D(t)$
$P(t+1) = P(t)\cdot \exp(-\alpha D(t)) + s T(t)$
$D(t) = D_0 + \beta I_{AI}(t)$

Here, Equation (1) captures the net stock of commons data after accounting for human input, natural decay, and AI-driven extraction cost; Equation (2) models participatory motivation and its decline under AI-driven demand; Equation (3) relates extraction demand to both baseline use and the expansion of AI users.

Metrics for evaluating the “health” of the intelligence commons extend these models:

Replenishment ratio: Rate of human contributions per unit of AI-generated usage
Contamination index: Proportion of AI-generated vs. human-generated content
Diversity/entropy of outputs, accuracy rates, fairness statistics, and Gini coefficients for usage/concentration (Huang et al., 2023)

Socially-minded intelligence formalizes the multi-agent perspective, modeling individual ( $\mathrm{ISMI}$ ) and group ( $\mathrm{GSMI}$ ) socially-minded intelligence as functions of agent abilities, shared identity, and goal alignment (Bingley et al., 8 Sep 2024).

3. Governance Structures and Institutional Arrangements

Effective commons governance requires institutional designs that implement Ostrom’s core principles—clear boundaries, collective-choice, monitoring, sanctions, conflict resolution, recognition of rights, and nested governance (Quillivic et al., 19 Mar 2024). Applied to intelligence as commons, key structures include:

Mechanism	Examples	Functions
Commons Stewardship Councils	Multi-stakeholder bodies (contributors, AI developers, GLAMs, policymakers)	Monitoring, rule-setting, dispute adjudication
Data Trusts & Fiduciary Models	Community-governed legal vehicles	Negotiating data use, distributing royalties
Open-source platform governance	Hugging Face, OSS foundations	Transparency, access, community moderation
AI-Commons Licenses	Modular open data licenses	Delineate training uses, enforce reciprocity
Technical standards (AI-purpose signals, unlearning APIs, provenance metadata)	robots.txt extensions, W3C PROV, REST endpoints	Consent management, data traceability, revocation
Monitoring dashboards	Real-time visualization of $P(t)$ , $C(t)$ , $D(t)$ , $F(t)$	Policy feedback, early-warning

These mechanisms address multi-tier access controls, provenance, community governance, data withdrawal/unlearning, and equitable distribution of the environmental and financial externalities of AI (Noroozian et al., 8 Aug 2025, Quillivic et al., 19 Mar 2024).

4. Risks, Failure Modes, and Sociotechnical Challenges

The emergence of AI has introduced complex failure modes for the intelligence commons:

Undersupply from Closed AI Adoption: Widespread use of closed AI systems diminishes contributions and user traffic to commons platforms, triggering a negative feedback cycle for participation, especially in low-resource languages or domains (Noroozian et al., 8 Aug 2025, Huang et al., 2023).
Extractive Crawling and Privatization: Blunt content controls fail to discriminate responsible (scholarly) versus exploitative (commercial) use, risking blanket access bans that harm both research and public benefit. Large commercial actors leverage power asymmetries to negotiate exclusive access (Noroozian et al., 8 Aug 2025, Huang et al., 2023).
Synthetic Content Proliferation: The introduction of unmarked or low-quality AI-generated material degrades the commons, increasing moderation burdens and eroding trust (Noroozian et al., 8 Aug 2025).
Cognitive Stratification: As detailed in "Cognitive Castes," AI interfaces amplify epistemic stratification, benefiting cognitively sophisticated users while “pacifying” others, leading to collapse of deliberative capacity and the creation of informational aristocracies (Wright, 16 Jul 2025).
Economic Concentration: High fixed costs for training and deploying cutting-edge models induces centralization among well-resourced actors, undermining accessibility and equity (Huang et al., 2023, Quillivic et al., 19 Mar 2024).

Commons Dissolution Dynamics

Information and intelligence cease being true commons under conditions of access asymmetry, privatized interface control, lack of interpretive agency, and when feedback loops reinforce cognitive or economic stratification (Wright, 16 Jul 2025).

5. Design Principles and Cultivation Strategies

To maintain a robust intelligence commons, recommended design principles include:

Transparency: Publicly accessible registries documenting dataset/model provenance, training logs, and environmental impacts (Huang et al., 2023, Noroozian et al., 8 Aug 2025).
Reciprocity: Mandatory contribution of improved datasets, model weights, or feedback from those extracting value from the commons (Huang et al., 2023, Noroozian et al., 8 Aug 2025).
Inclusivity and Equity: Governance structures that empower marginalized communities, support non-English domains, and address labor precarity (Noroozian et al., 8 Aug 2025, Quillivic et al., 19 Mar 2024).
Adaptability: Periodic revision of standards and governance based on empirical monitoring of commons health metrics (Noroozian et al., 8 Aug 2025, Huang et al., 2023).
Sustainability: Access fees or carbon offsets for high-volume extractors, with reinvestment in commons infrastructure (Noroozian et al., 8 Aug 2025, Quillivic et al., 19 Mar 2024).
Interpretive Agency: Guarantee “epistemic sovereignty” through legal and technical rights to adversarial interfaces, model audits, and output provenance (Wright, 16 Jul 2025).
Socially-Minded Intelligence in AI: Architectures should support dynamic switching between individual and group modes, using social-landscape modeling, self-relevance calculations, and social-influence algorithms (Bingley et al., 8 Sep 2024).

6. Applications and Future Socio-Technical Architectures

Intelligence as commons informs both human and AI system design. In multi-agent environments, systems endowed with socially-minded intelligence facilitate efficient pooling and resource reallocation without rigid centralization. In human–AI and human–human collaborations, cultivating group identification and goal alignment dynamically boosts both individual and collective problem-solving capacity (Bingley et al., 8 Sep 2024).

Scenarios include open knowledge platforms, federated AI foundations, democratized compute clusters, and data trusts where governance and benefit sharing are distributed (Quillivic et al., 19 Mar 2024, Noroozian et al., 8 Aug 2025).

Future architectures should embed continuous commons monitoring (e.g., ISMI/GSMI metrics), support identity-fluid groups and agents, and couple human oversight with algorithmic transparency to maintain open, resilient, and contestable epistemic infrastructure (Bingley et al., 8 Sep 2024, Wright, 16 Jul 2025, Huang et al., 2023).

7. Open Questions and Research Directions

Outstanding research questions include:

Preventing undersupply and “paradox of reuse” as public contributions dwindle under closed AI adoption (Noroozian et al., 8 Aug 2025, Huang et al., 2023).
Balancing essential openness with protection against exploitative extraction (“multi-tier access protocols” and reciprocity enforcement) (Noroozian et al., 8 Aug 2025, Quillivic et al., 19 Mar 2024).
Updating legal frameworks (licenses, unlearning protocols, data trusts) for new forms of collective data/knowledge stewardship (Noroozian et al., 8 Aug 2025, Quillivic et al., 19 Mar 2024).
Measuring and mitigating synthetic pollution, ensuring robustness and trust in commons repositories (Noroozian et al., 8 Aug 2025, Huang et al., 2023).
Accounting for infrastructural, labor, and ecological costs (e.g., “carbon + commons” footprint) and ensuring equitable redistribution (Noroozian et al., 8 Aug 2025, Quillivic et al., 19 Mar 2024, Huang et al., 2023).
Formalizing the reconstruction of rational autonomy and interpretive rights in civic settings where algorithmic mediation is now ubiquitous (Wright, 16 Jul 2025).

Plausible implications are that sustainable intelligence commons require systemic, multi-layer institutional support; research into open cognitive infrastructure, legal codification of epistemic rights, incentive design, and cross-disciplinary metrics will be central to ongoing developments (Noroozian et al., 8 Aug 2025, Wright, 16 Jul 2025, Huang et al., 2023).