= "where Creative Commons and open source attach conditions to intellectual property, the AIC Framework attaches conditions to the deployment and use of AI capabilities". [1]

"currently hosted at CLEA (Center Leo Apostel for Interdisciplinary Studies), Vrije Universiteit Brussel"

Description

AIC:

"The agrarian and industrial revolutions each transformed civilisation by reorganising what humans and their tools do - but in every case, humans remained the coordinating intelligence that directed the system. The integration of artificial intelligence is different in kind: for the first time, a new autonomous agent is entering the picture, capable of replacing human coordination at a scale and pace without precedent. Because AI systems can replicate at near-zero marginal cost, the terms on which they are adopted lock in rapidly through self-reinforcing dynamics - and whether these dynamics will concentrate gains or distribute prosperity depends on the conditions under which AI enters the economic game. So does whether the costs - environmental and social - are accounted for at each step or allowed to accumulate out of sight.

Shaping those conditions is the central design challenge of our time, and the purpose of the AI Commons Framework.

In any complex adaptive system, the emergent properties of the whole follow from the rules of interaction between agents, not from the characteristics of any agent taken alone. The same principle applies to the AI transition. The conditions under which AI affects society are shaped at several layers: what a system is designed to deliver, how it works, and what regulation permits and prohibits. All these matter. But from a systems perspective, the highest-leverage layer may be this: the operational arrangements through which AI capabilities are put to work and exchanged between actors. A well-intended, well-designed system, operating within the law, can still produce harmful systemic outcomes depending on the terms governing its use, whether anyone can see how it operates, who captures the gains, and who counts as a stakeholder. The architecture of the deal matters as much as the architecture of the model.

The existing legal and economic instrumentarium at this layer - the logic of value capture, profit maximisation, and cost externalisation that structures commercial relationships - is already straining. Concentration of gains, opacity of operations, ecological costs pushed downstream: these are familiar perils of the pre-AI economy. AI does not even need to introduce any new perils: if it runs the existing ones through systems that replicate at near-zero marginal cost and accelerate without human friction, the toolset that has been producing problematic outcomes at human pace will produce catastrophic ones at machine pace. New interaction patterns at this layer are structurally necessary.

In practice, these interaction patterns live in contracts - API terms, procurement clauses, platform conditions, arrangements both standardised and bespoke. Whether by default or by design, each one settles governance questions that no current regulatory framework reaches: how gains and environmental costs distribute through the value chain, what operational evidence is disclosed, who holds standing to contest the terms. Most pass without public sight; the few that attract attention confirm what the rest configure quietly.

A contract, however carefully drafted, remains a private arrangement between two parties. It produces no shared vocabulary and creates nothing others can build on. System dynamics shift when choices become repeatable - when a recognisable pattern emerges that actors adopt because it works for them, and that third parties can read and compare - or demand.

Creative Commons is the clearest precedent. A small family of standardised terms for sharing knowledge and culture, required by no government, adopted by scholars and creators because it made certain choices easy and legible. Individual decisions, over time, aggregated into something that had not existed before: a commons of openly accessible resources. Open-source licensing achieved the same for code, and now increasingly for AI model weights. Neither spread by mandate; both reshaped entire sectors through accumulation alone, and both remain enforceable through existing legal infrastructure..

The AIC Framework applies a similar logic to the conditions under which AI capabilities are contracted into use. Its vehicle is a licence - not in the narrow intellectual-property sense, but in the functional sense that open-source developers already recognise: a set of terms that attaches to a capability and travels with it through the value chain. When a developer chooses an open-source licence, they set conditions on how their code circulates; across thousands of projects, those individual choices built an ecosystem. The AIC licence does the same for AI capabilities - where Creative Commons and open source attach conditions to intellectual property, the AIC Framework attaches conditions to the deployment and use of AI capabilities."

(https://clea.paddlecms.net/aic)

Characteristics

"The framework is organised around six governance profiles, each independently optional. An adopter selects the profiles relevant to its context, composing a recognisable profile:

Sustainability - A commitment that the environmental impact of AI operations is neutral at minimum, and where feasible, regenerative. The adopter sets defined targets for energy consumption, emissions, and ecological footprint, measured and reported against standardised benchmarks. Accountability sharpens when this data becomes comparable across providers, making environmental performance a visible and legible factor in how AI capabilities are chosen.

Value - A commitment to directing a share of the gains from AI operations toward the communities bearing their social-economic consequences. The adopter allocates a defined share of gains to specified groups of beneficiaries through a commons fund with transparent governance. Adopted across many cases, these commitments aggregate into a distributed (many-to-many) mechanism for addressing the social-economic effects of AI from within economic processes, rather than through tax-based redistribution after the fact.

Access - A commitment to open access to the full capability, without discrimination by user type or ability to pay. The adopter commits that the service is freely accessible to all users, with volume as the only adjustable constraint. As more providers adopt comparable access commitments, a floor of universal availability emerges across the ecosystem - not mandated centrally, but accumulating through decentralised decisions.

Reciprocity - A commitment to recognising and returning value to those whose resources made a system possible: the datasets, labour, and knowledge it was built on. The adopter registers these upstream contributions and allocates a share of revenue to a contributor fund, addressing the persistent pattern in which value flows downstream while the inputs sustaining it are treated as free. Across many adopters, these registries make visible the actual dependency structure of AI value chains - who contributed what, and whether the return was proportionate.

Openness - A commitment that the system's full operational logic - its code, decision architecture, and the principles governing its behaviour - is legible and available for scrutiny. This is not a predefined disclosure checklist; it is an open-ended obligation that the system will demonstrate responsible handling of whatever societal concerns arise, including those not yet articulated. An AIC-O system cannot run undisclosed operations in the background, even where existing law would permit it: if it cannot be shown, it cannot be run. Where specific operational detail must remain confidential for defined reasons, the restriction itself and its justification are public. Across many adopters, these disclosures accumulate into a shared, comparable record of how AI systems operate in practice - a commons of operational evidence that civil society, regulators, and other stakeholders can draw on independently of any single provider's willingness to cooperate.

Governance - A commitment that all groups materially affected by an AI system are represented in decisions that alter how it affects them. The adopter defines the stakeholder composition, selection method, and the categories of decision that activate the governance mechanism - including scope expansion to new populations or use cases, transfer of control to a new operator, significant changes to the system's capabilities, modifications to access or data practices, and discontinuation. When governance profiles are declared publicly, a norm of participatory oversight develops across sectors - visible, comparable, and increasingly expected.

These profiles are modular: an adopter may take up any combination. Each commitment is voluntary, but standardised and visible. Once adopted, the commitment may extend into a requirement on others in the value chain: upstream suppliers or downstream operators. A foundation model provider, for instance, might commit to sharing automation gains, and require that every downstream operator who builds on their system does the same - so that the obligation travels through the chain regardless of how many times the capability is repackaged. These are not regulatory requirements imposed from above; they are requirements that peers set for each other through ordinary contracts, as a condition of doing business together.

Enforcement operates through two complementary layers: contractual commitments that are bilateral and litigable under ordinary contract law, and declaratory commitments - public visibility of declared profiles - that create reputational accountability. The combination works within existing legal systems and requires no new legislation."

(https://clea.paddlecms.net/aic)

Discussion

From the AIC Framework to the AI Economy

Em Lenartowicz:

"the AI Commons (AIC) Framework, developed by Em Lenartowicz et al. at CLEA out of the centre’s own theoretical tradition. That tradition — Evolution, Complexity and Cognition (ECCO), the research paradigm Francis Heylighen founded and leads — explains how order arises among processes that nobody coordinates: variation and selection assemble ever more complex structures, the structures maintain themselves as self-reproducing loops of activity, and the loops, stacked and refined, are cognition, at every scale from the cell to the society. Cognition, wherever it is active, chooses: every self-maintaining structure steers itself by making distinctions and selections, and every such choice changes the conditions its neighbours select under. Governance, read from this paradigm, is the whole field of those distributed instances of evolutionary selection-making that collectively shape the selections of others. The AIC Framework is that account made into a working instrument for the AI economy.

The AI economy is exactly such a field: everyone in it shapes the selections of others, while the field as a whole answers to nobody in particular. Jurisdictions, corporations, public agencies, open-source communities, and customers reach one another’s selections only in patches: the developers who release a new capability hoping it will improve the quality of life can decide everything about how it works, while what it is deployed for is decided further down the value chain, in deals they are no longer party to; the regulator can rule on what a system may do, while the terms on which it is bought and sold live in private contracts it never sees; the municipality hosting the servers can meter the energy and water they draw, while the deployment drawing them was contracted on another continent. And two parties hold no patch at all: the people whose accumulated work the capabilities were built from, spread across every jurisdiction at once, so that none can speak for them; and the workers whose occupations a deployment reorganises, party to none of the contracts that decide it. Governance responses so far answer from inside the patches.

AIC enters elsewhere. The paradigm’s route into a system nobody commands is the repetitive pattern: find the minimal relational unit present in every configuration where AI is active, introduce a consistent change there, and the change, recurring wherever the unit recurs, reshapes dynamics that no authority can reach whole. In the AI economy, as in the economy at large, that unit is the contract — the instrument by which every actor, beginning with a single customer, already binds parties it could never rule: across jurisdictions, across sectors, along the full length of every value chain. The ordinary deal has always been the place where systemic effects take shape, and where the shaping is carried out by market participants. Its classical logic has been sending costs outward and keeping gains inside, with the accumulative effects of social, environmental, and economic externalities. The transition to the AI economy adds to the power of contracts a new speed at which they can be made and carried out — each deal setting conditions for the next, the accumulating terms hardening into arrangements nobody chose.

AIC provides templates for writing standardised public-interest commitments into AI deployment contracts, and for propagating requirements of analogous commitments across value chains. The contracts it addresses are those that shape AI deployments — an AI capability made available to someone and taken into use, on specific terms. The commitments come in six declarable profiles: Reciprocity — value returns to those the capability draws on; Sustainability — impact is measured and bounded; Openness — workings admit scrutiny; Governance — the affected are represented; Access — the capability is available without discrimination; Value — a share of the gains is paid out unconditionally. Each profile is a standardised clause set with adjustable parameters, adopted the way Creative Commons and open-source licences are: chosen ready-made and stacked on ordinary contractual relationships.

The AIC profiles are modular: an adopter activates any combination, written as a deployment code — AIC-SOG is Sustainability, Openness, and Governance; AICRSOGAV is all six; the code names the profiles a deployment holds for itself. Each commitment is voluntary, but standardised and visible, and each is also directional — a profile binds at one or more of three positions along the value chain: Core, Upstream, and Downstream, written as the profile’s propagation pattern: a row of dots — upstream, core, downstream — filled where the duty binds and open where it does not."

(August 2026)

More information

Community-Controlled Artificial Intelligence

Bibliography

Lenartowicz, E.M. (2025). Impact-Oriented Licensing for Artificial Intelligence: A Conceptual Framework for a New Domain of AI Governance. SSRN id=5794362

Lenartowicz, E.M. (2025). Shaping AI Impacts Through Licensing: Illustrative Scenarios for the Design Space. SSRN id=5835702

Lenartowicz, E.M. (2025). AI Commons (AIC) Licence Suite: A Modular Framework for Impact-Oriented AI Governance. SSRN id=5848523

AI Commons Framework

Contents