A New Blueprint for AI Governance

In November 2025, Metagov released a document that reframes artificial intelligence from a single, opaque product to a layered set of operations that can be individually understood, modified, and governed. The authors—Nathan Schneider, Cormac Callanan, and a host of collaborators—argue that the prevailing narrative of AI as a monolithic, corporate‑owned technology is both misleading and disempowering.

Breaking Down the Stack

The core premise is simple: just as a complex mathematical problem is solved by breaking it into smaller, tractable sub‑problems, an AI system can be decomposed into distinct layers:

  1. Foundational Model Design – architecture, training data, and hyper‑parameters.
  2. Infrastructure & Resources – hardware, energy consumption, and environmental impact.
  3. Fine‑Tuning & Deployment – domain‑specific adjustments and runtime environments.
  4. Interface & User Experience – how users interact with the model and how the system communicates intent.
  5. Governance & Policy – mechanisms for accountability, consent, and value distribution.

Each layer presents a point of intervention where communities can introduce feedback loops, standards, or collective decision‑making processes.

“Collective governance is a way of introducing powerful feedback loops that draw on diverse knowledge and experience.” – Metagov

Leveraging Donella Meadows’ Leverage Points

The framework borrows heavily from Meadows’ Leverage Points essay, emphasizing that the most effective changes often occur at higher‑level system properties—such as goals, paradigms, and feedback loops—rather than at the low‑level code. By positioning governance mechanisms at these strategic junctures, Metagov proposes a model where:

  • Community‑curated datasets replace proprietary data silos.
  • Open‑source model architectures allow for peer review and rapid iteration.
  • Decentralized value‑sharing protocols ensure that economic benefits return to the contributors.

A Sample Governance Flow

flowchart TD
    A[Data Collection] --> B[Model Training]
    B --> C[Fine‑Tuning]
    C --> D[Deployment]
    D --> E[User Interaction]
    E --> F[Feedback Loop]
    F --> A
    subgraph Governance
      G[Community Oversight] --> B
      H[Ethics Review] --> C
      I[Policy Enforcement] --> D
    end

The diagram illustrates how governance nodes can be inserted at critical junctures, creating a cyclical feedback loop that continually refines the system.

From Theory to Practice

Metagov’s document is not merely an academic exercise; it references existing projects that already embody these principles:

  • Open‑AI‑Model‑Registry – a public repository for vetted, community‑reviewed model weights.
  • Decentralized Training Networks – federated learning frameworks that keep data on local devices.
  • Token‑Based Access Control – mechanisms that reward contributors and enforce usage limits.

By mapping these initiatives onto the proposed layers, the authors demonstrate that a distributed, participatory AI ecosystem is not only feasible but already in motion.

The Road Ahead

The paper concludes with a sober reminder that democracy is an opportunity, never a guarantee. It calls on developers, researchers, and policy makers to actively engage with the proposed governance structures, ensuring that AI systems evolve in ways that reflect the needs and values of the communities that build and use them.

“May our interventions begin with care and consideration for others.” – Metagov

Source: Metagov, Made for, November 2025 – https://metagov.org/cg-ai/