1. Background

In traditional OEM and ODM supply chains, delivery is accompanied by a manifest.
This manifest specifies what has been delivered, under what conditions, and according to which contractual expectations.

The manifest establishes three boundaries:

  • Acceptance criteria
  • Dispute evidence
  • Responsibility attribution

In contemporary AI delivery, these boundaries are blurred.
What is delivered is often described as “an AI model,” but operationally this description is incomplete.

2. Clarifying the Object of Delivery

AI is not a model.

An AI system in production consists of:

  • One or more models
  • Training and evaluation data
  • Inference and runtime environments
  • Deployment configurations
  • Operational workflows
  • Human and machine agents interacting with the system

Treating AI delivery as model delivery collapses this system into a single artifact, obscuring responsibility and weakening acceptance mechanisms.

This architecture explicitly treats AI as an operational system, not a file or API endpoint.

3. Problem Statement

AI delivery today exhibits structural asymmetry:

  • Suppliers control construction knowledge and system boundaries.
  • Purchasers inherit operational, legal, and reputational risk.

Binary or API-based delivery prevents purchasers from verifying:

  • Whether the deployed system matches declared properties
  • Which component caused failure
  • Where responsibility should be attributed

Responsibility becomes diffuse, while accountability remains centralized.

4. Architectural Reframing

This architecture reframes AI OEM delivery as a verifiable, system-level acceptance process.

The core primitive is the Verifiable Statement.

A Verifiable Statement is a machine-verifiable declaration of system-level operational claims, functionally equivalent to an OEM manifest, but executable and auditable.

It does not certify trust in abstract terms.
It defines what must hold true for delivery to be considered valid.

5. System Components

5.1 Verifiable Statement

A Verifiable Statement specifies:

  • Model identity and versioning
  • Training data lineage and constraints
  • Training and evaluation procedures
  • Expected operational behavior
  • Reproducible inference environments
  • Declared deployment assumptions
  • Explicit responsibility boundaries

It defines the acceptance surface of the AI system.

5.2 DAO as a Multi-Agent Release and Coordination Structure

The release and acceptance process is governed by a DAO composed of multiple specialized agents, rather than a single authority.

These agents may include:

  • Validation agents (statement structure and completeness)
  • Reproducibility agents (environment and inference verification)
  • Risk and insurance agents (premium and coverage assessment)
  • Contract coordination agents (commitment enforcement)

The DAO functions as a coordination and arbitration layer.
It does not replace legal institutions, but produces verifiable technical and economic signals consumable by them.

5.3 Asset-Bearing Contract Coordination

The DAO coordinates assets associated with each Verifiable Statement, such as:

  • Escrowed tokens
  • Insurance premiums
  • Performance bonds
  • Conditional release guarantees

These assets enable contractual coordination under distributed responsibility:

  • No single party bears total risk
  • Misrepresentation triggers economic consequences
  • Responsibility is resolved through verifiable claims

6. Delivery and Acceptance Flow

  1. System declaration
    Supplier declares the AI system via a Verifiable Statement.

  2. DAO coordination
    Agents validate statement structure, required commitments, and asset pledges.

  3. Artifact delivery
    Supplier delivers source code or auditable build artifacts.

  4. Re-execution and validation
    Purchaser re-runs the system under declared conditions.

  5. Acceptance or escalation

    • Consistency leads to acceptance.
    • Divergence activates dispute pathways with technical evidence.

7. Distributed Responsibility Attribution

This architecture enables responsibility to be distributed and resolved across layers:

  • Data provenance failures
  • Training process deviations
  • Environment mismatches
  • Deployment violations
  • Agent coordination errors

Failure is no longer attributed to “the AI” as a monolith, but to specific system components.

8. Procurement and Insurance Interface

To be institutionally effective, the Verifiable Statement must be consumable by procurement and insurance systems without interpretive translation.

Procurement and insurance are treated as first-class interfaces of AI delivery.

8.1 Procurement Mapping

From a procurement perspective, the Verifiable Statement functions as:

  • A machine-verifiable acceptance definition
  • An executable acceptance test
  • A reference object for dispute resolution

Acceptance no longer relies on reputational trust or descriptive capability claims, but on verifiable operational declarations.

8.2 Insurance Mapping

By treating AI as a system rather than a model, the Verifiable Statement provides insurers with:

  • Clearly scoped risk surfaces
  • Explicit operational boundaries
  • Reproducible technical evidence
  • Traceable responsibility layers

Insurance instruments can be attached to specific statements, enabling:

  • Premium pricing against declared scope
  • Coverage exclusions tied to stated constraints
  • Claims adjudication based on reproducible execution

8.3 Asset-Coupled Acceptance

By binding procurement acceptance and insurance coverage to the same Verifiable Statement:

  • Suppliers commit capital or premiums to their claims
  • Purchasers gain enforceable acceptance criteria
  • Insurers gain auditable risk definitions

Acceptance becomes a conditionally executable event, not a subjective judgment.

8.4 Institutional Consequence

This interface allows AI delivery to participate in existing institutional workflows:

  • Procurement gains technical rigor
  • Insurance gains definable risk objects
  • Legal systems gain reproducible evidence

Rather than introducing new trust frameworks, this architecture reuses existing institutions, correcting their current inability to reason about AI systems.

9. Scope and Limits

This architecture does not:

  • Guarantee correct behavior
  • Eliminate misuse
  • Replace regulation or courts

It provides an institutional interface between AI systems, economic commitments, and responsibility resolution.

10. Positioning

The Verifiable Statement should be understood as:

an architectural primitive for system-level AI delivery and accountability,
not a model artifact, certification label, or trust badge.

Its value emerges only when AI is treated as a system and responsibility is allowed to be distributed.