Proof of Attributed Intelligence (PoAI): How AI Contribution Becomes Value

Artificial intelligence is increasingly created, composed, and executed by networks—not by a single lab or cloud. Models are trained on open datasets, fine‑tuned by community contributors, and run across decentralized compute. Yet most AI value today is captured at the application layer, while upstream contributors (data providers, model builders, evaluators, and node operators) have no native claim on the economics their work enables.

Proof of Attributed Intelligence (PoAI) is an emerging design pattern that turns AI contribution into on-chain value. It combines provenance, verifiable compute, and programmable incentives so that any AI artifact—inference, dataset, weight update, prompt, or evaluation—can be cryptographically attributed, priced, and paid in a market-native way.

This article explains what PoAI is, why it’s needed, how to build it with today’s crypto primitives, and where it’s already happening.

Why PoAI now

• AI provenance is going mainstream. The C2PA standard for content authenticity allows media to carry signed provenance metadata from creation to distribution, forming a foundation for attributing outputs to creators and tools. See the Coalition for Content Provenance and Authenticity and specifications at C2PA for details (c2pa.org/specifications) — referenced here as C2PA and C2PA Specification.
• Verifiable compute is maturing. Zero‑knowledge and verifiable compute frameworks can attest that a given model produced a result from specified inputs without revealing sensitive weights. Explore RISC Zero’s zkVM approach to verifiable compute and attested ML via their overview on RISC Zero.
• On‑chain attribution rails exist. Ethereum’s typed data signatures and attestation protocols make it easy to record who did what, when, and under which terms. See EIP‑712 and the Ethereum Attestation Service (EAS) docs.
• Regulators are asking for transparency and accountability. The EU AI Act begins phased application from 2025, with risk‑based obligations that increase the need for traceability across AI supply chains. See the European Parliament’s explainer on the EU Artificial Intelligence Act.
• Oracles and hybrid compute bridge AI and crypto. Chainlink and others are positioning to deliver verifiable data, execution, and settlement across off‑chain AI and on‑chain contracts. See Chainlink’s perspective on blockchain and AI.

In short: the primitives for AI attribution and settlement are here. PoAI ties them together into a repeatable pattern.

What is Proof of Attributed Intelligence?

PoAI is a protocol and market design that:

1. Captures provenance: cryptographically signs and traces every contribution to an AI output (data, model, prompt, inference, evaluation).
2. Verifies compute: proves that the stated model and inputs produced the result (or that a training update was valid).
3. Prices and pays: allocates value to contributors using programmable splits, micro‑payments, and staking/penalty mechanisms.
4. Preserves privacy: reveals no more than necessary via zero‑knowledge proofs, selective disclosure, and private settlement channels.

Think of PoAI as “royalties for AI, enforced by cryptography”.

The four pillars of PoAI

• Identity and attestations

  • Use EIP‑712 signatures and attestations (e.g., EAS) to bind a contributor’s key to an action: “Address X provided dataset Y under license Z.” Reference: EIP‑712, EAS.
  • Bind off‑chain media provenance with C2PA, then anchor hash commitments on‑chain for immutability. Reference: C2PA.

• Verifiable compute and zkML

  • Prove correct inference or training steps without revealing model weights, using zkVMs or specialized zkML toolchains such as ezkl and research on verifiable training like Proof‑of‑Learning.
  • Use zk proofs to enforce service‑level agreements (e.g., latency, accuracy) and trigger on‑chain payments only when proofs verify. Reference: RISC Zero.

• Data rights and licensing

  • Publish machine‑readable licenses and consent artifacts for datasets, tying royalties to downstream usage. Data marketplaces like Ocean Protocol pioneered datatokens/NFTs for access control and revenue sharing.
  • Track derivative lineage: if Model B fine‑tunes on Model A and Dataset C, provenance should reflect that tree.

• Incentive‑compatible markets

  • Use staking, slashing, and reputation to discourage hallucinations and reward accurate outputs. Decentralized AI networks like Bittensor and agent economies like Olas showcase token‑incentivized contribution and evaluation.
  • Split revenue automatically across the attribution graph, e.g., 40% to model developer, 40% to data providers, 10% to evaluators, 10% to compute operators.

A reference architecture for PoAI applications

• Step 1: Contributor identity and consent

  • Contributors register keys; optional off‑chain identities via W3C Verifiable Credentials. See W3C VC Data Model 2.0.
  • Data creators sign C2PA manifests; on‑chain contracts store content hashes and license terms.

• Step 2: Verifiable inference or training

  • Inference/training runs within a verifiable environment (zkVM/TEE with remote attestation). Proofs include the model hash, input commitments, and output hash.
  • Smart contracts verify proofs and emit attestations linking to the original contributions (EAS schema).

• Step 3: Attribution graph and programmable splits

  • A registry contract records “edges” between contributions (e.g., dataset → model → inference).
  • Payment contracts disburse micro‑rewards to addresses on this graph upon successful verification.

• Step 4: Settlement and storage

  • Payments settle via native tokens or stablecoins; for recurring flows, use streaming payments.
  • Content and proofs stored on IPFS/Filecoin or cloud with on‑chain hash commitments.

• Step 5: Governance and upgrades

  • DAOs define scoring rules (e.g., evaluator weightings, accuracy benchmarks) and can evolve policies as models and attacks change.

Threat model and mitigations

• Model spoofing: prove inference with zk proofs; require model hash commitments and upgrades via governance.
• Data license violations: signed C2PA manifests with on‑chain verification and slashing for misuse.
• Collusion among evaluators: diversify evaluators; weight by stake and historical accuracy; use randomized audits.
• Prompt injection and jailbreaks: isolate prompt/context commitments; reward detectors that flag unsafe or policy‑violating outputs.
• Privacy leakage: aggregate payouts with privacy‑preserving techniques; minimize revelation via zero‑knowledge claims.

For risk governance frameworks that integrate with compliance programs, see the NIST AI Risk Management Framework.

Real projects pointing toward PoAI

• Bittensor: token‑incentivized network where miners provide model services and validators score outputs, distributing emissions accordingly. Documentation: Bittensor Docs.
• Olas (Autonolas): on‑chain agent economies with revenue shares for model/agent/service contributors. Docs: Olas Documentation.
• Ocean Protocol: datatokens and data NFTs for permissioning and monetization, composable with DeFi primitives. Learn more at Ocean Protocol.
• zkML and verifiable compute: proving correctness of AI execution with frameworks such as RISC Zero and tooling like ezkl.
• AI x blockchain infrastructure: oracles, data feeds, and hybrid computation for trustworthy AI settlement discussed by Chainlink.

These components can be composed into full PoAI stacks today.

Designing a PoAI token economy

• Supply and emissions
  • Reward pools pay contributors proportional to verified impact; emissions taper as fee revenue grows.
• Pricing
  • Dynamic pricing for inference based on latency and accuracy proofs; training markets priced per verified step.
• Curation and discovery
  • Evaluator rewards for surfacing reliable models and flagging regressions.
• Slashing conditions
  • Penalties for fraudulent proofs, license violations, or degraded service.

Importantly, value capture must flow to the attribution graph automatically, avoiding rent‑seeking intermediaries.

UX matters: hiding the crypto without hiding the guarantees

• Wallet‑less onboarding via account abstraction so agents and users can sign typed data without managing seed phrases manually. See EIP‑4337.
• Human‑readable attestations and per‑use receipts: “This answer used Model 0xAB… on Data 0xCD…; your payment split went to 12 contributors.”
• Compliance‑friendly logs: exportable, signed provenance and proof artifacts that can satisfy enterprise and regulatory audits while preserving user privacy.

A minimal PoAI blueprint you can build now

• Provenance
  • Adopt C2PA for media/data; anchor SHA‑256 hashes on‑chain.
• Attestations
  • Use EAS schemas for “dataset licensed,” “model published,” “inference served.”
• Verifiable compute
  • Wrap inference in a zkVM and output a succinct proof; fallback to TEE attestations while proofs are expensive.
• Economics
  • Implement a royalty splitter contract keyed by contribution IDs; trigger payments only when proofs verify.
• Discovery
  • Index the attribution graph on a subgraph and expose a simple “who got paid for this output?” API.

This gets you end‑to‑end attribution, verifiable execution, and automated rewards with today’s tooling.

Outlook for 2025

• Regulation will prefer auditable AI. Expect procurement and platform policies to require provenance and verifiability; PoAI provides both. See the EU’s trajectory via the EU Artificial Intelligence Act.
• Decentralized training markets will expand, with proof‑of‑useful‑work primitives and verifiable training steps likely moving from research to production; foundational ideas appear in Proof‑of‑Learning.
• Hybrid systems will dominate. On‑chain settlement with off‑chain proofs will become standard; oracles and verifiable compute infrastructure like RISC Zero and oracle networks such as Chainlink will be connective tissue.

Security note: keys for humans and agents

PoAI assumes many actors—people, services, and autonomous agents—hold signing keys to issue attestations, proofs, and payments. This makes key management a first‑class concern:

• Isolate high‑value keys in hardware.
• Use per‑role keys for models, data, and evaluators with scoped permissions.
• Prefer wallets that clearly display EIP‑712 details so you understand exactly what you’re signing.

If your organization is deploying AI agents or running attribution registries, consider a hardware wallet like OneKey to protect administrative and treasury keys, and to safely approve EIP‑712 attestations and contract upgrades. OneKey supports clear signing, multi‑chain compatibility, and secure element protection—useful when your protocols depend on the integrity of every signature and payment authorization.

Conclusion

PoAI reframes AI as an attribution‑first economy: anything that contributes provably can be rewarded programmatically. By combining provenance standards, verifiable compute, attestations, and crypto‑native incentives, we can pay the right actors—data creators, model builders, evaluators, and operators—at the right time, with transparency and privacy.

The primitives exist. The market need is clear. If you’re building AI infrastructure or applications in 2025, bake PoAI in from day one—and protect your keys accordingly.