The Inevitable Protocol for AI Model Attribution Splits

Attribution is a financial primitive. Every AI model is a composite of data, code, and compute contributions, but the value flow stops at the final model owner. This creates a massive misalignment between contributors and value capture, stifling open collaboration.

The solution is on-chain splits. Platforms like Ethereum and Solana provide the settlement layer for transparent, automated revenue sharing. This mirrors the creator economy logic of platforms like Superfluid or Sablier, but applied to model weights instead of content.

Protocols win by default. Just as Uniswap automated market-making, a dedicated attribution protocol will automate model royalties. The technical primitives—oracles (Chainlink), identity (Worldcoin), and modular DAOs (Syndicate)—already exist and are waiting for composition.

AI models are composite assets built from data, compute, and algorithms. The current web2 stack treats this as a single corporate asset, but the value flow is inherently multi-party. A protocol like EigenLayer for AI is inevitable to track provenance and automate revenue splits.

Attribution is a coordination problem that markets solve better than corporations. The oracle problem for AI is verifying contributions, not price feeds. Systems like Chainlink Functions or Witness Chain demonstrate the template for external verification, applied to training data and compute proofs.

The protocol is the new API. Instead of bespoke licensing deals, a standard like ERC-7641 for revenue splits enables composability. This creates a liquidity layer for AI contributions, similar to how Uniswap created one for tokens, allowing capital to flow to the most verifiably valuable inputs.

The core is a registry. The standard's foundation is a canonical, on-chain registry mapping model identifiers (hashes) to a split contract address. This creates a single source of truth for attribution logic, analogous to an ERC-20 token contract defining its own transfer rules.

Attribution is a payment split. The protocol must define a standard interface for a split contract, similar to EIP-2981 for NFT royalties. This contract receives inference fees or downstream revenue and programmatically distributes them to contributors based on immutable, on-chain weights.

Weights require consensus, not perfection. Determining exact contribution percentages is a social/legal problem. The protocol's role is to enforce agreed-upon splits, not to calculate them. Oracles like Chainlink or DAOs can be used to resolve and update weights for contentious lineages.

Evidence: On-chain enforcement works. The success of EIP-2981 proves that simple, standardized royalty hooks create enforceable economic flows. For AI, a similar standard would turn attribution from a legal footnote into a programmable revenue stream.

Centralized platforms cannot credibly commit. A corporate entity can unilaterally change terms, as seen with API pricing shifts from OpenAI or Google. This creates an irreducible counterparty risk that a smart contract eliminates by encoding rules into immutable logic.

The incentive structure is misaligned. A platform's goal is to maximize its own profit and control, not the creator's long-term value. This leads to platform rent-seeking and data siloing, whereas a protocol like EigenLayer for restaking or a marketplace like Ocean Protocol demonstrates neutral, composable infrastructure.

Legal contracts are insufficient. They are slow, expensive to enforce, and lack granular, automated execution. A programmable revenue split on-chain, akin to Superfluid's streaming payments, executes trustlessly across borders and in real-time, which a corporate ledger cannot replicate.

Evidence: The migration of creators from Web2 to platforms like Mirror.xyz and Farcaster demonstrates demand for ownership and portability that centralized models inherently oppose, validating the market need for credibly neutral infrastructure.

A universal attribution protocol emerges as the only viable solution to the model provenance crisis. The current patchwork of custom licensing and opaque training data logs creates legal and financial friction that stifles commercial deployment. A standard like OpenAI's Model Spec or Hugging Face's model cards will be formalized on-chain, but the settlement layer is missing.

The winning design is a non-custodial splitter. This protocol will function like a Squads multisig for revenue, automatically distributing micropayments to data contributors, model trainers, and compute providers based on verifiable, on-chain attestations of usage. It outcompetes centralized escrow by eliminating counterparty risk and enabling composable financialization of model royalties.

Adoption is driven by enterprise, not crypto natives. The initial catalyst is a major AI lab like Anthropic or Mistral AI integrating the protocol to transparently compensate data partners, turning a compliance cost into a competitive moat. This creates a network effect for verifiable AI, forcing all commercial models to adopt the standard for market access.

Evidence: The infrastructure is already being built. Projects like EigenLayer AVSs for attestation and Hyperliquid's intent-based order flow demonstrate the primitives for decentralized verification and automated settlement. The 24-month timeline is set by the maturation of these components, not by AI industry willingness.