Artificial intelligence today hinges on a paradox: the data used to train leading models is often collected without explicit permission or compensation. ERC 7007 proposes a bridge between machine learning and non-fungible tokens, enabling the monetization of AI training data and model ownership. At its core, ERC 7007 extends the familiar ERC-721 NFT standard by embedding a proof of generation that verifies outputs were produced using specific training data and model architectures.
The mechanism relies on zero-knowledge proofs to confirm validity without exposing private data, allowing trustworthy monetization in professional settings. The standard also defines a metadata schema that records the model URI, the prompt used, and the execution proof, promoting interoperability across marketplaces.
This framework opens multiple monetization pathways. Datasets can be minted as NFTs to prove origin and license usage, while models themselves can be traded or licensed with access controls. Prompts that yield reliable results can be sold, and early projects can issue fractional ownership through Initial Model Offerings, enabling communities to share in future value. Royalties can be embedded so ongoing usage flows back to the original creators.
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