Why AI Provenance Is the Foundation for a Verifiable Web

The Problem: AI training is centralized and unverifiable. The Solution: A decentralized network that anchors the entire AI lifecycle—data, compute, and inference—to a blockchain state root.

• Sovereign Execution: Models run in secure enclaves (TEEs) with outputs cryptographically signed to the chain.
• Provenance Graph: Creates an immutable record of which data, code, and hardware produced a specific model checkpoint.

The Problem: Off-chain AI assertions require decentralized security. The Solution: Leverage Ethereum's restaked capital to create an Actively Validated Service (AVS) for AI attestations.

• Cryptoeconomic Security: Operators stake ETH via EigenLayer, slashed for fraudulent AI output claims.
• Universal Verifiability: Any AI workload (training run, inference result) can request a costly-to-fake attestation, backed by $10B+ in restaked TVL.

The Problem: On-chain provenance data is useless if it's not queryable. The Solution: Subgraphs that index and structure the relationships between AI models, training data NFTs, and usage licenses.

• Data Composability: Enables applications to easily query a model's full lineage and attribution trail.
• Foundation for Apps: Powers discoverability markets, royalty enforcement, and compliance dashboards on top of raw provenance logs.

The Problem: Provenance is meaningless if the referenced assets disappear. The Solution: Permanent, low-cost storage for model checkpoints, training datasets, and audit logs.

• Data Integrity: Content-addressed storage (like IPFS) paired with a permanent economic guarantee.
• Cost Structure: ~$5 for 1GB stored forever, creating a viable ledger for massive AI artifacts.

The Problem: Smart contracts cannot natively verify AI outputs. The Solution: A protocol for verifiable machine learning via optimistic and zero-knowledge proofs.

• zkML: Generates succinct proofs of correct model execution for lightweight on-chain verification.
• Optimistic ML: A faster, cheaper alternative for high-stakes settlements, with fraud proofs.

The Problem: Provenance data is a public good; who pays for its creation? The Solution: Protocol tokens to coordinate and reward the supply of verifiable AI work.

• Work Token Model: Token stakers earn fees for providing attestations, compute, or storage.
• Alignment: Creates a cryptoeconomic flywheel where valuable, verifiable AI begets more security and usage.

Training on unverified, web-scraped data creates models with inherent biases and vulnerabilities. Adversarial examples and backdoor attacks become impossible to audit or trace back to source.

• Attack Vector: Injecting malicious data during pre-training corrupts the model's core logic.
• Consequence: A single poisoned dataset can propagate to millions of downstream applications.
• The Gap: Current ML pipelines lack an immutable audit trail from raw data to model weights.

Generative models remix copyrighted and licensed content without compensation or credit, creating a legal and ethical minefield. Stable Diffusion and Midjourney lawsuits highlight the multi-billion dollar liability.

• The Problem: No technical mechanism to prove a model's training lineage or attribute generated outputs.
• Financial Risk: Platforms face existential copyright infringement suits and retroactive licensing fees.
• Solution Space: On-chain registries (e.g., Story Protocol, Alethea AI) for verifiable asset provenance.

AI-generated synthetic data is now polluting the training corpus for future models, leading to irreversible model collapse and degradation of output quality. The web loses its grounding in reality.

• Mechanism: LLMs trained on their own outputs amplify errors and invent facts.
• Systemic Risk: Erodes trust in all AI systems, making them useless for high-stakes applications.
• Verification Need: Cryptographic proofs of human vs. synthetic origin are required for data ingestion.

Relying on a single entity (e.g., OpenAI, Google) to self-attest model provenance recreates the web2 trust problem. It's a single point of failure and censorship.

• The Flaw: A centralized attestation can be altered, revoked, or gated for competitive advantage.
• Bear Case: Creates AI monopolies that control the definition of 'truth' and verifiability.
• The Antidote: Decentralized verification networks (like EigenLayer AVS for AI) and on-chain proofs.

Bringing AI inferences on-chain (for DeFi, gaming, autonomous agents) requires trust in the off-chain computation. Without verifiable provenance, it's just a black-box oracle.

• Vulnerability: A malicious or compromised AI model can manipulate smart contract outcomes (e.g., loan approvals, insurance claims).
• Stakes: $10B+ DeFi TVL becomes exposed to adversarial AI inputs.
• Architecture Need: ZK-proofs of model integrity and execution (e.g., Giza, Risc Zero) are mandatory.

Full cryptographic verification of every inference is currently computationally prohibitive (~seconds vs. ~milliseconds). This creates a market for 'good enough' security that will be exploited.

• Practical Reality: Applications will skip verification for user experience, creating attack surfaces.
• Economic Incentive: Cost to attack << potential profit from manipulating high-value transactions.
• Innovation Frontier: Specialized hardware and lighter proof systems (e.g., zkSNARKs, Proof of Sampling) are critical paths.