The MIT license is extractive. It allows any entity to fork, commercialize, and monetize an AI model without compensating its original creators. This incentive misalignment mirrors the early NFT market where platforms like Blur bypassed creator royalties.
ai-x-crypto-agents-compute-and-provenance
Blog
Why On-Chain Royalties Are Non-Negotiable for AI Model Creators
Static licenses are broken promises for AI. This analysis argues that programmable, on-chain royalties via smart contracts are the only viable mechanism to ensure original contributors are compensated for downstream commercial value, creating a sustainable open-source ecosystem.
introduction
THE INCENTIVE MISMATCH
The Broken Promise of the MIT License
The permissive MIT license, while fueling open-source AI, creates a fundamental incentive failure for model creators by enabling unchecked value extraction.
On-chain royalties are non-negotiable infrastructure. They embed a verifiable payment rail directly into the model's usage, enforced by smart contracts on networks like Ethereum or Solana. This creates a persistent economic layer absent from traditional licensing.
Compare this to open-source software. A web2 company using an MIT-licensed library captures value internally. An on-chain AI model's value is its public inference calls—a direct, measurable revenue stream that royalties can automatically tax.
Evidence: The EIP-2981 standard. This NFT royalty standard, adopted by OpenSea and others, proves the technical viability of programmable creator fees. For AI, an analogous standard would attach fees to model queries, not just asset sales.
thesis-statement
THE NON-NEGOTIABLE
The Core Argument: Royalties as Programmable Property Rights
On-chain royalties are a fundamental, non-negotiable property right for AI model creators, enabling sustainable funding and verifiable attribution.
Royalties are a property right for AI model creators, not a feature. Off-chain, this right is unenforceable; on-chain, it becomes programmable and irrevocable through smart contracts like those on Ethereum or Solana.
Programmable rights create new markets. Unlike static licensing, on-chain royalties enable dynamic revenue streams from secondary inference, fine-tuning, and composability, similar to how Uniswap's fee switch monetizes protocol usage.
The alternative is data theft. Without enforceable royalties, the dominant model becomes parasitic scraping and replication, as seen in the web2 AI training data landscape. On-chain attribution via standards like ERC-7641 prevents this.
Evidence: The Bittensor subnet ecosystem demonstrates this, where model creators earn continuous, verifiable royalties from subnet usage, creating a direct link between model utility and creator compensation.
key-trends
AI'S VALUE CAPTURE CRISIS
The Market Context: Why This Matters Now
AI model creators face a fundamental misalignment: their work generates immense value, but the current infrastructure offers no direct, enforceable claim on downstream revenue.
01
The Problem: The API Revenue Black Box
Creators have zero visibility or control over how their models are used commercially. A model fine-tuned for a specific enterprise use case could generate millions in API fees for the platform, with the original creator receiving nothing. This disincentivizes high-quality, specialized model development.
$0
Creator Share
100%
Platform Capture
02
The Solution: Programmable Royalties as a Primitive
On-chain royalties transform model inference into a verifiable, fee-generating transaction. Every API call or model execution can be linked to a smart contract that automatically routes a percentage (e.g., 1-5%) back to the creator's wallet. This creates a direct, immutable financial feedback loop.
1-5%
Per-Use Royalty
100%
Auto-Enforced
03
The Catalyst: The Open-Source Model Wars
As open-source models like Llama, Mistral, and OLMo compete with closed giants, sustainable funding is critical. On-chain royalties provide a native monetization layer that doesn't rely on venture capital or corporate patronage, aligning economic incentives with community-driven development.
$0
Venture Dependency
Direct
Value Flow
04
The Precedent: NFTs and the Creator Economy
The $40B+ NFT market proved that digital assets with embedded royalties are viable. Projects like Art Blocks and Bored Ape Yacht Club established that code-enforced secondary sales royalties (typically 5-10%) can fund ongoing development and community growth. AI models are the next logical digital asset class.
$40B+
Market Proof
5-10%
Royalty Standard
05
The Problem: Fragmented Attribution & Provenance
Modern AI pipelines involve chaining models, fine-tuning, and RAG. Determining which original model contributed value to a final output is impossible off-chain. This kills attribution and makes fair compensation a legal nightmare, stifling collaborative development.
0
Traceable Contributions
Fragmented
Value Chain
06
The Solution: On-Chain Model Graphs
By registering models and their training data fingerprints on-chain (e.g., using EigenLayer AVS or Celestia DA), we can create a verifiable provenance graph. Smart contracts can then algorithmically split royalties across the contributor graph based on verifiable usage, enabling complex, incentivized collaboration.
Verifiable
Provenance
Automated
Revenue Splits
AI MODEL MONETIZATION
Static License vs. On-Chain Royalty: A Feature Matrix
A first-principles comparison of revenue models for AI model creators, quantifying the trade-offs between traditional licensing and programmable on-chain alternatives.
| Feature / Metric | Static License (Status Quo) | On-Chain Royalty (Programmable) | Hybrid Model (e.g., EIP-721C) |
|---|---|---|---|
Revenue Enforcement Mechanism | Legal contract, manual audits | Automated smart contract logic | Optional on-chain enforcement |
Royalty Collection Granularity | Per-enterprise deal, quarterly | Per-inference transaction, real-time | Per-transaction with off-chain triggers |
Default Royalty Rate (Typical) | 20-30% of SaaS fee | 0.1-5% per inference call | Configurable 0-100% |
Payout Latency | 30-90 days net terms | < 5 minutes per transaction | Configurable (real-time or batched) |
Composability with DeFi | |||
Transparent Revenue Dashboard | |||
Automated Treasury Management (e.g., to Aave, Compound) | |||
Integration Overhead for Developer | High (legal, billing systems) | Low (SDK, smart contract call) | Medium (SDK + optional legal) |
Global, Permissionless Payouts | |||
Residual Earnings from Secondary Model Sales |
deep-dive
THE ENFORCEMENT GAP
Mechanics: How Smart Contracts Enforce What Licenses Cannot
On-chain logic replaces legal threats with automated, non-negotiable execution of creator terms.
Code is the final arbiter. A smart contract's logic executes deterministically, removing the need for costly legal discovery and enforcement actions that plague traditional licensing.
Royalties become a protocol parameter. Platforms like Ethereum and Solana enable royalties to be programmed as a hard-coded fee on every secondary sale, a feature natively supported by marketplaces such as Tensor and Magic Eden.
Licenses rely on goodwill. Off-chain agreements are only as strong as a user's willingness to comply and a creator's resources to litigate, creating a massive enforcement gap for independent AI developers.
Evidence: The ERC-721C standard demonstrates this shift, allowing creators to enforce custom royalty rules directly on-chain, making non-payment a technical impossibility rather than a legal dispute.
protocol-spotlight
ON-CHAIN ROYALTIES FOR AI
Protocol Spotlight: Early Implementations
Smart contracts are the only credible mechanism to enforce creator compensation in a permissionless, composable AI economy.
01
The Problem: AI Models Are Non-Rivalrous Goods
Digital models can be copied infinitely, destroying traditional licensing value. On-chain royalties create scarcity at the transaction layer, turning each inference or fine-tuning event into a micro-transaction.
- Enforces value capture where it's impossible to control distribution.
- Enables pay-per-use business models without centralized gatekeepers.
- Creates verifiable provenance for model usage and derivative works.
100%
Marginal Cost
0
Natural Scarcity
02
The Solution: Bittensor & Subnet Royalties
Bittensor's subnet architecture bakes royalties into its consensus mechanism. Miners (model servers) and validators split rewards, with a configurable share automatically routed to the original model creator.
- Protocol-native monetization eliminates need for off-chain enforcement.
- Dynamic reward curves align incentives for quality and usage.
- ~$2B+ network demonstrates market validation for on-chain AI value flows.
$2B+
Network Cap
500+
Active Subnets
03
The Solution: EIP-7508 & On-Chain Inference
This proposed Ethereum standard for ZK-verified AI inference creates a canonical settlement layer. Royalties are programmed into the inference smart contract, ensuring creators are paid for provable usage of their model weights.
- Enables trustless composability between models, data, and payments.
- Auditable revenue streams for investors and creators.
- Foundation for an on-chain AI stack akin to DeFi's money legos.
ZK-Proof
Verification
EIP-Standard
Settlement
04
The Problem: Web2 Platforms Extract 30-70%
Centralized AI platforms (OpenAI, Hugging Face) act as rent-seeking intermediaries, capturing the majority of value. On-chain royalties return economic sovereignty to creators through transparent, programmable splits.
- Removes platform risk of arbitrary fee changes or de-platforming.
- Enables direct creator-to-user markets with minimized friction.
- ~30-70% is the typical revenue take rate by incumbent platforms.
30-70%
Platform Take
Direct
Value Flow
05
The Solution: Ritual & Infernet Nodes
Ritual's decentralized network separates execution from settlement. Infernet Nodes perform off-chain compute, while on-chain smart contracts handle payment routing with embedded royalty logic, inspired by systems like UniswapX for intents.
- Flexible royalty models (fixed fee, revenue share, subscription).
- Cryptoeconomic security ensures node operators are slashed for non-payment.
- Composable with DeFi for lending, staking, and bundling AI services.
Off-Chain
Compute
On-Chain
Settlement
06
The Verdict: A Prerequisite for Open AI Economies
Without enforceable on-chain royalties, open-source AI model development is economically unsustainable. This isn't a nice-to-have feature; it's the fundamental primitive that allows permissionless innovation to compete with centralized capital.
- Turns open-source from a cost center into a revenue engine.
- Aligns with crypto's core ethos of credibly neutral, user-owned infrastructure.
- Early traction in Bittensor and EIP-7508 proves the concept is viable now.
Core Primitive
Not a Feature
Viable Now
Market Proof
counter-argument
THE INCENTIVE MISMATCH
Steelman: The 'Fork and Strip' Counter-Argument
The argument that AI models can be forked and stripped of royalties ignores the economic and technical realities of on-chain deployment.
Forking destroys composability and liquidity. A forked model loses its canonical status, severing integration with established on-chain inference marketplaces like Ritual or Bittensor. This isolates the model from the network effects and pooled demand that generate revenue in the first place.
Royalties fund verifiable compute attestation. The royalty payment stream directly funds the cost of running verifiers (e.g., using EZKL or RISC Zero) to prove correct off-chain execution. A forked model without this funding loses its cryptographic guarantee of integrity, becoming a black-box service.
The stripped version is a commodity. Without the funded attestation layer, the forked model competes solely on raw inference cost, a race to the bottom dominated by centralized providers. The original, verifiably executed model captures a premium for trust.
Evidence: Observe the NFT marketplace wars. Platforms like Blur that bypassed royalties saw short-term volume gains but eroded creator ecosystems and long-term asset value, demonstrating that stripping value extraction destroys the primitives that attract users.
risk-analysis
ON-CHAIN ROYALTIES FOR AI
Risk Analysis: What Could Go Wrong?
Implementing on-chain royalties for AI models introduces novel attack vectors and systemic risks that must be mitigated.
01
The Oracle Manipulation Attack
Royalty calculations often rely on external oracles for price feeds and usage data. A compromised oracle can lead to massive revenue leakage or incorrect payouts. This is a single point of failure for the entire royalty system.
- Attack Vector: Manipulate the price of an inference call or token to skew royalty payouts.
- Mitigation: Use decentralized oracle networks like Chainlink with multiple data sources and economic security.
>$1B
Oracle TVL at Risk
3-5s
Finality Latency
02
The Sybil & Wash-Trading Problem
Bad actors can create thousands of fake identities to generate artificial, low-value inference requests. This inflates usage metrics without generating real revenue, diluting legitimate payouts and wasting compute resources.
- Attack Vector: Sybil farms spam the model with queries using negligible gas.
- Mitigation: Implement proof-of-personhood (Worldcoin) or costly signaling mechanisms to disincentivize spam.
>10k
Fake Identities
-99%
Revenue Dilution
03
Protocol-Level Censorship & Capture
The smart contracts governing royalty distribution become high-value censorship targets. Entities (e.g., governments, corporations) could pressure foundational layers (like L1 validators or L2 sequencers) to block or filter payments to specific model creators.
- Attack Vector: Censor transactions to a blacklisted creator's royalty address.
- Mitigation: Build on censorship-resistant chains (Ethereum) and utilize permissionless bridging (Across, LayerZero) for fund distribution.
33%
Validator Threshold
~0%
Recovery Chance
04
The Liquidity Fragmentation Trap
Royalty tokens representing future revenue streams will fragment across dozens of chains and L2s. This creates illiquid, worthless tokens for creators who cannot aggregate or sell their future cash flows efficiently.
- Attack Vector: Revenue is locked in illiquid pools on obscure chains.
- Mitigation: Integrate with intent-based aggregation protocols (UniswapX, CowSwap) and universal liquidity layers to consolidate value.
50+
Fragmented Chains
90%
Illiquidity Discount
05
Smart Contract Immutability as a Liability
Once deployed, royalty logic is immutable. A bug in the payout formula, tax handling, or upgrade mechanism can permanently lock or misdirect funds. Traditional SaaS can patch bugs instantly; on-chain systems cannot.
- Attack Vector: Exploit a logic flaw to divert all future royalties.
- Mitigation: Use time-locked, multi-sig upgradeable proxies and extensive audits before mainnet deployment.
Irreversible
Bug Consequence
$500k+
Audit Cost
06
Regulatory Arbitrage Creates Legal Risk
Creators using on-chain royalties to bypass jurisdiction may face retroactive regulatory action. Authorities could classify royalty tokens as unregistered securities, leading to fines and seizure of funds, especially if marketed for investment.
- Attack Vector: SEC or other agency targets the most successful royalty pools.
- Mitigation: Structure payouts as pure utility fees, not investment returns, and engage in proactive legal structuring.
100%
Retroactive Risk
Multi-Year
Legal Timeline
future-outlook
THE INCENTIVE ALIGNMENT
Future Outlook: The 24-Month Horizon
On-chain royalties create a sustainable, verifiable economic layer for AI model training and inference.
Royalties enforce sustainable economics for AI development. Current centralized API models like OpenAI's create a winner-take-all data moat. On-chain payments, using standards like ERC-7641 for intractably linked revenue streams, ensure creators are compensated per-use, aligning incentives for continuous model improvement.
On-chain provenance is non-negotiable for model trust. Systems like EigenLayer AVSs for verifiable inference or Bittensor subnet rewards require an immutable audit trail. Royalty payments embedded in this trail provide the financial proof-of-work for model quality and data sourcing.
The counter-intuitive insight is that royalties reduce, not increase, end-user cost. Without a sustainable payout, model providers resort to rent-extractive data licensing or invasive user surveillance. A transparent fee, routed via intents through UniswapX or CowSwap, becomes a competitive feature for high-value enterprise use.
Evidence: Platforms like Livepeer and Render Network demonstrate that micro-payments for compute create more efficient markets than upfront licensing. For AI, this translates to a 24-month shift from closed API endpoints to open, royalty-fueled model ecosystems.
takeaways
ON-CHAIN ROYALTIES
TL;DR for Busy Builders
For AI creators, on-chain royalties are a non-negotiable mechanism for sustainable value capture, not a feature.
01
The Problem: Off-Chain is a Black Box
API calls and cloud deployments create zero visibility into model usage. You can't price or bill for inference compute, fine-tuning, or downstream commercial applications.
- No Audit Trail: Impossible to verify who used the model, when, or for what purpose.
- Revenue Leakage: High-value enterprise use cases pay the same as a hobbyist API call.
0%
Usage Visibility
100%
Trust Assumed
02
The Solution: Programmable Royalty Streams
Embed royalty logic directly into the model's on-chain representation (e.g., an NFT or token). Every interaction triggers a micro-payment to the creator.
- Granular Pricing: Set fees per inference, per parameter accessed, or per commercial license.
- Automatic Enforcement: Smart contracts guarantee payment settlement; no middleman or collection agency needed.
~100%
Capture Rate
24/7
Settlement
03
The Architecture: Model-as-an-Asset
Treat the AI model as a composable financial primitive. This enables new DeFi and DAO structures for collective ownership and governance.
- Liquidity & Valuation: Royalty streams can be tokenized, fractionalized, or used as collateral.
- DAO Governance: Communities can fund, govern, and upgrade models, aligning incentives between creators and users.
10x+
Liquidity Options
DAO-native
Governance
04
The Precedent: NFT Royalties Failed. Learn Why.
The NFT market's optional royalty disaster (Blur, OpenSea) provides a perfect case study. The flaw was making royalties a marketplace policy, not a protocol-level rule.
- Enforcement at the Asset Layer: Royalties must be inseparable from the asset itself, not a feature of the trading venue.
- Critical Lesson: Build the payment mechanism into the token's transfer logic, like ERC-721C or ERC-6956, not the UI.
-95%
NFT Royalty Collapse
ERC-6956
Blueprint
05
The Stack: Oracles, ZK, and Autonomous Agents
Proving off-chain compute (inference) happened requires a specialized tech stack. This is where crypto infrastructure shines.
- Verifiable Compute: Use zkML (like Modulus, EZKL) or optimistic proofs to cryptographically verify model execution.
- Oracle Networks: Chainlink Functions or API3 can fetch and attest to usage data from private environments.
ZK Proofs
Verification
Oracles
Data Bridge
06
The Outcome: Aligned Incentives & Open Markets
On-chain royalties transform AI from a service into a tradable asset class. This creates a flywheel for open-source model development.
- Sustainable OSS: Top-tier models can be open-source, funded by usage, not venture capital.
- Composable Ecosystem: Models become lego bricks; royalties ensure creators benefit from every new application built on top.
New Asset Class
AI Models
Permissionless
Innovation
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