How AI Protocols Are Creating New On-Chain Business Models

Yael

/Nov 4, 2025

Key Takeaways

• AI protocols are enabling new on-chain business models through decentralized compute and data marketplaces.

• Cost-efficient L2 settlements make micropayments for AI services feasible.

• Verifiable computation technologies like zkML are crucial for ensuring trust in AI outputs.

• Autonomous agents can operate on-chain, providing services without centralized intermediaries.

• Security and key management are critical for the success of AI protocols in the blockchain space.

The convergence of artificial intelligence and crypto is no longer a thought experiment—it is rapidly materializing into protocols, tooling, and new economic primitives. From decentralized GPU markets to verifiable inference and autonomous agent economies, AI protocols are pushing business models on-chain, enabling transparent pricing, programmable incentives, and global liquidity. As Ethereum and its L2s mature, and as verifiability tech like zkVMs improves, builders now have credible paths to monetize AI services natively on-chain while keeping security and settlement guarantees at the base layer.

This article surveys how AI protocols are creating new on-chain business models, the patterns that are working, and the operational considerations for teams and treasuries.

Why AI On-Chain Now

Cost-efficient settlement and abundant blockspace on L2s make micropayments for AI feasible; see the landscape and costs across rollups on L2BEAT.
Crypto’s native incentives and slashing can enforce quality-of-service for AI compute and data markets.
Verifiable computation is maturing, with zero-knowledge machine learning (zkML) and optimistic verification offering ways to attest to training, datasets, or inference results. For an overview, see a16z crypto’s primer on zkML.
Ethereum-native identity, account abstraction, and token-bound accounts enable AI agents to own assets, receive revenue, and operate autonomously. See EIP-4337 for account abstraction and EIP-6551 for token-bound accounts.
The builders’ playbook for AI x crypto is clarifying; Vitalik Buterin’s reflection on crypto + AI highlights both the opportunities and the pitfalls.

The New On-Chain Business Models

1) Decentralized compute marketplaces

Networks aggregate underutilized GPUs and sell compute cycles on-chain with transparent pricing and crypto-native SLAs.

Rendering and AI workloads: Render Network coordinates GPU providers for rendering and ML tasks with on-chain payments and reputation.
General GPU marketplaces: io.net focuses on distributed GPU clusters for inference and training with Solana-native settlement.
Verifiable training: Gensyn explores cryptographic verification and incentive design for large-scale training jobs.

Revenue model: requesters pay per job or per token (inference) with escrowed payments and slashing for providers who fail QoS. Restaking frameworks like EigenLayer can support AI-specific validation oracles and service-level assurance modules.

Further reading: Render Network, io.net, Gensyn, EigenLayer.

2) Inference-as-a-service and AI coprocessors

Instead of relying on centralized APIs, protocols turn inference into a programmable market.

Bittensor coordinates “subnets” competing to provide ML services, with open incentives for model quality and routing.
Ritual is building an on-chain AI coprocessor that dapps can call to execute model inference with crypto-economic guarantees.

Revenue model: pay-per-inference, subscription, or staking-to-access. Marketplaces can add curator rewards for routing high-quality models and penalize spam or low-quality outputs.

3) Data marketplaces and data DAOs

AI models are only as good as their data. On-chain data markets enable licensing, curation, and streaming fees for datasets.

Ocean Protocol provides tooling for tokenized data assets, access control, and data-usage monetization.
Decentralized storage like Filecoin and Arweave allows datasets and model weights to be stored with verifiable provenance and long-term availability.

Revenue model: dataset tokens gate access; consumers pay per access or subscribe; curators earn from accurate labeling and useful curation.

Further reading: Ocean Protocol, Filecoin, Arweave.

4) Autonomous agents as on-chain service providers

Agents can run strategies, fulfill tasks, and negotiate payments on-chain—without centralized intermediaries.

Autonolas (OLAS) provides tooling for multi-agent services, including revenue-sharing and incentive alignment for maintainers and operators.
Account abstraction and token-bound accounts let agents hold funds, pay fees, and receive income. Developers can sponsor gas or use session keys for UX and safety.

Revenue model: subscription to agent services, usage-based metering, and performance fees routed via programmable splits.

Further reading: Olas Network, EIP-4337, EIP-6551.

5) Verifiable AI: proof-of-inference and optimistic verification

Trust-minimization is essential when money meets models.

zkML via general-purpose zkVMs (e.g., RISC Zero) or specialized circuits allows on-chain verification that a given model and input produced a claimed output.
Modulus Labs explores practical pathways to verify ML inside or alongside smart contracts.
Optimistic approaches use economic guarantees and dispute windows to verify claims; UMA’s optimistic oracle is a canonical design adaptable to AI attestations.

Revenue model: provers, challengers, and validators earn fees for keeping systems honest; requesters pay for verified proofs or for dispute insurance.

Further reading: RISC Zero, Modulus Labs, UMA.

6) Oracles and API bridges for AI workflows

Dapps often need to call off-chain AI APIs or ingest embeddings and features. Secure bridges and oracles make these flows programmable.

Chainlink Functions lets smart contracts fetch data and trigger computation from arbitrary APIs with cryptographic attestations.
Combined with streaming payments and task bounties, developers can compose fully on-chain AI pipelines.

Design Patterns That Work

Usage-metered payments: Stream tokens per inference or per minute using streaming rails like Superfluid or Sablier to match cash flow with compute consumption.
Curate-to-earn and stake-for-quality: Let curators stake on models, datasets, or providers; slash poor performance; reward good routing.
Stablecoin-first UX on L2s: Minimize price volatility and transaction costs—monitor the L2 landscape and security assumptions on L2BEAT.
Model provenance: Hash model weights and dataset snapshots to Arweave or Filecoin; log versioning on-chain; reference these hashes in access tokens and invoices.
Agent safety: Use EIP-4337 with session keys and spending limits; separate hot operational keys from cold treasury keys.

What Builders Are Asking in 2025

Can crypto sustainably pay for AI? The answer is increasingly “yes,” if you combine low-cost L2 execution, streaming payments, and verifiable or economically-secured outputs. See the zkML perspective from a16z crypto.
How do we prevent compute spoofing? Adopt challenge games, remote attestation, and zk/optimistic verification. Consider redundancy with multi-provider consensus for critical inference.
How do we manage model IP? Tokenize access rights, watermark outputs, and enforce licensing via cryptographic gating; keep raw weights under controlled storage with on-chain access registries.
Which chains? Pick based on fee environment, verification tooling, and ecosystem fit. Many AI protocols opt for Ethereum L2s for security and tooling, or high-throughput chains for inference calls; you can bridge revenues back to Ethereum for settlement and treasury management.

Risks and Mitigations

Economic attacks and spam: Use allowlists for early markets, progressive decentralization, and slashing with restaking-backed guarantees.
Data leakage and privacy: Favor privacy-preserving training/inference and access-controlled storage; avoid exposing raw datasets unnecessarily.
Regulatory uncertainty: Structure tokens around utility and governance, keep revenue accounting clean, and avoid implicit securities characteristics where not intended.
Operational key risk: Smart wallets and hardware-based signing should be standard for teams running AI treasuries and agent ops.

Getting Started: A Practical Stack

Compute and storage: Render Network or io.net for GPUs; Filecoin or Arweave for datasets and model weights.
Verification: RISC Zero for zk proofs; UMA for optimistic claims; Modulus Labs for zkML patterns.
Orchestration: Chainlink Functions to bridge APIs; Autonolas for agent services.
Payments: Superfluid or Sablier for streams; settle in stablecoins on L2s to reduce friction.
Security: Account abstraction with EIP-4337; token-bound accounts for agent-owned wallets; multi-sig for treasuries.

Why Secure Key Management Matters For AI Protocols

AI protocols move real value continuously: revenue streams, collateral, slashing bonds, and access tokens. Mishandling keys can halt your entire service or leak IP. A hardware wallet provides offline, tamper-resistant signing for multi-chain operations, DAO treasuries, and agent wallets.

If you are operating an AI marketplace, running an inference subnet, or managing a data DAO, consider anchoring treasury and admin keys in a hardware wallet such as OneKey. OneKey is an open-source hardware wallet brand known for multi-chain support, strong security design, and smooth integrations via WalletConnect—useful when interacting with EVM L2s, Solana, or cross-chain bridges that power AI payment flows. Teams can pair OneKey with multisig or smart accounts to separate day-to-day ops from cold storage, reducing the blast radius of compromised infrastructure.

Closing Thoughts

AI protocols are turning models, data, and compute into on-chain goods with programmable economics. The most promising business models pair verifiability with flexible payments and agent-native UX: compute marketplaces with slashing, inference-as-a-service with proofs, data DAOs with streaming licenses, and autonomous agents with account abstraction. The tooling is here; the next step is careful design, secure operations, and relentless focus on user value.

References and further reading: