The Future of AI Services: Composable, Cross-Chain Micro-Tasks

Today's on-chain AI is a cost-prohibitive, single-chain oracle. Running a full model like Llama costs ~$10 per inference and locks you into one execution environment, creating vendor lock-in and high latency.

• Cost Barrier: Prohibitive for high-frequency DeFi or gaming use.
• Chain-Locked: No native ability to source data or serve users across Ethereum, Solana, or Avalanche.
• Opaque Execution: No verifiable proof of correct model or data used.

Decouple AI into verifiable sub-tasks (inference, data fetching, fine-tuning) and auction them to a permissionless network of solvers, similar to UniswapX or Across Protocol. Users submit an intent ("summarize this text"), not a transaction.

• Cost Efficiency: Solvers compete, driving inference costs to <$0.01.
• Chain Abstraction: A solver on Solana can fetch data from Arweave and deliver result to Base via LayerZero or Axelar.
• Verifiable Proofs: Zero-knowledge proofs or TEE attestations for task correctness.

Ritual is building an infernet—a decentralized network for AI inference and fine-tuning. It's not an oracle; it's a sovereign execution layer where models are first-class citizens.

• Sovereign Chain: Dedicated chain for AI ops, composable with any L1/L2 via IBC-style bridges.
• Incentive-Model Alignment: Node operators are staked and earn fees for providing GPU power, creating a ~$1B+ potential compute marketplace.
• Native Privacy: Built-in support for FHE and ZK for private model inference.

Modulus Labs' core thesis: trustless AI requires economically viable cryptographic proofs. They make ZK proofs for small models (~1M params) cheap enough for on-chain use.

• Cost Scaling: Reduced proof cost from ~$100 to ~$0.10 via custom proof systems (e.g., RISC Zero).
• GameFi & DeFi Use-Case: Enables verifiable AI opponents in autonomous worlds or risk engines.
• The Verifiable Stack: Provides the critical "proof of correct execution" layer for micro-task markets.

Building an AI-powered dapp today means stitching together 5+ brittle services: a model API, a data oracle, a payment rail, and a cross-chain messenger. ~80% of dev time is spent on integration, not logic.

• Integration Hell: No standard for composing AI services with smart contracts.
• No Liquidity: AI service providers and consumers cannot discover each other efficiently.
• Siloed Economies: Fees and data are trapped within single protocol walls.

The end-state is vertical app-chains (e.g., dYmension RollApps) optimized for specific AI tasks—a prediction market chain, a content generation chain. They settle to a shared AI data availability and security layer, like Celestia or EigenLayer.

• Optimized Execution: Custom VM for tensor operations, not EVM opcodes.
• Shared Security: Leverages restaked ETH via EigenLayer for economic security.
• Composable Liquidity: Micro-task results become transferable assets across the settlement layer.

AI micro-tasks require deterministic verification of off-chain computation. Current oracle designs like Chainlink are built for simple data feeds, not complex AI outputs.

• Verification Cost: Proving a model's inference can cost 10-100x the task fee, killing economics.
• Consensus Latency: Multi-chain state synchronization for task results introduces ~2-30s delays, breaking real-time services.
• Adversarial Inputs: Maliciously crafted prompts can force models to generate verifiably incorrect or harmful outputs, poisoning the service layer.

Intents broadcasting AI tasks across chains via systems like UniswapX or Across create predictable, extractable value.

• Front-Running Models: Competitors can intercept a task spec, run the inference faster/cheaper, and submit the result first, stealing fees.
• Result Manipulation: Validators in chains with low decentralization (e.g., some EVM L2s) could censor or alter task results before cross-chain finality.
• Liquidity Fragmentation: Micro-payments for tasks scatter liquidity across 50+ chains, increasing settlement risk and slippage for solvers.

Composability means any service can be an input to another. A single poisoned model or corrupted data source propagates instantly.

• Unclear Liability: With tasks chained across Celestia DA, EigenLayer AVS, and an L2, fault attribution becomes legally and technically impossible.
• Data Lineage Gaps: Most decentralized storage (Arweave, IPFS) doesn't guarantee the provenance or licensing of training data, opening massive IP infringement risk.
• Adversarial Updates: A malicious or compromised model update via a decentralized registry (like Bittensor) could corrupt thousands of dependent micro-services before detection.

Users pay for AI tasks with any token via ERC-4337 account abstraction, but service providers need specific tokens for gas and staking.

• Volatility Sinkhole: A 20% token swing during the multi-chain task lifecycle can make providers operate at a loss, causing service dropout.
• Staking Slash Cascades: If an AI validator node on EigenLayer is slashed, it could insolvent linked staking pools on Cosmos or Solana, creating systemic risk.
• Fee Market Collapse: Priority fees for urgent AI tasks could be outbid by pure DeFi arbitrage bots, leading to unpredictable and unbounded latency for critical services.

Smart contracts demand deterministic, verifiable execution, but AI models are probabilistic and opaque. This creates a fundamental trust gap for direct on-chain integration.

• Solution: Decompose AI workflows into atomic, attestable micro-tasks (e.g., inference, proof generation, data fetching).
• Benefit: Enables cryptographic verification of each step via zkML (EZKL, Modulus) or optimistic fraud proofs, making AI outputs trust-minimized.

Monolithic AI APIs are expensive and inflexible. A decentralized network of specialized nodes allows builders to source and chain micro-services dynamically.

• Architecture: Use intent-based systems (like UniswapX, CowSwap) where users post desired outcomes, and solvers compete with optimal task bundles.
• Benefit: Drives costs down 50-80% via competition and enables cross-chain AI agents that can execute actions on Ethereum, Solana, and layerzero-connected chains seamlessly.

Agents need gas, non-custodial security, and the ability to trigger complex, conditional workflows across protocols.

• Mechanism: Deploy agents as smart contract wallets (Safe, Biconomy) with embedded logic and funded via account abstraction gas sponsorships.
• Benefit: Enables permissionless agent-to-agent commerce and autonomous treasury management (e.g., an agent that trades on Uniswap, bridges profits via Across, and stakes on Lido).

AI agents making decisions based on stale or incorrect cross-chain data will fail or be exploited. Existing oracles are not built for low-latency, high-frequency agent queries.

• Requirement: Integrate hyper-scalar oracles (Pyth, Switchboard) and interoperability layers (Wormhole, CCIP) directly into the agent's decision loop.
• Benefit: Achieves sub-second data freshness and atomic cross-chain transactions, turning multi-chain fragmentation into a single operational surface.

Raw compute is a commodity. The real value is in curated, high-quality task execution and specialized data. A pure pay-per-call model misaligns incentives.

• Model: Implement work token staking (like Livepeer) where node operators bond capital to perform tasks, slashed for poor performance.
• Benefit: Creates sybil-resistant networks and aligns operators with long-term network quality, not just short-term fee extraction.

The goal is not to "use AI in crypto," but for AI to become a first-class, economically sovereign actor within DeFi, governance, and NFTs.

• Vision: AI agents that provide liquidity, vote in DAOs, and co-create dynamic NFTs by participating directly in on-chain economies.
• Implication: Requires rethinking protocol design for non-human participants, including new fee structures and reputation systems.