The Cost of Not Standardizing Verifiable AI Proofs

Proof systems are proliferating without interoperability. Every new AI model or ZKML project like Modulus Labs or Giza creates a bespoke proving stack, forcing downstream infrastructure to support multiple, incompatible verification circuits.

This fragmentation is a direct subsidy to centralization. Validators and sequencers must choose which proof systems to support, creating gatekeeper risk similar to early MEV relay markets. Projects like EigenLayer that aggregate trust face integration complexity.

The cost is paid in security and capital efficiency. Each unique proof requires separate auditing, increases the trusted computing base, and locks liquidity in isolated verification pools. This is the oracle problem re-emerging at the compute layer.

Evidence: The Ethereum ecosystem spent years and billions in value standardizing token interfaces (ERC-20) and bridging (ERC-5169). The lack of a standard for AI proofs like a universal verifier interface will incur a similar, avoidable cost.

Proof systems are proliferating without interoperability. Projects like EigenLayer AVS and Ritual are building distinct proving stacks for AI inference. This creates a vendor lock-in scenario where dApps commit to a single, unproven proving backend.

Fragmentation destroys composability. A model proven on one system, like EZKL, cannot be verified by another, like Giza. This is the pre-TCP/IP era of AI crypto, where isolated networks cannot communicate.

The cost is paid in developer hours and security audits. Each new proving scheme requires custom integration and a new trusted setup ceremony. The industry is re-auditing the same cryptographic primitives repeatedly.

Evidence: The Ethereum Execution Layer scaled after standardizing the EVM. The current AI proving landscape resembles the pre-EVM era of custom virtual machines, which stifled developer adoption.

Incompatibility fragments liquidity. Each AI inference protocol that develops its own custom proof format, like a bespoke zkML circuit or a proprietary opML fraud proof, creates a walled garden. This prevents assets and data from flowing freely between systems like EigenLayer AVSs and Celestia rollups, replicating the early days of incompatible blockchain bridges.

Fragmentation increases execution costs. Users and applications pay a premium for cross-domain interoperability. A model's output verified on one chain requires expensive, trust-minimized bridging via LayerZero or Axelar to be used elsewhere, adding latency and fees that erode the value proposition of verifiable inference.

The end-state is systemic illiquidity. Isolated proof systems cannot aggregate security or share computational load. This leads to higher marginal costs for developers and thinner markets for AI-powered assets, mirroring the liquidity crisis seen in early fragmented DeFi pools before the dominance of Uniswap V3-style concentrated liquidity.

Evidence: The Ethereum L2 ecosystem demonstrates that standardization (e.g., EVM compatibility) drives composability and liquidity concentration. Chains without it, like early Solana or Flow, faced prolonged bootstrap phases despite technical superiority.

Fragmentation destroys composability. Without a standard like EIP-7212 for zkVMs, each AI inference marketplace (e.g., Ritual, Gensyn) builds a custom proof system. This prevents a proof generated for one application from being verified in another, replicating the pre-ERC-20 token chaos.

Vendor lock-in becomes protocol risk. Teams building on a non-standard stack from a single provider like Modulus or EZKL are hostage to that vendor's roadmap and security model. This centralizes risk in a space designed for decentralization.

Technical debt accrues exponentially. Every bespoke integration point—between an EigenLayer AVS and an AI oracle—requires custom verifier contracts and audit overhead. The eventual migration cost to a standard will dwarf early coordination efforts.

Evidence: The Oracle Extractable Value (OEV) problem in DeFi, where fragmented oracles like Chainlink and Pyth create arbitrage opportunities, demonstrates how lack of standardization directly leaks value and increases systemic fragility.

Fragmentation destroys composability. Each AI inference provider will build a custom verification circuit, forcing dApps to integrate dozens of bespoke SDKs. This is the EVM vs. Solana VM problem, but for AI. A dApp cannot natively use a proof from Gensyn to trigger a contract verified for Ritual.

Standardization is the liquidity layer. A universal interface, like ERC-20 for tokens or EIP-712 for signing, creates a shared language. This allows proofs from any compliant prover (e.g., EigenLayer AVS, Modulus) to be consumed by any smart contract, unlocking network effects.

The precedent is clear. The L2 interoperability mess (Arbitrum, Optimism, zkSync) required years and bridges like Across and LayerZero to partially solve. For AI, we must standardize the verification interface upfront, not the proving hardware ('the iron'), to avoid this costly fragmentation.

Evidence: Ethereum's EIP-721 standard enabled a $40B NFT market by creating a single, composable interface. Without a similar standard for AI proofs, the ecosystem will splinter, and the total addressable market for on-chain AI will remain a fraction of its potential.