Why Your zkEVM Needs a zkML Strategy

Every zkEVM offers cheap, fast EVM equivalence. The differentiator is now the application layer. Without a native strategy for verifiable AI, your chain becomes a passive settlement layer for innovators building elsewhere.

• Risk: Ceding the high-value use case frontier to competitors like zkSync and Starknet.
• Result: Becoming a cost center instead of a revenue-generating ecosystem hub.

Bake verifiable off-chain compute directly into your protocol's security model. This turns your chain into the default home for on-chain AI agents, decentralized prediction markets, and privacy-preserving identity.

• Attract: Modulus Labs, Giza, and EZKL developers seeking optimal proving infrastructure.
• Monetize: Capture value from proving fees and MEV-resistant sequencer revenue from complex intents.

Adopt a modular stack with a dedicated zkML co-processor (e.g., Risc Zero, SP1). Keep the core EVM lean for compatibility, while offloading heavy ML proofs to a specialized VM. This mirrors the Ethereum-Danksharding philosophy.

• Benefit: Maintain full Ethereum security and tooling (Hardhat, Foundry).
• Speed: Enable ~2-second finality for ML inferences without bloating the base layer.

The killer app is autonomous, verifiable agents. By offering the best proving latency and cost for models, you become the settlement layer for projects like OpenAI's o1-preview on-chain or Fetch.ai. This drives sustainable demand for your native token and blockspace.

• Outcome: Your sequencer becomes the central clearinghouse for intent-based transactions and agentic MEV.
• Metric: Daily Active Agents (DAAs) becomes your key growth indicator, not just TVL.

zkML isn't just about proving public model outputs; it's about private inputs. Competitors like Aztec and Aleo are building full privacy stacks. Without a strategy, you lose the entire vertical of private credit scoring, healthcare AI, and confidential DeFi strategies.

• Threat: Privacy-focused zkEVMs will siphon institutional capital and regulated applications.
• Defense: Implement selective disclosure proofs as a base layer primitive.

Treating zkML as a developer grant program is a failure mode. It must be a core R&D line with dedicated protocol-level resources. The proving market will be worth tens of billions; your chain's share is determined by architectural bets made today.

• Action: Allocate >20% of treasury grants to zkML infra and application incentives.
• Benchmark: Achieve sub-$0.10 cost for a GPT-4 scale proof by EOY 2025.

Feeding off-chain data (e.g., price feeds) or executing complex logic (e.g., DEX routing) requires trusting centralized oracles or validators, creating MEV extraction points and security risks.

• Vulnerability: Oracle manipulation attacks have drained >$500M.
• Inefficiency: Generalized compute oracles like Chainlink Functions are expensive and slow (~10s latency).

EZKL provides a toolkit to convert standard ML models (PyTorch, TensorFlow) into zk-SNARK circuits. This allows the model's output to be verified on-chain with a tiny proof.

• Interoperability: Proofs are EVM-verifiable, making them native to zkEVMs like zkSync, Scroll, and Polygon zkEVM.
• Scale: Can verify large models (~10M parameters) with sub-dollar proof costs on L2s.

Modulus is building a zkML-optimized rollup that acts as a co-processor for other chains. It's designed from the ground up for high-throughput ML inference, not general EVM computation.

• Performance: Aims for ~500ms end-to-end latency for inference tasks.
• Architecture: Decouples proof generation (off-chain) from verification (on any chain via light clients), similar to AltLayer or Espresso for ML.

zkML enables smart contracts that act based on proven AI judgment, moving beyond simple if-then logic. Think UniswapX with a zk-proven routing optimizer or a lending protocol that autonomously adjusts rates.

• Autonomy: Contracts can execute complex strategies without oracle latency or committees.
• Composability: A verifiable credit scoring model from one dApp can be reused trustlessly across the ecosystem.

Your zkEVM's liquidity is a static asset. Without zkML, AI agents cannot trustlessly verify on-chain actions, forcing them to default to centralized venues like Binance or Coinbase. This creates a permanent liquidity discount for decentralized L2s.

• AI-driven volume is projected to be >30% of all DEX trades by 2026.
• L2s without verifiable AI access become data silos, ceding the most valuable flow to CEXs and centralized bridges.

Searchers using private ML models already dominate MEV extraction. Without a native zkML stack, your chain's value leak accelerates as these opaque bots extract >90% of arbitrage profits. zkML enables transparent, verifiable MEV strategies that can be taxed or socialized for the protocol.

• Current opaque MEV: $500M+ annualized extraction.
• Verifiable MEV: Enables protocol-owned revenue from the most sophisticated actors.

Future DeFi primitives like dynamic AMMs, credit scoring, and insurance will require autonomous, verifiable logic. Without a zkML runtime, your zkEVM cannot host these applications, becoming a legacy settlement layer for simple swaps while EigenLayer, Espresso, and others capture the intelligence layer.

• Application Gap: Limits protocols to static if/then logic.
• Strategic Risk: Cedes the autonomous application stack to competing infra layers.

The argument that zkML proofs are too slow/costly is a temporary engineering challenge, not a fundamental limit. Projects like Modulus, Giza, and EZKL are driving 10-100x cost reductions yearly. Waiting for 'maturity' means your chain's developer mindshare and tooling will be years behind.

• Proof Cost Curve: Following Moore's Law for ZK.
• Opportunity Cost: ~24 month lead time lost to early adopters like StarkNet and Aztec.

zkML is the only viable path to using sensitive real-world data (RWA, identity, health) on-chain. Without it, your zkEVM's privacy is limited to financial transactions, missing the next trillion-dollar market for verifiable off-chain computation. This cedes the entire field to oasis, phala, and other privacy-centric networks.

• Market Missed: RWA, DeSci, Identity verticals require private ML.
• Architectural Lock-Out: Cannot bridge to TLS-N, HyperOracle data feeds.

In a multi-chain future, cross-chain intents and states will be coordinated by AI. If your chain cannot natively verify ML inferences, it becomes a composability dead-end. Aggregators like UniswapX, Across, and intent solvers will prioritize zkML-enabled chains, starving yours of integrated liquidity and users.

• Network Effect Inversion: Becomes a sink, not a source of liquidity.
• Final Outcome: Reduced to a niche settlement chain for legacy apps.

Chainlink and Pyth deliver data, not verifiable computation. You're trusting their nodes, not cryptographic truth. This is a systemic risk for DeFi and gaming primitives.

• Key Benefit 1: Replace trust with zero-knowledge proofs for price feeds, RNG, or any off-chain logic.
• Key Benefit 2: Enable novel applications like autonomous, verifiable AI agents (e.g., Modulus, Giza) directly in your smart contracts.

Treat your zkEVM not just as a VM, but as a verifiable court. Any complex state transition (DeFi risk models, gaming physics, KYC checks) can be computed off-chain and settled on-chain with a proof.

• Key Benefit 1: Drastically reduce on-chain gas costs by moving heavy computation to specialized provers (e.g., EZKL, Risc Zero).
• Key Benefit 2: Create composable verifiable services that other protocols can permissionlessly call, turning your chain into a utility layer.

Just as you need RPC endpoints for reads/writes, you'll need a decentralized prover network for verifiable compute. This is the next infrastructure battleground.

• Key Benefit 1: Decentralize the proving process to avoid single points of failure (look at Aleo, Succinct).
• Key Benefit 2: Monetize idle compute by allowing validators/stakers to also serve as provers, creating a new revenue stream.

StarkNet with Cairo and zkSync with their LLVM compiler are architecting for native zkML integration. Generic zkEVMs that treat ML as a second-class citizen will lag.

• Key Benefit 1: Future-proof your developer ecosystem by offering first-class tooling for on-chain AI (e.g., Orbis, zkMatrix).
• Key Benefit 2: Attract the next wave of differentiated dApps that are impossible on legacy EVM chains, securing long-term TVL.

Don't boil the ocean. Identify one high-value, compute-heavy data input your ecosystem depends on (e.g., a DEX's TWAP, a prediction market's resolution).

• Key Benefit 1: Immediate security & marketing win by showcasing verifiability where competitors are opaque.
• Key Benefit 2: Build internal expertise on proof systems (Groth16, Plonk, STARKs) and prover economics, de-risking the broader rollout.

Track the cost (in gas + prover fees) to verify a floating-point operation. This is the zkML equivalent of cost per transaction. Optimizing this is a core competitive moat.

• Key Benefit 1: Drive efficiency in your proving stack, attracting cost-sensitive applications like on-chain gaming and large-scale simulations.
• Key Benefit 2: Create transparent benchmarks for developers, making your chain the obvious choice for verifiable compute workloads.