Why zkML Protocols Are the Real Infrastructure Play

Smart contracts cannot trust off-chain AI inferences, creating a massive security and composability gap. This blocks AI agents from managing wallets, executing trades, or governing protocols autonomously.

• Security Risk: Unverifiable AI outputs are attack vectors for DeFi and DAOs.
• Composability Gap: AI logic remains siloed, unable to interact with on-chain money legos.

Protocols like EZKL, Modulus, and Giza act as a trust layer, generating cryptographic proofs that an AI model ran correctly without revealing its weights. This turns any AI into a transparent, on-chain service.

• Enables Autonomous Agents: Verifiable inference allows AI to sign transactions and control assets.
• Unlocks New Primitives: Private credit scoring, verifiable gaming AI, and MEV-resistant trading bots.

The real value accrues to protocols that standardize the proving stack and aggregate demand, similar to how The Graph indexes data or LayerZero standardizes messaging.

• Prover Commoditization: Specialized hardware (e.g., Cysic, Ingonyama) will race for latency, but the protocol layer captures the fee flow.
• Data Flywheel: The most-used zkML protocols will amass proprietary datasets for fine-tuning and optimization, creating a defensible barrier.

zkML is the missing piece for truly decentralized intent architectures, solving the trust problem in solvers. Projects like UniswapX and CowSwap rely on off-chain solvers; zkML makes them verifiable and anti-collusive.

• Solver Accountability: Prove optimal execution was found without revealing strategy.
• MEV Transformation: Enables complex, private trading strategies that are provably fair, reshaping the Flashbots landscape.

The Problem: Proving AI inferences on-chain is prohibitively expensive, often costing >$1 per proof, killing utility. The Solution: Specialized proving systems like RISC Zero and SP1 that achieve ~1000x cost reductions vs. general-purpose zkVMs, making on-chain AI agents economically viable.

• Key Benefit: Enables sub-$0.01 inference proofs for models like Stable Diffusion.
• Key Benefit: Proof aggregation for batch verification, amortizing costs across users.

The Problem: AI models are trapped in siloed, trusted environments. Oracles like Chainlink Functions introduce latency and centralization. The Solution: A standardized proving stack that converts any PyTorch/TensorFlow model into a zk-SNARK, creating a universal verification standard for off-chain compute.

• Key Benefit: On-chain verifiability for any AI-driven DeFi strategy or game logic.
• Key Benefit: Interoperable proofs that work across EVM, SVM, and Move-based chains.

The Problem: Autonomous on-chain agents (e.g., trading bots, NPCs) require deterministic, verifiable logic. Current systems are opaque and unaccountable. The Solution: End-to-end frameworks that train, compile, and prove ML models, creating accountable agents whose decisions are cryptographically auditable.

• Key Benefit: Intent-based systems (like UniswapX) can use verified AI for optimal routing.
• Key Benefit: Enables complex, conditional DeFi strategies without trust in an operator.

The Problem: Full privacy (e.g., zkSNARKs) requires trusted setups and complex circuits. Full transparency (e.g., OP Stack fault proofs) is slow and gameable. The Solution: Hybrid architectures using zkVM proofs for deterministic logic (like RISC Zero) and optimistic schemes for data availability, optimizing for cost and finality.

• Key Benefit: Selective privacy: keep model weights private while proving correct execution.
• Key Benefit: ~10s finality for complex inferences vs. 7-day challenge periods.

zkML's core value is trustless, verifiable compute. If proving times remain >10 seconds for complex models, it's unusable for real-time applications like on-chain gaming or high-frequency trading. This bottleneck could relegate zkML to niche, asynchronous use cases only.

• Latency vs. Security Trade-off: Faster proving often requires weaker security assumptions or trusted setups.
• Hardware Dependency: The need for specialized FPGA/ASIC provers could recentralize infrastructure, mirroring PoW mining pools.

Most zkML today relies on centralized off-chain provers (e.g., EZKL, Giza) to generate proofs. This recreates the oracle problem it aims to solve. If the prover network is controlled by a few entities, the system is only as secure as their honesty.

• Data Input Problem: Verifying the model is pointless if the input data (e.g., price feed, sensor data) isn't also trustlessly sourced.
• Economic Capture: Prover services could become extractive monopolies, killing application-layer margins.

For many proposed use cases, a simple cryptographic commitment to an off-chain result is sufficient. The ~1000x cost premium for a full zk proof may not be justified. Projects like Worldcoin use zk for privacy, not for on-chain model inference, highlighting the market's current preference.

• Market Fit Uncertainty: Is there $1B+ of sustainable demand for verifiable inference, or is it a solution in search of a problem?
• Developer Friction: The tooling stack (Circom, Halo2, Noir) remains esoteric versus traditional ML frameworks like PyTorch.

zkML isn't one stack; it's a fragmented landscape of proof systems (Groth16, Plonk, STARK), circuit languages, and specialized VMs. This risks protocol-level incompatibility, stifling composability—the core innovation of DeFi. A project built for RISC Zero may not work with Succinct.

• Winning Standard Risk: The space could consolidate around a single player (e.g., Polygon zkEVM's approach) or fracture irreparably.
• Audit Complexity: Each new zk circuit is a unique cryptographic attack surface, requiring specialized, scarce audit talent.

On-chain apps need off-chain data and computation, but relying on centralized oracles like Chainlink introduces a single point of failure and trust. This breaks the decentralized promise for DeFi, prediction markets, and insurance.

• Trust Minimization: Replaces trusted oracles with cryptographically verifiable state.
• New App Design: Enables autonomous, logic-driven protocols that react to real-world data without human committees.

Protocols that generate zero-knowledge proofs for ML model inferences, turning any data feed into a verifiable fact. This creates a new primitive as fundamental as a blockchain itself.

• Universal Verifiability: Any chain (Ethereum, Solana, rollups) can consume the same proven output.
• Market Creation: Unlocks on-chain AI agents, dynamic NFTs, and risk models that were previously impossible.

The real infrastructure value accrues to the proof acceleration layer, not the application. Winning protocols will own the proving stack.

• Vertical Integration: Leaders like RISC Zero and Succinct control the toolchain (zkVM, provers, circuits).
• Hardware Advantage: Specialized zkML ASICs (e.g., from Cysic, Ingonyama) will create unbeatable cost and speed barriers.

The first massive adoption will be in finance, where the cost of trust is highest. Imagine a DEX with a zk-verified risk engine or a lending protocol with real-time, proven credit scores.

• Regulatory Arbitrage: Provides auditable compliance (e.g., sanctions screening) without leaking private data.
• Capital Efficiency: Enables sub-second margin calls and complex derivatives backed by verified market signals.

Ethereum with EIP-4844 and dedicated L2s like Taiko are integrating native zkVM support. Why use a separate zkML protocol if the base layer absorbs its function?

• Native Advantage: L2s can offer cheaper, faster settlement for proofs as a core feature.
• Survival Play: Standalone zkML protocols must offer superior prover performance or become a commodity service for chains.

The market will initially over-index on proving speed. The long-term winner will be the protocol that provides the most robust, developer-friendly verifiability, even at a latency premium. Trust is the scarce resource.

• Standardization: The "EVM for zkML" will emerge, creating network effects.
• Protocol Revenue: Fees from proof generation and verification will dwarf application-layer fees.