CoinClear

Modulus

4.4/10

ZK proofs for AI model verification — cutting-edge zkML technology enabling trustless AI outputs on-chain, but extremely early with no production-scale deployments.

Updated: February 16, 2026AI Model: claude-4-opusVersion 1

Overview

Modulus is building infrastructure for zero-knowledge machine learning (zkML) — a cryptographic approach that allows anyone to generate a mathematical proof that an AI model produced a specific output given a specific input, without revealing the model's weights or the full computation. This proof can then be verified cheaply on-chain, enabling smart contracts to trustlessly incorporate AI model outputs.

The core problem Modulus addresses is the "oracle problem for AI." Currently, when a smart contract needs an AI prediction (e.g., credit scoring, fraud detection, price prediction), it must trust the entity providing the AI output. There's no way to verify the computation was performed correctly. Modulus enables verifiable AI inference — a smart contract can confirm that a specific model produced a specific result, with mathematical certainty.

Modulus has developed a specialized ZK proving system optimized for neural network inference operations (matrix multiplications, activation functions, normalization layers). Traditional ZK systems (Groth16, PLONK) are not optimized for ML workloads, making proof generation prohibitively expensive. Modulus claims significant speedups through ML-specific circuit optimizations.

This technology sits at the bleeding edge of both ZK cryptography and AI integration, addressing a genuine need as AI increasingly interfaces with on-chain systems. However, zkML is still in its research-to-production transition, with proof generation times and costs that limit practical deployment.

Technology

ZK Proving System

Modulus has developed a custom ZK proving system specifically optimized for neural network inference. Standard ZK circuits are poorly suited for the dense matrix arithmetic that characterizes neural networks. Modulus optimizes for common ML operations: linear layers (matrix multiplication), ReLU activations, softmax, batch normalization, and convolutional layers. The system supports models exported from standard frameworks (PyTorch, ONNX).

Proof Generation Performance

Proof generation for ML models remains computationally intensive. Modulus reports significant improvements over general-purpose ZK systems, but generating proofs for large models (billions of parameters) is still impractical. Current capabilities are best suited for smaller models (sub-100M parameters), which limits applicability to simpler classification and prediction tasks rather than large language models or diffusion models.

Verification

On-chain proof verification is relatively cheap — a smart contract can verify a zkML proof in a single transaction with modest gas costs. This asymmetry (expensive proof generation, cheap verification) is fundamental to ZK systems and makes the technology viable for blockchain integration once proof generation becomes practical.

Network

Current State

Modulus is pre-mainnet, operating primarily as a developer toolkit and proof-of-concept platform. There is no decentralized prover network yet — proof generation is done locally or through centralized infrastructure. The transition to a decentralized proving network is on the roadmap but has not been implemented.

Developer Access

The SDK is available for developers to experiment with zkML proof generation. Documentation covers model conversion, circuit compilation, and proof verification. The developer community is small but technically sophisticated, drawn from the intersection of ZK cryptography and ML engineering.

Adoption

Pilot Integrations

A handful of projects have experimented with Modulus for verifiable AI outputs in DeFi (price predictions, risk scoring) and gaming (verifiable AI opponents). These are primarily proof-of-concept integrations rather than production deployments processing real value.

Market Timing

The zkML market is nascent — the concept gained attention in 2023-2024 but practical deployment remains limited across the entire space, not just Modulus. The technology needs 2-3 more years of optimization before it can support production-scale AI inference verification for meaningful model sizes.

Competition

Modulus competes with other zkML approaches including EZKL, Giza, and Axiom's coprocessor. The space is small enough that competition is primarily about technical capability rather than market share. Ritual, which takes an optimistic verification approach rather than ZK, represents an alternative architectural paradigm.

Tokenomics

Token Status

Modulus's token economics are still being designed. The anticipated model involves a proof marketplace where provers earn tokens for generating zkML proofs and users pay tokens for verification. The tokenomics are speculative at this stage, with no live token economy to evaluate.

Future Value Drivers

If zkML achieves practical deployment, the demand for proof generation could create genuine token utility. The value proposition depends entirely on technical maturation — if proof generation becomes fast and cheap enough for production use, the token economy could be meaningful. This remains a significant "if."

Decentralization

Open Technology

The zkML proving system is designed to be open — any entity with sufficient compute can generate proofs. This inherent openness supports decentralization, as proof generation is not gatekept by centralized infrastructure.

Verification Trust Model

The beauty of ZK proofs is that verification is trustless — anyone can verify a proof without trusting the prover. This is genuinely decentralized trust, superior to optimistic or reputation-based approaches. The mathematical guarantee is the strongest form of trustless computation available.

Risk Factors

  • Extremely early-stage: Pre-mainnet with no production deployments or live token.
  • Technical risk: zkML is at the research frontier; proof generation for large models may remain impractical.
  • Performance limitations: Current capabilities limited to small models, excluding most commercially relevant AI.
  • No revenue or adoption: Pure R&D stage with speculative future value.
  • Competition: Multiple teams pursuing zkML with different approaches.
  • Market timing: The zkML market may not materialize at scale for several years.

Conclusion

Modulus represents some of the most technically ambitious work in the crypto-AI space — building the cryptographic infrastructure to make AI outputs verifiable on-chain. The technology addresses a genuine problem (how can smart contracts trust AI predictions?), and the ZK approach provides the strongest possible trust guarantees.

The 4.4 score reflects exceptional technology (8.0) weighted down by the reality of extreme early-stage development (adoption 2.5, network 3.0). This is a research-stage project with a potentially transformative vision but no proven market. Investors should view Modulus as a high-risk bet on the zkML thesis materializing within a reasonable timeframe. The technology is real; the market isn't — yet.

Sources