Verifiable Inference: The Missing Piece of AI Trust

The Problem

When an AI agent makes a decision, how do you prove it actually ran the model it claims? In DeFi, this isn't academic — it's a multi-million dollar trust question.

Current Approaches

ZK-ML

Zero-knowledge proofs for ML inference. Mathematically proves the model ran correctly without revealing weights. Current state: works for small models, too expensive for LLMs.

Optimistic Verification

Assume correct, challenge if suspicious. Lower cost, but introduces delay. Good for non-time-critical decisions.

TEE-Based

Run inference in a Trusted Execution Environment (Intel SGX, ARM TrustZone). Hardware attestation proves execution integrity.

My Prediction

Hybrid approach wins: TEE for real-time decisions, ZK proofs for audit trails, optimistic verification as a fallback. 2027 will be the year of verifiable AI.