Verifiable AI and ZKML

Zero-knowledge machine learning (ZKML) aims to prove a model executed correctly on specific inputs without revealing model weights or private data. For crypto, that means a smart contract could trust an inference result because the math verifies — not because a centralized API said so.

Applications include fair gaming (hidden information revealed only via proof), credit scoring without exposing raw financial records, and anti-cheat for AI competitions where participants must prove they used an approved model.

Tradeoffs are stark: proof generation can take minutes and significant CPU; developers often prove a subset of layers or use optimistic schemes with fraud proofs. Expect hybrid models — TEEs for speed, ZK for high-stakes settlement — for years to come.