Series · ongoing
A series on verifiable machine learning: how zero-knowledge proofs, optimistic schemes, and crypto-economic incentives can make AI inference trustworthy on-chain.
Zero-knowledge proofs promised to make machine learning trustless. A field survey of where zkML actually stands — proving systems, quantization tradeoffs, and what's deployable today.
Zero-knowledge proofs aren't the only path to trustworthy on-chain AI. Optimistic schemes trade latency for a 1000x cost reduction — here's how dispute games over inference actually work.
zkML costs 1000x, optimistic schemes cost a challenge window. Hardware attestation verifies AI inference at under 7% overhead and ~$0.26 on-chain — if you're willing to trust Intel. Part 3 weighs the third leg of verifiable inference.
FHE left the lab: confidential ERC-7984 tokens settle on Ethereum for ~$0.09 in gas, with 48,000 transfers since December. We dissect a live encrypted transaction, the 13-node MPC committee underneath, and why encrypted inference is still 11 seconds per token.
zkML, optimistic, TEE, FHE all prove the computation. Restaking takes the other road: bond it and slash liars. We do the cost-of-corruption math behind EigenLayer's $18B AI-AVS security, the overloading attack that breaks it, and the probabilistic-audit tax.