Training, inference, optimization, and the engineering of learning systems.
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.
Intent-based DEXes don't route your trade — they auction it. Solvers compete as autonomous optimizers to settle a batch at one clearing price. Inside CoW Protocol's contract, the optimization problem, and the metaheuristic solvers now winning it.
The gradients never touch the chain. What Solana actually stores when Psyche trains a 36B model across 24 nodes, how TOPLOC audits untrusted GPUs in 258 bytes, and why the flagship 'decentralized' model still shipped from a 512-GPU cluster.
Bittensor let AMM prices decide which AI subnets earn 3,600 TAO a day — until a memecoin subnet gamed the formula. Inside the TaoFlow upgrade: the constant-product math that got exploited, the EMA flow accounting that replaced it, and why refundable manipulation is the design smell to hunt for.
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.
The Elliptic benchmark made GNNs the default for on-chain AML. A 2026 leakage-free re-evaluation flips the script: random forests win by 13 F1 points, randomly rewired edges beat the real graph, and every model falls off a cliff at time step 43.
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.
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.