Journal
136 articles on the craft of AI × blockchain engineering. or filter by topic:
DataDAOs promise to pay you for your data — but 'pay you fairly' hides a cooperative-game problem that's O(2ⁿ). Inside Data Shapley, the KNN trick that makes it tractable, why rankings flip under SGD noise, and what Vana's Proof of Contribution actually computes instead.
To slash a dishonest AI operator on EigenLayer you need a proof. To produce one, you need determinism. EigenAI solves both: seeded inference + optimistic re-execution inside a TEE collapses verification to a byte-equality check — slashable at Ethereum-scale security.
A Uniswap v3 LP position in range [Pa, Pb] is mathematically a short strangle — short put at Pa, short call at Pb. Derive the equivalence, compute the Black-Scholes fair premium, and compare it to actual fee income at current ETH vol.
Bond-and-slash verifiable AI assumes someone audits. But checking costs money and pays nothing, so a rational verifier won't — the verifier's dilemma. Proof of Sampling fixes it with a bounty, and the math says honest inference holds at a 0.74% sampling rate where nobody ever arbitrates.
Olas turned an off-chain AI task into an on-chain contract: 2.1M requests across 100 mechs, a 60-300s priority window, and a Karma ledger that docks no-shows. We read the marketplace off Gnosis and dissect its crypto-economic SLA.
DiFR's seed-commitment round-trip is the last synchronization barrier to async verifiable inference. TOPLOC removes it: a locality-sensitive hash of intermediate activations compresses to 258 bytes per 32 tokens — 1,000× smaller than raw embeddings — and validates faster than inference ran.
38.6% of Ethereum blocks today go through censoring relays. FOCIL (EIP-7805) ends builder exclusion with a 16-validator committee and a 1-out-of-16 honesty assumption hardwired into the fork-choice rule — landing in the Hegota upgrade, late 2026.
Solana agents get 400 ms — but only if they declare every account they'll touch before execution starts. The Sealevel write-lock model, hot-account contention, Jito tip economics, and what the declarative execution contract means for AI agent design.
Federated learning keeps raw data local — but the gradients it publishes on-chain are not private. Inversion attacks reconstruct training images from them, and a public ledger makes that window permanent.
Five builders now win 96.7% of Ethereum blocks. Their edge isn't compute — it's exclusive orderflow: 12% of transactions that generate 54.59% of block value. AI agents transacting in public have to understand where they sit in that hierarchy — and when a preconfirmation changes the math.
DeepSeek-R1 emits 8,400 thinking tokens before a 341-token answer on hard math. Every existing on-chain verification scheme — TEE, optimistic, ZK, sampling — was designed when computation and output were proportional. They aren't anymore.
Cross-chain bridges have lost $2.5B to hacks because receiving contracts accept messages they cannot cryptographically verify. AI agents operating across chains inherit this exact problem. Here are the four trust models, their real security parameters, and a principled agent policy.