Consensus, networking, and the core architecture of distributed ledgers.
Most DeFi protocols are upgradeable, and most on-chain AI agents don't know it. Here's how proxy architecture invalidates agent assumptions — and a tiered defense framework to build around it.
Controlling one intermediate pipeline stage is enough to inject a backdoor into a decentralized post-training run — 94% ASR, no data access required. The mechanism, why training loss stays clean, and what it means for networks fine-tuning LLMs across untrusted nodes.
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.
EIP-4844 blobs look like cheap on-chain storage, but they vanish in 18 days. Here is why DA windows break AI training pipelines and what to use instead.
Uniswap v4 hooks make every liquidity pool programmable. Here's how the 14-bit permission bitmap works, what fires during a swap, and where AI agents fit into dynamic fee architecture.
On OP Stack chains, 'confirmed' is a four-layer promise stretching from 2 seconds to 7 days. AI agents that collapse these into one boolean expose themselves to sequencer reorgs, bridge lockups, and state dispute windows. Here's the full settlement stack, and an action taxonomy for each rung.
Every ECDSA signature broadcasts the wallet's public key. A March 2026 Google/EF paper tightens the quantum break estimate to ~500k physical qubits — here's what that means for autonomous AI agent wallets.
ETH's 3.46% daily volatility buries a 0.1% trading edge so deeply that on-chain P&L needs 4,600 trades — 230 days — before you can tell skill from luck at 95% confidence. Process rewards fix it in 23.
Three require() calls in Ondo Finance's CashKYCSenderReceiver block every address without KYC from $3.2 billion in permissioned US Treasury tokens — and AI agents can never pass them.
A new paper quantifies what everyone suspected: AI agent tokens trade at >10,000x the capital they actively manage. Here's the mechanics of why, and what it means for builders.
Decentralized RL splits the actor from the learner across the internet: the policy a worker acts with runs several steps behind the one that learns. INTELLECT-2 held reward at 4 steps stale; SparrowRL cut the broadcast 79x. The bandwidth and staleness taxes, and what the chain secures.
Most 'on-chain AI' keeps the model off-chain and posts a proof. But you can also just run the forward pass in Solidity. We do the gas math — ~106k gas per weight, an MNIST net that needs 180 Ethereum blocks — and find the thin band of models that fit.