Any permissionless network that rewards the best gradient contribution faces a Nash equilibrium where every rational miner copies instead of computes — same reward, zero cost. Gauntlet's commit-reveal mechanism closes that trap, and already trained a 1.2B LLM on Bittensor with real token payouts.
Llama-3.1-8B starts at 15.9% on MATH. Put it in an auction economy and it reaches 57.0% — beating a stronger monolithic baseline. The mechanism is Hayek: decentralized price signals, wealth accumulation, and economic selection pressure do what central orchestration can't.
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.
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.
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.
LLMs collude in on-chain auctions even when written rules forbid it — only enforceable, automatic penalties slash severe collusion from 50% to 5.6%. Here's how governance graphs make that concrete.
A flash loan gives an AI agent $1M with no collateral — and demands it back plus 0.05% before the block ends. The atomic callback constraint is both what makes flash loans safe and what forces an agent to solve its entire strategy before touching the chain.
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.
MORPH shows that a TPU v6e8 pod runs NTT at 40× CPU — 10× faster than an H100. The same silicon that accelerates AI inference now accelerates ZK proof generation, and the math that explains it is the same in both cases.
Long-context AI's real bottleneck isn't proof overhead — it's the KV cache. At 128K tokens, Llama 3.1 8B's KV state equals its own weights (15 GiB), pinned to one node per request. The formula, the GPU math, and why prefix caching demands stickiness that decentralized routing can't provide.