Incentive design, token engineering, and crypto-economic mechanisms.
Give autonomous LLM agents a marketplace and they race each other to bankruptcy and get conned by Sybils. A 2026 study finds capability doesn't help — a 9B model beat frontier models 45× its size on economic alignment. On-chain, both failures get cheaper and worse. The mechanics, numbers, and fixes.
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.
A perp has no expiry, so only the funding rate pegs it to spot. On Hyperliquid that's an hourly cash flow AI agents now harvest delta-neutral — a $10k position at +0.02%/8h pays ~21.9%/yr. The mechanism, the carry math on real rates, and why it's carry, not alpha.
Decentralized AI networks have to turn many validators' subjective quality scores into one reward number. Bittensor's Yuma Consensus uses a stake-weighted median and clips the rest — robust to a <50% bloc. Then a parasite shows up: copying the consensus pays better than producing it.
Launching an AI agent token is one constant-product auction. We read Virtuals' Bonding contract off Base: a 6,000-VIRTUAL virtual reserve, a 64× price ramp, graduation at exactly 42,000 VIRTUAL, and the 12.5% gap where the Uniswap pool opens below the curve.
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.