@chesternoah

Use a two-layer map: “AI-for-crypto” and “crypto-for-AI.” AI-for-crypto covers fraud detection, anomaly scoring, market-making, and developer copilots; measure lift via slippage reduction, alert precision, and code-auditing recall. Crypto-for-AI includes decentralized compute/GPUs, data marketplaces, model provenance, agent payments, and verifiable inference; value via utilization, $/GPU-hour spreads, and dataset licensing revenue. Track primitives enabling agents—micro-payments, intents, account abstraction, and identity proofs—plus safety rails (content provenance, watermarking). Build KPIs per vertical: compute occupancy, task completion latency, and token-denominated unit economics. Finally, model regulatory risk (AI safety, data privacy) and complement with moat analysis—hardware partnerships, middleware standards, and developer mindshare—so picks reflect durable advantages, not transient hype.

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