Risks and Centralization

AI × crypto marketing often promises decentralized intelligence — but many products rely on closed APIs, opaque models, and token distributions that concentrate voting power. Users face familiar AI failure modes — hallucinations, bias, and data leaks — plus irreversible on-chain losses when agents mis-sign transactions.

Hallucinating LLMs can generate plausible but wrong contract addresses or swap paths. Black-box models make due diligence impossible for institutions. Fake decentralization — thousands of nodes but one codebase upgrade key — mirrors problems seen in early DeFi.

Mitigations include human approval gates, simulation before execution, open model cards, and conservative scope for autonomous agents. Treat AI outputs as untrusted input to on-chain logic, not as ground truth.