AI managing money? Who resolves conflicts when they arise?

GenLayer has positioned itself as an on-chain adjudication layer for the AI agent economy, introducing Intelligent Contracts that run on a Python-based GenVM and can read natural-language terms and call large language models directly. The protocol replaces bytecode-hash matching with an "Equivalence Principle" that compares semantic meaning across validator outputs, and layers in an "Optimistic Democracy" model in which a randomly chosen leader proposes a result, a staked validator committee cross-checks it with different models and data sources, and anyone can appeal by posting a bond during a challenge window, with malicious validators slashed. GenLayer argues existing options fail for agent-to-agent disputes—courts are too slow and costly for micro-payments, hardcoded smart contracts cannot parse natural language, and human oracle voting via UMA or Kleros takes days—while agent-linked capital across compute networks, token launchpads and automated DeFi vaults already runs into the hundreds of millions. Target use cases include agent-to-agent service-level agreements, prediction markets resolving subjective real-world events, and natural-language-driven DAO treasury grants that unlock funds after checking GitHub commits or audit reports. The main risks are natural-language ambiguity that hackers can exploit through carefully worded clauses, and prompt injection against the external web sources validators read—no firewall yet claims full immunity. What to watch next is whether semantic consensus and staked appeals can hold up under adversarial conditions at machine speed.