PANews reported on January 24th that a16z Crypto published an article titled "How AI judges can scale prediction markets," which points out that the biggest challenge facing prediction markets is not "pricing the future," but rather determining what actually happened. Similar problems frequently arise in small-scale events. Incorrect or opaque settlement mechanisms can undermine market trust, liquidity, and the accuracy of price signals. AI-driven judgment mechanisms can significantly improve the efficiency and scalability of prediction market settlements while ensuring transparency and fairness. Industry experts recommend introducing Large Language Models (LLMs) as arbitrators in prediction markets, including on-chain rule commitments, resistance to manipulation, increased transparency, and enhanced neutrality. For example, when contracts are created, the specific LLM model, timestamps, and judgment prompts are encrypted and recorded on the blockchain. Traders can understand the complete decision-making mechanism in advance. Fixed model weights cannot be easily tampered with to reduce the risk of cheating. The settlement mechanism is public and auditable, without arbitrary human judgment. Developers can experiment on low-risk contracts, standardize best practices, build transparency tools, and conduct continuous meta-level governance.