[a16z Crypto: AI Judgment Mechanisms May Solve Prediction Market Scalability Bottlenecks] According to a report by Jinse Finance, a16z Crypto published an article titled 'How AI "Judges" Can Scale Prediction Markets,' which pointed out that the biggest challenge faced by prediction markets is not 'pricing the future,' but determining what actually happened. Similar issues frequently arise in small-scale events, where errors or opaque settlement mechanisms can undermine market trust, liquidity, and the accuracy of price signals. Industry experts suggest introducing large language models (LLMs) as adjudicators in prediction markets, including measures such as committing rules on-chain, ensuring resistance to manipulation, enhancing transparency, and improving neutrality. For example, during contract creation, specific LLM models, timestamps, and judgment prompts could be encrypted and recorded on the blockchain. This would allow traders to understand the complete decision-making mechanism in advance. Fixed model weights would prevent easy tampering, reducing the risk of cheating. Open and auditable settlement mechanisms would eliminate arbitrary human rulings. AI judgment mechanisms could significantly improve the settlement efficiency and scalability of prediction markets while ensuring transparency and fairness.