a16z Crypto: AI-based decision-making mechanisms may solve the bottleneck of prediction market expansion.

A16z Crypto article, "What to do when prediction markets fail," 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, and flawed or opaque settlement mechanisms can undermine market trust, liquidity, and the accuracy of price signals. Industry experts recommend introducing Large Language Models (LLMs) as arbitrators in prediction markets. This includes ensuring rules are committed to being on-chain, resisting manipulation, increasing transparency, and enhancing neutrality. For example, when contracts are created, the specific LLM model, timestamps, and decision 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. AI-driven judgment mechanisms can significantly improve the settlement efficiency and scalability of prediction markets while ensuring transparency and fairness.