a16z: Who Decides the Truth? The Institutional Dilemma of Prediction Markets [Plain Language Guide] This article explores the core bottleneck faced by prediction markets: contract settlement disputes. By analyzing failed cases such as the Venezuelan elections, the author proposes combining large language model (LLM) judges with blockchain technology. By pre-locking specific model versions and prompts on-chain, automated settlement can be achieved that is manipulation-resistant, highly transparent, and publicly trustworthy, thereby replacing human voting with conflicts of interest and helping to scale prediction markets. For more details, please see:
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a16z: Who Decides the Truth? The Institutional Dilemma of Prediction Markets [Plain Language Guide] This article explores the core bottleneck faced by prediction markets: contract settlement disputes. By analyzing failed cases such as the Venezuelan elections, the author proposes combining large language model (LLM) judges with blockchain technology. By pre-locking specific model versions and prompts on-chain, automated settlement can be achieved that is manipulation-resistant, highly transparent, and publicly trustworthy, thereby replacing human voting with conflicts of interest and helping to scale prediction markets. For more details, please see: