Prediction markets, which allow participants to trade on the outcomes of future events, are gaining traction as forecasting tools. However, they face significant structural inefficiencies that limit their reliability. Key issues include the lack of "dumb money," persistent arbitrage opportunities, and the influence of bots and algorithmic trading, which can distort market prices and signals. These markets also suffer from self-reinforcing feedback loops, misinformation, insider trading, and low liquidity in niche markets. Such inefficiencies can lead to misleading probabilities and unfair outcomes, challenging the markets' effectiveness as forecasting tools. Addressing these issues requires rethinking the underlying architecture to improve accuracy and trust in prediction markets.