In November 2024, prediction markets accurately forecasted the election outcome, giving Trump a 60% chance while traditional polls hesitated. This performance underscored the potential of prediction markets to aggregate diverse information into reliable forecasts. However, the article highlights a significant flaw: the information generated is available to everyone immediately, diminishing its value, especially for hedge funds and serious buyers who prefer exclusive insights. As a result, prediction markets often focus on events like elections and sports, where people are willing to gamble, leaving more complex geopolitical and economic questions unaddressed. The author proposes a shift toward "cognitive finance," which aims to create a new system designed to harness collective intelligence effectively. Traditional markets are slow and limited, relying on price as the primary signal. In contrast, cognitive finance would use private markets to provide exclusive insights, allowing entities like hedge funds and defense contractors to pay for tailored predictions without worrying about information leaks. Trusted execution environments (TEEs) could facilitate this by keeping sensitive data secure while allowing for market interactions. Combinatorial prediction markets are another innovation suggested in the article. These markets would consider the interconnections between different events, allowing for a more nuanced understanding of probabilities. For example, if inflation exceeds a certain level, it could influence the likelihood of interest rate changes. By capturing correlations effectively, these markets would create a continuously updated probability model that reflects real-world complexities. The piece also points out the growing role of automated trading systems in these markets, which can quickly identify mispricings and leverage external information, suggesting a future where AI plays a central role in market dynamics.