#预测市场 Seeing Kalshi's research report, I felt a sense of familiarity. The comparison of 25 months of data shows that prediction markets have an error rate 40% lower than Wall Street consensus, and during periods of high volatility, they can be up to 67% more accurate—this is nothing new, just history repeating its own story.

I still remember the days around 2008 when traditional economists collectively fell silent in the face of the subprime mortgage crisis. At that time, I realized a truth: the trader community that pools real money is always more sensitive than predictors sitting in offices. Because those betting real cash won't deceive themselves, and the market's price discovery mechanism is inherently more honest than any single authority.

Now, prediction markets finally have quantitative proof, and Kalshi has anchored this logic with data on the table. Especially during periods of high economic volatility, this advantage becomes even more apparent—demonstrating what "certainty in uncertainty" really means. Collective intelligence tends to be clearer under extreme conditions because incorrect judgments are immediately shattered by market feedback.

But this also makes me think of past failures. The success of prediction markets fundamentally depends on participant diversity and aligned incentives. If market participation is insufficient or dominated by a single voice, this mechanism can fail. Therefore, Kalshi's discovery is more significant because it reminds us: institutional design is more crucial to success than mere technological innovation.

In this cycle, what I see is a slow shift from "authoritative predictions" to "market predictions." History tells us that more and more institutions will ultimately recognize this fact.