> For the complete documentation index, see [llms.txt](https://insightx-2.gitbook.io/whitepaper/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://insightx-2.gitbook.io/whitepaper/insightx-whitepaper/2.-pain-points-and-market-opportunities-of-traditional-forecasting-markets.md). # 2. Pain Points and Market Opportunities of Traditional Forecasting Markets Despite the significant growth in trading volume in prediction markets over the past few years, their underlying architecture still faces several systemic challenges that severely hinder the industry's long-term healthy development and institutional adoption. InsightX is designed based on a deep understanding of these pain points, offering a systematic solution through dual innovation in technology and economic models. #### 2.1 Capital Lock-up and Opportunity Cost Funds are locked up throughout the process, with a utilization rate of less than 10%, leaving billions of dollars idle for a long period. In traditional prediction markets, once a user opens a position, their funds are locked up until the event is settled. This design results in extremely low capital utilization, typically below 10%. According to industry data, billions of dollars in locked value remain dormant for extended periods, unable to generate additional returns or be used for other DeFi strategies or opportunistic investments. This high opportunity cost not only reduces user participation but also limits the overall market's capital liquidity and depth. #### 2.2 Single revenue stream Profits can be made simply by making accurate predictions; there is a lack of strategies for compounding returns. Traditional prediction markets operate on a highly simplistic revenue model: users are only rewarded for accurately predicting event outcomes. There are no additional revenue tiers or capital-efficient strategies. This "all-or-nothing" revenue structure prevents users from enjoying stable returns or passive income during the holding period, further amplifying the negative effects of idle funds. Even with increased overall market trading volume, users struggle to hedge risks or enhance returns through diversification strategies. #### 2.3 Severe liquidity shortage The active market is less than 20%, and most events have limited trading depth. Liquidity is another core bottleneck in market prediction. Data shows that active trading accounts for less than 20% of the market, with the vast majority of events facing a lack of liquidity. Users struggle to efficiently enter and exit positions when needed, leading to increased slippage, a poor trading experience, and ultimately suppressing overall market activity. Low liquidity also creates a vicious cycle: shallow liquidity further reduces user participation and limits the accuracy of price discovery. #### 2.4 Inefficient information and transaction processes Mainstream events account for more than 80%, industry concentration is more than 97%, and long-tail events lack price discovery and high-frequency trading. The limited speed at which information enters the market and the lag in market response to new information lead to insufficient trading efficiency and price discovery capabilities. Even with total trading volume reaching tens of billions of dollars, single-category events still account for over 80% of the trading volume, resulting in an industry concentration of over 97%. A large number of long-tail events lack high-frequency trading and adequate price discovery mechanisms, leading to overall low market information efficiency and an inability to effectively reflect the complex expectations and dynamic changes of the real world. These pain points collectively make it difficult for traditional prediction markets to attract mainstream capital and institutional participation, and also limit their application potential in broader DeFi, RWA, and real-world asset pricing scenarios. InsightX addresses these four structural problems by building a brand-new AI-native InfoFi architecture, injecting prediction markets with sustainable capital efficiency, multiple returns, deep liquidity, and intelligent information processing capabilities, thereby opening up unprecedented growth opportunities in the InfoFi field. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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