> For the complete documentation index, see [llms.txt](https://torch-1.gitbook.io/litepaper/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://torch-1.gitbook.io/litepaper/solution-limitations.md).
# Solution limitations
### **Perpetual futures**
Perpetual futures provide reliable predictive insights into short-term price movements within tens of minutes. Over a one-week period, changes in perpetual futures funding rates explain only a small portion of price changes. Their predictive power diminishes over longer periods.
### **AI forecasting**
While AI models enhance trend analysis, risk assessment, and other forecasting techniques, their predictive power is limited to these specific applications and does not capture broader market dynamics. In contrast, crowdsourced insights have historically yielded more accurate predictions by aggregating diverse market perspectives.
### **Prediction markets**
While effective for discrete event forecasting, traditional markets fail to offer a continuous price discovery mechanism. Fragmented liquidity across multiple event questions reduces their efficiency for evaluating token price trajectories.
### **Token launchpads**
Token launchpads concentrate speculative activity around token listings, but provide little insight into longer-term price performance. They primarily incentivize short-term hype, not informed forecasting. Liquidity is often thin and controlled, making post-launch price signals noisy and unreliable.
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