Prediction markets are undoubtedly one of the most watched sectors in the crypto industry. Leading project Polymarket boasts over $36 billion in cumulative trading volume and recently completed a strategic funding round at a valuation of $9 billion. Meanwhile, platforms including Kalshi (valued at $11 billion) have also received substantial capital injections.

 ▲Source: Dune

However, behind the continuous influx of capital and impressive data growth, we find that prediction markets, as a type of trading product, still face many problems.

In this article, I attempt to set aside the mainstream optimism and offer some different perspectives.

01

Prediction is event-based—events are inherently discontinuous and non-replicable. Unlike the price fluctuations of assets such as stocks and forex over time, market prediction relies on a finite number of discrete events in the real world. It is low-frequency compared to trading.

In the real world, there are very few events that truly garner widespread attention, have clear outcomes, and are settled within a reasonable timeframe—presidential elections are held every four years, the World Cup every four years, the Oscars every year, and so on.

Most social, political, economic, and technological events do not generate sustained demand for transactions. These events are limited in number and frequency each year, making it difficult to build a stable trading ecosystem.

In other words, the low-frequency nature of prediction markets cannot be easily changed by product design or incentive mechanisms. This fundamental characteristic determines that, in the absence of major events, the trading volume of prediction markets will inevitably not remain at a high level.

02

Unlike the stock market, prediction markets do not rely on fundamentals: the stock market's value comes from a company's intrinsic value, including its future cash flow, profitability, assets, and so on. Prediction markets, however, ultimately point to an outcome and depend on users' "interest in the outcome of the event itself."

In this context, the amount people are willing to bet on is significantly positively correlated with the importance of the event, market attention, and time frame: scarce and high-profile events such as the finals and presidential elections attract a large amount of money and attention.

Naturally, an average fan would be more concerned about the outcome of the annual finals and would place heavy bets on it, rather than performing the same way during the regular season.

On Polymarket, the 2024 presidential election accounted for over 70% of the platform's total online activity (OI). Meanwhile, the vast majority of events remained in a state of low liquidity and high bid-ask spreads for extended periods. From this perspective, it's difficult for the prediction market to expand exponentially.

03

Prediction markets have a gambling nature, but they are unlikely to generate the retention and expansion that gambling does.

We all know that the real mechanism of gambling addiction lies in instant feedback—slot machines go every few seconds, Texas Hold'em is played every few minutes, and contracts and memecoin transactions change rapidly every second.

However, the feedback cycle in prediction markets is very long, with most events taking weeks to months to resolve. Events with rapid feedback may not be interesting enough to warrant a large bet.

Immediate positive feedback significantly increases dopamine release frequency and reinforces user habits. Delayed feedback, on the other hand, fails to create stable user retention.

04

In some types of events, there is a high degree of information asymmetry among the participants.

In competitive sports, in addition to the teams' theoretical strength, the outcome largely depends on the athletes' performance on the day, which means there is still considerable uncertainty.

However, political events involve opaque processes involving inside information, channels, and connections. Insiders have a significant information advantage, making their bets much more certain.

Just like the vote counting process in elections, internal polls, and organizational details in key areas, it's difficult for outsiders to obtain this information. Currently, there is no clear definition of "insider trading" in prediction markets from regulatory bodies, leaving this area in a gray area.

In general, in these types of events, the party at an informational disadvantage is more likely to exit liquidity.

05

Due to the ambiguity of language and definitions, it is difficult to be completely objective in predicting market events.

For example, whether Russia and Ukraine will cease hostilities in 2025 depends on the statistical methodology used; whether a cryptocurrency ETF will be approved at a certain time can be determined by factors such as full approval, partial approval, or conditional approval. This raises the issue of "social consensus"—when both sides are evenly matched, the losing side will not readily concede defeat.

Such ambiguity necessitates the establishment of a dispute resolution mechanism on the platform. However, once prediction markets encounter linguistic ambiguity and dispute resolution, they cannot rely entirely on automation or objectivity, leaving room for human manipulation and corruption.

06

The main value proposition of prediction markets in the market is "collective intelligence," which means that, compared to the low level of trust in the media and mainstream discourse, prediction markets can gather the best information from around the world, thereby achieving collective consensus.

However, before prediction markets achieve widespread adoption, this "information sampling" will inevitably be one-sided and the sample will not be diverse enough. The user base of prediction market platforms may be highly homogeneous.

For example, in the early stages of a prediction market, it is certainly a platform mainly composed of cryptocurrency users whose views on political, social, and economic events may be highly convergent, thus forming an information cocoon.

In this situation, the market reflects the collective bias of a specific group, and is still quite far from "collective wisdom".

Conclusion

The core message of this article is not to predict a bearish market, but rather to encourage us to remain calm amidst heightened FOMO, especially after the ups and downs of popular narratives like ZK and GameFi.

Over-reliance on special events like elections, short-term sentiment on social media, and airdrop incentives often amplifies the superficial aspects of data and is insufficient to support judgments about long-term growth.

Nevertheless, from the perspective of user education and user acquisition, prediction markets will remain important in the next three to five years. Similar to on-chain yield savings products, they have an intuitive product format and a lower learning curve, making them more likely to attract users from outside the crypto ecosystem than on-chain transaction protocols. Based on this, prediction markets are highly likely to develop further and, to some extent, become an entry-level product for the crypto industry.

Future prediction markets may also occupy certain vertical sectors, such as sports and politics. They will continue to exist and expand, but they do not have the fundamental conditions for exponential growth in the short term. We should consider investing in prediction markets from a cautiously optimistic perspective.