$2 billion "probability game": Is the prediction market迎来 the "singularity" moment?

Written by: Bitget Wallet Research

From the public discussions about “whether Zelensky wears a suit” to the global focus on the U.S. elections and the Nobel Prize attribution, the prediction market always seems to periodically “get hot.” However, since Q3 2025, a real storm seems to be brewing:

At the beginning of September, industry giant Polymarket received regulatory approval from the U.S. CFTC, returning to the U.S. market after three years.

At the beginning of October, the parent company of the New York Stock Exchange, ICE, plans to inject up to $2 billion into Polymarket;

In mid-October, the predicted market weekly trading volume reached a historic high of 2 billion dollars.

The huge wave of capital, the opening of regulations, and the market's carnival have all arrived at the same time, along with rumors of the Polymarket token launch—where does this wave of enthusiasm come from? Is it merely another brief speculation, or is it a “value singularity” in a brand new financial track? Bitget Wallet Research will take you through an in-depth analysis of the underlying logic and core value of the prediction market in this article, and will provide a preliminary assessment of its core dilemmas and development direction.

1. From “Distributed Knowledge” to “Dual Oligopoly”: The Evolution of Prediction Markets

Prediction markets are not unique to the crypto world; their theoretical foundation can even be traced back to 1945. Economist Hayek proposed in his classic discourse that fragmented, local “dispersed knowledge” can be effectively aggregated by the market through price mechanisms. This idea is considered to lay the theoretical cornerstone of prediction markets.

In 1988, the University of Iowa launched the first academic prediction platform - the Iowa Electronic Markets (IEM), which allowed users to trade futures contracts on real-world events such as presidential elections. Over the following decades, extensive research generally confirmed that a well-designed prediction market often surpasses the accuracy of traditional opinion polls.

However, with the emergence of blockchain technology, this niche tool has found new scalable ground. The transparency, decentralization, and global accessibility of blockchain provide an almost perfect infrastructure for prediction markets: automated execution of settlements through smart contracts can break the entry barriers of traditional finance, allowing anyone in the world to participate, thereby greatly expanding the breadth and depth of “aggregated information.” Prediction markets are gradually evolving from a niche gambling tool into a powerful on-chain financial sector, beginning to deeply integrate with the “crypto market.”

Data source: Dune

Data from the Dune platform intuitively confirms this trend. On-chain data shows that the current crypto prediction market has presented a highly monopolized “dual oligopoly” pattern: two giants, Polymarket and Kalshi, share more than 95% of the market share. With both capital and regulatory incentives stimulating the market, this sector is being overall activated. In mid-October, the weekly trading volume of the prediction market exceeded $2 billion, surpassing the historical peak before the 2024 U.S. elections. In this explosive growth phase, Polymarket has gained a slight edge over Kalshi in intense competition due to its key regulatory breakthroughs and potential token expectations, further consolidating its leading position.

2. “Event Derivatives”: Beyond Gambling, Why is Wall Street Betting?

To understand why ICE has heavily invested in Polymarket, one must peel away the “gambling” facade of prediction markets and see the core of its “financial instrument.” The essence of prediction markets is an alternative trading contract, which belongs to a type of “event derivative.”

This is different from the “price derivatives” we are familiar with, such as futures and options. The trading targets of the latter are the future prices of assets (such as crude oil and stocks), while the trading target of the former is the future outcome of specific “events” (such as elections and climate). Therefore, the contract price does not represent asset value, but rather the market's collective consensus on the “probability of the event occurring.”

With the support of Web3, this difference is further amplified. Traditional derivatives rely on complex mathematical models such as Black-Scholes for pricing and are cleared through brokers and centralized exchanges; meanwhile, on-chain prediction markets automatically execute through smart contracts, relying on oracle-driven clearing, with their pricing (e.g., AMM algorithms) and liquidity pools being completely transparent on-chain. This significantly lowers the barriers to entry but also brings new risks (such as oracle manipulation and contract vulnerabilities), which sharply contrasts with the counterparty risk and leverage risk of traditional finance.

Comparison Table of Prediction Markets and Traditional Financial Derivatives

This unique mechanism is the core of its appeal to mainstream financial institutions. It offers a triple core value that traditional markets cannot reach, which is also the key reason why giants like ICE are making significant bets.

First, it is an advanced “information aggregator” that reshapes the landscape of information equality. In today's world, where AI-generated content, fake news, and information silos are rampant, “truth” has become expensive and difficult to discern. Prediction markets provide a radical solution: truth is not defined by authorities or media, but is “bid” into existence by decentralized, economically driven markets. It responds to the growing distrust (especially among the younger generation) of traditional information sources, offering a more honest alternative that realizes “voting with money”. More importantly, this mechanism transcends the traditional “aggregation of information” itself, achieving real-time pricing of “truth”, forming a highly valuable “real-time sentiment indicator”, ultimately realizing information equality across various dimensions.

Secondly, it will assetize the “information asymmetry” itself, opening up a completely new investment track. In traditional finance, investment targets are stocks, bonds, and other “property certificates.” The prediction market has created a brand new tradable asset - “event contracts.” This essentially allows investors to directly convert their “beliefs” or “information advantages” about the future into tradable financial instruments. For professional information analysts, quantitative funds, and even AI models, this represents an unprecedented profit dimension. They no longer need to express their views indirectly through complex secondary market operations (such as going long/short on related company stocks) but can directly “invest” in the events themselves. The enormous trading potential of this new asset class is the core interest point that attracts exchange operators like ICE.

In the end, it created a risk management market where “everything can be hedged,” greatly expanding the boundaries of finance. Traditional financial instruments struggle to hedge the uncertainty of the “event” itself. For example, how can a shipping company hedge the geopolitical risk of “whether the canal will be closed”? How can a farmer hedge the climate risk of “whether rainfall in the next 90 days will be below X millimeters”? Prediction markets provide a perfect solution for this. They allow participants in the real economy to transform abstract “event risks” into standardized tradable contracts for precise risk hedging. This is equivalent to opening up an entirely new “insurance” market for the real economy, providing a new entry point for empowering the real economy with finance, with potential far beyond imagination.

III. Concerns Beneath Prosperity: The Triple Dilemma of Prediction Markets Yet to Be Resolved

Despite a clear value proposition, the market still faces three interlinked real challenges on the road from “niche” to “mainstream,” which together form the ceiling of industry development.

The first dilemma: the contradiction between “truth” and “decision-maker,” namely the oracle problem. Prediction markets are “outcome-based trading,” but who announces the “outcome”? A decentralized on-chain contract paradoxically relies on a centralized “decision-maker”—the oracle. If the definition of the event itself is ambiguous (such as the definition of “wearing a suit”), or if the oracle is manipulated or malfunctions, the entire market's foundation of trust can collapse in an instant.

The second dilemma: the contradiction between “breadth” and “depth”, that is, the liquidity exhaustion of the long tail. Currently, the prosperity is highly concentrated on major focal events like the “U.S. elections”. However, the true value of prediction markets lies in serving those vertical, niche “long tail markets” (such as the aforementioned agricultural and shipping risks). These markets naturally lack attention, resulting in extreme liquidity exhaustion, making prices highly susceptible to manipulation, thereby losing their actual functions of information aggregation and risk hedging.

The third dilemma: the contradiction between “market makers” and “informed traders”, namely the “adverse selection” problem of AMMs. In traditional DeFi, AMM market makers (LPs) bet on market volatility to earn trading fees. However, in prediction markets, LPs are directly betting against “informed traders”. Imagine a market where the question is whether a “certain new drug will be approved”; when LPs bet against scientists with insider information, this is a guaranteed loss due to “adverse selection”. Therefore, in the long run, automated market makers find it extremely difficult to survive in such markets, and platforms must rely on expensive human market makers to maintain operations, greatly limiting their scale expansion.

Looking to the future, the breakthrough points in predicting the market industry will inevitably revolve around the three major dilemmas mentioned above: more decentralized and manipulation-resistant oracle solutions (such as multi-party verification and AI-assisted review) are the foundation of trust; guiding liquidity into long-tail markets through incentive mechanisms and better algorithms (such as dynamic AMM) is key to realizing its intrinsic value; and more sophisticated market maker models (such as dynamic fees and information asymmetry insurance pools) serve as the engine for its scalability.

IV. Conclusion: From “Probability Game” to “Financial Infrastructure”

The CFTC's approval and ICE's entry are a clear signal: prediction markets are starting to be viewed as a serious financial tool rather than a marginalized “crypto toy.” It is centered around the core value of “aggregating truth” and has “event derivatives” as its financial core, providing a new dimension of risk management for modern finance. Indeed, the path from “probability games” to “financial infrastructure” is not an easy one. As mentioned earlier, the oracle dilemma of “deciders,” the liquidity challenges of long-tail markets, and the “adverse selection” of market makers are all real challenges that the industry must calmly face after the frenzy.

But regardless, a new era that integrates information, finance, and technology has begun. When top-tier traditional capital starts to heavily invest in this sector, the amount it leverages will far exceed a weekly trading volume of $2 billion. This may be a true “singularity” moment – it signifies that a completely new asset class (the pricing power of “belief” and “future”) is being accepted by the mainstream financial system.