1. Historical Evolution and Industrial Landscape of Prediction Markets

Prediction markets, as a mechanism for pricing future events, have evolved over more than thirty years from academic experiments and gray-area gambling to an independent asset class that combines informational value, liquidity scale, and financial attributes. These markets are centered around the structure of "price equals probability," using real funds to reflect the collective judgment of market participants regarding the probability of a certain event occurring: a binary contract that settles at $1 or $0 has a trading price between $0 and $1, directly presenting market consensus. For example, when the price of a certain event contract is $0.62, it means "the market believes the probability of the event occurring is about 62%." This mechanism of aggregating views from decentralized participants essentially constructs a quantifiable, verifiable, and real-time updated public good of information, which is not only different from recreational gambling but also distinct from the dealer structure of binary options. Instead, it is a hybrid financial infrastructure that combines market efficiency, collective wisdom, and dynamic trading capabilities. Unlike the zero-sum mechanism of gambling, the overall structure of prediction markets presents a "positive-sum information output": the platform charges a small fee, while the core value comes from the probability signals aggregated by the market. These signals can be cited by the media, modeled by research institutions, used by companies for risk management, and directly embedded as pricing nodes in other financial derivatives and Web3 protocols, possessing strong externalities and social value.

The foundation of modern prediction markets can be traced back to the Iowa Electronic Markets (IEM) established in 1988. This early experiment led by academic institutions allowed participants to trade contracts representing the probability of candidates winning or their vote shares with small amounts of money, clearly aimed at improving prediction accuracy. Numerous studies have shown that between 1988 and 2004, IEM's predictions for U.S. elections significantly outperformed most traditional polls, with its probability signals reflecting real trends earlier.

What truly propelled the industrial leap of prediction markets was the emergence of a new generation of platforms after 2020, underpinned by the maturity of Layer 2, stablecoins, and cross-chain infrastructure, exemplified by the "dual oligopoly" formed by Polymarket and Kalshi in 2024-2025. Polymarket represents the comprehensive maturity of the decentralized route: based on Polygon and multi-chain expansion, it has achieved a product form that combines experience and censorship resistance through an order book model (CLOB), low-friction deposits, gas-free trading, and UMA's optimistic oracle. During the 2024 U.S. election, its monthly trading volume reached $2.6 billion, with annual cumulative trading exceeding $10 billion. Its significant media and social network dissemination effects constructed a flywheel of "opinions → positions → dissemination," making it the preferred platform for Web3 users entering prediction markets. Even after being penalized by the CFTC, its acquisition of the licensed exchange QCEX to re-establish its presence in the U.S. market further indicates that compliance has become the core direction of the sector's development. In parallel, Kalshi represents a completely different path: compliance, regulatory certainty, and penetration into mainstream financial channels. Kalshi obtained CFTC Designated Contract Market (DCM) status in 2021 and subsequently secured a clearing license (DCO), becoming a federally compliant event contract exchange in the U.S. Its centralized matching structure is closer to traditional exchanges, supporting deposits in USD and USDC, and directly providing event contracts through partnerships with brokers like Robinhood on mainstream investor interfaces. After the explosion of sports and macroeconomic contracts in 2025, Kalshi's weekly trading volume once reached $800-900 million, capturing a market share of 55-60%, effectively becoming the infrastructure for domestic prediction markets in the U.S. Unlike Polymarket's on-chain openness, Kalshi's advantage lies in the institutional participation, brand trust, and traditional channel distribution capabilities brought by compliance certainty, with both forming an orthogonal dual core of "on-chain composability" and "compliance usability."

Beyond the dual oligopoly, new platforms and vertical tracks are rapidly emerging, further expanding market boundaries. Opinion leveraged BSC ecosystem traffic and airdrop incentives to surpass hundreds of millions in scale within its first week of launch; Limitless meets the demand of crypto traders for volatility products through short-cycle price predictions within the Base ecosystem; PMX Trade in the Solana ecosystem directly tokenizes Yes/No contracts, exploring deep integration of prediction markets and DEX liquidity. Sports-related platforms like SX Network, BetDEX, and Frontrunner have become the largest vertical scenes due to their high frequency and stickiness, while "creator economy prediction markets" represented by Kash, Melee, and XO Market directly financialize opinions, turning KOL views into tradable assets. Meanwhile, tools like Flipr, Polycule, and okbet are rapidly developing as another direction, compressing complex prediction interactions into chat interfaces, providing cross-platform price tracking, arbitrage, and fund flow monitoring, forming a new ecosystem of prediction markets akin to "1inch + Meme Bot."

Overall, prediction markets have gradually completed three leaps in their thirty-year evolution: from academic experiments to commercial gambling exchanges, then from on-chain experiments to dual-core platforms of compliance and scale, and finally diversifying into highly varied forms in vertical scenes such as sports, crypto markets, and the creator economy. The window for general platforms is narrowing, while true incremental growth is more likely to come from deeply verticalized scenes, the data and tool layers surrounding the ecosystem, and the degree of integration between prediction market signals and other financial systems. Prediction markets are accelerating their transition from a "gray-area toy market" to "an important infrastructure of the global information and financial system."

2. Structural Challenges of Prediction Markets

After more than thirty years of iteration, prediction markets have transitioned from experimental products to a financial-grade infrastructure stage with gradual participation from global users and institutions. However, their development still faces three major structural bottlenecks that cannot be circumvented: regulation, liquidity, and oracle governance. These three are not independent; they are interlinked and mutually restrictive, determining whether prediction markets can grow from "gray innovation" to a "compliant and transparent global information and derivatives system." Regulatory uncertainty limits institutional capital entry, insufficient liquidity undermines the effectiveness of probability signals, and if oracle governance cannot provide a reliable adjudication mechanism, the entire system may fall into a quagmire of manipulation and result disputes, failing to become a trusted source of information for the external world.

Regulatory issues are the primary bottleneck for prediction markets, with their complexity particularly pronounced in the U.S. Whether prediction markets are classified as commodity derivatives, gambling, or a type of securities investment contract corresponds to different regulatory paths. If viewed as commodities and derivatives, they fall under CFTC regulation and are treated similarly to futures exchanges, requiring the application for DCM (Designated Contract Market) and DCO (Clearing Organization) licenses, which have high thresholds and costs. However, if successful, they gain legal status at the federal level, as exemplified by Kalshi. If classified as gambling, they must apply for gambling licenses in all 50 states, leading to exponentially rising compliance costs, which almost precludes the possibility of a national platform. If considered securities, it triggers strict SEC regulation, posing significant potential risks for DeFi prediction protocols with token designs or yield promises. The fragmented and overlapping U.S. regulatory system places prediction markets in a repeatedly contentious gray area. For instance, the lawsuit between Kalshi and the New York Gaming Commission centers on whether the CFTC has exclusive regulatory authority over event contracts. This ruling not only affects whether Kalshi can operate smoothly nationwide but also concerns the institutional trajectory of U.S. prediction markets for the next decade. Additionally, the CFTC's enforcement actions against Polymarket and its classification of Crypto.com's sports event contracts indicate that regardless of whether a platform's shell is "decentralized," as long as it provides a front end to U.S. users and facilitates transactions, it will essentially be viewed as an unregistered compliance activity based on derivatives or binary options, incurring corresponding legal responsibilities.

Outside the U.S., jurisdictions around the world generally follow a "binary framework": either incorporating prediction markets into gambling regulatory systems or into financial derivatives systems, with very few jurisdictions creating new laws specifically for prediction markets. Countries like the UK and France maintain an open attitude towards event betting under online gambling regulations, but regulatory tools such as geographic blocking, payment bans, and ISP blocking make it difficult for prediction market platforms to reach mainstream users before obtaining licenses. For entrepreneurs, the "technological neutrality" defense can no longer evade legal risks; offshore companies, DAOs, or decentralized front ends cannot ensure immunity from regulation. There are only three paths for long-term survival: either embrace licensing head-on like Kalshi; maintain complete offshore and fully open-source decentralization while accepting the cost of absence from the mainstream market; or pivot to building compliant infrastructure, providing technical services (KYC, risk control, prediction data API, etc.) for licensed institutions. Regulatory uncertainty limits institutional capital participation and restricts the depth of connections with traditional finance, making it difficult for prediction markets to truly scale.

3. Value Innovation and Future Opportunities in Prediction Markets

After several rounds of reshuffling under the constraints of regulation, liquidity, and oracle governance, truly valuable innovations in prediction markets are beginning to shift from "single platform competition" to the "primitive layer" and "infrastructure layer." Simply put, what has been done over the past decade is "creating a new prediction market website"; while in the next decade, incremental growth is more likely to come from "abstracting event contracts into informational derivatives and embedding them into the entire DeFi and financial system," transforming prediction markets from an application into a piece of DeFi Lego that can be assembled. The binary contracts of events themselves are just the starting point; once contracts become standardized, composable, and collateralizable asset units, a whole suite of derivatives such as perpetuals, options, indices, structured products, lending, and leverage can naturally grow around them. The "event markets" explored in designs like D8X, Aura, and parts of dYdX v4 essentially project "whether it happens" into a price space of 0-1, further allowing for high-leverage trading, enabling traders to not only bet on event directions but also trade volatility and sentiment. Protocols like Gondor allow users to collateralize Polymarket's YES/NO shares to borrow stablecoins, transforming originally static locked long-term event positions into reusable collateral assets. The protocol then dynamically adjusts LTV and liquidation logic based on market probabilities, financializing "opinions" into reusable capital tools. Further up are index and structured products similar to PolyIndex, which bundle a basket of events into ERC-20 index tokens, allowing users to gain comprehensive exposure to a certain theme with one click, such as the "U.S. Macro Policy Uncertainty Index" or "AI Regulation and Subsidy Implementation Event Basket." In the context of asset management, prediction markets are no longer isolated markets but become a new asset class that can be included in portfolio configurations by asset managers.

The truly valuable "shovel opportunities" for the medium to long term are concentrated in four areas. The first is the truth and rules layer, which involves the new generation of oracle and arbitration protocols. How to avoid the reoccurrence of disputes like those seen with UMA in terms of economic incentives and governance structures, and how to use standardized, modular tools to help ordinary users create "clearly defined and arbitrable" event markets, will directly determine the extent to which prediction markets can be trusted by institutions and public sectors. The second is the liquidity and capital efficiency layer, which includes AMMs customized for prediction markets, unified liquidity pools, collateralized lending, and yield aggregation protocols that can transform dormant event positions into reusable assets. This not only brings a new asset class to DeFi but also provides a thicker economic moat for the platforms. The third is the distribution and interaction layer, which encompasses social embedded SDKs/APIs, one-click media access components, professional terminals, and strategy tools. These directions determine the "entry forms" of prediction markets and who can stand at the intersection of information and trading to earn continuous transaction fees and technical service fees. The fourth is the compliance technology and security layer, which focuses on refined geofencing, KYC/AML, risk control monitoring, and automatic reporting across multiple jurisdictions, helping licensed institutions safely access prediction market data within the regulatory framework, allowing event prices to truly enter asset management, investment research, and risk management processes. Finally, the rise of AI provides a new closed loop for binding prediction markets with capital markets. On one hand, AI models can act as "super traders" in prediction markets, trading with stronger information processing and pattern recognition capabilities, thereby improving market pricing efficiency; on the other hand, prediction markets can serve as a "real-world scoring field" for AI capabilities, quantifying model quality through real profits and losses, long-term calibration metrics, and providing an external, hard-constraint evaluation system for "AI research reports, AI investment advisors, and AI strategies." For investors, projects that understand derivative design, can safely utilize event prices within regulatory boundaries, and build bridges between AI and traditional finance are likely to grow into key infrastructure assets in the entire "informational derivatives" sector in the next cycle.

4. Conclusion

From the betting on papal elections in the 16th century, to predicting presidential outcomes on Wall Street in the 20th century, to IEM, Betfair, Polymarket, and Kalshi, the evolution of prediction markets is essentially a history of humanity's attempts to approach "more accurate probabilities" through systems and incentives. Today, as mainstream media trust continues to decline and social platform signals are mixed with noise, prediction markets materialize the "cost of saying the wrong thing" through prices, compressing scattered global information and judgments into a quantifiable, verifiable probability curve. It is not a perfect truth machine, but it provides a more verifiable public signal than slogans and emotions. Looking ahead, the ultimate fate of prediction markets may not be the emergence of a single platform larger than Polymarket, but rather becoming an "information and opinion interaction layer" embedded in social media, news websites, financial terminals, games, and creator tools; ubiquitous like a "like button," allowing every opinion to naturally correspond to a tradable probability; continuously producing "collective predictions" constrained by incentives in the game involving both humans and AI, feeding back into decision-making and governance. To truly reach that point, the sector must first pass through three gates: the regulatory threshold, the liquidity threshold, and the oracle governance threshold. These three gates are the stage for the next generation of infrastructure and emerging primitives. For entrepreneurs and investors, prediction markets are by no means a sector that has already been "completed"; on the contrary, it has just completed the first stage from concept to industrial prototype. What will truly determine whether it can become "Web3-level information infrastructure" is the ongoing innovation and institutional adaptation around rules, liquidity, and oracle governance in the next 5-10 years. In this information war worth billions of dollars, the winners are often not the loudest voices, but those builders who quietly solidify the "shovels" and "roads."

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