Prediction Market: From Curated Platforms to Open Protocols

Author: Noveleader; Source: Castle Labs; Translation: Shaw, Jinse Finance

Over the past 18 months, the monthly trading volume of prediction markets has surged from approximately $2 billion to over $30 billion, rapidly evolving from a niche track into a mature industry ecosystem.

Prediction markets are gradually integrating multiple attributes: they serve as both information trading markets and unique hedging tools, while allowing users to trade and predict outcomes for various events such as sports, politics, and macroeconomics.

The range of trading targets is extremely broad, from niche topics like "what statements a certain podcast will make" to major events like "the Federal Reserve's interest rate cut decision," enabling users to trade almost any event outcome.

The current industry growth is almost entirely dependent on the platform control model: platforms designate a few entities to decide which trading markets are listed. Leading platforms like Polymarket and Kalshi both adopt this model, strictly controlling the tradable targets, and the market has responded positively in terms of trading volume.

While the platform control model is viable, it deviates from the original design intent of early products in this track. The initial goal of prediction markets was to achieve permissionless market creation—allowing anyone to open a trading market on any topic. However, most products built on this concept (Augur, Omen, Zeitgeist, etc.) have failed, continuously facing multiple challenges such as insufficient liquidity, event outcome determination, creator incentives, and regulatory compliance.

The industry has thus concluded that a fully open permissionless model cannot achieve large-scale adoption, but this assertion is now facing a new round of verification. A new wave of products is entering the scene, attempting to solve the various pain points left by early products by optimizing oracle infrastructure, building scalable liquidity mechanisms, and refining product design.

This report outlines the evolution of prediction markets, analyzing the root causes of the initial failure of permissionless models, the reasons for the success of the platform control model, and how current products are returning to the permissionless nature of prediction markets. The report also uses Limitless as a case study: the platform recently launched user-generated market (UGM) functionality, and this article will detail the product adjustments made to accommodate user-created markets.

Early Prediction Markets: The Permissionless Development Path

The odds provided by Polymarket and Kalshi during the 2024 U.S. presidential election were far more accurate than other channels, bringing the value of prediction markets into the spotlight for the first time. Since then, the business boundaries of prediction markets have expanded significantly, no longer limited to election forecasting.

Since the beginning of this year, the nominal trading volume of prediction markets has consistently exceeded $20 billion, surpassing $40 billion last month; the industry's trading volume is mainly contributed by the two leading platforms, Polymarket and Kalshi, forming a duopoly in the industry.

Prediction markets were among the earliest experimental infrastructure components in the crypto space. Relying on the core concepts of decentralization and permissionless participation in the crypto industry, early products all adopted a permissionless construction model.

Augur was one of the first products to launch in this track, officially going live in July 2018. The project team was established in 2014, and its Initial Coin Offering (ICO) was the first in the track, raising $5.5 million.

On the Augur platform, anyone could create prediction markets without permission. This permissionless architecture quickly exposed many risks: within weeks of launch, trading markets involving political assassinations, plane crashes, and other legal red lines appeared on the platform's frontend. In addition, various technical defects emerged one after another, such as high Ethereum gas fees and settlement determination delays (in case of disputes, the determination process could take up to 90 days).

The product had a unique design: the native token REP, holders could stake tokens to report the final outcome of events.

In 2026, the Lituus Foundation restarted the Augur project, transforming it into an event determination infrastructure that other prediction markets could call upon, shifting the competitive focus of the track to underlying infrastructure. The new determination engine mechanism: participants stake their own funds on a certain outcome; whenever a dispute arises, the staking threshold for all parties continues to rise, making the cost of deliberately lying or maintaining false conclusions increasingly high.

Gnosis was another early participant in the track, launching a conditional token framework in 2017, allowing anyone to create markets and convert event outcomes into tradable tokens, a framework later adopted by Polymarket. Gnosis eventually shut down its own prediction market product, facing challenges highly similar to Augur: high Ethereum gas fees, insufficient scalability, and rudimentary user tools.

In 2020, Omen was launched based on the Gnosis framework, supporting permissionless market creation and equipped with an Automated Market Maker (AMM) mechanism. However, its core pain point was liquidity fragmentation: anyone could open a market on any niche topic, leading to hundreds of highly similar trading pools for the same event, with most pools having no liquidity at all.

Additionally, the platform faced oracle-related issues. Omen used the decentralized external oracle Kleros for outcome determination: Kleros uses crowd-sourced jurors' votes, incentivizing jurors to follow the majority opinion, which may not necessarily align with objective facts and the true event outcome. Moreover, the determination process was slow, and gas costs remained high.

These were just technical and liquidity issues; the platform's economic model and product design flaws also had a significant impact.

In early 2018, Stox launched a permissioned prediction market covering sports, finance, and news events, raising $33 million in its ICO. The core reasons for Stox's failure were the lack of a profitable business model and mismatched token economic mechanisms. Although the platform charged trading fees, the revenue was insufficient to support market maker incentives; holders of the platform's native token STX were supposed to share platform revenue through fee distribution, but there was no on-chain mandatory distribution mechanism, failing to continuously attract users and liquidity. Technologically, Stox's determination oracle was highly centralized and completely dependent on the operating company, raising doubts about decentralization.

Another permissionless platform, Hedgehog Markets, was built on the Solana blockchain, allowing users to create markets autonomously. The project launched "principal-protected prediction markets," where users deposited 100 or 1000 USDC as principal, exchanging it for game tokens used for trading predictions; the interest generated from the deposited USDC pool was distributed entirely to winning traders. Although this mechanism seemed innovative, it significantly capped users' profit potential—users could only earn interest income without risking their principal. For investors willing to take principal risk and trade deeply, the mechanism had a clear imbalance in returns, greatly reducing their willingness to participate.

All the above products exposed industry pain points from different dimensions: liquidity exhaustion, disorderly market creation, failed event determination mechanisms, and a series of compliance risks concentrated.

Market Discovery Challenges

After the permissionless creation mechanism was opened, users could create new markets indefinitely, leading to a large number of duplicate trading pools for the same event. For major events, there would often be hundreds of markets with low liquidity, confusing descriptions, and overlapping functions.

A large number of redundant markets severely damage the user experience, easily confusing ordinary users with similar targets. Experienced traders will concentrate their funds on the pool with the best liquidity, but this problem cannot be completely solved. Either limit the duplicate creation of similar markets or optimize the front-end page to hide low-liquidity markets, only allowing users to manually search and retrieve them.

Event Determination Disputes

"The value of a prediction market depends entirely on its ability to determine outcomes based on objective facts."

Augur's REP-staked oracle and Omen's Kleros mechanism both incentivize stakers to vote with the majority. Similarly, Polymarket's UMA optimistic oracle has the same determination flaw: UMA token holders vote to decide the final outcome, but these voters themselves can trade on Polymarket, making it easy for them to intentionally bias a result in favor of their own positions, creating inherent conflicts of interest in voting.

Taking Polymarket as an example, the current dispute determination power is highly concentrated in 9 whale addresses, whose voting results always align with the final winning direction. These large holders can easily manipulate market directions to profit from their own trades.

Recent typical case: the MicroStrategy Bitcoin sell prediction market. From May 26 to 31, 2026, the company indeed sold 32 Bitcoins. The market should have been determined as "Yes"; even after two rounds of dispute appeals, the final determination still resulted in "No."

Liquidity Fragmentation

The permissionless market creation model causes liquidity fragmentation: users create multiple competing markets around the same topic, splitting overall funds. Omen's biggest challenge was liquidity fragmentation, with a large number of users creating highly similar market targets.

There are two solutions to this problem: one is to tighten the market creation review mechanism, for example, by limiting only one market per event; the other is to optimize the front-end page, aggregating all related markets for the same topic on one page, where the platform preferentially recommends the pool with the best liquidity, allowing users to choose their trading targets.

Manipulation Risk Surface

Prediction markets themselves are highly susceptible to manipulation through capital or information means. Users can significantly influence market prices through large holdings while also holding significant voting power in oracles, guiding the determination result towards their own positions.

Both permissioned and permissionless models have this vulnerability, but in the permissionless model, users can create an unlimited number of new markets, dramatically expanding the attack surface.

Recently, a Google employee was investigated by the U.S. Commodity Futures Trading Commission (CFTC) for trading Google search-related prediction targets on Polymarket. The employee used insider information to arbitrage, knowing the final outcome in advance, and profited millions of dollars.

Regulatory Compliance Issues

The U.S. Commodity Futures Trading Commission (CFTC) classifies event contracts as futures contracts, which, according to the Commodity Exchange Act, must be registered on a Designated Contract Market (DCM).

Most early prediction markets did not meet compliance requirements, which was a core obstacle to the industry's large-scale development. In 2022, Polymarket was fined $1.4 million for non-compliant operations. The U.S. market has strong demand for sports betting and event trading, with a huge market size. Therefore, Kalshi has consistently adhered to a compliance-first approach, while Polymarket also acquired QCEX in 2025 to complete U.S. regulatory qualifications.

Lack of Market Creator Incentives

Early permissionless market creation products did not design accompanying economic incentives, failing to consider that market creators could earn revenue from operating targets. This led to a misalignment of interests between the platform and market creators: even if a market's popularity and liquidity continued to increase, creators lacked motivation and could not obtain any economic returns.

Today's leading older platforms have recognized these pain points and have shifted to permissioned market creation models to address the issues.

Permissioned Market Review Model: The Development Path of Polymarket and Kalshi

Polymarket launched in 2020, while Kalshi launched in 2021. The two platforms initially followed different development paths, but recently their strategies in compliance and competing for the U.S. market have gradually converged.

As the industry developed, Kalshi and Polymarket formed a strong competitive relationship.

Before mid-2025, Polymarket held an 80% market share for a long time. Since then, Kalshi has gained a competitive advantage through partnerships with platforms like Robinhood, and now accounts for the majority of prediction market trading volume.

In terms of fee income, Kalshi's current annualized fee income is approximately $2 billion, while Polymarket's is only about $300 million. Additionally, in recent fundraising rounds, Kalshi was valued at $22 billion (currently targeting a valuation of $40 billion), while Polymarket was valued at $15 billion; in terms of price-to-sales (P/S) ratio, Polymarket appears overvalued relative to its competitor.

The two platforms have achieved their current scale because they have largely resolved the various pain points of early prediction markets mentioned earlier.

First, in terms of compliance, Kalshi's compliance-first strategy has been highly effective, helping it achieve rapid growth in the U.S. market. Polymarket has also launched Polymarket U.S., a U.S.-compliant version, gradually rolling out operations in phases.

In terms of market target management, both platforms have dedicated market review teams, adopting a permissioned management model, effectively avoiding problems such as liquidity fragmentation and duplicate markets.

Despite the current success of permissioned platforms, it does not mean that permissionless models cannot coexist in the track. A large number of users still have the need to create their own markets: they want to independently build niche topics of interest, share market creation fee income, and participate in providing liquidity.

Early permissionless prediction markets exposed many defects, which the leading platforms have resolved through permissioned models.

Even after years of operation and significant expansion, users still cannot create and trade markets on any topic autonomously. However, the ultimate vision of prediction markets is to allow anyone to create objective and credible price references for various events through capital betting.

Against this backdrop, as industry solutions continue to improve, the permissionless market creation model is experiencing a strong resurgence.

The next section will analyze the current state of the permissionless self-built market track and the feasible path for user-generated markets (UGM) to achieve scale.

The Permissionless Shift: A New Generation of Prediction Markets

Over the past year, the permissionless market creation track has continued to develop, with many new projects entering, including Melee, HIP-4, XO Market, etc.; The established platform Limitless has also expanded its business, launching user-generated markets (UGM) and entering this track.

Limitless recently launched permissionless market functionality. The platform has not fully opened permissionless creation permissions; for now, it only allows the creation of crypto-related markets, gradually expanding to other categories to gauge market demand and scale the business simultaneously.

Additionally, market creators on the platform can receive 50% of the market's fee income, deeply aligning the interests of the platform and market creators.

HIP-4, or Hyperliquid limited markets, sets a capital entry threshold: market deployers need to stake 1 million HYPE tokens to obtain a market deployment slot. Developers can set their own fee rate up to 50% on top of Hyperliquid's base fee (this feature just launched in early May, with all fees temporarily waived to stimulate trading). The high deployment threshold fundamentally eliminates junk and redundant markets.

HIP-4 markets also have an architectural advantage: they natively run on Hyperliquid's underlying Hypercore, sharing the same order book, account system, and margin engine as the platform's spot and perpetual contracts. Traders do not need to spread funds across multiple accounts to participate in prediction markets or hedge their portfolios.

Both types of platforms use an order book trading model, with liquidity initially provided by market makers (MMs). Other platforms adopt differentiated liquidity solutions.

XO Market uses a Liquidity-Sensitive Logarithmic Market Scoring Rule (LS-LMSR) automated market maker model, an upgraded version of the standard LMSR model commonly used in most prediction markets.

The core difference lies in liquidity management: in the standard LMSR model, market creators must pre-set fixed liquidity parameters, essentially anticipating the trading volume the market can attract. Setting parameters too low makes prices overly sensitive to individual trades; setting them too high requires huge capital. LS-LMSR makes liquidity parameters dynamically adjustable, automatically adapting market depth to trading activity, eliminating the need for manual presets.

When creating a market, users must also provide initial startup liquidity, which helps curb junk markets and alleviate liquidity exhaustion. Therefore, the platform is also called a "conviction market"—participants must commit capital to express their judgment.

In event outcome determination, XO uses a unique three-layer adjudication mechanism. The first layer is an AI priority channel, relying on MODRA (Market Outcome & Dispute Resolution Agent), using artificial intelligence to automatically and quickly determine simple events with clear facts; if disputes arise, it goes to the second layer Senate jury for manual review, and appeals can be made to the Supreme Court. The platform has accumulated over $250 million in trading volume, with over 2,800 trading markets and more than 30k completed trades.

Another prediction market project, Melee, iterates on the parimutuel betting model, naming its proprietary solution PMM. Traditional parimutuel betting pools aggregate all bet funds, with final payouts determined by the total bet amount and the number of winning bets. This model is common in scenarios like horse racing and has a rigid time limit: traders cannot enter or withdraw funds after the event officially starts.

The parimutuel betting model has many inherent flaws: users must wait for the market to settle before redeeming funds, unable to exit mid-process, resulting in insufficient trading flexibility and deterring many traders. Even if the market is still open, traders wanting to exit their positions can only buy the opposing outcome tokens; prediction market prices fluctuate in real-time, and position values change constantly. Additionally, this model requires markets to close trading before the event starts—during the event, participants can concentrate bets on the already advantaged side, exploiting loopholes for arbitrage.

Melee is optimizing the parimutuel betting model by supporting continuous trading to eliminate these shortcomings, but it has not yet officially launched.

Products like Xmarket use a unique initial liquidity mechanism: the minimum capital to create a market is only $1, but it must accumulate a soft liquidity threshold of $100 before the market officially launches; if it fails to meet the threshold, all participant funds are returned. This mechanism filters out meaningless junk markets at a basic level, screening for targets with genuine attention and assessing user demand. However, the low threshold can be easily manipulated by artificial volume; nonetheless, the mechanism effectively activates initial liquidity and market trading activity.

The chart below will intuitively compare the solutions of the above products regarding the various pain points exposed by the first generation of prediction markets. The industry has evolved multiple technical routes for the five core challenges: market creation, outcome determination, liquidity supply, economic incentives, and compliance. This fully reflects the industry's active innovation and diverse experimentation.

User-generated markets (UGM) have encountered many challenges in the past, and there is currently no conclusive evidence that any single solution is optimal—no user-generated market has yet achieved large-scale adoption.

In the next section, we will deeply analyze Limitless, a product recently launched, focusing on its user-generated market operation plan and how it addresses the various industry pain points discussed earlier.

Financial Prediction Markets: The Mechanism of Limitless User-Generated Markets

Limitless entered the prediction market track in 2024 and has achieved steady business growth. Its growth benefits also come from using the platform's native token as a distribution vehicle for user incentives.

Previously, Limitless adopted a permissioned market creation process, with all trading targets designed internally by the platform team. The platform has a standardized outcome determination mechanism: financial markets such as cryptocurrencies, stocks, commodities, and forex use data from Pyth and Chainlink oracles for settlement; sports, politics, and other categories are manually determined by the Limitless team. If a market cannot determine the final outcome, user funds are fully refunded.

Leveraging its established underlying infrastructure, the platform is expanding by launching permissionless market creation functionality.

On June 2, 2026, Limitless launched its first permissionless market category. To prudently advance scaling, the platform did not fully open market creation for any arbitrary topic; instead, it adopted standardized templates and a strategy limited to financial categories. In the initial phase, market creators could only choose targets from a designated pool of crypto assets (Bitcoin, Ethereum, Solana, XRP, Dogecoin), set range-bound price targets (price change range -5% to +5%), and select trading durations from 15 minutes to 1 day. The core purpose of this standardized template mechanism is to eliminate redundant markets with vague descriptions and strange phrasing.

In the first month after the feature launch, the cumulative trading volume of such permissionless markets reached $2.2 million.

User-generated markets (UGM) operate alongside officially created markets, making Limitless a hybrid prediction market platform. Currently, only crypto-category self-built markets are open; the team will verify market demand and stabilize platform operations before gradually introducing other categories.

As the scale of such self-built markets grows, UGMs can evolve into customized hedging tools, allowing users to create exclusive trading markets based on their perpetual/spot positions. After the platform adds more assets and prediction categories, such as stocks, commodities, sports, and esports, this application scenario will further expand.

Early prediction markets exposed various typical pain points. Limitless adopts a differentiated approach to address them, explained in different dimensions as follows:

• Market Creation: Limitless adopts a gradual strategy, initially opening only a few financial targets, primarily the five major crypto assets: Bitcoin, Ethereum, Solana, XRP, and Dogecoin. Users set price ranges and trading durations through standardized templates, significantly reducing markets with obscure descriptions and strange phrasing, and avoiding disputes in the subsequent settlement phase.

• Event Outcome Determination: Since the initial phase only involves crypto-related markets, the platform reuses the same oracle system as permissioned markets, integrating data sources like Chainlink and Pyth to ensure consistent and stable price feeds across the platform. Fully automated oracles eliminate the need for continuous manual monitoring, making market creation and settlement smoother and more flexible.

• Liquidity Supply: Limitless uniformly injects initial startup liquidity into self-built markets. All UGMs are built on a Central Limit Order Book (CLOB) architecture, with fully open underlying infrastructure, allowing any market maker to enter and provide liquidity, achieving efficient price discovery.

• Market Creator Incentives: The viability of user-generated markets depends on the creator's economic return model, i.e., how creators profit. Without substantial revenue sharing, creators will not build markets properly; instead, they will create a large number of targets with vague information, harming market searchability and failing to attract trading volume. The alignment of interests between the platform and creators is key to long-term stable operation. On Limitless, creating a market requires a fee of 100 to 1000 LMTS platform tokens (the fee increases with longer trading durations); creators receive 50% of the fees generated by the market. The team is also collecting user feedback to explore more flexible pricing models.

• Market Searchability: Currently, self-built markets are limited to price movements of a few crypto assets, with clear restrictions on asset types, trading durations, and price movement ranges, minimizing market fragmentation and highly similar duplicate targets at the source.

• Compliance: In early May, Limitless submitted an application to the U.S. Commodity Futures Trading Commission (CFTC), planning to obtain federal regulatory derivatives exchange qualifications in the U.S. (the CFTC has deemed the application complete and entered formal review). Once approved, the platform will have a clear compliance path, enabling large-scale expansion in the U.S. prediction market track, competing directly with Kalshi, compliant Polymarket, and Crypto.com's derivatives business.

Limitless's mechanism provides comprehensive solutions to the various defects of previous generations of prediction markets: standardized templates ensure platform order, eliminating low-quality markets with unclear information; creation fee mechanisms fundamentally filter out junk and volume-manipulated markets; 50% fee sharing gives creators real motivation to actively operate and stimulate trading; at the same time, the platform is awaiting CFTC approval, providing a clear compliance route, offering differentiated advantages over competitors.

However, the creation fee also presents a classic "chicken-and-egg" dilemma: users need to pay a fee upfront to create a market, so they must weigh the upfront cost against the potential revenue from the 50% fee sharing. Revenue depends on sufficient trading volume, while creation costs are fixed.

Under the current Limitless model, the fee to create a market is fixed and only payable in the platform's native token LMTS. Although this mechanism effectively intercepts junk markets, the platform could consider introducing a dynamic fee mechanism: reducing fees when demand for creating certain asset or market categories is low and increasing them when demand is high, optimizing supply-demand balance.

Future Development Outlook

The prediction market track started early, with first-generation products like Augur and Gnosis born during the crypto ICO craze. These early projects faced highly similar industry pain points, mainly concentrated in four areas: liquidity fragmentation, slow settlement determination with frequent disputes, lack of market creator incentives, and prominent compliance risks.

Today's new generation of projects in the permissionless market creation track are clearly aware of these shortcomings and adopt differentiated product designs to specifically address the existing challenges in prediction markets.

XO Market requires creators to inject initial liquidity themselves, combined with a multi-layer adjudication mechanism to strive for the most objective and accurate event outcome; Melee reconstructs the traditional parimutuel betting model to achieve continuous trading; Xmarket sets a soft capital threshold to filter out meaningless junk markets; HIP-4 sets a high token staking entry threshold, allowing only quality markets to launch; Limitless introduces a 50% fee sharing mechanism and charges market creation fees, improving the creator revenue system while preventing junk targets from proliferating.

But the real core difference between these projects is not their individual standalone solutions, but the completeness of their overall solutions across different dimensions. A product that only solves liquidity issues but fails to properly handle the determination process, or only optimizes settlement mechanisms but lacks creator incentive systems, will struggle to achieve large-scale adoption.

Against this background, the permissionless self-built market track is experiencing a new wave of development opportunities and deserves continued attention.

In the long run, the market will select the optimal design scheme that adapts to large-scale adoption, creating the best trading platform that can cover various niche segments and restore objective facts—and this is the core original intent of the underlying architectural design of prediction markets.