What are the core obstacles facing the popularization of prediction markets?

Author: Nick Ruzicka

Compiled by: SpecialistXBT, BlockBeats

Original title: Why Prediction Markets Are Still in the Exploration Stage

Prediction markets are entering a golden moment. Polymarket's coverage of the presidential election has made headlines, and Kalshi's regulatory victories have opened new avenues. Suddenly, everyone wants to talk about this “world truth machine.” But behind this wave of enthusiasm lies a more intriguing question: if prediction markets are indeed so good at predicting the future, why haven't they become widespread?

The answer is not sexy. The problem lies with the infrastructure - in the United States, it is regulation. (For example, Kalshi received approval from the Commodity Futures Trading Commission (CFTC), and Polymarket has established an offshore presence), but the infrastructure issues still persist. Even in regions where prediction markets are legal, the same fundamental challenges remain.

In 2024, the dominant platforms are throwing money at these problems. According to research analyst Neel Daftary from Delphi Digital, Polymarket invested about $10 million in market maker incentives, paying over $50,000 a day at one point to maintain the liquidity of its order book. Nowadays, these incentives have collapsed to just $0.025 per $100 trade. Kalshi has spent over $9 million on similar projects. None of these are sustainable solutions—they are merely band-aids on structural wounds.

Interestingly, the challenges hindering the development of prediction markets are not mysterious. They are clearly defined, interconnected, and— for the right entrepreneurs— easily solvable. After communicating with teams in the field and analyzing the current situation, we identified five recurring issues. It might be helpful to view them as a framework, a common terminology to help us understand why prediction markets, despite their bright prospects theoretically, remain in the testing phase.

These are not just problems; they are also a roadmap.

Question 1: Liquidity Paradox

The fundamental issue lies in liquidity. Or more precisely, it is the chicken-or-egg problem that causes most prediction markets to become ghost towns.

The mechanism is quite simple. When a new market is launched, liquidity is low. Traders face poor execution—high slippage and price impact make trading unprofitable. They exit one after another. Low trading volume scares away professional liquidity providers, as they need stable fees to offset risks. Without liquidity providers, liquidity continues to be scarce. This cycle repeats itself.

The data confirms this. On the Polymarket and Kalshi platforms, the trading volume of most markets is below $10,000. Even larger markets lack sufficient depth to attract institutional investors for meaningful participation. Any large positions lead to double-digit price fluctuations.

The root cause is structural. In a typical cryptocurrency liquidity pool (e.g., ETH/USDC), you deposit two assets and earn fees when traders make trades—regardless of whether the price is unfavorable to you, the value for both sides is preserved. Prediction markets are different: what you hold are contracts, and once they fail, these contracts become worthless. There is no rebalancing mechanism, no residual value—there are only two possibilities: half of the assets go to zero.

Worse yet, you will be “harvested.” As the market approaches settlement and the outcomes become clearer, informed traders know more than you do. They buy the winning side from you at advantageous prices while you are still pricing based on outdated probabilities. This “toxic order flow” continues to bleed market makers.

Polymarket switched from an Automated Market Maker (AMM) model to a central limit order book in 2024 for this reason: the order book allows market makers to immediately cancel quotes upon realizing they are about to be trapped. However, this does not address the fundamental issue—it merely provides market makers with some defensive tools to mitigate losses.

These platforms circumvent this issue by directly paying fees to market makers. However, subsidies cannot be scaled. This model works effectively for flagship markets—presidential elections, major sporting events, popular cryptocurrencies. Polymarket's election market has ample liquidity. Kalshi's NFL market competes with traditional sports bookmakers. The real challenge lies in all the other areas: markets where a large number of prediction markets could play a role, but due to insufficient trading volume, millions of dollars in subsidies cannot be supported.

The current economic model is difficult to sustain. Market makers do not profit from price differences but are compensated by the platform. Even protected liquidity providers, whose losses are capped (a maximum of 4-5% per market), require ecosystem subsidies to break even. The question is: how to make providing liquidity profitable without burning money?

Kalshi's successful model is gradually emerging. In April 2024, they introduced Wall Street's major market maker Susquehanna International Group as the first institutional supplier. The result: liquidity increased by 30 times, contract depth reached 100,000, and the spread was under 3 cents. However, this requires resources that retail market makers cannot provide: dedicated trading platforms, customized infrastructure, and institutional-level capital investment. The key to the breakthrough is not higher rebates, but getting the first institutional investor that truly values prediction markets to see it as a legitimate asset class. Once institutional participation occurs, other institutions will follow suit: lower risk, benchmark pricing, and naturally increasing trading volume.

But there is a problem here: institutional market makers need to meet specific conditions. For Kalshi, this means obtaining approval from the U.S. Commodity Futures Trading Commission (CFTC) and clear regulatory guidelines. However, for crypto-native and decentralized platforms—many of which lack regulatory moats or the scale of large platform developers—this path is not feasible. These platforms face different challenges: how to initiate liquidity without the ability to provide regulatory legitimacy or guaranteed trading volume? For platforms other than Kalshi and Polymarket, the infrastructure issue remains unresolved.

What are entrepreneurs trying?

Quality-weighted order rebate rewards liquidity, thereby improving trading—such as shortening transaction times, increasing quote sizes, and narrowing spreads. While this approach is pragmatic, it does not address the fundamental issue: these rebates still require funding support. Protocol tokens offer an alternative—subsidizing liquidity providers (LPs) through the issuance of tokens instead of utilizing venture capital funds, which is similar to the launch models of Uniswap and Compound. It remains unclear whether prediction market tokens can accumulate sufficient value to sustain issuance in the long term.

Tiered cross-market incentives provide diversified liquidity across multiple markets, spreading risks and making participation more sustainable.

Just-In-Time (JIT) liquidity provides funds only when users need them. The robots monitor large transactions in the liquidity pool, inject concentrated liquidity, charge fees, and withdraw immediately. This method is efficient in terms of capital, but requires complex infrastructure and does not solve the fundamental problem: the risk is still borne by others. JIT strategies have generated over $750 billion in trading volume on Uniswap V3, but trading activity is mainly dominated by well-funded participants, with minimal returns.

The continuous combination market itself challenges the binary structure. Traders are no longer limited to discrete “yes/no” options, but can express views within a continuous range. This gathers liquidity that was originally scattered across related markets (Will Bitcoin rise to $60,000? $65,000? $70,000?). Projects like functionSPACE are building this infrastructure, although it has not yet been tested on a large scale.

The most radical experiments completely abandon the order book. Melee Markets applies Bonding curves to prediction markets - each outcome has its own dedicated curve, early participants can enjoy better prices, and believers will be rewarded. No professional market makers are needed. XO Market requires creators to use LS-LMSR AMM to inject liquidity, and as funds flow in, market depth continually improves. Creators can earn fees, thereby linking the incentive mechanism to market quality.

Both solve the cold start problem without the need for professional market makers. The downside of Melee is its lack of flexibility (positions are locked until settlement). XO Market allows for continuous trading but requires the creator to invest funds in advance.

Question 2: Market Discovery and User Experience

Even if the liquidity issue is resolved, there is a more practical problem: most people cannot find the markets they care about, and even when they do, the experience is quite clumsy.

This is not just a “user experience issue,” but a structural problem. The lack of market discovery directly exacerbates the liquidity issue. Polymarket has thousands of markets online at any given time, but trading volume is concentrated in a very few areas: election markets, major sporting events, and trending cryptocurrency issues. Other markets go unnoticed. Even if a specific niche market has some depth, if users cannot naturally find it, trading volume will remain sluggish, ultimately leading to market makers exiting. A vicious cycle: without market discovery, there is no trading volume, and therefore no sustainable liquidity.

The concentration of market liquidity is extremely severe. During the 2024 election cycle, Polymarket's leading markets account for the vast majority of trading activity. After the election, the platform still sees a monthly trading volume of $650-800 million, but distributed across sports, cryptocurrencies, and viral markets. Other thousands of markets—such as local issues, niche communities, and curiosities—are hardly attended to.

User experience barriers exacerbate this situation. The interfaces of Polymarket and Kalshi are designed for those who already understand prediction markets. Regular users face a steep learning curve: unfamiliar terminology, conversion of odds to probabilities, what “buying a YES” means, and so on. This is acceptable for crypto-native users. But for others, these frictions can stifle conversion rates.

Better algorithms help, but the core issue is distribution: matching thousands of markets to the right users at the right moment, without causing choice paralysis.

What entrepreneurs are trying

The most promising approach is to provide services directly on the platforms that users already have, rather than requiring them to learn new platforms. Flipr allows users to trade markets like Polymarket or Kalshi by directly tagging the bot in their Twitter feed. For example, when users see a market mentioned in a tweet, they can simply tag @Flipr to trade without leaving the app. It embeds prediction markets into the conversational layer of the internet, turning social information streams into trading interfaces. Flipr also offers leverage of up to 10 times and is developing features like copy trading and AI analysis—essentially, it’s working to become a fully functional trading terminal that just happens to exist within Twitter.

The deeper insight is that for startups, distribution is more important than infrastructure. Instead of spending millions to launch liquidity like Polymarket, it is better to integrate existing liquidity and compete on distribution. Platforms like TradeFox, Stand, and Verso Trading are building unified interfaces that can aggregate odds from multiple platforms, route orders to the best trading venues, and integrate real-time news feeds. If you are a serious trader, why bother switching between multiple platforms when you can use a single interface that offers higher execution efficiency?

The most experimental approach is to view market discovery as a social issue rather than an algorithmic one. Fireplace, which is part of Polymarket, emphasizes investing with friends—recreating the vibrancy of collective betting instead of going it alone. AllianceDAO's Poll.fun goes a step further: it builds a P2P market among small circles of friends, allowing users to create markets on any topic, bet directly with peers, and have the outcome decided by votes from the creator or the group. This model is highly localized, highly social, and completely avoids the long tail problem by focusing on community rather than scale.

These are not just improvements in user experience, but also distribution strategies. The platform that ultimately wins may not necessarily have the best liquidity or the most markets, but rather the one that can best answer the question, “How can we deliver the prediction market to the right users at the right time?”

Question Three: Issues with User Expression of Opinions

The following data should concern everyone who is optimistic about prediction markets: 85% of Polymarket traders have a negative account balance.

To some extent, this is inevitable—predicting is inherently difficult. Part of the reason lies in the platform's hard flaws. Because traders cannot effectively express their views, the platform forces them to build suboptimal positions. Do you have a detailed theory? There's no way. You can only make binary bets: buy or not buy, or choose the size of the position. There is no leverage to amplify your beliefs, no way to consolidate multiple views into one position, and no conditional outcomes. When traders cannot effectively express their beliefs, they will either tie up too much capital or have positions that are too small. In either case, the traffic captured by the platform is less.

This issue can be divided into two completely different needs: traders who want to leverage their single bets and traders who want to combine multiple viewpoints for betting.

Leverage: Continuous Settlement Solution

Traditional leverage strategies are not suitable for binary prediction markets. Even if your prediction direction is correct, market volatility may wipe you out before settlement. For example, a leveraged position on “Trump winning” could be liquidated during a week of poor polling results, while Trump ultimately wins in November.

But there is a better way: continuous settlement perpetual contracts based on real-time data streams. Seda is building a true perpetual contract functionality based on Polymarket and Kalshi data, allowing positions to settle continuously instead of waiting for discrete event settlements. In September 2025, Seda enabled perpetual contracts for the real-time odds of the Canelo vs. Crawford fight on the testnet, initially at 1x leverage (, demonstrating the feasibility of this model in sports betting.

Short-term binary options are another increasingly popular trading method. Limitless surpassed $10 million in trading volume in September 2025, offering a binary option on cryptocurrency price trends. This type of market provides implicit leverage through its profit structure, while avoiding the liquidation risk that traders face during the contract's duration. Unlike fixed-income options, binary options settle at a fixed time, but their immediacy of settlement (a few hours or days rather than weeks) can provide retail traders with the quick feedback they need.

Infrastructure is rapidly maturing. Polymarket launched a 15-minute cryptocurrency price market in September 2025 in collaboration with Chainlink. Perp.city and Narrative are experimenting with a continuous information flow trading based on poll averages and social sentiment — a true perpetual contract that never yields binary outcomes.

Hyperliquid's HIP-4 “Event Perpetual Contracts” is a groundbreaking technology—it trades in continuously changing probabilities, not just final outcomes. For example, if Trump's winning probability rises from 50% to 65% after the debate, you can profit without waiting for election day. This addresses the biggest issue of leveraged trading in prediction markets: even if the final prediction is correct, you might still get liquidated due to market fluctuations. Platforms like Limitless and Seda are also gaining increasing attention with similar models, indicating that the market needs continuous trading rather than binary bets.

Combination Betting: Unresolved Issues

Combination betting is different. It expresses complex, multifaceted hypotheses, such as: “If Trump wins, Bitcoin price breaks 100,000 USD, and the Federal Reserve cuts interest rates twice.” Sports betting companies can easily do this because they operate like a centralized institution that manages decentralized risks. Conflicting positions offset each other, so they only need to provide collateral for the maximum net loss, rather than for each individual payout.

Prediction markets cannot do this. They act as custodial agents - once each transaction is completed, it must be fully collateralized. As a result, costs can quickly soar: even for relatively small portfolio bets, market makers need to lock up several orders of magnitude more capital than sports bookmakers would need to assume the same level of risk.

The theoretical solution is a net margin system that only collateralizes the maximum net loss. This requires a complex risk engine, real-time correlation modeling across unrelated events, and potentially centralized counterparties. Researcher Neel Daftary ) suggests initially underwriting a limited market portfolio by professional market makers and then gradually scaling up. Kalshi has adopted this approach—initially offering combination bets on events occurring in the same venue, as the platform can more easily model correlations and manage risks in the context of a single event. This approach is insightful but also acknowledges that a true combination market, the “choose as you like” experience, may be difficult to achieve without centralized management.

Most prediction market entrepreneurs believe that these novel prediction market mechanisms have limitations: for example, leverage restrictions on short-term markets, pre-audited event combinations, or simplified “leveraged trading” that platforms can hedge. Users' expression of opinion issues may be partially resolved (e.g., continuous settlement), but other aspects (such as arbitrary combination markets) remain out of reach for decentralized platforms.

Question 4: Permissionless Market Creation

Addressing market expression issues is one thing, but the deeper structural question is: who has the right to create the market?

Everyone agrees that the prediction market needs diversity—significant regional events, niche community-focused events, and unusual one-off events that traditional platforms would never touch, etc… However, the creation of permissionless markets has always been a challenge.

The core issue is that the lifecycle of trending topics is limited. The most explosive trading opportunities often arise from breaking news and cultural events. For example, a market like “Will the Academy revoke Will Smith's Oscar for slapping Chris Rock?” can generate massive trading volume within hours of the event occurring. However, by the time centralized platforms review and list it, people's interest has long since faded.

However, permissionless creation encounters three issues: semantic fragmentation (ten versions of the same problem split liquidity into useless pools), liquidity cold start (zero initial liquidity makes the chicken or egg problem extreme), and quality control (platforms are filled with low-quality markets, or worse—bets on assassination events that pose legal risks).

Polymarket and Kalshi have both chosen a platform selection model. Their teams will review all markets to ensure quality and clear resolution standards. While this helps build trust, it sacrifices speed—the platform itself becomes a bottleneck.

What are entrepreneurs trying?

Melee adopts a strategy similar to pump.fun to address the cold start phase. Market creators receive 100 shares, while early buyers' shares decrease (3 shares, 2 shares, 1 share…). If the market gains recognition, early participants will receive excess returns - with potential returns of up to 1000 times or more. This is a “market of markets,” where traders predict which markets themselves will grow by establishing positions early. The core idea is that only the highest quality markets - those created by top creators or products that truly meet market demand - can attract sufficient trading volume. Ultimately, high-quality markets will naturally stand out.

The XO Market requires content creators to provide liquidity using LS-LMSR AMM. Creators earn revenue by paying fees, linking the incentive mechanism to market quality. Opinion market platforms like Fact Machine and Opinions.fun allow influencers to monetize cultural capital by creating viral markets around subjective topics.

The theoretically ideal form is a hybrid, community-driven model: users invest reputation and liquidity when creating markets, which are then reviewed by community administrators. This model allows for rapid creation without permission while ensuring the quality of the content. However, no mainstream platform has successfully realized this model yet. The fundamental contradiction still exists: the lack of permission can bring diversity, while administrators can ensure quality. Disrupting this balance will unleash the localized, niche markets that the ecosystem requires.

Question Five: Oracles and Settlement

Even if you solve the issues of liquidity, discovery, expression, and creation, there remains a fundamental dilemma: who decides what happens?

Centralized platforms make decisions by the team, which is efficient but carries a risk of single points of failure. Decentralized platforms require oracle systems to handle any issues without the need for continuous human intervention. However, determining the outcomes of these issues remains the most challenging aspect.

As researcher Neel Daftary (Neel Daftary) articulated for Delphi Digital, the emerging solution is a multi-layered stack that can route problems to the appropriate mechanisms:

Automated data push is used for objective results. Polymarket will integrate Chainlink in September 2025, achieving instant settlement for cryptocurrency price markets. Fast and highly deterministic.

AI Agent is used to answer complex questions. Chainlink tested its AI oracle in 1660 Polymarket markets with an accuracy rate of 89% (with sports events accuracy as high as 99.7%). Supra's Threshold AI oracle uses a multi-agent committee to verify facts and detect manipulation, ultimately providing signed results.

Optimistic oracles like UMA are suitable for ambiguous issues, as they propose certain outcomes, and both parties in dispute stake funds to challenge these outcomes. While it is based on game theory, it is very effective for clear questions.

For high-risk disputes, a reputation-based jury is adopted, where voting rights are linked to on-chain performance records, not just capital.

The infrastructure is rapidly maturing, but market settlement remains the most challenging issue. If the settlement solution goes wrong, it will undermine trust; if the solution is correct, it can be scaled to millions of markets.

Why These Questions Are Important

Liquidity, market discovery, expression of trader views, market creation, and settlement are five interconnected issues. Solving the liquidity problem can enhance market attractiveness, thereby improving the market discovery mechanism. A better market discovery mechanism can attract more users, making permissionless market creation possible. More markets mean a greater demand for robust oracles. This is a system, and currently, there are bottlenecks in this system.

But opportunities also arise: existing projects are trapped in established models. The success of Polymarket and Kalshi is based on certain assumptions about how prediction markets operate. They optimize under established constraints. The new generation of developers has the advantage of completely ignoring these constraints.

Melee can try different Bonding Curves because their goal is not to become Polymarket. Flipr can embed leverage mechanisms into social information streams because they do not need regulatory approval in the United States. Seda can generate perpetual contracts based on continuous data streams because they are not constrained by binary resolution.

This is where the true advantage of entrepreneurs in the prediction market track lies. It is not about copying existing models, but rather directly tackling fundamental issues. These five major problems are basic requirements. Platforms that can solve these issues can not only gain market share but also unleash the full potential of prediction markets as a coordinating mechanism.

The year 2024 will prove that prediction markets can be adopted at scale. The year 2026 will prove that it can operate anywhere.