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Original Article Title: Why Prediction Markets Are Still in Beta
Original Author: Nick Ruzicka
Original Translation: SpecialistXBT, BlockBeats

Prediction markets are experiencing their moment in the spotlight. Polymarket's coverage of the presidential election made headlines, Kalshi's regulatory win opened up new territory, and suddenly, everyone wants to talk about this "world truth machine." However, behind this wave of excitement lies a more intriguing question: If prediction markets are truly so good at forecasting the future, why have they not become mainstream?

The answer is not sexy. The issue lies in infrastructure — in the U.S., it's regulatory (for example, Kalshi receiving approval from the U.S. Commodity Futures Trading Commission (CFTC), Polymarket achieving an offshore setup), but infrastructure issues remain widespread. Even in areas where prediction markets are legal, the same fundamental challenges persist.

The platforms poised to dominate in 2024 are addressing these issues through a money-throwing approach. According to Delphi Digital researcher Neel Daftary's analysis, Polymarket has invested around $10 million in market maker incentives, once paying over $50,000 a day to maintain liquidity in its order book. Today, these incentives have collapsed to just $0.025 for every $100 traded. Kalshi has spent over $9 million on similar initiatives. These are not sustainable solutions — they are merely band-aids on structural wounds.

Interestingly, the challenges hindering the development of prediction markets are not mysterious. They are well-defined, interrelated, and — for the right entrepreneur — easily fixable. After engaging with teams in the space and analyzing the current situation, we found five recurring issues. Let's consider them as a framework, a set of common terms, to help us understand why prediction markets, despite their theoretical promise, are still in the testing phase.

These are not just problems; they are roadmaps.

Issue One: The Liquidity Paradox

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

The mechanism is simple. When a new market launches, liquidity is low. Traders face poor execution — high slippage, price impact making trading unprofitable. They start to exit. Low trading volume scares off professional market makers, as they need stable fees to offset risks. Without liquidity providers, liquidity remains scarce. And so the cycle continues.

The data confirms this. On platforms like Polymarket and Kalshi, the majority of markets have a trading volume below $10,000. Even in larger markets, there is a lack of sufficient depth to attract institutional investors for meaningful participation. Any large position would cause 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 as traders transact—even if the price moves against you, the value of both sides is preserved. Prediction markets operate differently: you hold contracts that become worthless upon failure. There is no rebalancing mechanism, no residual value—resulting in only two outcomes: half of your assets to zero.

What's worse is that you get "front-run." As the market nears settlement and the outcome becomes clearer, informed traders know more than you. They purchase winning positions from you at favorable prices while you are still pricing based on outdated probabilities. This "toxic order flow" continually bleeds market makers.

In 2024, Polymarket switched from an Automated Market Maker (AMM) model to a centralized limit order book for this very 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 some defensive tools to mitigate losses for market makers.

These platforms circumvent this issue by directly compensating market makers. But subsidies are not scalable. This model works well for flagship markets—such as presidential elections, major sports events, and popular cryptocurrencies. Polymarket's election markets have ample liquidity. Kalshi's NFL markets compete with traditional sportsbooks. The real challenge lies in all other aspects: the vast number of markets prediction markets could serve, where the volume is insufficient to support multimillion-dollar subsidies.

The current economic model is unsustainable. Market makers are not profiting from spreads but receiving rewards from the platform. Even for protected liquidity providers with bounded losses (maximum 4-5% loss per market), ecosystem subsidies are required to achieve balance. The question is: how to make providing liquidity profitable without burning through funds?

Kalshi's successful model is gradually unfolding. In April 2024, they onboarded the Wall Street giant market maker Susquehanna International Group, making them the first institutional supplier. The result: liquidity increased by 30 times, contract depth reached 100,000 contracts, and the spread was below 3 cents. However, this requires resources that retail market makers cannot provide: a dedicated trading platform, customized infrastructure, and institutional-level capital input. The key to the breakthrough lies not in higher rebates but in getting the first institution truly interested in prediction markets to consider it a legitimate asset class. Once one institution participates, others will follow suit: lower risk, benchmark pricing, and naturally growing trading volume.

But there is a catch: Institutional market makers need to meet specific requirements. 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 — those lacking regulatory moats or the scale of platforms like Kalshi — this path is not viable. These platforms face different challenges: how to bootstrap liquidity when unable to offer regulatory legitimacy or trading volume certainty? For platforms other than Kalshi and Polymarket, the infrastructure issue remains unresolved.

What Entrepreneurs Are Trying

Quality-weighted order book rebate rewards liquidity to enhance trading — for example, by reducing transaction times, increasing quote sizes, and narrowing spreads. While practical, this approach does not address the fundamental issue: these rebates still require capital support. Protocol tokens offer an alternative — subsidizing liquidity providers (LPs) through token issuance rather than tapping into venture capital funding, mirroring the launch model of Uniswap and Compound. Whether prediction market tokens can accrue enough value to sustain issuance in the long term remains unclear.

Rank-based cross-market incentives provide diversified liquidity across multiple markets, spread risk, and encourage more persistent participation.

Just-in-Time (JIT) liquidity provides funds only when users need them. Bots monitor large trades in the liquidity pool, inject concentrated liquidity, charge fees, and withdraw immediately. This method is capital efficient but requires complex infrastructure and does not address the fundamental issue: risk still lies with others. JIT strategies have generated over $750 billion in trading volume on Uniswap V3, but trading activity is mainly driven by well-capitalized participants, with minimal returns.

Ongoing combinatorial markets themselves challenge the binary structure. Traders are no longer limited to discrete "yes/no" options but express views within a continuous range. This consolidates liquidity that was previously dispersed across related markets (Will Bitcoin reach $60,000? $65,000? $70,000?). Projects like functionSPACE are building this infrastructure, although it has not yet undergone large-scale testing.

The most radical experiments entirely discard the order book. Melee Markets apply a Bonding curve to a prediction market — each outcome has its own curve, with early participants getting a more favorable price, and steadfast believers receiving rewards. No professional market maker required. XO Market mandates creators to inject liquidity using the LS-LMSR AMM, where market depth continuously increases with capital inflows. Creators earn fees, aligning the incentive mechanism with market quality.

Both have solved the cold start problem without the need for professional market makers. Melee's drawback is its lack of flexibility (position locking until settlement). XO Market allows for continuous trading but requires upfront creator funding.

Issue Two: Market Discovery and User Experience

Even if the liquidity problem is solved, there is a more practical issue: most people can't find the markets they care about, and even if they do, the experience is clumsy.

This is not just a "user experience problem" but a structural one. The market discovery issue directly exacerbates the liquidity problem. Polymarket has thousands of markets online at any given time, but trading volume is concentrated in a few areas: election markets, major sports events, and hot cryptocurrency issues. Other markets go unnoticed. Even if a niche market has some depth, if users cannot naturally find it, trading volume remains low, leading to market maker exodus. Vicious cycle: lack of market discovery means no trading volume, which means no sustainable liquidity.

Market liquidity concentration is extremely severe. In the 2024 election cycle, Polymarket's top markets captured the majority of trading activity. Post-election, the platform still sees monthly trading volumes of $6.5-8 billion, but distributed across sports, cryptocurrency, and viral markets. The other thousands of markets—such as local issues, niche communities, oddities—get almost no attention.

User experience barriers exacerbate this situation. The interfaces of Polymarket and Kalshi are designed for those already familiar with prediction markets. Ordinary users face a steep learning curve: unfamiliar terms, odds-to-probability conversion, what "buying a YES" means, and so on. For native cryptocurrency users, this is acceptable. But for others, this friction kills conversion rates.

While better algorithms help, the core problem is distribution: matching thousands of markets to the right users at the right time without causing decision paralysis.

What Entrepreneurs Are Trying

The most promising approach is to provide the service directly on platforms users already have, rather than making them learn new platforms. Flipr allows users to trade in markets like Polymarket or Kalshi directly within their Twitter feeds by tagging the bot. For example, when a user sees a market mentioned in a tweet, they simply tag @Flipr to trade without leaving the app. It embeds prediction markets into the Internet's conversational layer, turning social information flows into trading interfaces. Flipr also offers up to 10x leverage and is developing features like copy trading and AI analytics—basically, it is striving to become a full-featured trading terminal that happens to reside within Twitter.

A deeper insight is that for early-stage companies, distribution is more critical than infrastructure. Rather than spending millions of dollars to bootstrap liquidity like Polymarket, it is better to integrate existing liquidity and compete on distribution. Platforms such as TradeFox, Stand, and Verso Trading are building unified interfaces that can aggregate odds from multiple platforms, route orders to the best execution 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 for more efficient execution?

The most experimental approach is to view market discovery as a social rather than algorithmic problem. Housed under Polymarket, Fireplace emphasizes co-investment with friends—recreating the vibrancy of communal betting rather than going it alone. AllianceDAO's Poll.fun goes further: it builds P2P markets among small circles of friends, where users can create markets on any topic, directly bet with peers, and have the outcome decided by the creator or group vote. This model is highly localized, highly social, and by focusing on community rather than scale, it completely bypasses the long tail issue.

These are not just improvements in user experience but also in distribution strategy. The eventual winning platform may not necessarily have the best liquidity or the most markets but the one that can best answer the question, "How can we deliver prediction markets to the right users at the right time?"

Question Three: User Expression Problem

The following data should be concerning for all prediction market enthusiasts: 85% of Polymarket traders have a negative account balance.

To some extent, this is inevitable—prediction is inherently difficult. But part of the reason lies in the platform's fundamental flaws. Since traders cannot effectively express their views, the platform forces them into suboptimal positions. Have a nuanced theory? Too bad. You can only make binary bets: buy, don't buy, or choose position size. There is no leverage to amplify your beliefs, no way to integrate multiple views into one position, and no conditional outcomes. When traders cannot effectively express their beliefs, they either tie up too much capital or have too small a position. In either case, the platform captures less traffic.

This issue can be divided into two dramatically different demands: traders who wish to use leverage to magnify a single bet and those who want to combine multiple views into a bet.

Leverage: Continuous Settlement Solution

Traditional leverage strategies do not apply to the binary prediction market. Even if your prediction direction is correct, market volatility may wipe you out before settlement. For example, a leveraged "Trump victory" position could be liquidated within a week of poor poll results, only for Trump to emerge victorious in November.

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

Short-term binary options are another increasingly popular trading method. In September 2025, Limitless surpassed $10 million in trading volume, offering cryptocurrency price trend binary options. These markets provide implicit leverage through their payoff structure while avoiding traders' exposure to liquidation risks during the contract's duration. Unlike fixed-income options, binary options settle at fixed times, but their immediate settlement (within hours or days, not weeks) can provide retail traders with the quick feedback they desire.

Infrastructure is rapidly maturing. In September 2025, Polymarket partnered with Chainlink to launch a 15-minute cryptocurrency price market. Perp.city and Narrative are experimenting with continual information flow trading based on poll averages and social sentiment—a true perpetual contract that never settles with a binary outcome.

Hyperliquid's HIP-4 "Event Perpetual Contract" is breakthrough technology—it trades on evolving probabilities, not just the final outcome. For example, if Trump's win probability increases from 50% to 65% after a debate, you can profit without waiting for election day. This addresses the key issue of leverage trading in prediction markets: even with a correct final prediction, you may be liquidated due to market fluctuations. Platforms like Limitless and Seda are also gaining increasing attention with similar models, indicating the market's need for continuous trading rather than binary opposing bets.

Portfolio Betting: The Unsolved Challenge

Portfolio betting, on the other hand, is different. It expresses complex, multifaceted assumptions, such as "Trump victory, Bitcoin price breaks $100,000, Fed cuts rates twice." Sportsbooks can easily do this because they operate like a centralized entity managing dispersed risks. Conflicting positions offset each other, so they only need to collateralize for the maximum net loss, not for each individual payout.

Prediction markets cannot achieve this. They act as custodial agents - once a transaction is complete, full collateral must be posted. As a result, costs escalate quickly: even for small-scale portfolio bets, market makers need to lock up funds several orders of magnitude more than what a sportsbook would need to take on equivalent risk.

Theoretical solutions involve a net margin system that only collateralizes the maximum net loss. However, this requires a complex risk engine, real-time correlation modeling across unrelated events, and possibly centralized trading counterparties. Researcher Neel Daftary suggests having professional market makers initially underwrite a limited market portfolio and then gradually scale up. Kalshi has adopted this approach - initially offering combination bets on events at the same venue as the platform finds it easier to model correlation and manage risk in the backdrop of a single event. While insightful, this approach also acknowledges that a true combination market, the "choose your own" experience, may be hard to achieve without centralized oversight.

Most prediction market entrepreneurs see these novel prediction market plays as having limitations: for instance, leverage restrictions on short-term markets, pre-audited event combinations, or a simplified version of hedgable "leverage trading." User-expressed views might partially address issues (e.g., continuous settlement), but others (e.g., arbitrary combination markets) remain largely out of reach for decentralized platforms.

Issue Four: Permissionless Market Creation

Solving the market expression issue is one thing, but a more profound structural problem is: who has the right to create markets?

There is a consensus that prediction markets need diversity - significant region-specific events, events of interest to niche communities, quirky one-off events traditional platforms would never touch, etc. However, permissionless market creation has always been a challenge.

The core problem is that the lifecycle of hot-button issues is finite. The most explosive trading opportunities often arise in breaking news and cultural events. For example, a market like "Will the committee revoke Will Smith's Oscar for slapping Chris Rock?" would see significant volume within hours of the event. But by the time centralized platforms vet and go live, interest has waned.

However, entirely permissionless creation faces three issues: semantic fragmentation (ten versions of the same issue fragment liquidity into useless pools), liquidity cold starts (zero initial liquidity poses an extreme chicken-or-egg problem), and quality control (platforms are flooded with low-quality markets, or worse - bets on assassination events that pose legal risks).

Both Polymarket and Kalshi have opted for a curated platform model. Their teams 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 Entrepreneurs Are Trying

Melee adopts a strategy similar to pump.fun to address the cold start phase. Market creators receive 100 shares, with early buyers' shares decreasing (3 shares, 2 shares, 1 share...). If the market gains acceptance, early participants receive an outsized return—potential returns of up to 1000x or more. This is a "market of markets," where traders predict which markets will grow through early positioning. The core idea is that only the highest-quality markets—built by top creators or products that truly meet market demand—can attract sufficient trading volume. Ultimately, quality markets will naturally rise to the top.

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

The theoretically ideal form is a hybrid, community-driven model: users stake reputation and liquidity when creating markets, which are then reviewed by community moderators. This model enables fast permissionless creation while ensuring content quality. However, no mainstream platform has successfully implemented this model. The fundamental contradiction remains: permissionlessness brings diversity, while moderation ensures quality. Breaking this balance will unlock the localization and niche markets necessary for the ecosystem.

Issue Five: Oracle and Settlement

Even if you solve liquidity, discovery, expression, creation—all these issues—a fundamental problem remains: who determines what happened?

Centralized platforms have teams making decisions, which is efficient but carries the risk of a single point of failure. Decentralized platforms rely on oracle systems to handle any issues without ongoing manual intervention. Yet, deciding the outcomes of these issues remains the most challenging.

As researcher Neel Daftary outlined for Delphi Digital, emerging solutions involve a multi-layered stack that can route issues to the appropriate mechanism:

For objective outcomes, automated data feeds are used. In September 2025, Polymarket integrated Chainlink, enabling real-time settlement of cryptocurrency price markets. Fast and highly deterministic.

An AI Agent is used to answer complex questions. Chainlink tested an AI oracle across 1660 Polymarket markets, achieving an 89% accuracy rate (with sports event accuracy reaching 99.7%). Supra's Threshold AI oracle employs a multi-agent committee to validate facts and detect manipulation, ultimately providing a signed result.

Omniscient oracles like UMA are suitable for ambiguous questions, where they propose certain outcomes and disputing parties then stake funds to challenge those outcomes. While based on game theory, it is effective for clear questions.

For high-stakes disputes, reputation-based juries are used, where voting power is tied to on-chain performance records, not just capital.

Infrastructure is rapidly maturing, but market settlement remains the most challenging problem. If the settlement scheme fails, it undermines trust; if successful, it can scale to millions of markets.

Why These Issues Matter

Liquidity, price discovery, trader expression, market creation, and settlement are interrelated. Solving the liquidity issue enhances market attractiveness, leading to improved price discovery mechanisms. Better price discovery brings more users, enabling permissionless market creation. More markets mean a greater demand for robust oracles. It's a system, and currently, this system has bottlenecks.

But opportunities arise: existing projects are stuck in established patterns. The successes of Polymarket and Kalshi are built on certain assumptions about how prediction markets operate. They are optimizing within existing constraints. However, the next generation of developers have the advantage of ignoring these constraints entirely.

Melee can explore different Bonding Curves as they are not aiming to become Polymarket. Flipr can embed leverage mechanisms in social feeds as they do not require regulatory approval in the U.S. Seda can generate perpetual contracts based on continuous data streams as they are not confined by binary resolution.

This is where the real advantage of prediction market startup founders lies. Not in replicating existing patterns, but in directly tackling fundamental issues. These five key issues are the baseline requirements. Platforms that can address these issues can not only capture market share but can also unlock the full potential of prediction markets as a coordination mechanism.

2024 proved that prediction markets can be widely adopted. 2026 will prove that they can operate anywhere.

「Original Article Link」

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