Why is it said that prediction markets are still in the exploratory stage? A look at the five major challenges of prediction markets

This article explores the five major challenges facing prediction markets: liquidity paradox, market discovery dilemma, user perspective expression restrictions, permissionless market creation issues, and oracle settlement difficulties. The content originates from an article by Nick Ruzicka, organized, translated, and written by BlockBeats. (Background: Multicoin Capital: What other entrepreneurial opportunities exist in prediction markets?) (Additional context: From Trump and CZ to Wall Street, why is everyone betting on “prediction markets”?) Prediction markets are experiencing a moment of prominence. Polymarket’s coverage of the presidential election made headlines, Kalshi’s regulatory victory opened new avenues, and suddenly, everyone wants to talk about this “truth machine.” But behind this wave of enthusiasm lies a more interesting question: if prediction markets are truly so good at forecasting the future, why haven’t they become widespread? The answer is not glamorous. The problem lies in infrastructure—in the US, regulation (for example, Kalshi’s approval by the CFTC, and Polymarket’s offshore setup)—but infrastructure issues remain widespread. Even in regions where prediction markets are legal, fundamental challenges persist. Leading platforms in 2024 are trying to solve these problems through significant investments. According to Neel Daftary, a researcher at Delphi Digital, Polymarket has invested about $10 million in market maker incentives, sometimes paying over $50,000 daily to maintain liquidity. Today, these incentives have collapsed to just $0.025 per $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 clearly defined, interconnected, and— for the right entrepreneurs—easily solvable. After engaging with teams in this field and analyzing the current state, we identified five recurring issues. Think of them as a framework, a shared terminology to help us understand why prediction markets, despite their promising prospects, remain in testing phases. These are not just problems—they are also a roadmap.
Challenge 1: Liquidity Paradox
The fundamental issue is liquidity—or more precisely, 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 trades unprofitable. They exit. Low trading volume discourages professional liquidity providers, who need stable fees to offset risks. Without liquidity providers, liquidity remains scarce. This cycle repeats. Data confirms this: on Polymarket and Kalshi platforms, most markets have trading volumes below $10,000. Even larger markets lack sufficient depth to attract institutional investors for meaningful participation. Any large position causes two-digit percentage price swings.
The root cause is structural. Unlike typical crypto liquidity pools (e.g., ETH/USDC), where depositing two assets earns fees from traders—preserving value even if prices move unfavorably—prediction markets are different: you hold contracts, which become worthless once they fail. Without a rebalancing mechanism or residual value, half of the assets can become zero. Worse, traders can “harvest” others. As markets approach settlement and outcomes become clearer, informed traders know more than you. They buy winning outcomes at favorable prices while you price based on outdated probabilities. This “toxic order flow” causes market makers to bleed. In 2024, Polymarket switched from an AMM model to a central limit order book, allowing market makers to cancel quotes when they realize they are about to be trapped. But this doesn’t solve the core problem—it only provides some defense tools to slow losses.
These platforms try to bypass this issue by paying market makers directly. But subsidies cannot scale. For flagship markets—presidential elections, major sports events, popular cryptocurrencies—this approach works well. Polymarket’s election markets are highly liquid. Kalshi’s NFL markets compete with traditional sportsbooks. The real challenge lies elsewhere: many prediction markets could be useful, but trading volume isn’t enough to support millions of dollars in subsidies.
Current economic models are unsustainable. Market makers don’t profit from spreads but from platform rewards. Even protected liquidity providers with bounded losses (max 4-5% per market) need ecosystem subsidies to break even. The question is: how to make liquidity provision profitable without burning money? Kalshi’s success model is gradually emerging. In April 2024, they introduced a major Wall Street market maker, Susquehanna International Group, making it the first institutional provider. As a result, liquidity increased 30-fold, contract depth reached 100,000, and spreads dropped below 3 cents. But this requires resources retail market makers cannot provide: dedicated trading platforms, customized infrastructure, and institutional-level capital. The breakthrough depends not on higher rebates but on attracting genuine institutional investors who see prediction markets as a legitimate asset class. Once institutions participate, others will follow—reducing risk, establishing benchmarks, and naturally increasing trading volume.
However, there’s a catch: institutional market makers need to meet specific conditions. For Kalshi, this means obtaining approval from the CFTC and clear regulatory guidelines. For native crypto and decentralized platforms—many of which lack regulatory shields or large-scale operations—this path is not feasible. These platforms face different challenges: how to bootstrap liquidity without regulatory legitimacy or guaranteed trading volume? For platforms other than Kalshi and Polymarket, infrastructure issues remain unresolved.
What Entrepreneurs Are Trying
Efforts include quality-weighted order rebate incentives to improve trading—such as shortening trade times, increasing quote sizes, and narrowing spreads. While pragmatic, these do not address the fundamental problem: rebates still require funding. Protocol tokens offer an alternative—subsidizing liquidity providers (LPs) through token issuance instead of venture capital, similar to Uniswap and Compound’s startup models. Whether prediction market tokens can accumulate enough value to sustain long-term issuance remains uncertain.
Tiered cross-market incentives diversify liquidity across multiple markets, dispersing risk and fostering more durable participation.
Real-time liquidity (JIT) provision supplies funds only when users need them. Bots monitor large trades in liquidity pools, …