The Next Stop for Prediction Markets: Evolutionary Paths and Ultimate Challenges
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The Next Stop for Prediction Markets: Evolutionary Paths and Ultimate Challenges
The trend is set, but challenges remain.
Navigating Web3 tides with focused insightsBy: KarenZ, Foresight News
Prediction markets aggregate collective intelligence and quantify uncertainty, providing a dynamic information pricing tool for politics, sports, finance, and crypto. They enable broad aggregation and sharing of information and are even referred to as the "truth engine."
More people are participating not only for potential financial returns but also to leverage collective wisdom through these platforms for more accurate foresight into future events.
In my previous article, “The Prediction Market Landscape: Two Giants Battle It Out—How Can Newcomers Break Through?”, I reviewed emerging prediction market players from Polymarket and Kalshi to Limitless and Opinion, as well as prediction-related expansions by Robinhood and Jupiter.
This article explores the development trends and core challenges of Web3 prediction markets based on the current landscape and the directions taken by new platforms.
What trends will prediction markets follow?
1. Regulatory Frameworks: From Chaos to Divergence, Compliance Paths Becoming Clearer
Global regulatory attitudes toward prediction markets show significant regional differences. The U.S., through the Commodity Futures Trading Commission (CFTC), has approved platforms like Kalshi and Polymarket, setting a benchmark for compliance.
In contrast, the EU and many parts of Asia still view prediction markets as high-risk, with most countries classifying them as "gambling" and banning cryptocurrency settlements.
Going forward, prediction markets must strike a balance between "decentralization" and "local compliance," such as using geofencing to restrict access in certain regions or partnering with locally licensed institutions to meet regulatory requirements.
2. AI Empowerment: From Tool to Participant, Reshaping Market Efficiency
As predictors: Machine learning analyzes historical data, social media sentiment, and real-time events to generate high-precision forecasting models, lowering the barrier to entry for ordinary users.
As infrastructure:
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AI oracles can automatically collect multi-source data and verify outcomes, reducing human intervention and enabling markets to access more accurate and timely data, providing reliable grounds for smart contract execution.
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Automated settlement systems empowered by AI enable fast and accurate payouts, significantly improving market efficiency.
3. Use Case Expansion: From Speculation to Practical Utility
Beyond popular domains like politics, sports, and cryptocurrencies, prediction markets are being explored for practical applications such as supply chain raw material price fluctuation alerts, insurance pricing, and corporate strategic decision-making. Their core value may shift from a "speculative tool" to an "information aggregation, hedging, and strategic decision support" system.
For example:
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Supply chains: By predicting fluctuations in raw material prices and logistics risks, prediction markets can provide early warnings for businesses, helping them formulate response strategies in advance and reduce supply chain risks. When a key raw material is predicted to sharply increase in price, companies can proactively increase inventory or find alternative suppliers to avoid cost pressures.
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In corporate strategy, prediction markets can play a significant role. Companies can launch predictions about market trends or competitor moves to gather diverse insights and inform strategic decisions.
4. Embedded in Countless Applications: Accelerating Mainstream Adoption
Financial apps are integrating prediction markets. For instance, Robinhood has integrated parts of Kalshi’s prediction markets, attracting younger investors.
Web3 wallets or DeFi protocols may also integrate prediction markets. Jupiter’s prediction market uses Kalshi for liquidity; World App offers prediction markets via Kalshi Mini App and Polymarket Mini App; Web3 wallets MetaMask and Rabby are set to integrate Polymarket directly into their interfaces.
5. Permissionless Market Creation
New platforms like Opinion, PMX, and The Clearing Company are exploring zero-barrier market creation. This model could unlock long-tail demand, but may also result in shallow depth or insufficient liquidity in long-tail markets.
6. Incentive Mechanisms
Most prediction markets attempt or are currently using tokens or reward mechanisms to attract liquidity providers, traders, and market creators. Polymarket, for example, offers rewards for holding USDC.
What are the core challenges facing prediction markets?
1. Regulatory Uncertainty
Differing national classifications of prediction markets lead to high compliance costs. Additionally, cross-border data flows and anti-money laundering (AML) requirements further complicate compliance.
2. Liquidity Fragmentation: The "Barrenness" of Long-Tail Markets
Mainstream markets (e.g., U.S. elections, Bitcoin price) enjoy relatively strong liquidity, while mid-to-long-tail markets suffer from few participants, leading to wide bid-ask spreads and high slippage. Some platforms try to incentivize liquidity provision, but long-term solutions depend on expanding use cases to attract diverse users.
3. Market Manipulation and Integrity Risks: "Big Fish Eating Small Fish" in Low-Liquidity Environments
In illiquid markets, large funds can manipulate odds with minimal capital, misleading other participants.
Moreover, oracles are critical in terms of data sources and resolution mechanisms. If oracles are attacked, bribed, or overly reliant on centralized data, incorrect settlements may occur.
Summary
The ultimate goal of Web3 prediction markets is to build a "collective intelligence-driven global risk pricing network." Their success depends not only on technological breakthroughs but also on finding the optimal balance between innovation and compliance, decentralization and user experience.
With advances in AI, Web3 infrastructure, and expansion into practical use cases, the potential of prediction markets is immense.
However, only when the three core pain points—regulatory uncertainty, liquidity, and market integrity and manipulation—are effectively addressed can prediction markets truly break free from their status as a "niche tool" and become an indispensable part of the global information aggregation and risk hedging ecosystem.
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