The trend is set, but challenges remain.
Written by: KarenZ, Foresight News
Prediction markets aggregate collective intelligence and quantify uncertainty, providing dynamic information pricing tools for fields such as politics, sports, finance, and cryptocurrency, achieving widespread information aggregation and sharing, and are even referred to as "truth engines."
An increasing number of people are participating, not only to gain potential economic benefits but also to leverage this platform to utilize collective intelligence for a more accurate grasp of the development trends of future events.
In the previous article "The Landscape of Prediction Markets: How Emerging Players Break the Deadlock in the Duel of Titans?" I reviewed emerging prediction market players from Polymarket, Kalshi to Limitless, Opinion, and the expansion of prediction services by Robinhood and Jupiter.
This article explores the development trends and core challenges of Web3 prediction markets in conjunction with the current state of prediction markets and the exploratory directions of emerging platforms.
What trends will prediction markets present?
1. Regulatory Framework: From Chaos to Differentiation, Compliance Path Gradually Clarified
The global regulatory attitude towards prediction markets shows significant regional differences. The approval of platforms like Kalshi and Polymarket by the Commodity Futures Trading Commission (CFTC) in the United States has set a compliance benchmark for prediction markets.
In contrast, the European Union and many regions in Asia still view them as high-risk areas, with most countries directly classifying them as "gambling" and prohibiting cryptocurrency settlement.
In the future, prediction markets need to find a balance between "decentralization" and "local compliance," such as by using geofencing technology to restrict access for users in specific regions or collaborating with local licensed institutions to meet regulatory requirements.
2. AI Empowerment: From Tools to Participants, Reshaping Market Efficiency
As predictors: By analyzing historical data, social media sentiment, and real-time events through machine learning, high-precision predictive models can be generated, lowering the participation threshold for ordinary users.
As infrastructure:
AI oracles can automatically scrape multi-source data and verify results, reducing human intervention, allowing the market to obtain more accurate and timely data, providing a reliable basis for the execution of smart contracts.
Automated settlement systems, empowered by AI, can achieve fast and accurate settlements, greatly enhancing market operational efficiency.
3. Scenario Expansion: From Speculation to Practicality
In addition to the currently popular predictions in politics, sports, and cryptocurrencies, prediction markets are exploring applications in practical scenarios such as supply chain raw material price fluctuation warnings, insurance pricing, and corporate strategic decision-making. The core value is expected to shift from "speculative tools" to "information aggregation, hedging, and strategic decision support."
For example:
In the supply chain: Prediction markets can provide risk warnings for companies by predicting fluctuations in raw material prices and logistics transportation risks, helping companies formulate response strategies in advance to reduce supply chain risks. When predicting a significant price increase for a key raw material, companies can increase inventory in advance or seek alternative suppliers to avoid cost pressures from rising raw material prices.
In corporate strategic decision-making, prediction markets can also play an important role. Companies can initiate predictions on market trends, competitor dynamics, etc., in prediction markets to gather various viewpoints and information, providing references for strategic decisions.
4. Embedding in Thousands of Applications: Accelerating the Mainstreaming of Prediction Markets
Financial applications are integrating prediction markets. For instance, the Robinhood app has integrated some prediction markets from Kalshi, attracting young investors.
Web3 wallets may integrate prediction markets within DeFi protocols. For example, the Jupiter prediction market is provided liquidity by Kalshi, and World App offers prediction markets through Kalshi Mini App and Polymarket Mini App. Web3 wallets MetaMask and Rabby are set to integrate the Polymarket prediction market within the wallet.
5. Permissionless Market Creation
Emerging platforms like Opinion, PMX, and The Clearing Company are exploring zero-threshold prediction market creation. This model will further unleash long-tail market demand but may also lead to insufficient depth or liquidity in long-tail markets.
6. Incentive Mechanisms
Most prediction markets are also attempting or are in the process of attracting liquidity providers, traders, and market creators through tokens or reward mechanisms, with Polymarket also offering USDC holding rewards.
What are the core challenges of prediction markets?
1. Regulatory Uncertainty
The qualitative differences in how different countries classify prediction markets lead to high compliance costs. Additionally, cross-border data flow and anti-money laundering (AML) requirements further complicate compliance.
2. Liquidity Stratification: "Desolate" Tail Markets
Mainstream prediction markets (such as the U.S. elections and Bitcoin prices) have relatively sufficient liquidity; however, mid-tail markets suffer from low participation, leading to large bid-ask spreads and high slippage. Some platforms attempt to incentivize users to provide funds through "liquidity" incentives, but long-term reliance on scenario expansion to attract diverse users is still necessary.
3. Market Manipulation and Integrity Risks: "Big Fish Eating Small Fish" in Low Liquidity
In markets with insufficient liquidity, large amounts of capital can manipulate odds with minimal investment, misleading other participants.
Moreover, oracles are crucial for data sources and adjudication mechanisms. If an oracle is attacked, bribed, or overly reliant on centralized data sources, it may lead to erroneous settlements.
Conclusion
The ultimate goal of Web3 prediction markets is to build a "collective intelligence-driven global risk pricing network." Its success depends not only on technological breakthroughs but also on how to find the optimal solution between innovation and compliance, decentralization and user experience.
With advancements in AI and Web3 infrastructure, as well as the expansion into practical scenarios, the potential of prediction markets is enormous.
However, only by effectively addressing the three core pain points of regulatory uncertainty, liquidity, and market integrity and manipulation can prediction markets truly break free from the shackles of being "niche tools" and become an indispensable part of the global information aggregation and risk hedging system.
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