The Next Stop for Prediction Markets: Evolutionary Paths and Ultimate Challenges

The trend is set, but challenges remain.

Written by: KarenZ, Foresight News

Prediction markets aggregate collective wisdom and quantify uncertainty, providing dynamic information pricing tools for fields such as politics, sports, finance, and crypto. They enable broad aggregation and sharing of information, and are even referred to as "truth engines."

An increasing number of people are participating, not only to gain potential economic returns, but also to leverage the platform and collective intelligence for more accurate insights into future trends.

In my previous article, "Prediction Market Panorama: The Duel of Two Giants, How Can Emerging Players Break Through?", I reviewed emerging prediction market players from Polymarket and Kalshi to Limitless and Opinion, as well as the expansion of prediction services by Robinhood and Jupiter.

This article explores the development trends and core challenges of Web3 prediction markets, combining the current state of prediction markets with the exploration directions of emerging platforms.

What trends will prediction markets present?

1. Regulatory Framework: From Chaos to Differentiation, Compliance Paths Gradually Clarified

Global regulatory attitudes toward prediction markets show significant regional differences. In the United States, the Commodity Futures Trading Commission (CFTC) has approved platforms such as Kalshi and Polymarket, setting a compliance benchmark for prediction markets.

Meanwhile, many regions in the European Union and Asia still regard them as high-risk areas, with most countries directly classifying them as "gambling" and prohibiting crypto settlements.

In the future, prediction markets will need to find a balance between "decentralization" and "local compliance," for example, by using geofencing technology to restrict access from certain regions or cooperating with locally licensed institutions to meet regulatory requirements.

2. AI Empowerment: From Tool to Participant, Reshaping Market Efficiency

As predictors: By using machine learning to analyze historical data, social media sentiment, and real-time events, high-precision prediction models can be generated, lowering the participation threshold for ordinary users.

As infrastructure:

• AI oracles can automatically capture multi-source data and verify results, reducing human intervention, enabling the market to obtain more accurate and timely data, and providing reliable support for smart contract execution.
• Automated settlement systems empowered by AI can achieve fast and accurate settlements, greatly improving market operational efficiency.

3. Scenario Expansion: From Speculation to Practicality

In addition to the current popular predictions in politics, sports, and cryptocurrencies, prediction markets are being explored for practical applications such as supply chain raw material price fluctuation warnings, insurance pricing, and corporate strategic decision-making. The core value is expected to shift from a "speculation tool" to "information aggregation, hedging, and strategic decision support."

For example:

• In supply chains: Prediction markets can forecast raw material price fluctuations, logistics risks, and other factors to provide risk warnings for enterprises, helping them formulate response strategies in advance and reduce supply chain risks. If a significant price increase in a key raw material is predicted, companies can increase inventory or seek alternative suppliers in advance to avoid cost pressures caused by 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., on prediction markets to collect various viewpoints and information, providing references for strategic decisions.

4. Embedded in Thousands of Applications: Accelerating Prediction Market Mainstream Adoption

Financial applications are integrating prediction markets. For example, the Robinhood app has integrated some Kalshi prediction markets, attracting young investors.

Web3 wallets or DeFi protocols may integrate prediction markets. For instance, Jupiter's prediction market is provided with liquidity by Kalshi, World App offers prediction markets via Kalshi Mini App and Polymarket Mini App, and Web3 wallets MetaMask and Rabby are about to integrate Polymarket prediction markets within their wallets.

5. Permissionless Market Creation

Emerging platforms such as 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 result in insufficient depth or liquidity in long-tail markets.

6. Incentive Mechanisms

Most prediction markets are attempting or already using tokens or reward mechanisms to attract liquidity providers, traders, and market creators. Among them, Polymarket also offers USDC holding rewards.

What are the core challenges for prediction markets?

1. Regulatory Uncertainty

The differences in how countries define prediction markets lead to high compliance costs. In addition, cross-border data flows and anti-money laundering (AML) requirements also increase compliance complexity.

2. Liquidity Stratification: "Desolation" in Tail Markets

Mainstream prediction markets (such as the US election, bitcoin price) have relatively sufficient liquidity; however, mid- and long-tail markets suffer from fewer participants, resulting in wide bid-ask spreads and high slippage. Some platforms attempt to incentivize users to provide funds through "liquidity" rewards, but in the long run, scenario expansion is still needed to attract a more diverse user base.

3. Market Manipulation and Integrity Risks: "Big Fish Eat Small Fish" in Low Liquidity Markets

In markets with insufficient liquidity, large funds can manipulate odds with small amounts of capital, misleading other participants.

In addition, oracles are critical to data sources and adjudication mechanisms. If oracles are attacked, bribed, or overly reliant on centralized data sources, erroneous 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 the advancement of AI, Web3 infrastructure, and the expansion into practical scenarios, the potential of prediction markets is enormous.

But 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 a "niche tool" and become an indispensable part of the global information aggregation and risk hedging system.