Future Trends and Ecosystem Competition in Prediction Markets

I. Why Are Prediction Markets Entering a New Phase in 2024–2025?

Prediction markets are not a new concept, but for a long time, they remained niche experiments. The real shift began after 2024—when prediction markets simultaneously met three key conditions: usability, necessity, and scalability.

First, usability: The maturity of Layer 2 solutions and lower on-chain transaction costs have made creating and trading prediction events far less expensive. Second, necessity: In an increasingly uncertain global environment, market participants have a much greater need for “probabilistic judgment” than for “certainty narratives.” Finally, scalability: Prediction markets are no longer limited to politics or entertainment—they are expanding into finance, technology, and on-chain behavior.

The combination of these factors has transformed prediction markets from “interesting experiments” into financial modules with infrastructure potential.

II. EventFi: From “Prediction” to “Event Financialization”

1. From Prediction Market to EventFi

At its core, a prediction market answers the question: What is the probability of an event occurring? EventFi seeks to answer: How many financial expressions can be built around an event?

From the EventFi perspective, prediction markets are just the foundational layer—they provide probabilistic anchors, not the final product.

2. Advanced Structures in EventFi

Building on prediction markets, several new forms may emerge:

• Conditional event markets: For example, “If A happens, will B occur?”
• Composite event markets: Bundling and unbundling probabilities of multiple events
• Event indices: Weighted probabilities for a group of related events
• Event hedging tools: Linked with spot, options, and perpetual contracts

This suggests that prediction markets may no longer exist as standalone products but instead become the probability layer within a broader derivatives ecosystem.

III. AI × Prediction Markets: From Human Judgment to Human-Machine Consensus

1. The True Role of AI in Prediction Markets

A common misconception is: “If AI becomes powerful enough, do prediction markets still matter?” In reality, prediction markets and AI address different types of uncertainty.

• AI excels at processing historical data and structured information.
• Prediction markets excel at aggregating dispersed cognition, subjective judgment, and unstructured signals.

Thus, AI is more likely to act as an amplifier for prediction markets rather than a replacement.

2. Key Areas Where AI Can Be Embedded

In practice, AI can play a role in:

• Event selection: Identifying which events are worth being marketized
• Anomaly detection: Spotting manipulation, wash trading, or irrational trades
• Probability comparison: Analyzing deviations between model forecasts and market prices

When AI predictions and market probabilities consistently diverge, that itself becomes a trading or research signal.

IV. Privacy and Compliance: Potential Paths for ZK Prediction Markets

1. The “Compliance Ceiling” of Prediction Markets

Prediction markets inherently touch several sensitive boundaries:

• Future events
• Monetary incentives
• Information asymmetry

As a result, they often occupy a legal gray area in most jurisdictions. For institutions, the biggest obstacle isn’t technology—it’s the inability to reconcile compliance and privacy.

2. Structural Breakthroughs Possible with ZK

Zero-knowledge proofs offer a new path to balance in prediction markets:

• User identity can be verified without being publicly disclosed.
• Trading activity can be audited without exposing strategies.
• Result settlement is trustworthy while keeping processes private.

With this model, prediction markets could evolve from “high-risk applications” to controllable, auditable institutional-grade tools.

V. Platform Competition and Business Model Differentiation

1. Traffic-Driven Prediction Markets

• Use trending events to drive user growth
• Emphasize ease of use and engagement
• Resemble content and information platforms

Risks include:

• Short event lifecycles make user retention difficult.

2. Professional Prediction Markets

• Focus on capital efficiency and depth
• Cater to professional traders and research institutions
• Complex mechanisms but high-quality signals

These platforms are more likely to become the “Probability Bloomberg.”

3. Tool-Based Prediction Markets

• Do not use trading volume as the core KPI
• Provide decision-making support for DAOs, funds, and research teams
• Serve as internal tools rather than public-facing products

In the future, these three models may coexist rather than replace each other.

VI. Structural Challenges in Prediction Markets

Even with a long-term outlook, prediction markets face persistent challenges:

• Highly concentrated liquidity: Most funds focus on very few events.
• High event creation costs: Quality events are much rarer than quality trading interfaces.
• Steep user education curve: Probabilistic thinking is not intuitively accessible.
• Ongoing regulatory uncertainty

These constraints mean prediction markets are unlikely to experience explosive growth like Meme coins or DeFi. Instead, they’re positioned as a slow-moving sector.

VII. The Ultimate Form of Prediction Markets: Probability as Infrastructure

From a macro perspective, the ultimate value of prediction markets may not lie in trading revenue but in the information they provide for the entire system.

When prediction market prices are:

• Cited by research institutions
• Used for protocol governance reference
• Fed as inputs to AI models
• Used as signals for macro decision-making

They cease to be just applications—they become probability infrastructure.