I. Why Prediction Markets Entered a New Phase in 2024–2025
Prediction markets are not a new concept, but for a long time they remained confined to niche experimentation. The real shift began after 2024, when prediction markets simultaneously satisfied three critical conditions for the first time: usability, necessity, and scalability.
First, usability. The maturation of Layer 2 solutions and the sharp reduction in on-chain transaction costs made creating and trading prediction events far less expensive and more accessible. Second, necessity. In an increasingly uncertain global environment, market participants have shown a growing demand for probabilistic judgment rather than deterministic narratives. Third, scalability. Prediction markets are no longer limited to politics or entertainment. They are expanding into finance, technology, and on-chain behavioral forecasting.
Together, these factors have transformed prediction markets from an “interesting experiment” into a financial primitive with infrastructure-level potential.
II. EventFi: From “Prediction” to the Financialization of Events
1. From Prediction Markets to EventFi
At its core, a prediction market answers a single question: What is the probability that a given event will occur? EventFi, however, aims to answer a broader question: How many different financial expressions can be built around an event?
From an EventFi perspective, prediction markets represent only the most foundational layer. They provide a probability anchor, rather than a final product form.
2. Advanced Structures in EventFi
On top of prediction markets, multiple financial structures may emerge, including:
- Conditional event markets : e.g., “If A occurs, will B occur?”
- Composite event markets: Bundling and unbundling probabilities across multiple events
- Event indices: Weighted probabilities of a group of related events
- Event hedging instruments: Integrated with spot markets, options, and perpetual contracts
This implies that prediction markets may no longer exist as standalone products in the future, but instead evolve into the probability layer of a broader derivatives ecosystem.
III. AI × Prediction Markets: From Human Judgment to Human–Machine Consensus
1. The Real Role of AI in Prediction Markets
A common misconception is: “If AI becomes powerful enough, will prediction markets still matter?” In reality, AI and prediction markets 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.
For this reason, AI is far more likely to become an amplifier of prediction markets, rather than a replacement.
2. Key Integration Points for AI
In practical implementations, AI may be applied to several critical layers:
- Event selection: Identifying which events are worth turning into markets
- Anomaly detection: Detecting manipulation, wash trading, or irrational behavior
- Probability benchmarking: Comparing model-based forecasts with market-implied probabilities
When AI-generated forecasts and market probabilities diverge persistently, the divergence itself becomes a valuable trading and research signal.
IV. Privacy and Compliance: Possible Paths for ZK-Enabled Prediction Markets
1. The “Compliance Ceiling” of Prediction Markets
Prediction markets inherently operate at the intersection of several sensitive boundaries:
- Future events
- Financial incentives
- Information asymmetry
As a result, they exist in a regulatory gray zone across most jurisdictions. For institutional participants, the primary barrier is not technology, but the trade-off between compliance and privacy—a balance that has historically been difficult to achieve.
2. Structural Breakthroughs Enabled by Zero-Knowledge Technology
Zero-knowledge proofs introduce a new equilibrium for prediction markets:
- User identities can be verified without being disclosed
- Trading activity can be audited without revealing strategies
- Outcome settlement remains trustworthy while preserving transactional privacy
Under this model, prediction markets have the potential to evolve from high-risk experimental applications into controlled, auditable, institution-grade tools.
V. Platform Competition and Business Model Divergence
1. Traffic-Driven Prediction Markets
- User growth driven by high-profile, trending events
- Strong emphasis on ease of use and participation
- Function more like content or information platforms
Key risk:
Event lifecycles are short, making long-term user retention difficult.
2. Professional Prediction Markets
- Focus on capital efficiency and market depth
- Target professional traders, funds, and research institutions
- More complex mechanisms, but significantly higher signal quality
These platforms are more likely to evolve into a “Bloomberg for probabilities.”
3. Tool-Oriented Prediction Markets
- Trading volume is not the primary KPI
- Designed to support decision-making for DAOs, funds, and research teams
- Prediction markets function as internal analytical tools
In the long run, these three models are likely to coexist rather than replace one another, serving different user segments and use cases.
VI. Structural Challenges Facing Prediction Markets
Even from a long-term perspective, prediction markets cannot avoid several structural constraints:
- Highly concentrated liquidity: The vast majority of capital focuses on only a small number of high-profile events
- High event creation costs: Well-defined, high-quality events are far scarcer than well-designed trading interfaces
- Persistent regulatory uncertainty: Legal ambiguity remains a long-term overhang
- Persistent regulatory uncertainty: Legal ambiguity remains a long-term overhang
These limitations suggest that prediction markets are unlikely to experience explosive growth like Memecoins or DeFi. Instead, they are more likely to evolve as a slow-moving, structurally important sector within the crypto ecosystem.
VII. The Ultimate Form of Prediction Markets: Probability as Infrastructure
From a broader perspective, the ultimate value of prediction markets may not lie in trading revenue, but in the information they provide to the entire system.
When prediction market prices are:
- Cited by research institutions
- Referenced in protocol governance
- Used as inputs for AI models
- Interpreted as signals for macro-level decision-making
It is no longer just an application, but a form of probability infrastructure.
