Kalshi and Polymarket account for 97.5% of the prediction market share in 2025.

Author: MetaHub Research

Introduction: Redefining the Boundaries of Prediction Markets

Prediction markets are platforms that allow participants to trade contracts based on the outcomes of future uncertain events, with contract prices reflecting the market’s collective consensus on the probability of those events occurring. They have demonstrated significantly greater accuracy than expert forecasts and opinion polls in areas such as political elections, macroeconomic indicators, sports events, cryptocurrency assets, and corporate events.

Prediction markets are essentially tools for "financializing information"—price equals probability. In areas of high uncertainty and strong subjective judgment, prediction markets offer significant advantages.

The global forecast market is projected to have a total trading volume of approximately $50.25 billion in 2025. If maturity is measured by trading volume rather than narrative, the forecast market will truly transition from [event-driven short-term curiosity] to [sustained market] in 2025.

Kalshi has demonstrated that the industry is not just about "volume," but is beginning to show commercial viability—its report claims to have generated approximately $260 million in fee revenue. Nevertheless, prediction markets have not yet entered a true growth phase; compared to the global futures market’s annual trading volume of hundreds of trillions of dollars, today’s prediction markets resemble financial futures in 1982, not cryptocurrency in 2020.

Unlike most financial innovations, prediction markets have not experienced long-tail competition; instead, they have rapidly consolidated into two dominant platforms: Kalshi and Polymarket, which together hold over 97.5% of the market share, while all other platforms combined account for only about $1.25 billion in trading volume, representing a marginal ecosystem.

I. The Nature of Prediction Markets: An Information Production Mechanism Beyond Attention Economics

Prediction markets are no longer merely an innovation in trading formats; they are evolving into an information production mechanism outside the attention economy.

The key difference from traditional attention economy lies in:

• Value is not determined by clicks, traffic, or popularity

Core assets are knowledge and information quality.

Market participants seek verifiable, tradable, and referenceable judgments, not short-term attention exposure.

Under this logic, the competitors in prediction markets have also shifted:

• Brokerage research system

• Consulting Firm Evaluation System

• Media narrative control

• Probability output after AI training

In other words, this is a market that prices future knowledge.

The true dividing line in the industry at this stage is not technology, but three things: whether it can establish sustained information liquidity; whether it has entered the “weakly regulated tolerance zone” rather than the gray arbitrage zone; and whether it is treated by institutions as a decision-making input rather than a retail entertainment tool. Once these three conditions are met, prediction markets will resemble a hybrid of Bloomberg, an exchange, and a polling agency—not a Web3 application.

Problem Definition Rights: A Core Asset Severely Undervalued

Most people underestimate the most critical asset in prediction markets—not liquidity, but the ability to define questions.

Who controls the definition of the question controls: the point of information access, the context of trading, and the authority to interpret probabilities. This mirrors the power structure of index providers like MSCI. A well-designed market question is, at its core, a tradable cognitive framework.

II. Why was the value of prediction markets suddenly reevaluated during the 2024–2026 cycle?

2025 is not accidentally a turning point, but the result of three structural factors converging.

2.1 Expectation of Regulatory Clarification

• In 2024, multiple U.S. states and the CFTC adopted clearer regulatory stances on event contracts.

• Kalshi’s legal pathway opens the door for traditional institutional funds, leading to a sudden surge in institutional trading volume.

• Traditional investors are beginning to view prediction markets as an "alpha-generating event trading tool," rather than gray-area gambling.

2.2 Trade Volume Concentration + Continuous Event Supply

• In the past, prediction markets focused mainly on political events or one-time occurrences, with short trading cycles and high volatility.

• In 2025, high-frequency events (sports, corporate KPIs, crypto market events) will enable the market to continuously absorb capital.

• Continuous events create a self-reinforcing cycle of liquidity: liquidity generates deeper information → attracts more trading → more accurate price signals

2.3 Marginal Amplification of Information Needs

• Although data is abundant in the AI era, "probability credibility" has become a scarce asset.

Quantitative funds, hedge funds, and corporate decision-making departments are beginning to treat price predictions as legitimate sources of signals.

Core logic: It’s not user growth from traffic, but capital and information demands triggering liquidity concentration—this is the true inflection point for prediction markets.

2.4 Three Structural Forces Converge

Power One: The "limit of effectiveness" of traditional research systems is becoming apparent.

Over the past decade, sell-side research has significantly lagged in predicting macro inflection points; buy-side firms have increasingly come to view "the speed of consensus formation" as a source of alpha; expert models are becoming more like narrative engineering than probabilistic discovery.

Prediction markets offer a different paradigm: not "who is smarter," but "who is willing to pay for their judgment." The exposure of capital itself becomes an information filter.

Power Two: After the Rise of AI, Human Society Needs "True Sources of Signals" Even More

Large models can generate judgments, but they cannot assume risk. The uniqueness of prediction markets lies in their irreplaceable mechanistic advantages:

Therefore, it has become one of the very few systems in the AI era with a fact-based anchoring mechanism, which is why an increasing number of quantitative funds are beginning to treat predicted market prices as an exogenous variable.

Power Three: Web3 solves a critical constraint—settlement trustworthiness

The biggest problem with early prediction markets wasn't lack of participation, but rather: Who would act as the bookmaker? How to prevent default? How to enable global participation? On-chain settlement reduces trust from "trusting the operator" to "trusting code execution," giving prediction markets the ability to scale across jurisdictions for the first time.

III. Comparison of Leading Platform Sizes (Actual Scale in 2025)

① Kalshi — Current Liquidity Hub

• Notional trading volume in 2025 amounted to approximately $23.8 billion, representing a year-over-year growth of over 1,100%.

• Once accounted for 55%–60% of weekly industry trading volume, becoming the most liquid market

• During part of the reporting period, the global market share rose to 62.2%.

• Monthly trading volume once reached the $1.3 billion level

• Growth is primarily driven by the opening of compliant channels that attract traditional capital, rather than an expansion of crypto users.

Kalshi adopted a completely different strategy: proactively engaging with the regulatory framework and defining prediction markets as an "event contract exchange," aiming to replicate the path to legitimacy followed by futures markets. Growth is slower in the short term, but if successful, it will open the floodgates for allocations from pension funds, RIA, and institutional capital.

② Polymarket — Crypto-native liquidity hub

• Total trading volume for 2025 is approximately $22 billion

• Maintained monthly trading volumes in the hundreds of millions of dollars in some months

Polymarket follows a global permissionless liquidity model: rapidly achieving high event coverage density, leveraging on-chain technology to reduce participation friction, and substituting trading activity for regulatory depth.

Its true value lies not in trading volume, but in creating the world's first "real-time political probability curve"—a type of data that has never existed in traditional systems.

③ Second-tier platforms (representing a very small share of the total but signaling future differentiation trends)

Despite high market concentration, several exploratory platforms such as Azuro and TrendleFi have emerged. Collectively, these platforms account for only about $1.25 billion in trading volume, indicating that the industry has not yet entered a phase of widespread innovation but remains in the stage of infrastructure validation.

Augur exemplifies the limitations of first-generation decentralized experiments: an overemphasis on "trustlessness," neglect of real trader experience, and failure to address issue distribution and liquidity acquisition. This demonstrates that prediction markets are not purely technical problems, but rather problems of market design.

Logical conclusion: The core advantage of prediction markets is not technology, but a composite moat built on liquidity and event design capabilities. Platforms with low liquidity struggle to succeed through decentralized competition.

Four: Why do most prediction markets fail?

Historically, failed platforms did not lose because of technology, but because of market microstructure.

4.1 Treat prediction markets as an "event casino"

This error causes: high-frequency noise to overwhelm information-based traders, making it impossible for market-making capital to remain long-term, and rendering the Sharpe Ratio unsustainable. A successful prediction market must grant information-based traders a structural advantage.

4.2 Mismatch of Liquidity Sources

Prediction markets don't need retail traders—they need macro traders, policy researchers, industry experts, and risk hedgers. These participants provide information-driven trading flows, not speculative ones.

4.3 Settlement Frequency Design Error

If the market settlement cycle is too short, it devolves into gambling; if too long, it loses capital efficiency. The optimal range is typically events with a half-life of information between two weeks and six months, which aligns precisely with the real-world time window where分歧 can form yet remain verifiable.

Five: Vertical Market Analysis: Four High-Growth Segments

As the window for general-purpose prediction markets closes, opportunities in the space are increasingly concentrating on verticalized niches. Sports, creator economies, AI predictions, and social bot interactions have become the four fastest-growing细分segments.

5.1 Sports Sector

Key logic

Sports events inherently feature frequent schedules and clear outcomes, making them easy to quantify and predict, while also fostering highly engaged user bases. The platform can rapidly deploy trading markets and odds systems using middleware such as Azuro Protocol, lowering technical barriers.

Representative project

• Football.fun: Short-term TVL exceeded $10 million, with high user engagement

• Overtime: Create a closed-loop ecosystem by integrating community engagement with derivatives trading

• SX Network, Azuro Protocol: Provide public blockchain and middleware support for sports betting

User behavior characteristics

• High participation, instant betting, and active trading around events

• User actions are easily influenced by community and social recommendations

• Prefers leveraged instruments and short-term contracts for rapid feedback

Trends and Opportunities

Over the next 1-3 years, the sports sector will become increasingly specialized: high-frequency derivatives, leveraged trading, and cross-chain aggregation will become standard features, fostering a composite growth model of "sports prediction + community economy" through communities and event ecosystems.

5.2 Creator Economy Sector

Key logic

Integrating prediction markets with the creator economy empowers KOLs by directly enabling market creation and revenue distribution. Users, while participating in predictions, also become community content creators, forming a closed-loop ecosystem through creator revenue-sharing incentives, resulting in significant viral growth.

Representative project

• Melee: Offers a 20% creator revenue share to incentivize KOLs to drive market engagement.

• Index.fun: 30% creator earnings—pack prediction results into a "Creator Index" to enhance secondary trading and community engagement.

Trends and Opportunities

The future creator economy will become indexed and platformized: platforms can transform creator influence into tradable assets through predictive indices, NFT-based incentives, and revenue-sharing mechanisms.

5.3 AI Prediction Arena

Key logic

AI is evolving from a supporting tool into a core product, handling market generation, event analysis, content creation, and settlement. Through intelligent agents and Copilot, the platform enables zero-cost creation, infinite supply, and automated settlement, significantly reducing operational costs.

Representative project

• OpinionLabs: AI agents generate event markets with automated settlement of prediction outcomes

• BuzzingApp: AI-powered with zero manual intervention, supporting high-speed event iteration and settlement

Trends and Opportunities

Over the next 1-3 years, AI will become standard in market prediction: automation of market generation, intelligent settlement, event analysis, and end-to-end AI-driven risk control will give rise to new high-frequency and highly intelligent products, while attracting professional quantitative traders.

5.4 Social Bot Interaction Track

Key logic

Lightweight frontend and social integration lower the barrier to user interaction by embedding predictive trading directly into Telegram posts, X platform tweets, or content wallets, creating a "social-as-trading"闭环.

Representative project

• Flipr, Noise: One-click ordering via Telegram bot, simplifying complex trading operations

• XO Market: Aggregates orders from multiple platforms, offering leverage and take-profit/stop-loss features to meet the needs of professional traders.

Trends and Opportunities

The future social bot赛道 will deeply integrate platform aggregators and leverage tools to achieve cross-chain liquidity consolidation, and expand user reach further through social embedding, becoming the "growth engine" of prediction markets.

Summary: The rise of vertical niches reflects the evolution of prediction markets from general-purpose information tools toward a model characterized by "derivatization, data service integration, AI embedding, and creator/social ecosystem development." Each niche has established a complete logical chain: market-driven demand → user behavior → technological support → investment opportunities.

Six: The Breakthrough for Small-Scale Prediction Markets

Even with extremely high industry concentration, small platforms still have several "blue ocean" opportunities to enter:

6.1 Verticalization / Niche Markets

• Sports professional events, esports, industry KPIs

• Internal corporate prediction markets, professional association events

• Industry-specific or regional policy events

Logic: Deep or specialized events not covered by mainstream platforms can create high-value, low-volume markets.

6.2 Data Productization + B2B Integration

• Does not engage in direct trading; instead, turns price signals into API or index products sold to funds or enterprises.

• Key advantages are low regulatory risk and a sustainable business model

6.3 Experience Differentiation / Information Enhancement

• Provides predictive pre-analysis tools and community consensus mechanisms

• Turn predictions into "cognitive value rather than pure trading" to enhance user retention

Core logic: Small platforms should avoid direct competition in liquidity and instead focus on high-value, low-volume scenarios combined with data-driven business models.

Seven: Investment Perspective—Structural Infrastructure Is the True Area to Bet On

Future high-value directions may include:

• Predictive Market Data API (sold to quantitative funds)

• Enterprise-grade decision-making market SaaS

Market Making and Risk Intermediation

• Probability Index Product (similar to the VIX Future Expectation Index)

The true moat will belong to those who control probability distribution, not those who match trades.

7.1 Overview of Actual VC Investment Directions

7.2 Interpreting Key Funding Signals

The Clearing Company has raised approximately $15 million, with investors including Union Square Ventures, Coinbase Ventures, Haun Ventures, and Variant. This is a significant signal: capital is beginning to treat prediction markets as a formal asset class requiring a clearinghouse.

Kalshi's valuation has risen to $5 billion; FanDuel and CME are also preparing to launch prediction market products to compete; by 2025, institutional capital is expected to account for approximately 55% of prediction market funding. This indicates it is undergoing an evolution similar to the trajectory of 2017 DEX → 2021 DeFi → 2024 prediction market tech stack.

Eight, Future Trends and Evolution Directions

8.1 Mechanism Evolution: Deepening Derivatization

The market is expected to gradually shift from "event outcome prediction" toward high-frequency trading, structured options, and leveraged contracts. Typical path:

• Short-term event contracts (e.g., Limitless 30-minute contracts) → High-frequency volatility trading tools

• Leveraged trading (Flipr 5x) → Integrated with DeFi leverage protocols to form an on-chain derivatives ecosystem

• Range prediction, spread arbitrage → gradually evolve into structured options and financial derivatives

Meanwhile, cross-chain and cross-platform liquidity integration has become a core competitive advantage. Aggregators combine order books from different platforms to provide optimal prices and settlement solutions, similar to a "predictive market 1inch."

8.2 Product Evolution: Data as a Service + AI Integration

The predicted market price already reflects "event probabilities" and will become a core data source for institutional quantitative trading, asset allocation, and risk management. Product formats will include:

• Data Subscription: Real-time market probabilities, top account activities, and arbitrage opportunities

• Indexing: Combine various prediction outcomes into a "Creator Index" or "Event Index" to facilitate secondary trading or integration into DeFi.

• Visual Terminal: A Polysights-style "Predictive Market Bloomberg Terminal" that delivers strategy signals directly

Meanwhile, AI will be involved in market creation, automated settlement, content analysis, and risk control: automatically generating event markets (with zero human intervention), intelligent settlement and odds adjustment, and AI agents/copilots assisting in trade prediction.

8.3 Infrastructure Evolution: Modularity and Composability

The market will resemble DeFi LEGO: modular components such as market creation, settlement, liquidity, oracles, and AI agents will be plug-and-play, lowering technical barriers and enabling multi-chain deployment.

• Gnosis CTF → Standardized Asset Issuance

• Azuro Protocol → Gambling Middleware

• Polymarket/Kalshi → Core Settlement Layer

Multi-chain deployment and cross-chain order aggregation have become standard: chains such as Base, Polygon, and Solana serve as the foundational infrastructure for vertical sectors.

8.4 User Experience Evolution

Frontend interactions are evolving toward socialization, lightweight design, and real-time functionality: Bots (on Telegram/social platforms), one-click trading, and leveraged trading are integrated into content ecosystems. AI combined with intelligent oracles reduces manual effort and costs, while automated settlement and intelligent event parsing enhance platform scalability.

Over the next 1-3 years, the market is forecast to accelerate through four driving forces: derivatives integration, data serviceization, AI embedding, and composable infrastructure. It will evolve from a simple information aggregation tool into an integrated ecosystem encompassing derivatives markets, data services, AI ecosystems, and creator/vertical sector consolidation. Investment value will be concentrated in infrastructure modules, data services, vertical sector applications, and innovations in AI and interaction layers.

Conclusion: A New Social Infrastructure

Prediction markets are not a fringe innovation in finance, but rather an attempt to solve a fundamentally basic problem:

How humans form actionable consensus in the face of uncertainty.

The importance of this mechanism is only beginning to emerge as information overload, AI generalization, and expert failure occur simultaneously.

It is more like a new social infrastructure than an asset class.