Two companies account for 97% of the market, and transaction volume surges by 1100%: Predicting the reshaping of the market landscape and the next wave of entrepreneurial opportunities.

Author: MetaHub Research

Introduction: Redefining the Boundaries of Prediction Markets

Prediction markets are markets that allow participants to trade on the outcomes of uncertain future events, with contract prices reflecting the market's consensus on the probability of that event occurring. They have demonstrated significantly higher accuracy than expert predictions and polls in areas such as political elections, macroeconomics, sporting events, crypto assets, and corporate events.

Prediction markets are essentially tools for "financializing information"—price equals probability. They possess significant advantages in areas of high uncertainty and strong subjective judgment.

The total trading volume of the global prediction market is estimated at approximately US$50.25 billion in 2025. If maturity is defined by trading volume rather than narrative, the prediction market will only truly complete its leap from event-driven short-term curiosity to a sustainable market in 2025.

Kalshi has validated that the industry is not just about "trading volume," but is beginning to demonstrate commercial viability—its report claims to have generated approximately $260 million in fee revenue. Nevertheless, the prediction market has not yet truly entered a growth phase. Compared to the trillions of dollars in annual trading volume of mature global futures markets, it is more like financial futures in 1982 than cryptocurrencies in 2020.

Unlike most financial innovations, the prediction market did not experience long-tail competition, but instead rapidly collapsed to two platforms: Kalshi and Polymarket together accounted for more than 97.5% of the market share, while all other platforms combined had a trading volume of only about $1.25 billion, belonging to the peripheral ecosystem.

I. The essence of market prediction: the information production mechanism in a non-attention economy

Prediction markets are no longer simply innovative trading formats, but are evolving into an information production mechanism in a non-attention economy.

The core difference from the traditional attention economy lies in:

• Value does not depend on clicks, traffic, or popularity.

• Core assets are cognition and information quality

Market participants seek verifiable, tradable, and referable judgments, rather than short-term exposure to attention.

Under this logic, the competitors in the prediction market have also changed:

• Brokerage Research System

Consulting firm judgment system

• Media narrative power

• Probability output after AI training

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

The real watershed moment for the industry at this stage lies not in technology, but in three things: whether it can achieve sustained information flow; whether it enters a "weakly regulated and tolerable zone" rather than a gray arbitrage zone; and whether it is used by institutions as decision-making input, rather than as a tool for retail investors' entertainment. Once these three points are met, the prediction market will resemble a hybrid of Bloomberg + exchange + polling agency, rather than a Web3 application.

The right to define the problem: severely undervalued core assets

Most people underestimate the most crucial asset in predicting the market—not liquidity, but the ability to define the problem.

Whoever controls the definition of the problem controls: the information gateway, the trading context, and the power to interpret probabilities. This is highly similar to the power structure of index companies (such as MSCI). A well-designed market problem is essentially a tradable cognitive framework.

II. Why is the value of the forecast market suddenly being reassessed in the 2024–2026 cycle?

The turning point in 2025 is not accidental, but rather the result of the combination of three structural factors.

2.1 Expectations for clearer regulation

• In 2024, the regulatory attitudes of many US states and the CFTC towards event contracts became clearer.

Kalshi's legitimate channels opened up access to traditional institutional funds, leading to a sudden surge in institutional trading volume.

Traditional investors are beginning to view prediction markets as "event trading tools that can be accounted for with alpha," rather than grey market gambling.

2.2 Increased Transaction Scale + Continuous Event Supply

• In the past, market event predictions mostly focused on political or single events, with short trading cycles and high volatility.

• 2025 will see a surge in high-frequency events (sports, corporate KPIs, crypto market events), allowing the market to continuously absorb funds.

• Successive events create a self-reinforcing cycle of liquidity: liquidity brings information depth → attracts more transactions → more accurate price signals.

2.3 Marginal amplification of information demand

• In the AI ​​era, while data is abundant, "probabilistic credibility" has become a scarce asset.

• Quantitative funds, hedge funds, and corporate decision-making departments are beginning to view market price predictions as a source of reliable signals.

The core logic is not about user growth, but rather the concentration of liquidity triggered by capital and information demand – this is the true turning point in predicting the market.

2.4 The superposition of three structural forces

Over the past decade, sell-side research has significantly lagged behind in predicting macroeconomic turning points; buy-side research has gradually regarded the "speed of consensus formation" as a source of alpha; and expert models have become more and more like narrative engineering than probabilistic discovery.

Prediction markets offer a different paradigm: instead of "who is smarter," it's "who is willing to pay for judgment." Capital exposure itself becomes an information filter.

Large models can generate judgments, but they cannot bear the risks. The uniqueness of prediction markets lies in their irreplaceable mechanism advantages:

Therefore, it has become one of the very few systems in the AI ​​era with a fact-anchoring mechanism, which is why more and more quantitative funds are beginning to treat market price prediction as an exogenous variable.

The biggest problem with early prediction markets wasn't the lack of predictions, but rather: who would be the market maker? How to prevent default? How to achieve global participation? On-chain settlement reduces trust from "trusting the operator" to "trusting code execution," enabling prediction markets to expand across jurisdictions for the first time.

III. Comparison of the size of leading platforms (actual size in 2025)

① Kalshi — Current Liquidity Center

• Nominal trading volume is projected to reach approximately $23.8 billion in 2025, representing a year-on-year increase of over 1100%.

• Once accounting for 55%–60% of the industry's weekly trading volume, it became the most liquid market.

• During some statistical periods, the global market share rose to 62.2%.

• Monthly transaction volume once reached the level of $1.3 billion.

• Growth is primarily driven by compliant pathways to access traditional funding channels, rather than by the expansion of crypto users.

Kalshi has chosen a completely different strategy: proactively entering the regulatory framework, defining the prediction market as an "event contract exchange," and attempting to replicate the legalization path of the futures market. Short-term growth will be slow, but if successful, it will open the floodgates for pension funds, RIAs, and institutional fund allocation.

② Polymarket — Crypto-native liquidity hub

• Total transaction volume in 2025 is estimated at approximately US$22 billion.

• Monthly transaction volume remained in the hundreds of millions of dollars in some months.

Polymarket follows a global permissionless liquidity path: rapidly building event coverage density, reducing participation friction on-chain, and replacing compliance depth with trading activity.

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

③ Second-tier platforms (accounting for a very small percentage of the total, but representing the future direction of differentiation)

Despite the high market concentration, several exploratory platforms have emerged, such as Azuro and TrendleFi. These platforms collectively contributed only about $1.25 billion in transaction volume, indicating that the industry has not yet entered a phase of "flourishing diversity" but is still in the stage of infrastructure ownership confirmation.

Augur represents the limitations of first-generation decentralized experiments: an overemphasis on "trustlessness," neglect of the real trader experience, and failure to address issues of distribution and liquidity access. This illustrates that prediction markets are not purely a technical problem, but rather a market design problem.

Logical conclusion: The core of market prediction is not technology, but a composite moat of liquidity and event design capabilities. Low-liquidity platforms will find it difficult to succeed through decentralized competition.

IV. Why do most market predictions fail?

Historically, failed platforms did not fail due to technological limitations, but rather due to flaws in market microstructure.

4.1 Treating prediction markets as "event casinos"

This error leads to: high-frequency noise overwhelming information traders, market-making funds unable to remain for long, and the Sharpe Ratio becoming unsustainable. Successful market prediction requires giving information traders a structural advantage.

4.2 Liquidity source mismatch

Predicting the market doesn't require retail investors; instead, it requires macro traders, policy researchers, industry experts, and risk hedgers. They provide information-driven trading flows, not speculative ones.

4.3 Errors in the design of settlement frequency

If the market settlement cycle is too short, it degenerates into gambling; if it is too long, it loses capital efficiency. The optimal range is usually 2 weeks to 6 months for information half-life events, which corresponds precisely to the real-world time window where "disagreements can arise but verification is still possible."

V. Vertical Track Analysis: Four High-Growth Sub-Sectors

As the window of opportunity for general-purpose prediction markets gradually closes, opportunities in various sectors are concentrating towards vertical specialization. Sports, the creator economy, AI prediction, and social bot interaction are currently the four fastest-growing sub-sectors.

5.1 Sports Track

Key Logic

Sports events inherently feature high-frequency schedules and predictable outcomes, making them easy to quantify and predict, while also fostering a highly engaged user base. Platforms can quickly build trading markets and odds systems using middleware (such as Azuro Protocol), lowering the technical barrier to entry.

Representative projects

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

• Overtime: Combining community interaction with derivatives trading to form a closed-loop ecosystem.

• SX Network and Azuro Protocol: Providing public blockchain and middleware support for sports prediction.

User Behavior Characteristics

• High-frequency participation, instant betting, and active trading around the event

• User behavior is easily influenced by community and social recommendations.

• Prefers leveraged instruments and short-term contracts, seeking rapid feedback.

Trends and Opportunities

In the next 1-3 years, the sports sector will become more professional: high-frequency derivatives, leveraged trading and cross-chain aggregation will become standard features, forming a compound growth model of "sports prediction + community economy" through communities and event ecosystems.

5.2 Creator Economy Track

Key Logic

By combining prediction markets with the creator economy, KOLs are directly empowered with market generation and revenue distribution. While participating in predictions, users also become community content producers, forming a closed-loop ecosystem through creator revenue sharing incentives, resulting in significant viral growth.

Representative projects

• Melee: Offers a 20% revenue share to creators, incentivizing KOLs to drive market growth.

• Index.fun: 30% creator revenue, packaging prediction results into a "Creator Index" to enhance secondary transactions and community engagement.

Trends and Opportunities

The future of the creator community will move towards indexation and platformization: platforms can transform creators' influence into tradable assets through predictive indices, NFT-based incentives, and revenue sharing mechanisms.

5.3 AI Prediction Track

Key Logic

AI is evolving from an auxiliary tool to a core product, taking on functions such as market generation, event analysis, content production, and settlement. Through intelligent agents and Copilot, the platform achieves zero-cost creation, unlimited supply, and automated settlement, significantly reducing operating costs.

Representative projects

• OpinionLabs: AI-powered agent generates event marketplaces and automatically settles prediction results.

• BuzzingApp: AI-driven with zero human intervention, supporting high-speed event iteration and settlement.

Trends and Opportunities

In the next 1-3 years, AI will become a standard feature of the prediction market: AI will be applied to the entire chain of market generation automation, intelligent settlement, event analysis and risk control, which will give rise to new high-frequency and highly intelligent products and attract professional quantitative traders.

5.4 Social Bot Interaction Track

Key Logic

Lightweight front-end and social integration lower the barrier to entry for users, allowing predictive transactions to be directly embedded in Telegram, X platform tweets, or content wallets, forming a closed loop of "social as transaction".

Representative projects

• Flipr, Noise: Telegram Bots simplify complex trading operations with one-click order placement.

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

Trends and Opportunities

In the future, the social bot sector will deeply integrate platform aggregators and leverage tools to achieve cross-chain liquidity integration, and further expand user coverage through social embedding, becoming a "growth engine" for the prediction market.

In summary, the rise of vertical markets reflects the trend of prediction markets evolving from general-purpose information tools towards "derivative products + data services + AI embedding + creator/social ecosystem." Each market segment forms a complete logical chain: market-driven → user behavior → technological support → investment opportunities.

VI. Breakthrough Point for Small-Scale Forecasting Markets

Even in industries with extremely high concentration, smaller platforms still have several "blue ocean" opportunities to tap into:

6.1 Verticalization/Niche Markets

• Professional sports events, e-sports, industry KPIs

• Internal corporate forecasting of market events and professional association events

• Specific industry or regional policy events

Logic: In-depth or specialized events that mainstream platforms cannot cover can form a high-value but low-volume market.

6.2 Data Productization + B2B Embedding

• Instead of trading directly, they create price signals as API/index products and sell them to funds or companies.

• The core advantages are low regulatory risk and a sustainable business model.

6.3 Experience Differentiation / Information Value-Added

• Provides predictive pre-analysis tools and community consensus mechanisms

• Make predictions a form of "cognitive enhancement rather than pure transaction" to increase user engagement.

The core logic is that smaller platforms should avoid direct competition in liquidity and instead focus on high-value, low-scale scenarios and data-output-oriented business models.

VII. Investment Perspective: Structural Infrastructure is the Real Betting Direction

Potential high-value sectors in the future include:

• Predicting market data API (sold to quantitative funds)

• Enterprise-level decision-making market SaaS

• Market making and risk intermediaries

• Probability index products (similar to the VIX Future Expectation Index)

The real moat will belong to those who control the probability distribution, rather than those who facilitate transactions.

7.1 Overview of Actual VC Investment Directions

7.2 Interpretation of Key Financing Signals

The Clearing Company has raised approximately $15 million in funding from investors including Union Square Ventures, Coinbase Ventures, Haun Ventures, and Variant. This is a very crucial signal: capital is beginning to recognize prediction markets as a legitimate asset class that requires 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 funds will account for about 55% of prediction market capital. This means that it is undergoing an evolutionary path similar to the prediction market technology stack of 2017 DEX → 2021 DeFi → 2024.

VIII. Future Trends and Evolutionary Directions

8.1 Mechanism Evolution: Deepening of Derivativeization

The prediction market will gradually shift from "event outcome prediction" towards high-frequency trading, structured options, and leveraged contracts. A typical path:

• Short-term event contracts (such as Limitless 30-minute contracts) → High-frequency volatility trading tools

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

• Range forecasting and spread arbitrage gradually evolved into structured options and financial derivatives.

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

8.2 Product Evolution: Data as a Service + AI Embedded

Predicting market prices already reflects "event probabilities" and will become a core data source for institutional quantitative analysis, asset allocation, and risk management in the future. Product formats will include:

• Data subscription: Provides real-time market probabilities, top account behavior, and arbitrage opportunities.

• Indexing: Combining different prediction results into "Creator Index" or "Event Index" facilitates secondary trading or integration into DeFi.

• Visualization Terminal: A Polysights-style "Prediction Market Bloomberg Terminal" that directly provides strategy signals.

At the same time, AI will participate in market generation, automatic settlement, content analysis and risk control: automatically generating event markets (zero human intervention), intelligent settlement and odds adjustment, and AI Agent/Copilot participating in transaction prediction.

8.3 Infrastructure Evolution: Modularization and Composability

Prediction markets will be more like DeFi Lego: modular elements such as market generation, settlement, liquidity, oracles, and AI agents will be available, allowing projects to be plugged in and used, lowering the technical threshold, and supporting 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 the standard: chains such as Base, Polygon, and Solana have become the foundation for vertical tracks.

8.4 Evolution of User Experience

Front-end interaction is evolving towards socialization, lightweighting, and immediacy: Bots (Telegram/social platforms), one-click ordering, and leveraged operations are embedded in the content ecosystem. AI + intelligent oracles reduce manual operations and costs, while automated settlement and intelligent event analysis improve platform scalability.

Over the next 1-3 years, the market is projected to experience accelerated development driven by four engines: "derivatives + data servitization + AI embedding + composable infrastructure." It will evolve from a simple information aggregation tool into a comprehensive system integrating financial derivatives markets, data services, AI ecosystems, and creator/vertical sector integration. Investment value will be concentrated in infrastructure modules, data services, vertical sector applications, and AI and interaction layer innovation.

Conclusion: A New Social Infrastructure

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

How can humans form actionable consensus in the face of uncertainty?

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

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