Two Platforms Dominate 97%, Trading Volume Soars 1100%: The Reshaping of Prediction Markets and the Next Wave of Entrepreneurial Opportunities

 Introduction: Redefining the Boundaries of Prediction Markets

A prediction market is a market that allows participants to trade on the outcomes of future uncertain events, where the contract price reflects the market's consensus judgment on the probability of that event occurring. It has demonstrated significantly superior accuracy compared to expert predictions and polls in areas such as political elections, macroeconomics, sports events, crypto assets, and corporate events.

At its core, a prediction market is a tool for "financializing information"—price equals probability. In areas of high uncertainty and strong subjective judgment, prediction markets possess significant advantages.

The total global trading volume of prediction markets in 2025 was approximately $50.25 billion. If maturity is defined by trading volume rather than narrative, prediction markets truly completed the transition from [event-driven short-term curiosity] to [a persistent market] in 2025.

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

Unlike most financial innovations, prediction markets did not experience long-tail competition but rapidly consolidated towards two platforms: Kalshi and Polymarket together account for over 97.5% of market share. The combined trading volume of all other platforms is only about $1.25 billion, constituting a marginal ecosystem.

1. The Essence of Prediction Markets: An Information Production Mechanism in a Non-Attention Economy

Prediction markets are no longer merely an innovation in trading forms; they are evolving into an information production mechanism within a non-attention economy.

The core differences from the traditional attention economy are:

• Value does not depend on clicks, traffic, or popularity
• The core asset is cognition and information quality
• Market participants pursue verifiable, tradable, and citable judgments, not short-term attention exposure

Under this logic, the competitive landscape for prediction markets has also shifted:

• Brokerage research systems
• Consulting firm judgment systems
• Media narrative power
• Probability outputs from AI training

In other words, this is a market for pricing future cognition.

The real watershed for the industry at its current stage lies not in technology, but in three things: whether it can form continuous information liquidity; whether it enters a "weakly regulated tolerable zone" rather than a gray arbitrage zone; and whether it is treated by institutions as a decision-making input, not as a retail entertainment tool. Once these three points are established, the form of prediction markets will resemble a hybrid of Bloomberg + an exchange + a polling agency, rather than a Web3 application.

The Power to Define Problems: A Severely Undervalued Core Asset

Most people underestimate the most core asset of prediction markets—it's not liquidity, but the ability to define problems.

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

 2. Why Was the Value of Prediction Markets Suddenly Reassessed in the 2024–2026 Cycle?

2025 was not an accidental inflection point; it was the result of the convergence of three types of structural factors.

2.1 Regulatory Clarification Expectations

• In 2024, regulatory attitudes towards event contracts became clearer in multiple US states and at the CFTC

• Kalshi's legal pathway opened the entry point for traditional institutional capital, leading to a sudden surge in institutional trading volume

• Traditional investors began viewing prediction markets as "event trading tools that can generate alpha," not as gray gambling

2.2 Trading Scale Intensification + Continuous Event Supply

• In the past, prediction market events were mostly concentrated in politics or one-off events, with short trading cycles and high volatility

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

• Continuous events created a self-reinforcing cycle of liquidity: liquidity leads to information depth → attracts more trading → price signals become more accurate

2.3 Marginal Amplification of Information Demand

• Although the AI era is flooded with data, "probability credibility" has become a scarce asset
• Quantitative funds, hedge funds, and corporate decision-making departments began treating prediction market prices as a source of genuine signals

Core logic: It's not about user growth from traffic, but the concentration of liquidity triggered by capital and information demand—this is the real inflection point for prediction markets.

2.4 The Overlap of Three Structural Forces

Force One: The "failure margin" of the traditional research system is becoming apparent

Over the past decade, sell-side research has shown significant lags in predicting macro inflection points; buy-side firms have gradually come to view "consensus formation speed" as a source of alpha; expert models increasingly resemble narrative engineering rather than probability discovery.

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

Force Two: After the rise of AI, human society actually needs "real signal sources" more

Large models can generate judgments but cannot bear risk. 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 factual anchoring mechanism, which is why more and more quantitative funds are starting to treat prediction market prices as exogenous variables.

Force Three: Web3 solved a key constraint—settlement credibility

The biggest problem with early prediction markets was not a lack of predictors, but: Who acts as the market maker? How to prevent default? How to enable global participation? On-chain settlement reduces trust from "believing the operator" to "believing code execution," giving prediction markets cross-jurisdictional scalability for the first time.

3. Head Platform Scale Comparison (2025 Actual Size)

① Kalshi — The Current Liquidity Center

• 2025 nominal trading volume approximately $23.8 billion, a year-over-year increase of over 1100%

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

• In some statistical periods, its global market share rose to 62.2%

• Monthly trading volume once reached the $1.3 billion level

• Growth momentum primarily came from the compliance pathway opening traditional capital entry points, not from crypto user expansion

Kalshi chose a completely different strategy: proactively entering the regulatory framework, defining prediction markets as "event contract exchanges," attempting to replicate the legitimacy path of futures markets. Short-term growth is slower, but if successful, it will open the floodgates for pension/RIA/institutional capital allocation.

② Polymarket — The Crypto-Native Liquidity Hub

• Full-year 2025 trading volume approximately $22 billion

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

Polymarket follows the path of global permissionless liquidity: rapidly achieving event coverage density, using on-chain mechanisms to reduce participation friction, and substituting trading activity for compliance depth.

Its real value is not trading volume, but establishing the world's first "real-time political probability curve"—such data never existed in traditional systems.

③ Second-Tier Platforms (Minimal total share but representing future differentiation directions)

Despite high market concentration, several exploratory platforms have emerged, such as Azuro, TrendleFi, etc. These platforms collectively contribute only about $1.25 billion in trading volume, indicating the industry has not yet entered a "blooming" phase but is still in the infrastructure establishment stage.

Augur represents the limitations of the first-generation decentralized experiment: overemphasizing "trustlessness," neglecting real trader experience, and failing to solve problem distribution and liquidity acquisition. This shows that prediction markets are not purely a technical problem but a market design problem.

Logical conclusion: The core of prediction markets is not technology, but the composite moat of liquidity and event design capability. Low-liquidity platforms are unlikely to succeed through fragmented competition.

 4. Why Do Most Prediction Markets Fail?

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

4.1 Treating Prediction Markets as "Event Casinos"

This mistake leads to: high-frequency noise overwhelming information traders, market-making capital unable to stay long-term, and unsustainable Sharpe Ratios. Successful prediction markets must give information-based traders a structural advantage.

4.2 Liquidity Source Mismatch

Prediction markets need not retail speculators, but: macro traders, policy researchers, industry experts, and risk hedgers. They provide information-driven trading flow, not gambling-driven trading flow.

4.3 Settlement Frequency Design Error

If the market settlement cycle is too short, it degenerates into gambling; if too long, it loses capital efficiency. The optimal range is typically events with an information half-life of 2 weeks to 6 months, which corresponds to the time window in the real world where "disagreements can form but are still verifiable."

5. Vertical Sector Analysis: Four High-Growth Segments

As the window for general-purpose prediction markets gradually closes, opportunities are concentrating towards verticalization. Sports, creator economy, AI prediction, and social bot interaction have become the four fastest-growing segments.

5.1 Sports Sector

Key Logic

Sports events inherently have high-frequency schedules and clear outcomes, making them easy to quantify for prediction, while also forming highly sticky user groups. Platforms can quickly build trading markets and odds systems through middleware (like Azuro Protocol), lowering technical barriers.

Representative Projects

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

• Overtime: Combines community interaction with derivative trading, forming a closed-loop ecosystem

• SX Network, Azuro Protocol: Provide public chain and middleware support for sports prediction

User Behavior Characteristics

• High-frequency participation, instant betting, active trading around events

• User actions easily influenced by community and group recommendations

• Preference for leverage tools and short-cycle contracts, seeking quick feedback

Trends & Opportunities

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

5.2 Creator Economy Sector

Key Logic

Combining prediction markets with the creator economy directly empowers KOLs with market creation and revenue distribution. Users become community content producers while participating in prediction, forming a closed-loop ecosystem through creator share incentives, leading to significant viral growth effects.

Representative Projects

• Melee: Offers 20% creator share, incentivizing KOLs to drive market creation

• Index.fun: 30% creator revenue, packaging prediction results into "creator indices," enhancing secondary trading and community engagement

Trends & Opportunities

The creator sector will move towards indexification and platformization: platforms can convert creator influence into tradable assets through prediction indices, NFT-based incentives, and sharing mechanisms.

5.3 AI Prediction Sector

Key Logic

AI is upgrading from an auxiliary tool to a core product, taking on market creation, event analysis, content production, and settlement functions. Through intelligent agents and Copilots, platforms achieve zero-cost creation, infinite supply, and automated settlement, significantly reducing operational costs.

Representative Projects

• OpinionLabs: AI agents generate event markets and automatically settle prediction results

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

Trends & Opportunities

In the next 1-3 years, AI will become standard for prediction markets: automation of market creation, intelligent settlement, event analysis, and risk control will be fully AI-driven, giving rise to new high-frequency and highly intelligent products while attracting professional quantitative traders.

5.4 Social Bot Interaction Sector

Key Logic

Frontend lightweighting and social embedding lower user operational barriers, embedding prediction trading directly into Telegram, X platform tweets, or content wallets, forming a "social is trading" closed loop.

Representative Projects

• Flipr, Noise: Telegram Bot one-click orders, simplifying complex trading operations

• XO Market: Aggregates orders from multiple platforms, provides leverage and stop-loss/take-profit functions, meeting professional user needs

Trends & Opportunities

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

Summary: The rise of vertical sectors reflects the trend of prediction markets evolving from general-purpose information tools towards "derivatization + data servitization + AI embedding + creator/social ecosystem integration." Each sector forms a complete logical chain: market driver → user behavior → technical support → investment opportunity.

6. Breakthrough Points for Small Prediction Markets

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

6.1 Verticalization / Niche Markets

• Professional sports events, esports, industry KPIs

• Corporate internal prediction markets, professional association events

• Specific industry or regional policy events

Logic: Deep or professional events not covered by mainstream platforms can form high-value but low-volume markets.

6.2 Data Productization + B2B Embedding

• Don't directly facilitate trading, but package price signals into API / index products sold to funds or corporations

• Core advantages are low regulatory risk + sustainable business model

6.3 Experience Differentiation / Information Value-Add

• Provide pre-prediction analysis tools, community consensus mechanisms

• Make prediction "cognitive value-add rather than pure trading," increasing user stickiness

Core logic: Small platforms should avoid head-on liquidity competition and instead focus on high-value, low-scale scenarios + data-output business models.

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

Future high-value directions may include:

• Prediction market data APIs (sold to quantitative funds)

• Enterprise-level decision market SaaS

• Market making and risk intermediation

• Probability index products (similar to a VIX-like Future Expectation Index)

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

7.1 Panorama of Actual VC Investment Directions

7.2 Interpretation of Key Financing Signals

The Clearing Company completed a roughly $15 million funding round, with investors including Union Square Ventures, Coinbase Ventures, Haun Ventures, Variant. This is a very critical signal: capital is starting to treat prediction markets as a formal asset class requiring a clearinghouse.

Kalshi's valuation rose to $5 billion; FanDuel and CME are also preparing to launch prediction market products to compete; by 2025, institutional capital accounted for about 55% of prediction market capital. This means it is undergoing an evolution path similar to 2017 DEX → 2021 DeFi → 2024 prediction market tech stack.

8. Future Trends & Evolution Directions

8.1 Mechanism Evolution: Deepening Derivative Nature

Prediction markets will gradually shift from "event outcome prediction" towards high-frequency trading, structured options, and leveraged contracts. Typical paths:

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

• Leveraged trading (Flipr 5x) → integration with DeFi leverage protocols, forming an on-chain derivatives ecosystem

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

Simultaneously, cross-chain and cross-platform liquidity integration becomes a core competency. Aggregators merge order books from different platforms, providing optimal pricing and settlement solutions, similar to a "1inch for prediction markets."

8.2 Product Evolution: Data Servitization + AI Embedding

Prediction market prices already reflect "event probabilities" and will become