Two companies dominate 97%, trading volume soars by 1100%: reshaping the landscape of prediction markets and the next wave of entrepreneurial opportunities

Introduction: Redefining the Boundaries of Prediction Markets

Prediction markets are platforms that allow participants to trade on the outcomes of uncertain future events. Contract prices reflect the market’s consensus probability of those events occurring. They outperform expert forecasts and polls in areas like political elections, macroeconomics, sports, crypto assets, and corporate events.

At its core, a prediction market is an “information financialization” tool—prices equal probabilities. In high-uncertainty, highly subjective domains, prediction markets have clear advantages.

In 2025, the global trading volume of prediction markets is approximately $50.25 billion. If we measure maturity by trading volume rather than narrative, prediction markets will have truly transitioned from “short-term curiosity driven by events” to “sustainable markets” only in 2025.

Kalshi has demonstrated that the industry is not just about “having trading volume,” but is beginning to show signs of commercialization—its reports claim around $260 million in fee revenue. Nonetheless, prediction markets have not yet entered a growth phase. Compared to the hundreds of trillions of dollars in annual trading volume of mature financial futures markets worldwide, today’s prediction markets are more like 1982 financial futures rather than 2020 cryptocurrencies.

Unlike most financial innovations, prediction markets did not undergo a long tail competition. Instead, they rapidly consolidated into two platforms: Kalshi and Polymarket, which together hold over 97.5% of the market share. All other platforms combined account for only about $1.25 billion in trading volume, placing them on the fringe.

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

Prediction markets are no longer just simple trading innovations; they are evolving into information production mechanisms within a non-attention economy.

The key differences from traditional attention economies are:

• Value is not based on clicks, traffic, or popularity
• Core assets are cognition and information quality
• Participants seek verifiable, tradable, and citeable judgments, not short-term attention exposure

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

• Brokerage research systems
• Consulting judgment frameworks
• Media narrative control
• Probabilistic outputs from AI after training

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

The industry’s true watershed at this stage is not technology but three critical factors: whether continuous information liquidity can be established; whether it can enter a “weak regulation tolerable zone” rather than a gray arbitrage zone; and whether it can be used by institutions as decision inputs rather than retail entertainment tools. Once these are in place, prediction markets will resemble a hybrid of Bloomberg + exchanges + polling agencies, rather than Web3 applications.

2. The Underestimated Core Asset: The Power to Define Problems

Most people underestimate the core asset of prediction markets—not liquidity, but the ability to define the problem.

Whoever controls problem definition also controls: information entry points, trading contexts, and probability interpretation rights. This is highly similar to the power structure of index providers like MSCI. A well-designed market question essentially forms a tradable cognitive framework.

3. Why Will the Value of Prediction Markets Be Revalued in 2024–2026?

2025 is not an accident; it results from the convergence of three structural factors.

3.1 Clearer Regulatory Expectations

• In 2024, US states and the CFTC are clarifying their stance on event contracts.

• Kalshi’s legal pathway opens traditional institutional capital, leading to a sudden increase in institutional trading volume.

• Traditional investors begin to see prediction markets as “event trading tools that can contribute to alpha,” rather than gray-area gambling.

3.2 Intensified Trading Scale + Continuous Event Supply

• Previously, prediction market events focused on politics or single occurrences, with short trading cycles and high volatility.

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

• Continuous events create a self-reinforcing liquidity cycle: liquidity deepens information → attracts more trading → price signals become more accurate.

3.3 Marginal Expansion of Information Demand

• In the AI era, while data is abundant, “probability credibility” becomes a scarce asset.
• Quant funds, hedge funds, and corporate decision departments are starting to treat prediction market prices as genuine signals.

Core logic: It’s not about user traffic growth but capital and information demand-driven liquidity concentration—this is the real inflection point for prediction markets.

3.4 The Confluence of Three Structural Forces

Force One: The “failure margin” of traditional research systems is emerging

Over the past decade, sell-side research has lagged significantly in predicting macro turning points; buy-side increasingly views “speed of consensus formation” as an alpha source; expert models resemble narrative engineering rather than probabilistic discovery.

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

Force Two: Post-AI rise, society needs “authentic signal sources” more than ever

Large models can generate judgments but cannot bear risks. The unique advantage of prediction markets lies in their irreplaceable mechanism:

This makes them one of the few systems in the AI era with a fact anchoring mechanism, which is why more quant funds are starting to treat prediction market prices as exogenous variables.

Force Three: Web3 solves a key constraint—settlement trustworthiness

The biggest problem early prediction markets faced was not lack of prediction but “who acts as the market maker? How to prevent default? How to enable global participation?” On-chain settlement shifts trust from “trusting operators” to “trusting code execution,” enabling prediction markets to expand across jurisdictions for the first time.

4. Comparing Leading Platforms (Actual Scale in 2025)

① Kalshi — The current liquidity hub

• Nominal trading volume in 2025: approximately $23.8 billion

• Over 55–60% of weekly industry trading volume at peak, making it the most liquid market

• Market share in some periods rose to 62.2%

• Monthly trading once reached around $1.3 billion

• Growth driven mainly by opening regulatory pathways for traditional capital, not crypto user expansion

Kalshi’s strategy is markedly different: actively engaging with regulatory frameworks, defining prediction markets as “event contract exchanges,” aiming to replicate the legitimacy path of futures markets. Short-term growth is slow, but success could unlock pension, RIA, and institutional allocations.

② Polymarket — Native crypto liquidity hub

• Total trading volume in 2025: about $22 billion

• Maintains several hundred million dollars monthly in some months

Polymarket pursues a permissionless global liquidity approach: rapidly building event coverage density, leveraging on-chain friction reduction, and replacing compliance depth with trading activity.

Its true value is not just volume but the creation of the world’s first “real-time political probability curve”—a dataset that has never existed in traditional systems.

③ Second-tier platforms (small share but indicative of future divergence)

Despite high industry concentration, exploratory platforms like Azuro, TrendleFi, etc., have emerged. These combined contribute only about $1.25 billion, indicating the industry is still in infrastructure validation rather than full bloom.

Augur exemplifies the limitations of first-generation decentralized experiments: overemphasizing “trustless” without addressing real trader experience, problem distribution, or liquidity acquisition. This shows prediction markets are not purely a technical issue but a market design challenge.

Logical conclusion: The core of prediction markets is not technology but the combined moat of liquidity and event design capability. Low-liquidity platforms will struggle to compete through decentralization alone.

5. Why Do Most Prediction Markets Fail?

Historical failures are less about technology and more about market microstructure.

5.1 Treat prediction markets as “event casinos”

This mistake causes high-frequency noise to drown out informational traders, making market-making capital unsustainable, and Sharpe ratios unviable. Successful prediction markets must give informational traders a structural advantage.

5.2 Mismatch in liquidity sources

Prediction markets need not retail traders but macro traders, policy analysts, industry experts, and hedgers. They provide information-driven trading flows, not gambling-like flows.

5.3 Poor settlement cycle design

If settlement is too frequent, it degenerates into gambling; if too infrequent, capital efficiency is lost. The optimal window is typically 2 weeks to 6 months—aligning with the real-world “disagreement but still verifiable” timeframe.

6. Vertical Sector Analysis: Four High-Growth Subfields

As the window for general prediction markets closes, opportunities are shifting toward vertical niches. Sports, creator economy, AI prediction, and social bot interactions are currently the fastest-growing sectors.

6.1 Sports Sector

Key logic

Sports events have high frequency and clear outcomes, making them easy to quantify and predict. They also foster highly engaged user bases. Platforms can quickly build trading markets and odds systems via middleware (e.g., Azuro Protocol), lowering technical barriers.

Representative projects

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

• Overtime: Combines community engagement with derivative trading, forming a closed ecosystem

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

User behavior

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

• User actions influenced by communities and social recommendations

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

Trends & Opportunities

In 1–3 years, sports prediction will further professionalize: high-frequency derivatives, leverage trading, and cross-chain aggregation will become standard, creating a “sports prediction + community economy” growth model.

6.2 Creator Economy Sector

Key logic

Combining prediction markets with creator economy empowers KOLs directly. Users participate in predictions and become content creators, forming a closed-loop ecosystem with creator revenue sharing, leading to viral growth.

Representative projects

• Melee: Offers 20% creator share, incentivizing KOL-driven market creation

• Index.fun: 30% creator revenue, packaging prediction results into “creator indices” to boost secondary trading and community engagement

Trends & Opportunities

The creator sector will trend toward indexing and platformization: platforms can turn prediction indices, NFTs, and revenue sharing into tradable assets, amplifying creator influence.

6.3 AI Prediction Sector

Key logic

AI is shifting from an auxiliary tool to a core product, handling market generation, event analysis, content creation, and settlement. With intelligent agents and Copilot, platforms can create markets at zero cost, supply infinitely, and automate settlement, drastically reducing operational costs.

Representative projects

• OpinionLabs: AI agents generate event markets and automatically settle predictions

• BuzzingApp: AI-driven, zero-human operation supporting rapid event iteration and settlement

Trends & Opportunities

In 1–3 years, AI will become standard in prediction markets: automated market creation, intelligent settlement, event analysis, and risk control will be fully AI-driven, spawning new high-frequency, high-intelligence products, attracting professional quant traders.

6.4 Social Bot Interaction Sector

Key logic

Lightweight front-end and social embedding lower user entry barriers, integrating prediction trading directly into Telegram, X posts, or content wallets, forming a “social-as-trading” closed loop.

Representative projects

• Flipr, Noise: Telegram bots for one-click orders, simplifying complex operations

• XO Market: Aggregates orders from multiple platforms, offers leverage and stop-loss features, catering to professional users

Trends & Opportunities

Future social bot sectors will deeply integrate platform aggregators and leverage tools, enabling cross-chain liquidity aggregation and expanding user reach through social embedding—becoming the “growth engine” of prediction markets.

Conclusion: The rise of vertical sectors reflects prediction markets’ evolution from general information tools toward “derivatives + data services + AI embedding + creator/social ecosystems.” Each sector forms a complete logical chain: market drivers → user behavior → technological support → investment opportunities.

7. Breakthroughs in Small Prediction Markets

Even with high industry concentration, small platforms still have several “blue ocean” entry points:

7.1 Vertical/Niche Markets

• Specialized sports, esports, industry KPIs

• Internal corporate prediction markets, professional association events

• Specific industry or regional policy events

Logic: Deep or professional events that mainstream platforms cannot cover can form high-value but low-trade-volume markets.

7.2 Data Productization + B2B Embedding

• Not directly trading but packaging price signals as APIs or indices for funds or enterprises

• Advantages include low regulatory risk and sustainable business models

7.3 Experience Differentiation / Information Value-Add

• Providing pre-prediction analysis tools, community consensus mechanisms

• Making prediction a form of “cognitive value-add” rather than pure trading, increasing user stickiness

Logic: Small platforms should avoid direct competition on liquidity and instead focus on high-value, low-scale scenarios and data-driven business models.

8. Investment Perspective: Infrastructure as the True Bet

High-value directions in the future include:

• Prediction market data APIs (sold to quant funds)

• Enterprise decision-making SaaS platforms

• Market-making and risk intermediaries

• Probabilistic index products (similar to VIX futures)

The real moat belongs to those controlling probability distribution, not just matching trades.

8.1 VC Investment Landscape

8.2 Key Financing Signals

The Clearing Company completed approximately $15 million in funding, with investors including Union Square Ventures, Coinbase Ventures, Haun Ventures, and Variant. This is a critical signal: capital is beginning to treat prediction markets as a formal asset class requiring clearinghouses.

Kalshi’s valuation has risen to $5 billion; FanDuel and CME are preparing to launch prediction market products. By 2025, institutional capital will account for about 55% of prediction market assets. This mirrors the evolution path from 2017 DEX to 2021 DeFi to 2024 prediction market tech stacks.

9. Future Trends and Evolution Directions

9.1 Mechanism Evolution: Deepening Derivatives

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

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

• Leveraged trading (e.g., Flipr 5x) → integration with DeFi leverage protocols, forming on-chain derivatives ecosystems

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

Cross-chain and cross-platform liquidity aggregation will become core competitive advantages. Aggregators will merge order books across platforms, offering optimal prices and settlement solutions—similar to “Prediction Market 1inch.”

9.2 Product Evolution: Data as a Service + AI Embedding

Prediction market prices already reflect “event probabilities.” In the future, they will become key data sources for institutional quantification, asset allocation, and risk management. Product forms include:

• Data subscriptions: real-time market probabilities, top account behaviors, arbitrage opportunities

• Indexing: combining different prediction results into “creator indices” or “event indices” for secondary trading or DeFi embedding

• Visualization terminals: “Prediction Market Bloomberg Terminal” style, providing direct strategy signals

Meanwhile, AI will participate in market generation, automated settlement, content analysis, and risk control: automatically generating event markets (zero manual intervention), intelligent odds adjustment, and AI agents/Copilots involved in trading predictions.

9.3 Infrastructure Evolution: Modular and Composable

Prediction markets will resemble DeFi Lego blocks: modules for market creation, settlement, liquidity, oracles, and AI agents—plug-and-play, lowering technical barriers, supporting multi-chain deployment.

• Gnosis CTF → Standardized asset issuance

• Azuro Protocol → Betting middleware

• Polymarket/Kalshi → Core settlement layers

Multi-chain deployment and cross-chain order aggregation will become standard: Base, Polygon, Solana, and other chains will serve as the backbone for vertical sectors.

9.4 User Experience Evolution

Frontend interactions will become more social, lightweight, and real-time: bots (Telegram, social platforms), one-click trading, embedded leverage tools. AI and smart oracles will reduce manual operations and costs, enabling automated settlement and intelligent event analysis, enhancing platform scalability.

Over the next 1–3 years, prediction markets will accelerate driven by “derivatives + data services + AI embedding + modular infrastructure.” From simple information aggregation tools, they will evolve into comprehensive systems combining financial derivatives, data services, AI ecosystems, and creator/vertical sector integrations. Investment focus will be on infrastructure modules, data services, vertical applications, and innovations in AI and interaction layers.

Conclusion: A New Social Infrastructure

Prediction markets are not just fringe financial innovations but are attempting to solve a fundamental problem:

How can humanity form actionable consensus on uncertainty?

As overload of information, AI generalization, and expert failure occur simultaneously, the importance of this mechanism is just beginning to emerge.

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