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

Author: MetaHub Research

Introduction: Redefining the Boundaries of Prediction Markets

Prediction markets are platforms that allow participants to trade on the outcomes of uncertain future events, with contract prices reflecting the market’s consensus on the probability of those events. They have demonstrated significantly higher accuracy than expert forecasts and polls in areas such as political elections, macroeconomics, sports, cryptocurrencies, and corporate events.

At its core, a prediction market is a “financialization of information”—price equals probability. In high-uncertainty, highly subjective domains, prediction markets hold clear advantages.

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

Kalshi has proven that the industry is not just about “having trading volume,” but is beginning to demonstrate commercialization—its reports claim around $260 million in fee revenue. Nevertheless, prediction markets have not yet entered a growth phase; compared to the hundreds of trillions of dollars in annual trading volume of mature global futures markets, 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 but quickly collapsed into two dominant 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 ecosystem.

1. The Essence of Prediction Markets: An Information Production Mechanism in the Attention Economy

Prediction markets are no longer just simple innovations in trading formats; they are evolving into an information production mechanism under the attention economy.

The core difference from traditional attention economy:

• Value is not based on clicks, traffic, or popularity

• The core asset is cognitive and informational quality

• Participants seek verifiable, tradable, and citeable judgments, not short-term attention exposure

In this logic, the competitive landscape of prediction markets is also shifting:

• Brokerage research systems

• Consulting judgment frameworks

• Media narrative authority

• Probabilistic outputs from AI 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 key aspects:

• Whether continuous information liquidity can be established

• Whether it can enter a “weak regulation tolerable zone” rather than a gray arbitrage zone

• Whether it is used by institutions as decision inputs rather than retail entertainment tools

Once these are achieved, prediction markets will resemble a hybrid of Bloomberg + exchanges + polling agencies, rather than a Web3 application.

The issue of defining the core asset: severely underestimated

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 is essentially a tradable cognitive framework.

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

2025 is not an accidental inflection point but the result of the superposition of three structural factors.

2.1 Clearer Regulatory Expectations

• In 2024, US states and the CFTC are moving toward clearer regulation of event contracts

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

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

2.2 Intensified Trading Scale + Continuous Event Supply

• Historically, 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 emerge, allowing markets to absorb capital continuously

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

2.3 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 beginning to treat prediction market prices as genuine signals

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

2.4 Superimposition of Three Structural Forces

Force 1: The “failure margin” of traditional research systems is becoming apparent

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

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

Force 2: 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:

Thus, prediction markets become one of the few systems in the AI era with a fact anchoring mechanism, which is why more quantitative funds are starting to treat prediction market prices as exogenous variables.

Force 3: Web3 solves a key constraint—settlement trustworthiness

The biggest problem early on was not the 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 the operator” to “trusting the code execution,” enabling prediction markets to expand across jurisdictions for the first time.

3. Comparison of Leading Platforms (Actual Scale in 2025)

① Kalshi — Current liquidity hub

• Nominal trading volume in 2025: approximately $23.8 billion, over 1100% YoY growth

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

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

• Monthly trading volume once reached around $1.3 billion

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

Kalshi’s strategy is quite different: actively entering regulatory frameworks, defining prediction markets as “event contract exchanges,” attempting to replicate the legitimacy path of futures markets. Short-term growth is slow, but if successful, it will open the gates for pension funds, RIAs, and institutional allocations.

② Polymarket — Native crypto liquidity hub

• Full-year trading volume in 2025: about $22 billion

• Maintains several hundred million dollars monthly trading volume in some months

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

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

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

Despite high market 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” mechanisms, neglecting real trader experience, and failing to solve problem distribution and liquidity acquisition. This shows prediction markets are not just a technical issue but a market design challenge.

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

4. Why Do Most Prediction Markets Fail?

Historically, failed platforms did not fail due to technology but due to microstructure issues.

4.1 Treat prediction markets as “event casinos”

This mistake leads to high-frequency noise overwhelming information traders, market makers unable to sustain long-term presence, and unsustainable Sharpe ratios. Successful prediction markets must give information-driven traders a structural advantage.

4.2 Mismatch in liquidity sources

Prediction markets require not retail traders but macro traders, policy analysts, industry experts, and hedgers—those providing information-driven trading flows, not gambling-like flows.

4.3 Poor settlement cycle design

If the market settlement cycle is too short, it degenerates into gambling; too long, it loses capital efficiency. The optimal window is typically 2 weeks to 6 months, aligning with the real-world “disagreement but still verifiable” timeframes.

5. Vertical Track Analysis: Four High-Growth Sub-sectors

As the window for general prediction markets closes, opportunities are concentrating vertically. Sports, creator economy, AI prediction, and social bot interaction are the four fastest-growing sub-sectors.

5.1 Sports Sector

Key logic:

Sports events inherently have high-frequency schedules and clear outcomes, making them easy to quantify and predict, while also forming a highly engaged user base. 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 interaction and derivatives trading, forming an 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 community and social recommendations

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

Trends & opportunities:

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

5.2 Creator Economy

Key logic:

Combining prediction markets with creator economy enables direct empowerment of KOLs in market generation and revenue sharing. Users participate in predictions and become content creators, forming a viral ecosystem driven by creator incentives.

Representative projects:

• Melee: Offers 20% creator revenue share, incentivizing KOLs to generate markets

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

Trends & opportunities:

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

5.3 AI Prediction

Key logic:

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

Representative projects:

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

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

Trends & opportunities:

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

5.4 Social Bot Interaction

Key logic:

Lightweight front-end and social embedding lower user operation 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 multiple platform orders, offers leverage and stop-loss features, catering to professional users

Trends & opportunities:

Future social bot tracks will deeply integrate platform aggregators and leverage tools, enabling cross-chain liquidity aggregation and expanding user reach, becoming a growth engine for prediction markets.

Summary: 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 drive → user behavior → technology support → investment opportunities.

6. Breakthroughs for Small Prediction Markets

Even with high industry concentration, small platforms can find “blue ocean” niches:

6.1 Vertical / Niche Markets

• Sports, esports, industry KPIs

• Corporate internal prediction markets, professional associations

• Specific industry or regional policy events

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

6.2 Data Productization + B2B Embedding

• Instead of direct trading, package price signals as APIs or indices for funds or enterprises

• Advantages: low regulatory risk + sustainable business model

6.3 Differentiated Experience / Information Value-Add

• Provide pre-prediction analysis tools, community consensus mechanisms

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

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

7. Investment Perspective: Structural Infrastructure as the True Bet

Future high-value directions include:

• Prediction market data APIs (sold to quant funds)

• Enterprise decision-making market SaaS

• Market-making and risk intermediaries

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

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

7.1 VC Investment Landscape

7.2 Key Funding Signals

The Clearing Co. raised about $15 million, 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 formal asset classes requiring clearinghouses.

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

8. Future Trends and Evolution Directions

8.1 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 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

Simultaneously, cross-chain and cross-platform liquidity aggregation will become core. Aggregators will combine order books from different platforms to offer optimal prices and settlement solutions, akin to “Prediction Market 1inch.”

8.2 Product Evolution: Data Services + 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.

8.3 Infrastructure Evolution: Modular and Composable

Prediction markets will resemble DeFi Lego blocks: market creation, settlement, liquidity, oracles, AI agents, etc., as modular components that can be plugged in, reducing technical barriers and supporting multi-chain deployment.

• Gnosis CTF → Standardized asset issuance

• Azuro Protocol → Betting middleware

• Polymarket/Kalshi → Core settlement layer

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

8.4 User Experience Evolution

Frontend interactions will become more social, lightweight, and instant: bots (Telegram/Social platforms), one-click orders, leverage embedded in content ecosystems. AI + smart oracles will reduce manual operations and costs, enabling automated settlement and intelligent event analysis, enhancing platform scalability.

In the next 1–3 years, prediction markets will accelerate driven by “derivatives + data services + AI embedding + composable infrastructure.” From being simple information aggregation tools, they will evolve into comprehensive systems combining financial derivatives, data services, AI ecosystems, and creator/vertical track 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?

When 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.