Prediction Markets Poised for $10 Billion Revenue by 2030 as Kalshi and Polymarket Lead Institutional Adoption

Key Takeaways

• Prediction market platforms could generate $10 billion in annual revenue by 2030, according to Citizens Bank research projections
• Kalshi dominates regulated markets with CFTC approval, while Polymarket leads decentralized prediction volume
• Platform differentiation centers on regulatory compliance, market breadth, and liquidity provision mechanisms
• Institutional adoption accelerating as prediction markets demonstrate superior accuracy to traditional polling

Prediction markets are moving from niche forecasting tools to mainstream financial instruments, with Citizens Bank projecting annual revenue could reach $10 billion by 2030. This growth positions platforms like Kalshi and Polymarket as leaders serving different institutional needs, each building distinct competitive advantages as demand for accurate forecasting grows.

Platform Architecture and Market Structure

Two primary models now dominate the prediction market space: regulated exchanges and decentralized protocols. Kalshi operates under CFTC oversight, offering institutional comfort through regulatory compliance and traditional order book mechanics. Platform data shows Kalshi processes election contracts, Federal Reserve policy predictions, and economic indicator markets with transparent fee structures averaging 3-7% of gross winnings.

Polymarket takes a different approach, leveraging Polygon blockchain infrastructure to enable global participation through USDC-denominated contracts. The decentralized model eliminates geographic restrictions while maintaining pseudonymous trading. Volume data from the platform shows Polymarket capturing 60% of crypto-native prediction market activity, with average daily volumes exceeding $2 million during major political events.

How each platform handles market making reveals their strategic differences. Kalshi employs traditional market makers providing two-sided liquidity, while Polymarket utilizes automated market maker (AMM) pools supplemented by professional liquidity providers. These structural differences impact pricing efficiency and spread compression, with Kalshi typically maintaining tighter spreads on high-volume political contracts.

Regulatory Framework and Competitive Positioning

Regulatory status separates these platforms more than any other factor. Kalshi's CFTC approval enables direct institutional participation and marketing to U.S. residents, providing significant distribution advantages. The platform's recent legal victories, including successful appeals of CFTC restrictions on congressional election contracts, have established precedent for expanding permissible market categories.

Polymarket faces different constraints, restricting U.S. users while serving global markets. This limitation creates unexpected advantages in controversial political markets and long-term forecasting where regulatory approval proves elusive. The platform's performance during manipulation attempts in the 2024 election cycle demonstrated oracle reliability through UMA's dispute resolution mechanism.

Expansion strategies reflect these regulatory realities. Kalshi pursues regulated market entry through partnerships with licensed operators, while Polymarket leverages blockchain infrastructure for permissionless global access. Both target the estimated $300 billion global gambling market, positioning prediction markets as information products rather than entertainment betting.

Information Aggregation and Market Efficiency Analysis

Accuracy data favors prediction markets over traditional forecasting methods across multiple categories. Analysis of 2024 election predictions shows Kalshi and Polymarket maintaining superior calibration compared to polling aggregators, with Brier scores averaging 0.15 versus 0.23 for conventional polls, according to academic research compiled by Metaculus.

Prediction markets outperform through specific structural advantages: real-time price discovery, financial incentives for accuracy, and aggregation of dispersed information. Participants risk actual capital on predictions, creating stronger incentive alignment than survey responses or pundit predictions.

Liquidity depth varies dramatically by market category. Political event contracts often achieve $10-50 million in total volume, while niche scientific prediction markets may trade under $100,000. This disparity reflects both public interest and the challenge of pricing low-probability, long-duration events.

Revenue Model Evolution and Growth Projections

Platform monetization centers on transaction fees, market making spreads, and data licensing. Kalshi charges percentage-based fees on winning positions, generating estimated revenue of $15-25 million annually based on disclosed trading volumes. Polymarket captures value through token mechanisms and partnership revenue, though exact figures remain private.

Reaching the projected $10 billion revenue target requires dramatic expansion beyond current political and sports betting applications. Citizens Bank research identifies several potential growth drivers:

• Corporate earnings and M&A prediction markets serving institutional hedging needs
• Central bank policy prediction enabling macro strategy implementation
• Real estate and commodity price forecasting for commercial applications
• Scientific research outcome markets supporting R&D investment decisions

Institutional adoption demands additional infrastructure development. Custody solutions, prime brokerage services, and risk management tools lag behind traditional derivatives markets. Platforms investing in institutional infrastructure may capture disproportionate revenue share as the market matures.

Competitive Landscape and Market Consolidation

Secondary platforms serve specialized niches beyond Kalshi and Polymarket. Metaculus focuses on long-range scientific forecasting with strong academic integration. PredictIt operates under research exemptions with position limits restricting commercial scale. Manifold Markets gamifies prediction through play money, developing user engagement before potential real-money conversion.

Market consolidation appears inevitable as regulatory costs and liquidity requirements favor larger platforms. Leading prediction markets will likely emerge from current players expanding market breadth rather than new entrants challenging established positions. Network effects prove particularly strong here—liquidity attracts informed traders, improving price discovery and attracting additional participants.

Oracle integration represents a crucial competitive differentiator. Platforms requiring human judgment for outcome determination face scalability constraints and manipulation risks. Automated settlement through blockchain oracles enables broader market categories but introduces technical risks around data accuracy and availability.

Risk Assessment and Market Limitations

Prediction market growth faces substantial regulatory headwinds. State-level gambling restrictions limit market access, while federal agencies maintain skeptical positions toward information markets. Platform operators navigate complex compliance requirements that constrain product development and marketing efforts.

Market manipulation concerns persist despite structural protections. Large capital deployments can distort pricing, particularly in low-liquidity markets. Detection mechanisms include unusual volume analysis and cross-platform price monitoring, though sophisticated manipulation attempts may evade current surveillance systems.

Liquidity constraints affect market efficiency across numerous categories. Niche prediction markets often lack sufficient trading volume for accurate price discovery, limiting their value as information aggregation tools. This creates barriers to expansion into specialized forecasting applications.

Institutional Integration and Future Applications

Corporate adoption of prediction markets for internal forecasting shows promising early results. Technology companies use internal markets to predict product launch success, while financial institutions experiment with employee prediction markets for economic forecasting. These applications suggest significant untapped demand for specialized prediction market infrastructure.

Integration with traditional financial systems remains limited but growing. Prediction market prices increasingly influence options pricing and volatility forecasts, while institutional investors explore these markets as alternative data sources. Full integration requires additional regulatory clarity and risk management framework development.

Decentralized finance convergence creates new product possibilities. Synthetic asset protocols could reference prediction market prices, while lending protocols might use prediction market outcomes for collateral valuation. These applications remain largely theoretical but represent significant revenue expansion potential.

Conclusion

Prediction markets face a critical juncture between experimental forecasting tools and mainstream financial infrastructure. Kalshi's regulated approach and Polymarket's decentralized model represent complementary strategies for capturing the projected $10 billion market opportunity by 2030.

Success depends on platforms' ability to navigate regulatory challenges while building institutional-grade infrastructure. Leading prediction markets must combine regulatory compliance, deep liquidity, and broad market coverage to serve both retail traders and institutional forecasting needs.

The evolution from curiosity to necessity reflects broader trends toward data-driven decision making and real-time information aggregation. As traditional forecasting methods prove inadequate for complex, rapidly evolving events, prediction markets offer superior accuracy through market-based price discovery.

Risk Considerations: Prediction markets face regulatory uncertainty, liquidity constraints, and potential manipulation. Platform concentration risks and oracle reliability issues may impact market integrity. Investors should consider position sizing limitations and jurisdiction-specific restrictions before participating.

Sources: Citizens Bank research report, platform volume data from Kalshi and Polymarket, CFTC regulatory filings, Metaculus academic research compilation. Analysis as of February 24, 2026. Sources cited:

• CoinDesk (https://coindesk.com/prediction-market-firms-10-billion-revenue-2030)
• Kalshi (https://kalshi.com)
• Polymarket (https://polymarket.com)