Gate Research Institute: Kalshi Case Study, Brokerage Entry Promotes Centralized Prediction Market Scalability

Summary

• Relying on CFTC DCM qualifications, Kalshi integrates event contracts into broker platforms like Robinhood/Webull/IB for distribution, with Robinhood contributing over half of the trading volume across multiple periods.
• During the intensive schedule window following the NFL and NBA seasons, high-frequency, standardized sports-themed contracts and continuous new offerings have driven trading activity and increased user engagement, maintaining Kalshi’s market share above 50%.
• Kalshi is exploring tokenization on Solana, expanding access to wallets and aggregators, but faces regulatory uncertainties at the state and federal levels, as well as challenges related to the consistency and risk management of off-chain main markets and on-chain mappings.

I. Introduction

Prediction markets are essentially a type of event contracts or outcome-based derivatives, which express the probability of a future event occurring through tradable prices.

1.1 Functionality of Prediction Markets

The core functions of prediction markets can generally be broken down into three layers:

• The first layer is information aggregation and “probability pricing,” which compresses dispersed information, opinions, and capital preferences into a continuous price or implied probability, allowing observation and comparison of “how market expectations change over time”;
• The second layer involves hedging and risk transfer, providing more direct risk management tools when event outcomes impact asset, business, or policy risks (e.g., interest rate paths, regulatory trends, or elections), beyond just being a “place to express opinions” for trading;
• The third layer, under broader financialization trends, standardizes “judgments about the future” into settleable contracts, enabling expectations scattered across public opinion and research reports to be quantified and priced within a unified trading mechanism.

1.2 Global Development Trends

Over the past year, the global trend of prediction markets has clearly expanded from a few native crypto products to broader financial distribution channels and larger user bases:

I. Monthly trading volume of prediction markets

• Significant growth in scale and visibility: Monthly trading volume in the prediction market sector increased from approximately $2.3 billion in 2024 to over $11 billion in 2025, with daily trading volume stabilizing between $400 million and $600 million; market participants shifted from a single dominant player, Polymarket, to a multi-competitor landscape.
• Expansion of themes from politics to sports and macroeconomics: The path from political event-driven surges to sports becoming mainstream is now clearer. Since September NFL and October NBA seasons started, Kalshi’s trading volume quickly outpaced Polymarket, with over 90% of its volume coming from sports events.
• Regulatory boundaries and compliance are becoming key variables influencing industry development: After rapid expansion of high-frequency sports-themed contracts, discussions and debates around how these contracts should be classified and regulated have intensified among multiple regulatory levels. This constrains business expansion and poses challenges for further institutionalization and mainstream adoption of prediction markets. Those who excel in compliance and distribution are more likely to attract incremental users and liquidity.
• Centralized versus decentralized development paths: These two paths differ systematically in regulatory frameworks, user structures, and innovation boundaries, rather than just technical implementation. Centralized platforms (like Kalshi) operate within clear regulatory frameworks such as CFTC, ensuring contract legality but with strict content review; decentralized platforms (like Polymarket) rely on on-chain contracts for global access but face regulatory uncertainties regarding identity verification; in user distribution, the former reaches traditional financial users via broker channels, while the latter depends on crypto wallets and DeFi communities; in trading architecture, centralized platforms use continuous matching and fiat settlement, standardizing high-frequency sports contracts with deep order books and risk controls, whereas decentralized platforms adopt hybrid off-chain matching and on-chain settlement, resulting in more fragmented liquidity.

II. Overview of Kalshi and Industry Position

2.1 Company and Product Introduction

Founded in 2018 by Tarek Mansour (CEO, former high-frequency trading engineer) and Luana Lopes Lara (co-founder), Kalshi’s team combines technical and financial backgrounds. Its core goal is to standardize “event outcomes” into tradable financial contracts and operate within regulatory frameworks. Unlike many native crypto prediction markets, Kalshi’s design from inception emphasizes legal compliance, trading rules, and clearing mechanisms for event contracts.

In terms of regulatory qualification, Kalshi obtained CFTC (Commodity Futures Trading Commission) designated contract market (DCM) status in November 2020, establishing an independent clearing entity under the commodity futures regulatory system. Its platform products are defined as event contracts, distinct from traditional futures or gambling contracts, and are listed, traded, and settled within a compliant framework. This also provides a regulatory foundation for integrating traditional account systems, payment channels, and broader user groups.

II. Kalshi’s Positioning and Distribution

The core trading categories focus on two high-frequency, standardized scenarios:

• Sports events (single-game outcomes, season MVP, scoring leader, etc.), due to dense schedules and objective results, constitute the largest trading segment;
• Political events (elections, summits, etc.), attracting risk management and professional traders;
• Secondary categories include entertainment, economics, and crypto, which contribute to a secondary or long-tail volume.

Among these, sports contracts, with their high frequency, clear rules, and definite settlement, have gradually become Kalshi’s most liquid and scaled product line, dominating trading volume.

2.2 Market Status: Broker Distribution Model and Growth Structure

Kalshi’s unique moat is its broker-centric distribution approach, where event contracts are not solely dependent on Kalshi’s own platform for customer acquisition and conversion. Instead, they are productized and distributed through broker apps, expanding into broader retail trading scenarios. Channels like Robinhood (and Webull) play a key role in amplifying trading volume.

III. Robinhood accounts for over 50% of Kalshi’s monthly trading volume

According to Robinhood’s financial disclosures, Robinhood has contributed over half of Kalshi’s nominal trading volume across multiple periods:

• Q2 2025: Kalshi’s quarterly trading volume was $1.88 billion, with Robinhood accounting for $1 billion (53.17%);
• Q3 2025: Kalshi’s quarterly trading volume was $4.48 billion, with Robinhood at $2.3 billion (51.36%);
• October 2025: Kalshi’s monthly trading volume was $4.4 billion, with Robinhood at $2.5 billion (56.85%).

This data indicates that Kalshi’s growth is not just driven by its own product strength but is deeply tied to the distribution efficiency of broker channels—once event contracts are embedded into broker account systems, prediction markets become a new category directly tradable by mainstream retail users, with significantly lowered entry barriers and simplified user pathways.

2.3 Market Share: From 10% to Over 50% in Just One Year

From the perspective of trading volume market share, Kalshi has achieved a rapid leap from low awareness to a dominant share within about a year: from a relatively limited influence during the 2024 election window, to now maintaining over half of the prediction market’s trading volume amid a more diverse market participant landscape.

IV. Prediction Market Trading Volume Market Share

Kalshi’s growth trajectory can be divided into three phases:

• Q4 2024: Breakthrough period During the November 2024 election-related trading window, Kalshi’s monthly trading volume first reached the billion-dollar level, demonstrating that event contracts can support large-scale trading under current regulatory frameworks. However, in terms of real-world influence, Polymarket was the main player, frequently appearing on major media headlines; Kalshi’s trading volume was solid but its visibility and attention were comparatively lower.

• H1 2025: Broker distribution deployment period In the first half of 2025, Kalshi leveraged its compliance advantages to expand broadly into traditional finance and broker institutions. As channels like Robinhood launched event contract products, Kalshi’s quarterly nominal trading volume reached $1.88 billion in Q2 2025, with market share steadily rising and emerging from the post-election lull. Sports-related contracts became a key trading category, laying the foundation for explosive growth in the second half.

• H2 2025: Significant increase in sports supply boosting market share

V. Kalshi’s Daily Trading Volume

In 2025, as the sports season entered a dense supply window, NFL and NBA seasons started in September and October, respectively. These seasons brought continuous, high-frequency, highly standardized contracts. Sports events often start on weekends, providing Kalshi with a steady weekend trading rhythm, with weekend volumes significantly higher than weekdays. Notably, on January 11 and 12, trading volumes exceeded $450 million, setting new records. As the events progressed, the attention and betting on these events on Kalshi created a feedback loop, further increasing user engagement and maintaining market share above 50%.

III. On-Chain Exploration and Technical Pathways

3.1 Background for On-Chain Development

Following the growth driven by broker distribution and high-frequency sports supply, Kalshi’s strategic focus remains unchanged: deepening channel distribution while exploring on-chain solutions to extend trading reach from off-chain fiat scenarios to on-chain liquidity networks.

On-chain infrastructure inherently offers low-cost distribution. Once tokenized, event contracts can seamlessly integrate into wallets, DEX aggregators, and DeFi protocols without complex KYC procedures. Kalshi has explicitly expressed interest in connecting tokenized prediction to on-chain liquidity, expanding sports contracts from broker channels to global crypto-native scenarios.

Furthermore, as market scale and participant diversity increase, users and integrators increasingly demand verifiability of holdings, settlements, and position changes when comparing Kalshi with platforms like Polymarket. On-chain assetization can more easily provide transparent, verifiable states and settlement records.

It’s important to emphasize that on-chain development does not mean Kalshi abandons its existing compliance framework. Its approach is more about building on top of the compliant market, mapping some contract risk exposures into tokens on-chain to expand distribution and integration boundaries.

3.2 Why Choose Solana for Tokenization

Kalshi’s on-chain deployment on Solana can be summarized into three points based on observable ecosystem synergies:

• Network performance and cost High-frequency trading and dense quoting in sports and similar themes demand fast confirmation and low fees. Solana’s low-cost, high-throughput environment aligns well with real-time/high-frequency event contracts.

• Overall size and competition in Solana prediction markets Current prediction market projects on Solana are limited in scale, with no dominant player. Although some projects are exploring this space, their trading volumes are relatively low, and the ecosystem does not yet have a “market leader” monopoly. This means entry costs for Kalshi are relatively low.

• Using “event contract tokenization” as a sustainable asset issuance model

VI. Prediction Market Contract Supply

Kalshi’s event contracts are inherently standardized, batch-generable, and highly time-sensitive. To date, Kalshi has “issued” over 7.2 million market contracts, with more than 6.8 million settled. Mapping many short-term event contracts into tradable on-chain tokens could resemble a continuous rollout of assets centered around hot topics, with rolling issuance and expiration dates. Solana’s large meme launchers, trading tools, and user base are naturally suited for this large-scale asset issuance. With expiration dates, funds can theoretically roll over as contracts expire and new ones are issued, potentially improving capital turnover and alleviating long-term liquidity stagnation in meme assets.

In this framework, competition in on-chain prediction markets is not just about capturing trading volume from existing meme or trading categories but also about competing for on-chain asset issuance and distribution portals—whether event contracts can become a new, scalable class of on-chain tradable assets, prompting existing trading terminals to provide dedicated display and trading zones.

3.3 Major Progress

Current progress in Kalshi’s on-chain development can be summarized along three main lines:

• Tokenized event contracts launched on Solana: In December 2025, Kalshi announced the launch of its Tokenized Predictions on Solana, leveraging ecosystems like Jupiter and DFlow for on-chain trading and integration. Since mid-December last year, DFlow integrators have processed over $6 million in trading volume, with daily volumes between $200,000 and $300,000.

VII. Distribution of Daily Trading Volume for Kalshi-supported DFlow Prediction Markets API

• Wallet distribution and modular trading experience: Mainstream Solana wallets like Phantom announced integration with Kalshi’s prediction markets (also via DFlow API) in December, including market display, trading, and community interaction modules, embedding event contracts into daily wallet usage.
• Data and oracle/interface layer development: Kalshi is collaborating with RedStone to bring market data into multi-chain environments, facilitating third-party reading and integration of event contract data across different chains.

3.4 Challenges and Constraints: Regulatory Adaptation and Migration Costs for Hybrid Architectures

On-chain development opens new distribution boundaries and ecosystem collaboration spaces but also introduces two key constraints: one related to regulatory reinterpretation risks, and another related to engineering costs of transitioning from a “centralized trading system—on-chain distribution/mapping” hybrid architecture.

• Regulatory adaptation uncertainties For sports event contracts, a major external constraint in recent years has been conflicts between state-level gaming regulators and federal derivatives frameworks: states tend to view some sports-related event contracts as unlicensed sports betting or gambling variants, while Kalshi argues these are designated contracts under CFTC regulation, with a stronger nationwide legal basis. Cases like lawsuits from Massachusetts AG and cease-and-desist orders from Tennessee exemplify this tension, with federal courts temporarily blocking enforcement. These developments show that even with federal regulatory qualification, state-level enforcement and legal uncertainties can disrupt product launches and business scope.

In this context, on-chain tokenization further complicates product attribute understanding, as tokenized positions on-chain may trigger additional regulatory scrutiny related to derivatives classification, AML, and gambling boundaries, especially when crossing jurisdictions. A practical challenge is that Kalshi must clarify its product boundaries, sales, and distribution methods with regulators to reduce the risk of reclassification.

• Engineering constraints in transitioning from centralized to hybrid architecture Moving from a fully centralized entity to partial on-chain distribution (tokenized positions) effectively expands a controlled, closed trading system into a more open, composable environment. This introduces engineering challenges: maintaining strong consistency between on-chain tokens and off-chain main markets to prevent arbitrage, mispricing, and risk mismatches. Consistency involves not only price anchoring but also contract specifications, expiration and settlement logic, and synchronization under extreme market conditions. Additionally, centralized risk management systems cannot easily achieve full visibility and real-time constraints on on-chain wallets, raising new requirements for permission boundaries, limits, risk controls, and collaboration with key integrators and front-end systems.

Overall, the on-chain development of prediction markets is not just a technical migration but a dynamic balancing act between regulatory certainty, on-chain composability, and distribution scalability. It aims to avoid triggering regulatory redefinitions while generating real incremental liquidity and maintaining the scale advantages of existing broker channels.

IV. Conclusion

4.1 Long-term Strategic Positioning of Kalshi

Kalshi’s long-term strategy can be summarized as a clear mainline: leveraging regulatory qualifications and broker distribution as growth foundations to deliver scalable products and trading volumes in high-frequency sports themes; and on this basis, exploring tokenization on Solana to extend event contract reach from broker accounts to on-chain liquidity networks, seeking new distribution channels and incremental liquidity sources.

Kalshi is thus moving toward a dual on-chain and off-chain development path:

• Off-chain continues to emphasize regulatory certainty, account systems, and distribution efficiency;
• On-chain emphasizes composability, integration, and low-threshold distribution, introducing a more open and international trading structure via tokenization.

However, it must be recognized that Kalshi’s compliance-driven distribution and on-chain assetization are still in early stages, or even the prediction market track itself remains early, especially regarding regulatory ambiguities on the on-chain side. The sustainability of this model ultimately depends on two variables: whether state-level gaming regulations and federal derivatives frameworks can be managed effectively, and whether on-chain trading can reach sufficient scale without amplifying compliance and risk management risks.

4.2 Insights

From an industry perspective, Kalshi’s approach offers a reference framework for how centralized prediction markets can enter the on-chain space, with three key insights:

• Distribution capability may be more decisive than product form in early scaling Prediction markets do not grow solely through thematic innovation; access to mature retail trading channels (brokerages/wallets/aggregators) directly impacts liquidity and user growth. Kalshi’s case reinforces the importance of “distribution as product” and “channels as king.”

• High-frequency, template-based themes are more conducive to scalable supply systems Sports season-based supply is critical: it provides continuous new events, stable trading rhythms, and repeatable listing mechanisms. This structure makes prediction markets resemble operational derivatives supply systems rather than short-term spikes around hot topics.

• The main challenge for centralized prediction markets entering on-chain lies in boundary management The hardest part is not just tokenizing contracts but ensuring economic consistency between off-chain main markets and on-chain mappings, risk control transparency, and cross-entry compliance and sales boundaries. For the industry and other prediction-oriented centralized entities, on-chain and off-chain hybrid operations involve ongoing trade-offs around permissions, limits, distribution portals, and product boundaries.

Overall, Kalshi’s case demonstrates that the scale of prediction markets depends heavily on distribution channels and high-frequency, standardized, scalable supply mechanisms—broker channels form the core distribution advantage, while on-chain exploration extends this boundary further into the blockchain ecosystem without fundamentally altering the main axis. The ultimate success still hinges on regulatory adaptation and hybrid architecture governance.

V. References

• Dune, https://dune.com/datadashboards/dflow-x-kalshi-prediction-markets
• Dune, https://dune.com/datadashboards/kalshi-overview
• Dune, https://dune.com/gateresearch/prediction-markets-overview
• Dune, https://dune.com/gateresearch/launchpad-but-prediction-market

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