The platforms will engage in fierce competition, while the infrastructure will continue to expand.
Author: Oliver B
Translation: Deep Tide TechFlow
The gold rush in the prediction market has already begun. Every founder in the crypto space, fintech entrepreneur, and contrarian believes they have found the secret to success. They are convinced they possess the prediction market platform that can outshine Polymarket and Kalshi. They have raised funds, built teams, launched dazzling interfaces, and promised better user experiences, faster settlement speeds, or niche markets overlooked by existing giants.
However, most are destined to fail.
This is not pessimism, but a mathematical outcome. In prediction markets, network effects are incredibly powerful. You need liquidity to attract traders, but at the same time, you need traders to build liquidity. In the crypto-native market, Polymarket already holds a significant advantage in scale. Meanwhile, in the U.S. event market, Kalshi has a regulatory high ground. The cost of shaking the positions of these two players is astonishingly high. From marketing to regulatory compliance to user acquisition, these expenses can accumulate rapidly. Even if new entrants achieve some success, they will only further dilute an already thin market. For platforms that rely on order book depth to survive, this is akin to a death sentence.
The "graveyard" of failed prediction market platforms illustrates this point. Remember the half-dozen markets launched after the 2024 election cycle? That's right, almost no one remembers them.
However, what venture capitalists should really focus on is that the true profit in prediction markets does not lie in operating these markets themselves, but in the infrastructure that supports their operation.
Why Infrastructure is a Superior Investment Choice
Looking back at the history of financial market development, the wealth of the stock market is not all created by stock exchanges, although some of it is. The real wealth comes from data providers, clearinghouses, trading infrastructure suppliers, market monitoring systems, and deeper analytical platforms. For example, Bloomberg did not earn billions by competing with the NYSE, but by becoming an indispensable piece of infrastructure.
Prediction markets are developing along the same trajectory, just lagging by several decades. Currently, their infrastructure layer is still in its early stages, fragmented and inefficient, which is precisely where the real opportunities lie.
Here are several specific areas that venture capitalists should focus on:
Data and Oracle Infrastructure
The core of prediction markets is "real data." They require authoritative data sources to provide key information such as which candidate wins, what the actual GDP data is, or whether a company meets its targets. This seems simple but is actually complex. Different markets require different data sources, and diverse verification and settlement mechanisms are needed to prevent data manipulation.
Oracle networks designed specifically for prediction markets are crucial. These companies are responsible for aggregating data, providing cryptographic proofs, and resolving disputes. As markets expand, a decentralized oracle ecosystem will become unsustainable. The ultimate winner will be the infrastructure provider that all platforms—even competitors—must rely on.
Cross-Market Infrastructure and Aggregation
Currently, liquidity is dispersed across different platforms. A savvy trader may want to arbitrage between Polymarket, Kalshi, and three other platforms, but there is currently no seamless way to achieve this. If a set of infrastructure could be built to allow traders to view order books across all markets, it would be immensely valuable. Through this system, traders could simultaneously execute hedging operations and manage risks across multiple venues, unlocking tremendous potential value. This represents the "Bloomberg Terminal" opportunity in prediction markets: every participant can benefit, and more efficient cross-market operations mean smaller spreads and deeper liquidity.
Analysis and Historical Data
As prediction markets gradually mature, researchers, quantitative analysts, and institutions will want to delve into historical prediction data. They will look for patterns and understand how markets price events at different times. Someone will establish an authoritative repository of prediction market data, which will be cleaned, standardized, and made queryable. This will become a reference dataset for academic research, institutional analysis, and model building, forming a high-profit and defensible business.
Processing and Settlement
As prediction markets expand and become more complex, their backend systems will also need to be upgraded in sync. More efficient settlement mechanisms, faster data processing capabilities, and improved market infrastructure are all crucial. Companies focused on building middleware will hold immense value. They will connect markets to clearing systems, automate settlement processes, and reduce operational risks. This can be seen as the "pipeline system" that keeps modern markets running smoothly.
Compliance and Risk Management Infrastructure
As prediction markets gradually move towards mainstream acceptance and gain more regulatory clarity, complexity will follow. Infrastructure for managing regulatory reporting will become essential. At the same time, large-scale KYC/AML (Know Your Customer/Anti-Money Laundering) capabilities will become a necessity. Additionally, detecting market manipulation and ensuring compliance across jurisdictions will be key. Such infrastructure may seem "boring," but it is a highly defensible and sticky area. Once embedded in the market system, it is nearly impossible to replace easily.
Trader-Focused Infrastructure Layout
Another key aspect of prediction markets is the infrastructure support provided for professional traders.
Currently, the users of prediction markets are primarily retail and enthusiasts. However, as the market matures, attracting institutional capital, quantitative traders, and algorithmic traders will lead to a significant shift in demand. These professional traders not only need access to the market but also a complete set of tools that institutional finance takes for granted.
Algorithmic Trading and Trading Bot Infrastructure
Professional traders will want to automate strategies across multiple markets. This requires providing APIs, execution infrastructure, and trading bot frameworks specifically designed for prediction markets. In the future, someone may create a "Zapier" or "Make.com" for the prediction market space, allowing professional users to easily create complex trading strategies. With such tools, they can execute hedging and manage risks without writing code. Furthermore, there may even be companies developing infrastructure specifically for professional quantitative traders, enabling them to efficiently achieve these functions.
Portfolio and Risk Management Tools
As traders accumulate positions across multiple prediction markets and platforms, they will need more advanced tools to support their operations. They need to track, manage, and understand their risk exposure. For example, what is the net exposure to political events? How correlated are these positions? What is the optimal hedging strategy? These questions may not trouble retail traders, but for institutions managing millions in prediction market capital, these will become core demands. The first platform to provide institutional-level portfolio analysis tools will have the opportunity to capture a significant market share of serious funds.
Backtesting and Research Framework
Before deploying capital, institutional traders want to backtest strategies based on historical prediction market data. However, currently, this data has not been organized into a format suitable for backtesting, nor are there corresponding tools to support this need. Therefore, there is a need for companies to build robust backtesting frameworks, providing clear historical data and realistic simulations of market microstructure. At the same time, these tools need to integrate easily into existing research tools. This type of infrastructure will become a key pillar for the quantitative trading community to enter prediction markets.
Market Microstructure and Intelligence Tools
Professional traders understand that the market is not just about correctly predicting outcomes, but also about a deep understanding of liquidity.
They need to identify market inefficiencies, detect information flow, and precisely time their entry and exit. As prediction markets mature, the demand for real-time market intelligence tools will grow rapidly. Microstructure analysis tools will become particularly important, such as heat maps showing the flow of "smart money," real-time alerts for unusual activity, and tools for identifying mispricing. These features will serve a similar role to Bloomberg terminals in traditional financial markets, but tailored for prediction markets.
Real-Time Aggregation and One-Click Trading: Essential Cornerstone for Institutional Capital
For professional traders, trading simultaneously across multiple platforms is a basic requirement. In the future, platforms will inevitably emerge that can aggregate the order books of Polymarket, Kalshi, and other prediction markets in real-time. Through such platforms, traders can view liquidity across all markets in one interface and execute one-click cross-platform trades. This is not only the dream of market makers but also a key infrastructure for the entire prediction market ecosystem to become more efficient.
This trader-focused infrastructure is as important as the market-side infrastructure. These tools are not optional "nice-to-haves," but necessary prerequisites for institutional entry. As institutional capital flows into prediction markets, these tools will become indispensable core elements. Companies building this layer of infrastructure will capture a different type of value than market operators. This value is not only highly defensible but also, to some extent, more scalable.
The Ultimate Question of Valuation: How Much Growth Potential is Left for Prediction Markets?
Recently, the financing dynamics of the two major players in prediction markets have attracted widespread attention. Kalshi recently reached a valuation of $5 billion, while Polymarket achieved a post-money valuation of $9 billion thanks to investment from Intercontinental Exchange, the parent company of the New York Stock Exchange.
This is not a small increase. Just a few months ago, Kalshi's valuation was still at $2 billion, while Polymarket's valuation at the beginning of 2025 was only $1.2 billion. In just a few months, these valuations skyrocketed by 2.5 to 7 times.
This raises an unsettling question for venture capitalists: how much upside potential is left for prediction markets?
Currently, these two companies have reached sufficiently high valuations that future exit multiples are constrained. Assuming Kalshi or Polymarket someday reaches a valuation of $50 billion to $100 billion, from the current base of $5 billion to $9 billion, this is undoubtedly a considerable but not astonishing return.
More importantly, these platforms are gradually becoming potential acquisition targets for traditional financial giants. Exchanges, brokers, and financial institutions have shown strong interest in them. Selling at 2 to 4 times the current valuation to ICE, CME, or other large brokers is entirely possible. But this is not the "power law" investment that venture capitalists pursue for 100x returns.
In contrast, investments in the infrastructure space exhibit a completely different return curve. Whether it is oracle service providers, analytical platforms, or cross-market execution layers, once they become core infrastructure of the prediction market ecosystem, their returns will cover all platforms, all traders, and all institutions.
The initial valuations of such infrastructure are usually low, but their expansion potential is virtually limitless.
The Asymmetry of Risk
In the fiercely competitive platform arena, venture capital firms often bet on multiple projects, hoping that one of them can become the next Polymarket. This is a typical "power law" bet: most projects will fail, and even the successful ones may struggle to create significant value due to market fragmentation and liquidity division.
In contrast, the risk curve for infrastructure investments is entirely different. For example, an oracle service provider does not care whether traders use Platform A or Platform B—regardless of which side wins, it can benefit. The value of analytical platforms increases with the number of markets, rather than decreases. In the infrastructure space, there is no need to choose a winner; it just needs to be practical for all platforms.
Moreover, infrastructure typically forms strong defensive capabilities through data advantages, network effects, or technological barriers. This is not just a "burning cash" competition; it is a contest of technological depth and ecological stickiness.
What does this mean for investors and entrepreneurs?
If you are evaluating a business plan focused on building a new prediction market platform, whether it is marketed with a better user experience (UX) or targeting an undeveloped niche market, you need to ask sharper questions:
How will you solve the liquidity problem?
How will you achieve profitability in the face of competitive pressure from existing giants?
Among the many competing platforms, how many are likely to succeed?
More importantly, even if successful, what is the actual possibility of exit multiples from a base of over $100 million in funding?
If you are focused on opportunities in the infrastructure space, then you are facing a completely different risk and return model. Build data layers, develop cross-market tools, design settlement mechanisms, create trader analysis tools, and establish intelligent intelligence platforms. These businesses will grow with the expansion of the entire market, rather than competing against a single rival. They benefit from the prosperity of the market, rather than suffering from its fragmentation. They offer the kind of unrestricted growth potential that venture capital truly seeks.
The ecosystem of prediction markets is still in its early stages, which means there are enormous opportunities. But the real opportunity lies not in replicating what Polymarket has already done, but in building the foundational layer that enables the entire ecosystem to operate more efficiently.
The platforms will engage in fierce competition, while the infrastructure will continue to expand.
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