The prediction market gold rush has begun. Every crypto founder, fintech entrepreneur, and contrarian is convinced they’ve cracked the code to success. They firmly believe they possess the one prediction market platform that will outcompete Polymarket and Kalshi. They raise funds, assemble teams, launch dazzling interfaces, promising superior user experiences, faster settlement speeds, or niche markets overlooked by existing giants.
Yet most are destined to fail.
This isn’t pessimism—it’s a mathematical inevitability. In prediction markets, network effects reign supreme. You need liquidity to attract traders, yet you also need traders to build liquidity. In crypto-native markets, Polymarket has already gained scale dominance. In U.S. IPO markets, Kalshi holds the regulatory high ground. Displacing these two players comes at staggering cost. Expenses from marketing to regulatory compliance to user acquisition pile up rapidly. Even if new entrants achieve some success, they merely further dilute an already thin market. For platforms reliant on order book depth for survival, this amounts to a death sentence.
The graveyard of failed prediction market platforms bears witness to this reality. Remember that half-dozen markets launched after the 2024 election cycle? Exactly—almost no one does.
Yet what venture capitalists should truly focus on is this: the real profits in prediction markets lie not in operating the markets themselves, but in the infrastructure that powers them.
Why Infrastructure Is the Superior Investment
Looking back at the evolution of financial markets, not all the wealth in the stock market was created by exchanges—though some certainly was. The real riches flowed to data providers, clearinghouses, trading infrastructure suppliers, market surveillance systems, and deeper analytics platforms. Take Bloomberg: it didn’t earn billions by competing with the NYSE, but by becoming indispensable infrastructure.
Prediction markets are following the same trajectory, albeit decades behind. Currently, their infrastructure layer remains in its infancy—fragmented and inefficient—and this is precisely where the real opportunities lie.
Here are specific areas venture capitalists should focus on:
Data and Oracle Infrastructure
The core of prediction markets lies in “real data.” They require authoritative data sources to provide critical information such as which candidate wins, what the actual GDP figures are, or whether a company meets its targets. This seems simple but is actually complex. Different markets require different data sources, along with diverse verification and settlement mechanisms to prevent data manipulation.
Oracle networks designed specifically for prediction markets are crucial. These companies aggregate data, provide cryptographic proofs, and resolve disputes. As markets expand, a fragmented oracle ecosystem becomes unsustainable. The ultimate winner will be the infrastructure provider that all platforms—even competitors—must rely on.
Cross-Market Infrastructure and Aggregation
Current liquidity remains scattered across different platforms. A savvy trader might seek arbitrage opportunities across Polymarket, Kalshi, and three other platforms, yet no seamless way exists to execute this today. Building an infrastructure enabling traders to view order books across all markets would be immensely valuable. Such a system would allow simultaneous hedging and risk management across multiple venues, unlocking significant potential. This represents the “Bloomberg Terminal” opportunity for prediction markets: every participant benefits, as more efficient cross-market operations mean tighter spreads and deeper liquidity.
Analysis and Historical Data
As prediction markets mature, researchers, quantitative analysts, and institutions will seek deep analysis of historical prediction data. They will identify patterns and understand how markets price events across different periods. Someone will establish an authoritative repository for prediction market data—cleaned, standardized, and query-enabled. This will become the reference dataset for academic research, institutional analysis, and model building, forming a highly profitable and defensible business.
Processing and Settlement
As prediction markets expand and grow more complex, their backend systems must evolve accordingly. More efficient settlement mechanisms, faster data processing capabilities, and enhanced market infrastructure are all critical. Companies focused on building middleware will hold significant value. They connect markets to clearing systems, automate settlement processes, and reduce operational risk. Think of them as the “pipeline systems” enabling modern markets to function.
Compliance and Risk Management Infrastructure
As prediction markets move toward mainstream adoption and gain greater regulatory clarity, complexity will inevitably increase. Infrastructure for managing regulatory reporting will become indispensable. Simultaneously, large-scale KYC/AML (Know Your Customer/Anti-Money Laundering) capabilities will become essential. Furthermore, detecting market manipulation and ensuring compliance across jurisdictions will be key. While this infrastructure may seem “uninteresting,” it represents a highly defensive and sticky domain. Once embedded within market systems, it becomes nearly impossible to displace.
Infrastructure Layout for Traders
Another critical aspect of prediction markets is the infrastructure supporting professional traders.
Currently, prediction market users are predominantly retail investors and enthusiasts. However, as the market matures and attracts institutional capital, quantitative traders, and algorithmic traders, demand will undergo a significant shift. These professional traders require not only access to the market but also a full suite of tools taken for granted in institutional finance.
Algorithmic Trading and Trading Bot Infrastructure
Professional traders will seek to automate strategies across multiple markets. This necessitates APIs, execution infrastructure, and trading bot frameworks specifically designed for prediction markets. In the future, a “Zapier” or “Make.com” for prediction markets may emerge, enabling professional users to effortlessly create complex trading strategies. Such tools would allow them to execute hedging and manage risk without coding. Furthermore, enterprises may develop specialized infrastructure tailored for professional quantitative traders, enabling them to efficiently implement these capabilities.
Portfolio and Risk Management Tools
As traders accumulate positions across multiple prediction markets and platforms, they will require more advanced tools to support their operations. They need to track, manage, and understand their risk exposures. For example: What is the net exposure to political events? How do these positions correlate? What is the optimal hedging strategy? While these questions may not concern retail traders, they become core requirements for institutions managing millions in prediction market capital. The first platform to offer institutional-grade portfolio analysis tools will have the opportunity to capture significant market share from serious capital.
Backtesting and Research Frameworks
Before committing capital, institutional traders seek to backtest strategies using historical prediction market data. However, this data currently lacks organized formats suitable for backtesting, and corresponding tools to support this need are absent. Consequently, companies must build robust backtesting frameworks that provide clear historical data and realistic simulations of market microstructure. Simultaneously, these tools must integrate seamlessly with existing research platforms. Such infrastructure will become a critical pillar for the quantitative trading community’s entry into prediction markets.
Market Microstructure and Intelligence Tools
Professional traders understand that markets are not merely about predicting outcomes correctly, but also about deeply understanding liquidity.
They must identify market inefficiencies, detect information flows, and precisely time entry and exit points. As prediction markets mature, demand for real-time market intelligence tools will surge. Microstructure analysis tools will become particularly vital—such as heatmaps revealing “smart money” flows, real-time alerts for unusual activity, and tools for spotting mispricing. These functions will mirror the role of Bloomberg terminals in traditional financial markets, but tailored specifically for prediction markets.
Real-Time Aggregation and One-Click Trading: Essential Building Blocks for Institutional Capital
For professional traders, simultaneous trading across multiple platforms is fundamental. Platforms that aggregate order books from Polymarket, Kalshi, and other prediction markets in real time will inevitably emerge. Such platforms enable traders to view liquidity across all markets within a single interface and execute cross-platform trades with a single click. This represents not only a market maker’s dream but also critical infrastructure for the entire prediction market ecosystem to achieve efficiency.
This trader-facing infrastructure is equally vital as market-facing infrastructure. These tools are not optional “nice-to-haves,” but essential prerequisites for institutional entry. As institutional capital floods 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 defensive but, in some respects, even more scalable.
The Ultimate Valuation Question: How Much Room for Growth Remains in Prediction Markets?
Recent funding rounds by two prediction market giants have drawn significant attention. Kalshi recently secured a $5 billion valuation, while Polymarket achieved a $9 billion post-money valuation following investment from Intercontinental Exchange (ICE), the parent company of the New York Stock Exchange.
This is no small increase. Just months ago, Kalshi was valued at $2 billion, while Polymarket stood at $1.2 billion in early 2025. Within a few short months, these valuations surged by 2.5x to 7x respectively.
This raises an unsettling question for venture capitalists: How much upside remains for prediction markets?
Currently, both companies have reached valuations high enough to constrain future exit multiples. Assuming Kalshi or Polymarket someday reaches a valuation of $50 to $100 billion, this would represent a substantial but not staggering return from their current $5 to $9 billion base.
More significantly, these platforms are increasingly becoming potential acquisition targets for traditional financial giants. Exchanges, brokers, and financial institutions have shown keen interest in them. A sale to Intercontinental Exchange (ICE), the Chicago Mercantile Exchange (CME), or other major brokers at 2 to 4 times their current valuation is entirely plausible. However, this is not the “power-law” investment with 100x returns that venture capitalists pursue.
In contrast, investments in the infrastructure sector exhibit a completely different return curve. Whether ornot it’s an oracle provider, analytics platform, or cross-market execution layer, once it becomes core infrastructure within the prediction market ecosystem, its returns will extend across all platforms, all traders, and all institutions.
The initial valuations for such infrastructure are typically low, yet their scaling potential is virtually limitless.
Asymmetry of Risk
In the fiercely competitive platform space, venture capital firms often bet on multiple projects, hoping one will become the next Polymarket. This represents a classic “power law” wager: most projects will fail, and even successful ones may struggle to generate significant value due to market fragmentation and liquidity silos.
In contrast, the risk curve for infrastructure investments is fundamentally different. For instance, an oracle provider remains indifferent to whether traders use Platform A or Platform B—it benefits regardless of which prevails. The value of analytics platforms, meanwhile, increases as the number of markets grows, not diminishes. Infrastructure doesn’t require picking winners; it simply needs to be useful across all platforms.
Furthermore, infrastructure typically builds formidable defenses through data advantages, network effects, or technological barriers. This isn’t merely a “burn money” race—it’s a contest of technical depth and ecosystem stickiness.
What does this mean for investors and entrepreneurs?
If you’re evaluating a business plan focused on building a new prediction market platform—whether it pitches a superior user experience (UX) or targets an untapped niche—you need to ask tougher questions:
How will liquidity issues be resolved?
How can profitability be achieved amid competition from established giants?
How many competing platforms will actually succeed?
More importantly, even if successful, what is the realistic possibility of an exit multiple from a base of over $100 million in funding?If you’re focused on infrastructure opportunities, you face a completely different risk-reward model. Build the data layer, develop cross-market tools, design settlement mechanisms, create trader analytics, establish smart intelligence platforms. These businesses grow alongside the entire market’s expansion, rather than competing against a single rival. They benefit from market prosperity rather than suffer from market fragmentation. They offer the kind of unrestrained growth potential that venture capital truly pursues.
The prediction market ecosystem remains in its early stages, meaning enormous opportunities exist. But the real opportunity lies not in replicating what Polymarket has already done, but in building the foundational layers that enable the entire ecosystem to operate more efficiently.
Platforms will fight each other, while infrastructure will continue to expand.
