Prediction market platforms battle it out, but infrastructure is where VCs truly find opportunities
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Prediction market platforms battle it out, but infrastructure is where VCs truly find opportunities
Platforms will battle each other, while infrastructure continues to expand.
Navigating Web3 tides with focused insightsAuthor: Oliver B
Translation: TechFlow
The gold rush in prediction markets has already begun. Every founder in crypto, fintech entrepreneur, and contrarian thinker is convinced they've found the winning formula. They believe they possess the one prediction market platform that can beat Polymarket and Kalshi. They raise funds, assemble teams, launch flashy interfaces, and promise better user experiences, faster settlements, or niche markets overlooked by the current giants.
Yet, most are destined to fail.
This isn't pessimism—it's a mathematical outcome. Network effects in prediction markets are incredibly powerful. You need liquidity to attract traders, but you also need traders to build liquidity. In crypto-native markets, Polymarket already holds a significant scale advantage. In regulated U.S. event markets, Kalshi possesses a regulatory high ground. Dislodging either player comes at an astronomical cost—from marketing and regulatory compliance to user acquisition—expenses that pile up quickly. Even if new entrants achieve some success, they merely fragment an already thin market, effectively delivering a death sentence for platforms dependent on deep order books.
The graveyard of failed prediction market platforms proves this point. Remember that half-dozen markets launched after the 2024 election cycle? Exactly—almost no one does.
However, what venture investors should truly focus on is this: the real profits in prediction markets don't lie in operating the markets themselves, but in the infrastructure that powers them.
Why Infrastructure Is the Superior Investment
Looking back at financial market history, wealth wasn't created solely by stock exchanges, though some did contribute. Real fortunes emerged from data providers, clearinghouses, trading infrastructure suppliers, market surveillance systems, and deeper analytical platforms. Bloomberg didn’t earn billions by competing with the NYSE; it became indispensable infrastructure.
Prediction markets are following the same trajectory—just decades behind. Today, their infrastructure layer remains nascent, fragmented, and inefficient—and that’s precisely where the real opportunity lies.
Here are specific areas venture investors should prioritize:
Data and Oracle Infrastructure
The core of prediction markets is “real-world data.” They require authoritative sources to determine who won an election, what the actual GDP figure was, or whether a company hit its target. This sounds simple but is highly complex. Different markets need different data sources, plus diverse verification and settlement mechanisms to prevent manipulation.
Oracle networks designed specifically for prediction markets are critical. These companies aggregate data, provide cryptographic proofs, and resolve disputes. As markets grow, a fragmented oracle ecosystem will become unsustainable. The ultimate winner will be the infrastructure provider that all platforms—including competitors—must rely on.
Cross-Market Infrastructure and Aggregation
Liquidity today is scattered across platforms. A sophisticated trader might want to arbitrage between Polymarket, Kalshi, and three other venues—but there’s currently no seamless way to do so. Building infrastructure that allows traders to view order books across all markets would create immense value. Such a system could enable simultaneous hedging and multi-venue risk management, unlocking tremendous latent potential. This is the “Bloomberg Terminal” opportunity in prediction markets: every participant benefits, and more efficient cross-market operations mean tighter spreads and deeper liquidity.
Analytics and Historical Data
As prediction markets mature, researchers, quants, and institutions will seek deep analysis of historical forecasting data. They’ll look for patterns and study how markets priced events over time. Someone will build the definitive prediction market data repository—cleaned, standardized, and queryable. This will become the reference dataset for academic research, institutional analysis, and model development: a high-margin, defensible business.
Clearing and Settlement
As prediction markets grow in scale and complexity, their backend systems must evolve too. More efficient settlement mechanisms, faster data processing, and robust market infrastructure are essential. Companies building middleware will hold enormous value—connecting markets to clearing systems, automating settlement, and reducing operational risk. Think of them as the “plumbing” that keeps modern markets running.
Compliance and Risk Management Infrastructure
As prediction markets move toward mainstream adoption and gain clearer regulation, complexity increases. Infrastructure for managing regulatory reporting becomes crucial. Large-scale KYC/AML capabilities will be mandatory. Detecting market manipulation and ensuring compliance across jurisdictions will also be key. This type of infrastructure may seem “boring,” but it’s highly defensible and sticky. Once embedded into market systems, it becomes nearly irreplaceable.
Trader-Facing Infrastructure
Another critical dimension of prediction markets is infrastructure tailored for professional traders.
Currently, users are mostly retail participants and enthusiasts. But as markets mature and attract institutional capital, quant traders, and algorithmic operators, demand will shift dramatically. These professionals need more than just market access—they require a full suite of tools taken for granted in institutional finance.
Algorithmic Trading and Bot Infrastructure
Professional traders will want to automate strategies across multiple markets. This requires APIs, execution infrastructure, and bot frameworks purpose-built for prediction markets. Someone may eventually create the “Zapier” or “Make.com” of prediction markets, allowing pros to easily design complex trading strategies. With such tools, users could execute hedges and manage risk without writing code. Further down the line, specialized infrastructure for quant traders could emerge, enabling highly efficient operations.
Portfolio and Risk Management Tools
As traders accumulate positions across multiple prediction markets and platforms, they’ll need advanced tools. They’ll need to track, manage, and understand their exposures. What’s their net exposure to political events? How correlated are their positions? What’s the optimal hedging strategy? These questions may not trouble retail traders, but they’re central for institutions managing millions in prediction market capital. The first platform offering institutional-grade portfolio analytics will capture serious market share among serious money.
Backtesting and Research Frameworks
Before deploying capital, institutional traders will want to backtest strategies using historical prediction market data. Yet today, such data isn’t formatted for easy backtesting, and tools are lacking. Companies need to build robust backtesting frameworks—providing clean historical datasets and realistic simulations of market microstructure. These tools must integrate smoothly with existing research environments. Such infrastructure will become a cornerstone for the quant community’s entry into prediction markets.
Market Microstructure and Intelligence Tools
Professional traders know that markets aren’t just about predicting outcomes—they’re about understanding liquidity deeply.
They need to identify inefficiencies, detect information flows, and time entries and exits precisely. As prediction markets mature, demand for real-time market intelligence will surge. Microstructure analysis tools will be vital—heatmaps showing “smart money” flows, real-time alerts for unusual activity, and tools to spot mispricing. These will serve a role similar to Bloomberg Terminals in traditional finance—but built specifically for prediction markets.
Real-Time Aggregation and One-Click Trading: Essential Foundations for Institutional Capital
For professional traders, trading across multiple platforms simultaneously is a basic requirement. In the future, a platform will inevitably emerge that aggregates order books from Polymarket, Kalshi, and other prediction markets in real time. Through such a system, traders could view liquidity across all markets in one interface and execute trades across platforms with a single click. This isn’t just a dream for market makers—it’s foundational infrastructure for an efficient prediction market ecosystem.
This trader-facing infrastructure is just as important as market-side infrastructure. These tools aren’t optional “nice-to-haves”—they’re prerequisites for institutional participation. As institutional capital floods into prediction markets, these tools will become indispensable. Companies building this layer will capture value differently from market operators—value that is not only highly defensible but potentially more scalable.
The Ultimate Valuation Question: How Much Room Is Left to Grow?
Recently, funding developments at the two major prediction market players have drawn widespread attention. Kalshi recently reached a $5 billion valuation, while Polymarket achieved a post-money valuation of $9 billion thanks to investment from Intercontinental Exchange (ICE), parent company of the New York Stock Exchange.
This isn’t incremental growth. Just months ago, Kalshi was valued at $2 billion, and Polymarket stood at just $1.2 billion at the start of 2025. In mere months, valuations have surged 2.5x to 7x.
This raises an uncomfortable question for venture investors: how much room is left for growth?
These companies have now reached levels where future exit multiples are constrained. Suppose Kalshi or Polymarket someday reaches a $50–100 billion valuation—impressive from today’s $5–9 billion base, but hardly extraordinary.
More importantly, these platforms are increasingly becoming acquisition targets for traditional financial giants. Exchanges, brokers, and financial institutions show strong interest. A sale to ICE, CME, or another large broker at 2x to 4x current valuation is entirely plausible. But this isn’t the “power law” 100x return venture capitalists dream of.
In contrast, infrastructure investments offer a completely different return curve. Whether oracle providers, analytics platforms, or cross-market execution layers, once established as core infrastructure within the prediction market ecosystem, their returns scale across all platforms, all traders, all institutions.
Such infrastructure typically starts with lower valuations, but its expansion potential is virtually limitless.
Asymmetric Risk Profiles
In the crowded platform race, VCs often bet on multiple projects, hoping one becomes the next Polymarket. This is a classic power law gamble: most will fail, and even winners may struggle to generate massive value due to market fragmentation and liquidity splitting.
Infrastructure investments, by contrast, follow a very different risk curve. An oracle provider doesn’t care whether traders use Platform A or B—if either wins, it benefits. An analytics platform becomes more valuable as more markets exist, not less. Infrastructure doesn’t need to pick winners—it just needs to be useful across all platforms.
Moreover, infrastructure often builds strong defenses through data advantages, network effects, or technical moats. It’s not just a “burn cash” race, but a contest of technical depth and ecosystem stickiness.
What This Means for Investors and Founders
If you're evaluating a business plan focused on launching a new prediction market platform—whether touting superior UX or targeting an untapped niche—you must ask sharper questions:
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How will you solve the liquidity problem?
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How will you achieve profitability under competitive pressure from incumbents?
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Out of the many competing platforms, how many can realistically succeed?
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More importantly, even if successful, what are the realistic exit multiples from a starting point of over $100 million in funding?
If you're looking at opportunities in infrastructure, you're facing a fundamentally different risk-return profile. Build the data layer, develop cross-market tools, design settlement mechanisms, create trader analytics, establish intelligent intelligence platforms. These businesses grow with the overall market, rather than competing against a single rival. They benefit from market prosperity, not suffer from fragmentation. They offer the kind of unconstrained growth potential that venture capital truly seeks.
The prediction market ecosystem is still in its early stages—meaning vast opportunities remain. But the real opportunity isn’t in replicating what Polymarket has already done. It’s in building the foundational layer that makes the entire ecosystem operate more efficiently.
Platforms will fight each other. Infrastructure will keep expanding.
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