Polymarket and Kalshi CEOs Both Bet on It—What is 5(c) Capital’s Angle?

Original Author: Anita AGI/acc (X: @Anitahityou)

When Archrivals Bet Together: The Real Signal Behind 5(c) Capital

On Wall Street, there's a classic signal: when competitors start placing bets on the same infrastructure, the industry has entered its next phase.

That's where prediction markets are today.

On one side is Polymarket — the crypto world's most viral event market. On the other is Kalshi — one of the only event contract exchanges to secure U.S. regulatory approval.

Two completely different paths:

• One is a global, on-chain, decentralized narrative
• One is a compliant, CFTC-regulated, traditional finance track

Yet, the CEOs of both companies have simultaneously invested in the same fund, 5(c) Capital.

This is more unusual than it appears on the surface.

5(c) Capital is relatively small, targeting around $35 million in fundraising. Polymarket CEO Shayne Coplan and Kalshi CEO Tarek Mansour have both placed bets on this fund. These two companies are the most important players in prediction markets and direct competitors.

The fund is spearheaded by two former Kalshi employees: Adhi Rajaprabhakaran and Noah Zingler-Sternig. The former was a Kalshi trader, the latter was Kalshi's Head of Operations.

Polymarket was founded in 2020. The real origin of 5(c) isn't an established fund investing since 2020; it's a group of people who grappled with foundational issues in Kalshi's early market structure, institutionalizing their experience. 5(c) isn't a traditional thematic fund. It's more like a capital vehicle organized by industry insiders.

5(c) Isn't Betting on Platforms, But on the Arsenal Behind the Platform War

Public materials show that 5(c) plans to invest in approximately 20 companies, focusing on market makers, index design, and prediction market infrastructure.

It's not trying to invest in "the next Polymarket" or "the next Kalshi."

Its wagers are on:

• Who provides liquidity for prediction markets;
• Who designs event indices;
• Who builds cross-platform data solutions;
• Who creates trading tools;
• Who handles risk management and monitoring;
• Who defines outcome settlement;
• Who transforms prediction markets from retail betting into an institutional asset class.

Platforms can compete, but infrastructure can be shared. Polymarket needs depth, and Kalshi needs depth too; Polymarket needs more reliable prices, and so does Kalshi; Polymarket needs institutional entry, and Kalshi needs it even more.

It's betting on the entire prediction market ecosystem, not just a single entry point.

Why Are People from the Kalshi Camp Behind This?

5(c)'s lineage is clear: Kalshi.

Kalshi's path is completely different from Polymarket's. Polymarket is a crypto-native growth machine, rapidly breaking out through globalization, on-chain assets, and event narratives. Kalshi, on the other hand, chose the U.S. regulatory path, dealing long-term with the CFTC, state regulators, and the boundaries of event contracts.

Therefore, people from Kalshi are naturally concerned with a few things:

1. What events can be designed as contracts;
2. What events should not be traded;
3. Which markets are easily manipulated;
4. Why market makers are reluctant to participate;
5. How traders can exploit non-public information;
6. Where regulators will eventually tighten boundaries.

This perspective differs from a typical crypto fund. A typical crypto fund sees the growth curve; the Kalshi camp sees the market structure.

For prediction markets, the biggest problem has never been "whether people want to bet." Humans have always wanted to bet. The problem is: can this betting behavior be packaged into a financial market that can withstand regulatory scrutiny, liquidity issues, manipulation, settlement disputes, and institutional review? 5(c), by choosing to invest in infrastructure, is trying to answer this question.

Will Prediction Markets Be Monopolized by a Few Giants?

Very likely.

Prediction markets might seem infinitely expandable because new events happen every day. But truly efficient trading markets are rare. Most events lack sufficient traders, liquidity, and clear settlement standards.

This leads to a result: The more concentrated the liquidity, the more reliable the price; the more reliable the price, the more concentrated the users; the more concentrated the users, the more willing market makers are to participate; the more willing market makers are to participate, the further liquidity concentrates. This is the classic network effect of exchanges.

Stock trading, options trading, and futures trading all work this way. Eventually, markets don't evenly distribute across 100 platforms; they concentrate in the hands of a few exchanges, clearing houses, market makers, and data terminals.

Prediction markets will be no exception. Over the next 12–24 months, prediction markets will likely form a three-layer monopoly:

Layer 1: Frontend Platform Monopoly

Polymarket and Kalshi are currently closest to this position.

Polymarket dominates crypto-native and global user mindshare; Kalshi holds the U.S. compliant entry point. Their paths differ, but both compete for the default position of the "event contract exchange."

Layer 2: Liquidity Monopoly

What's truly valuable might not be the platform, but the market-making network.

If an institution can simultaneously serve Polymarket, Kalshi, and other venues, providing cross-market market making, arbitrage, and price stability, it could become the Jane Street or Citadel of prediction markets.

This is likely what 5(c) wants to back the most.

Layer 3: Data Monopoly

When prediction market prices are used by media, funds, corporations, and AI agents, probability itself becomes a data product.

In the future, people might sell:

• Probability of a U.S. recession;
• Probability of interest rate cuts;
• War risk index;
• Election volatility;
• Probability of AI technological breakthroughs;
• Probability of corporate events.

This could become the Bloomberg of prediction markets. Whoever controls data distribution controls the interpretation rights.

Insider Trading Isn't a Peripheral Issue, It's the "Original Sin" of Prediction Markets

Prediction markets cannot escape insider trading, but insider trading is killing them.

In traditional finance, insider trading is a market flaw. In prediction markets, insider information is almost part of the product's appeal. Because prediction markets essentially sell "who knows the future sooner."

The problem is, if those who know the future sooner start placing bets, is the market discovering information or rewarding corruption?

Recent regulatory pressure already highlights the issue. AP reported that prediction markets are under greater scrutiny due to concerns over insider trading and illegal gambling, including cases of military personnel allegedly using non-public information to bet on sensitive military operations, and politicians participating in markets related to their own elections.

Kalshi recently penalized and suspended three congressional candidates who placed bets on markets related to their own races. Although the bet amounts were small, the events struck at the most vulnerable point of prediction markets: if candidates, government employees, military personnel, regulators, and corporate executives can all trade events where they possess non-public information, market prices are no longer just "wisdom of the crowd" but potentially "power monetization."

Several U.S. states are also taking action. New York, California, and Illinois have recently imposed restrictions on government employees using non-public information to trade on prediction markets. The New York Governor signed an executive order prohibiting state employees from profiting on prediction markets like Kalshi and Polymarket using insider information obtained through their positions.

This is regulators telling the market: if prediction markets want to enter the financial mainstream, they can't continue to grow on the back of gray information advantages.

Here lies a paradox.

Prediction markets are valuable because they absorb dispersed information. But dispersed information inevitably includes some that is non-public.

Company employees know about project progress.

Government employees know about policy direction.

Campaign teams know internal polls.

Military personnel know about operational plans.

Supply chain workers know about production changes.

Traders know order flow.

If these people are completely barred from participating, the market loses some of its informational edge. If they can participate, the market risks being accused of encouraging corruption and insider trading. This is the hardest institutional dilemma for prediction markets to resolve.

Economists love prediction markets because they aggregate information. Regulators hate prediction markets because they might reward illegal information acquisition.

Therefore, a truly mature prediction market in the future won't be a completely free market. It's more likely to become a highly stratified market:

• Retail investors can trade low-sensitivity events;
• Institutions can trade compliance-screened events;
• Government employees, candidates, and insiders are restricted from participation;
• Events like war, assassination, death, and military operations are strictly prohibited;
• Platforms must establish monitoring, KYC, suspicious activity reporting, and penalty mechanisms.

This may sacrifice some "openness" but trade it for mainstream adoption.

5(c)'s Opportunity Also Comes from This Regulatory Tightening

Many will see regulation as bearish for prediction markets. In the short term, yes. In the long term, not necessarily. The tighter the regulation, the more it benefits infrastructure companies.

Why?

Because once the industry begins to formalize compliance, platforms will need:

• Identity verification;
• Transaction monitoring;
• Insider trading detection;
• Market manipulation identification;
• Contract review;
• Settlement dispute resolution;
• Cross-platform risk management;
• Institutional-grade record keeping;
• Audit and reporting systems.

These aren't things that Polymarket or Kalshi can fully solve internally alone.

This is precisely 5(c)'s opportunity. The ecosystem it's betting on isn't just about "getting more people to bet." More importantly, it's about giving prediction markets the conditions to enter the financial system.

If the early prediction market grew on hype, traffic, political events, and crypto capital, the next stage relies on institutionalization. Institutionalization means slower growth, but it also means big money can enter.

It's betting on three things.

First, events will become an asset class

Financial markets have historically traded company profits, interest rates, commodities, currencies, and volatility. Prediction markets aim to trade "events." This could be a new asset class.

Second, prediction markets will consolidate

Truly liquid markets will only concentrate on a few platforms. Polymarket and Kalshi are currently the two strongest front-end portals.

Third, after the front-end, the greatest value lies in the back-end

Market making, data, indices, risk management, settlement, and compliance tools will become the profit pools of this industry. 5(c) doesn't need to judge whether Polymarket or Kalshi will ultimately win. It only needs to judge whether this industry will grow. If the answer is yes, then investment opportunities will appear in the infrastructure layer.

This is also why two competing CEOs can simultaneously become investors.

They aren't jointly backing a competitor. They are buying insurance for the market foundation they will both need in the future.

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