Polymarket and Kalshi CEOs Bet Together, What Exactly is 5(c) Capital?

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

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

On Wall Street, there's a classic signal: when competitors start betting on the same infrastructure, the industry has moved to the next stage.

That's where prediction markets are now.

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 with official U.S. regulatory approval.

Two completely different paths:

• One is a global, on-chain, decentralized narrative
• The other 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 far more unusual than it appears on the surface.

5(c) Capital is a modest-sized fund, targeting approximately $35 million. Polymarket CEO Shayne Coplan and Kalshi CEO Tarek Mansour have both placed bets on this fund. These two companies are the most significant players in prediction markets and the most direct competitors.

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

Polymarket was founded in 2020. The real origin of 5(c) isn't that of an established VC fund investing since 2020. Instead, it's a group of people who grappled with the underlying problems in Kalshi's early market structure and turned that experience into a fund. 5(c) is not 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 Platform Wars

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

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

Its bet is on:

• Who provides liquidity to prediction markets;
• Who designs event indices;
• Who builds cross-platform data infrastructure;
• Who creates trading tools;
• Who manages risk and surveillance;
• 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; Kalshi needs depth too. Polymarket needs more credible prices; Kalshi needs the same. Polymarket needs institutional adoption; Kalshi needs it even more.

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

Why Is It the Kalshi Alumni Driving This?

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

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

Therefore, people from Kalshi are naturally concerned with a specific set of issues:

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

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

The biggest problem for prediction markets 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 capable of withstanding regulation, liquidity issues, manipulation, settlement disputes, and institutional scrutiny? 5(c)'s choice to invest in infrastructure is an attempt to answer this question.

Will Prediction Markets Be Monopolized by a Few Giants?

Very likely.

Prediction markets seem infinitely expandable because the world generates new events every day. However, very few events actually create markets with effective trading. Most events lack enough traders, sufficient liquidity, or clearly defined settlement standards.

This leads to a specific outcome: the more concentrated the liquidity, the more credible the price; the more credible 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, the further liquidity concentrates. This is the classic network effect of exchanges.

Stock trading, options trading, and futures trading all follow this pattern. Ultimately, markets don't distribute evenly across 100 platforms; they concentrate in the hands of a few exchanges, clearinghouses, market makers, and data terminals.

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

Tier 1: Front-End Platform Monopoly

Polymarket and Kalshi are currently closest to this position. Polymarket captures the crypto-native and global user mindshare; Kalshi holds the U.S. compliant entry point. Their paths differ, but both compete for the default position as "the event contract exchange."

Tier 2: Liquidity Monopoly

What's truly valuable might not be the platform itself, but the market-making network. If one 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 very likely what 5(c) most wants to back.

Tier 3: Data Monopoly

Once prediction market prices are used by media, funds, corporations, and AI agents, probability itself becomes a data product. In the future, entities will sell: the probability of a U.S. recession, the probability of a rate cut, a war risk index, election volatility, the probability of an AI breakthrough, the probability of a corporate event. This could become the Bloomberg terminal of prediction markets. Whoever controls data distribution controls the narrative.

Insider Trading is Not a Marginal Issue, It's the Original Sin of Prediction Markets

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

In traditional finance, insider trading is a market flaw. In prediction markets, insider information is almost a part of the product's allure. Why? Because prediction markets sell the idea of "knowing the future sooner." The problem is, if those who know the future first start betting, is the market discovering information or rewarding corruption?

Recent regulatory pressure illustrates the issue. AP reports that prediction markets are facing greater scrutiny over concerns of insider trading and illegal gambling, citing cases involving military personnel using non-public information to bet on sensitive military operations, and politicians trading on markets related to their own elections. Kalshi recently penalized and suspended three congressional candidates who placed bets on their own election races. While the amounts were small, the incidents hit the most vulnerable point of prediction markets: if candidates, government employees, military personnel, regulators, and corporate executives can trade on events where they possess non-public information, the market price is no longer just "wisdom of the crowd," but potentially "monetization of power."

Multiple U.S. states are also taking action. New York, California, Illinois, and others have recently moved to restrict government employees from using non-public information to trade in prediction markets. New York's governor signed an executive order prohibiting state employees from profiting on platforms like Kalshi and Polymarket using insider information obtained through their positions.

This is the regulator telling the market: if prediction markets want to enter the financial mainstream, they cannot continue to rely on growth fueled by grey-area information advantages. This creates a paradox. Prediction markets are valuable because they can absorb dispersed information. But dispersed information inevitably includes some degree of non-public information.

• Company employees know about project timelines.
• Government employees know about policy directions.
• Campaign teams know internal polling numbers.
• Military personnel know about operational plans.
• Supply chain personnel know about production capacity changes.
• Traders know about order flow.

If these people cannot participate at all, 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 most difficult institutional dilemma for prediction markets to solve. Economists love prediction markets because they aggregate information. Regulators hate them because they might reward the illegal acquisition of information.

Therefore, a truly mature prediction market of the future will not be an entirely free market. It's more likely to become a highly stratified market:

• Retail investors can trade low-sensitivity events.
• Institutions can trade events that have passed compliance review.
• Government employees, candidates, and insiders will be restricted from participation.
• Events like wars, assassinations, deaths, and military operations will be strictly prohibited.
• Platforms must establish monitoring, KYC, suspicious activity reporting, and penalty mechanisms.

This will sacrifice some "openness" in exchange for mainstream adoption.

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

Many view regulation as a bearish factor for prediction markets. In the short term, yes. In the long term, not necessarily. The stricter the regulation, the more it favors infrastructure companies. Why? Because as the industry moves towards compliance, platforms will need:

• Identity verification;
• Transaction surveillance;
• Insider trading detection;
• Market manipulation identification;
• Contract review;
• Settlement dispute resolution;
• Cross-platform risk control;
• Institutional-grade data recording;
• Audit and reporting systems.

None of these can be fully solved internally by a single company like Polymarket or Kalshi. This is precisely where 5(c)'s opportunity lies. The ecosystem it's betting on isn't just about "letting more people place bets." More importantly, it's about equipping prediction markets with the conditions necessary to enter the broader financial system. If the early phase of prediction markets relied on hype, traffic, political events, and crypto capital for growth, the next phase depends 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. Past financial markets 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. Markets with genuine liquidity will only concentrate on a few platforms. Polymarket and Kalshi are currently the two strongest front-end entry points.

Third, after the front-end, the biggest value lies in the back-end. Market making, data, indices, risk control, settlement, and compliance tools will become the profit pool of this industry. 5(c) doesn't need to predict whether Polymarket or Kalshi will ultimately win. It only needs to determine if the industry will grow. If the answer is yes, then investment opportunities will emerge in the infrastructure layer.

This is also why the CEOs of two competing companies can simultaneously serve as investors. They aren't jointly supporting a competitor; they are buying insurance for the market foundation they will both need in the future.

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