Polymarket and Kalshi CEOs Place Concurrent Bets, What Exactly Is 5(c) Capital?

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

When archrivals start betting together: The real signal behind 5(c) Capital

On Wall Street, there is a classic signal: when competitors start betting on the same infrastructure, the industry has entered its next phase.

That is precisely where the prediction market stands today.

On one side is Polymarket — the most culturally impactful event market in the crypto world. On the other is Kalshi — one of the only event contract exchanges with official U.S. regulatory approval.

Two completely different paths:

• One is global, on-chain, and decentralized in narrative
• The other is compliant, CFTC-regulated, and built on traditional financial rails

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

This is far more unusual than it appears on the surface.

5(c) Capital is not a large fund; its target is to raise approximately $35 million. Polymarket CEO Shayne Coplan and Kalshi CEO Tarek Mansour have both invested in this fund. These two companies are the most important players in the prediction market and also its most 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 true origin of 5(c) is not that of an established fund investing since 2020, but rather a group of people who grappled with the underlying problems within Kalshi's early market structure, translating that experience into a fund. 5(c) is not a thematic fund in the traditional sense. It is more like a capital vehicle organized by industry insiders.

5(c) isn't betting on platforms, but on the arsenal behind the platform wars

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

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

Its bet is on:

• Who provides liquidity for prediction markets;
• Who designs event indices;
• Who builds cross-platform data infrastructure;
• Who develops trading tools;
• Who handles risk management 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, and so does Kalshi; Polymarket needs more reliable prices, Kalshi does too; Polymarket needs institutional entry, Kalshi needs it even more.

It is betting on the entire prediction market ecosystem, not on a single entry point.

Why did the Kalshi team become the ones to do this?

The lineage of 5(c) is clear: Kalshi.

Kalshi's path is entirely 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 coming from Kalshi are naturally concerned about a few things:

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

This perspective differs from that of a typical crypto fund. A typical crypto fund sees a growth curve; the Kalshi team sees 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 act of betting be packaged as a financial market, capable of withstanding regulatory scrutiny, liquidity issues, manipulation, settlement disputes, and institutional review? By choosing to invest in infrastructure, 5(c) is answering this question.

Will prediction markets be monopolized by a few giants?

Very likely.

Prediction markets may seem infinitely expandable because the world creates new events daily. However, only a few events can form markets with effective trading. Most events lack enough traders, sufficient liquidity, and clear settlement standards.

This leads to one 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 market makers participate, the further liquidity concentrates. This is the classic network effect of exchanges.

This happened with stock trading, options trading, and futures trading. Eventually, the market doesn't distribute evenly across 100 platforms; it concentrates in the hands of a few exchanges, clearing houses, market makers, and data terminals.

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

Layer 1: Front-end Platform Monopoly

Polymarket and Kalshi are currently closest to this position.

Polymarket occupies the minds of crypto-native and global users; Kalshi occupies the compliant U.S. entry point. Their paths differ, but both are vying for the default position of an "event contract exchange."

Layer 2: Liquidity Monopoly

What may be truly valuable isn't 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 would become the Jane Street or Citadel of prediction markets.

This is likely what 5(c) most wants to invest in.

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 will sell:

• Probability of a U.S. recession;
• Probability of an interest rate cut;
• War risk index;
• Election volatility;
• Probability of an AI technological breakthrough;
• Probability of a corporate event.

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

Insider trading isn't a fringe 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 allure. Because prediction markets sell the idea of "who knows the future first."

The problem is, if those who know the future first start betting, is the market discovering information or rewarding corruption?

Recent regulatory pressure has made this clear. An AP report noted that prediction markets are facing greater scrutiny over concerns of insider trading and illegal gambling. This includes 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.

Recently, Kalshi penalized and suspended three congressional candidates who placed bets on markets related to their own campaigns. Although the bet amounts were small, the incidents strike at 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 ceases to be "the wisdom of the crowd" and becomes "the monetization of power."

Several U.S. states have also begun to act. New York, California, and Illinois have recently taken steps to restrict government employees from using non-public information to trade in prediction markets. The governor of New York signed an executive order prohibiting state employees from using insider information obtained through their positions for profit on prediction markets like Kalshi and Polymarket.

This is the regulator telling the market: if prediction markets want to enter mainstream finance, they cannot continue to grow on the back of gray information advantages.

There is a paradox here.

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

Company employees know project progress.

Government employees know policy trends.

Campaign teams know internal polling.

Military personnel know operational plans.

Supply chain personnel know production capacity changes.

Traders know order flow.

If these people are completely barred from participating, the market loses some of its informational advantage. If they are allowed to participate, the market risks being accused of encouraging corruption and insider trading. This is the most difficult 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 will not be a completely free market. It is more likely to become a highly stratified market:

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

This will sacrifice some degree of "openness" but buy mainstream acceptance.

5(c)'s opportunity also comes from this regulatory tightening

Many people see regulation as a negative for prediction markets. Short-term, yes. Long-term, not necessarily. The stricter the regulation, the more advantageous it is for infrastructure companies.

Why?

Because once the industry moves towards 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 data records;
• Audit and reporting systems.

None of these are things that a single company like Polymarket or Kalshi can fully solve internally.

This is exactly 5(c)'s opportunity. The ecosystem it is betting on isn't just about "letting more people place bets." It's more importantly about equipping prediction markets with the conditions needed to enter the broader financial system.

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

It bets on three things.

First, events will become an asset class

Historically, 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 centralize

Markets with genuine liquidity will consolidate onto a few platforms. Polymarket and Kalshi are currently the two strongest front-end entry points.

Third, beyond 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 determine whether Polymarket or Kalshi will ultimately win. It only needs to determine: will this industry 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 become investors.

They are not jointly supporting a competitor; they are buying insurance for the market infrastructure they will both need in the future.

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