What is the background of 5(c) Capital, which has both Polymarket and Kalshi CEOs as investors?

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

This is the current state of the prediction market.

On one side is Polymarket ------ the most viral event market in the crypto world; on the other side is Kalshi ------ one of the only event contract exchanges licensed by U.S. regulators.

The two paths are completely different:

• One is a global, on-chain, decentralized narrative

• One is compliant, CFTC, traditional financial track

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

This is more unusual than it appears on the surface.

5(c) Capital is not large, aiming to raise about $35 million. Polymarket CEO Shayne Coplan and Kalshi CEO Tarek Mansour both placed bets on this fund. These two companies are the most important players in the prediction market and are also direct competitors.

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

Polymarket was founded in 2020. The true nature of 5(c) is not that of a traditional fund that has been investing in projects since 2020, but rather a group of individuals who have explored the underlying issues in Kalshi's early market structure, turning their experiences into a fund. 5(c) is not a thematic fund in the traditional sense. It is more like a capital tool organized by industry insiders.

5(c) is not betting on the platform, but on the arsenal behind the platform war

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

It is not looking to invest in the "next Polymarket," nor the "next Kalshi."

It is betting on:

• Who provides liquidity to the prediction market;

• Who designs event indices;

• Who does cross-platform data;

• Who creates trading tools;

• Who manages risk and monitoring;

• Who defines outcome settlements;

• 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; Polymarket needs more credible prices, and so does Kalshi; Polymarket needs institutional entry, and Kalshi needs it even more.

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

Why are people from Kalshi doing this?

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

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

Thus, those coming from Kalshi naturally care about several issues:

• What events can be designed as contracts;

• What events should not be traded;

• What markets are easy to manipulate;

• Why market makers are reluctant to enter;

• How traders exploit non-public information;

• Where regulators will ultimately tighten boundaries.

This perspective is different from that of ordinary crypto funds. Ordinary crypto funds see growth curves, while those from Kalshi see market structures.

The biggest problem with prediction markets has never been "Is there anyone who wants to bet?" Humans have always wanted to bet. The question is: can this betting behavior be packaged into a financial market that can withstand regulation, liquidity, manipulation, settlement disputes, and institutional scrutiny? 5(c) chooses to invest in infrastructure to answer this question.

Will prediction markets be monopolized by a few giants?

It is very likely.

Prediction markets seem infinitely expandable because new events occur every day. However, very few markets can form 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 prices; the more credible the prices, the more concentrated the users; the more concentrated the users, the more willing market makers are to come; the more willing market makers are to come, the further liquidity concentrates. This is a typical exchange network effect.

Stock trading, options trading, and futures trading all work this way. In the end, the market will not be evenly distributed across 100 platforms but will concentrate in the hands of a few exchanges, clearinghouses, market makers, and data terminals.

Prediction markets will not be an exception. In the next 12 to 24 months, prediction markets will likely form a three-tier monopoly:

First Tier: Frontend Platform Monopoly

Polymarket and Kalshi are currently closest to this position.

Polymarket occupies the crypto-native mindset of global users; Kalshi occupies the U.S. compliant entry. Both paths are different, but they are competing for the default position of "event contract exchange."

Second Tier: Liquidity Monopoly

What may truly be valuable is not the platform, but the market-making network.

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

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

Third Tier: Data Monopoly

When prediction market prices are used by media, funds, enterprises, and AI agents, probabilities themselves will become data products.

In the future, someone will sell:

• U.S. recession probabilities;

• Interest rate cut probabilities;

• War risk indices;

• Election volatility;

• AI technology breakthrough probabilities;

• Company event probabilities.

This will become a prediction market version of Bloomberg. Whoever controls data distribution controls the interpretation.

Insider trading is not a marginal issue, but the "original sin" of prediction markets

Prediction markets cannot avoid 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 temptation. Because what prediction markets sell is "who knows the future first."

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

Recent regulatory pressures have already indicated the problem. AP reported that prediction markets are under greater scrutiny due to concerns about insider trading and illegal gambling, including cases where military personnel were accused of 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 bet on their own election-related markets. Although the amounts were not large, the events themselves hit the most vulnerable point of prediction markets: if candidates, government employees, military personnel, regulators, and corporate executives can trade events based on non-public information they possess, market prices are no longer just "collective wisdom," but may become "monetization of power."

Several states in the U.S. have also begun to take action. New York, California, Illinois, and other states have recently implemented restrictions on government employees using non-public information to trade prediction markets. The governor of New York signed an executive order prohibiting state employees from profiting in 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 mainstream finance, they cannot continue to rely on gray information dividends for growth.

There is a paradox here.

The value of prediction markets lies in their ability to absorb dispersed information. But within that dispersed information, there will inevitably be a portion of non-public information.

• Company employees know project progress.

• Government employees know policy trends.

• Campaign teams know internal polling.

• Military personnel know operational arrangements.

• Supply chain personnel know capacity changes.

• Traders know order flow.

If these people cannot participate at all, the market will lose some information advantage. If these people can participate, the market will be accused of encouraging corruption and insider trading. This is the institutional dilemma that prediction markets find hardest to solve.

Economists like prediction markets because they can aggregate information. Regulators dislike prediction markets because they may reward illegal information acquisition.

Therefore, the truly mature prediction markets in the future will not be completely free markets. They are more likely to become a highly stratified market:

• Retail investors can trade low-sensitivity events;

• Institutions can trade events that have undergone compliance review;

• Government employees, candidates, and insiders are restricted from participating;

• Events such as wars, assassinations, deaths, and military operations are strictly prohibited;

• Platforms must establish monitoring, KYC, abnormal trading reporting, and penalty mechanisms.

This will sacrifice some "openness," but will bring about mainstream acceptance.

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

Many people view regulation as a negative for prediction markets. In the short term, it is. In the long term, it may not be. The stricter the regulation, the more beneficial it is for infrastructure companies.

Why?

Because once the industry begins to comply, platforms will need:

• Identity verification;

• Transaction monitoring;

• Insider trading detection;

• Market manipulation identification;

• Contract review;

• Settlement dispute handling;

• Cross-platform risk control;

• Institutional-level data recording;

• Audit and reporting systems.

These things cannot be fully resolved internally by Polymarket or Kalshi alone.

This is precisely the opportunity for 5(c). The ecosystem it bets on is not just about "getting more people to bet." More importantly, it is about enabling prediction markets to meet the conditions for entering the financial system.

If early prediction markets relied on topics, traffic, political events, and crypto funds for growth, then the next phase relies on institutionalization. Institutionalization means slow progress, but it also means big money can come in.

It bets on three things.

First, events will become an asset class

In the past, financial markets traded company profits, interest rates, commodities, currencies, and volatility. Prediction markets want to trade "events." This could be a new asset class.

Second, prediction markets will become centralized

Truly liquid markets will only concentrate on a few platforms. Polymarket and Kalshi are currently the two strongest frontend entries.

Third, after the frontend, the greatest value lies in the backend

Market making, data, indices, risk control, settlement, and compliance tools will become the profit pool of this industry. 5(c) does not need to determine who will ultimately win between Polymarket and Kalshi. It only needs to judge: will this industry grow? If the answer is yes, then investment opportunities will arise at the infrastructure level.

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

They are not jointly supporting a competitor; they are insuring the market foundation they will both need in the future.