What’s the real story behind the 5(c) Capital, which is jointly invested by the CEOs of Polymarket and Kalshi?

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Author: Anita

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 prediction market.

On one side is Polymarket — the most influential event market in the crypto world; on the other side is Kalshi — one of the few exchanges licensed by U.S. regulators for event contracts.

The two paths are completely different:

One is globalized, on-chain, decentralized narrative

The other is compliant, CFTC-regulated, traditional finance track

But the CEOs of these two companies have simultaneously invested in a fund, 5© Capital

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

5© Capital is not a large-scale fund, aiming to raise about $35 million. Polymarket CEO Shayne Coplan and Kalshi CEO Tarek Mansour both invested in this fund. These two companies are the two most important players in prediction markets and are direct competitors.

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

Polymarket was founded in 2020. The true background of 5© is not an old fund that has been investing since 2020, but a group of people who have explored underlying issues in Kalshi’s early market structure, turning their experience into a fund. 5© is not a traditional thematic fund. It’s more like a capital tool organized by insiders within the industry.

5© invests not in platforms, but in the arms depot behind platform wars.

Public materials show that 5© plans to invest in about 20 companies, focusing on market makers, index design, and prediction market infrastructure.

It’s not aiming to invest in “the next Polymarket,” nor in “the next Kalshi.”

It bets on:

Who provides liquidity to prediction markets;

Who designs event indices;

Who creates cross-platform data;

Who develops trading tools;

Who handles risk control and monitoring;

Who defines result 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 also needs depth; Polymarket needs more trustworthy prices, Kalshi also needs them; Polymarket needs institutional entry, Kalshi needs it even more.

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

Why is it the Kalshi team doing this?

The lineage of 5© is clear: Kalshi.

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

Therefore, people coming from Kalshi naturally care about a few things:

What events can be designed as contracts;

What events should not be traded;

Which markets are prone to manipulation;

Why market makers are reluctant to participate;

How traders can leverage non-public information;

Where regulations will tighten in the future.

This perspective differs from that of ordinary crypto funds. Ordinary crypto funds see growth curves; Kalshi insiders see market structure.

The biggest problem with prediction markets has never been “whether someone wants to bet.” Humans have always wanted to bet. The question is: can this betting behavior be packaged into financial markets and withstand regulation, liquidity, manipulation, settlement disputes, and institutional scrutiny? 5©’s investment in infrastructure is answering this question.

Will prediction markets be monopolized by a few giants?

Very likely.

Prediction markets seem infinitely expandable because new events occur daily worldwide. But markets that can truly facilitate effective trading are rare. Most events lack enough traders, liquidity, or clear settlement standards.

This leads to a result: the more concentrated liquidity is, the more trustworthy the prices; the more trustworthy the prices, the more users; the more users, the more willing market makers are to participate; the more market makers participate, the further liquidity concentrates. This is a typical network effect of exchanges.

Stock trading, options, futures—all follow this pattern. Ultimately, markets won’t be evenly distributed across 100 platforms but will concentrate in a few exchanges, clearinghouses, market makers, and data terminals.

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

First layer: Front-end platform monopoly

Polymarket and Kalshi are currently closest to this position.

Polymarket dominates the crypto-native and global user mindshare; Kalshi dominates the U.S. regulatory entry point. Their paths differ, but both are vying for the default position of “event contract exchange.”

Second layer: Liquidity monopoly

The real value may not be the platform itself but the market-making network.

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

This is likely what 5© most wants to invest in.

Third layer: Data monopoly

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

In the future, someone will sell:

The probability of a U.S. recession;

The probability of interest rate cuts;

War risk indices;

Election volatility;

AI breakthrough probabilities;

Company event probabilities.

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

Insider trading is not a fringe issue but the “original sin” of prediction markets.

Prediction markets rely on 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 “who knows the future earlier.”

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

Recent regulatory pressures have already illustrated this. An AP report states that prediction markets are under increased scrutiny due to concerns over insider trading and illegal gambling, including cases where military personnel are accused of betting on sensitive military operations with non-public information, and politicians participating in markets related to their own elections.

Kalshi recently penalized and suspended three congressional candidates who bet on markets related to their campaigns. Although the amounts were small, the event itself hit the prediction market’s most vulnerable spot: if candidates, government employees, military personnel, regulators, and executives can trade on non-public information they hold, market prices are no longer just “collective wisdom” but could be “power monetization.”

Multiple U.S. states have also begun action. Recent restrictions in New York, California, Illinois target government employees trading prediction markets with non-public information. The governor of New York signed an executive order banning state employees from profiting from insider information via Kalshi, Polymarket, and similar platforms.

This is regulators telling the market: if prediction markets want to enter mainstream finance, they cannot continue to grow on gray-area information dividends.

Here’s a paradox.

Prediction markets are valuable because they absorb dispersed information. But within dispersed information, some non-public information is inevitably included.

Company employees know project progress.

Government officials know policy trends.

Campaign teams know internal polls.

Military personnel know operational plans.

Supply chain staff know capacity changes.

Traders know order flow.

If these people are completely barred from participating, the market loses some informational advantage. If they can participate, the market is accused of encouraging corruption and insider trading. This is the most difficult institutional dilemma prediction markets face.

Economists love prediction markets because they can aggregate information. Regulators dislike them because they might reward illegal information acquisition.

Therefore, the future mature prediction markets are unlikely to be fully free markets. They are more likely to be highly layered markets:

Retail traders can trade low-sensitivity events;

Institutions can trade events that have passed compliance checks;

Government officials, candidates, insiders are restricted from participation;

Events like war, assassination, death, military operations are strictly prohibited;

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

This sacrifices some “openness” but enables mainstream adoption.

The opportunity for 5© also comes from this tightening regulation.

Many see regulation as a negative for prediction markets. Short-term, yes. Long-term, perhaps not. Stricter regulation benefits infrastructure companies.

Why?

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

Identity verification;

Transaction monitoring;

Insider trading detection;

Market manipulation recognition;

Contract review;

Settlement dispute handling;

Cross-platform risk control;

Institutional-grade data recording;

Audit and reporting systems.

None of these can be fully handled internally by Polymarket or Kalshi alone.

This is the opportunity for 5©. It bets on an ecosystem that’s not just about “getting more people to bet.” More importantly, it aims to make prediction markets capable of entering the financial system.

If early prediction markets relied on topics, traffic, political events, and crypto capital growth, the next phase depends on institutionalization. Institutionalization is slow but allows large capital to flow in.

It bets on three things:

First, events will become asset classes

Past financial markets traded profits, interest rates, commodities, currencies, volatility. Prediction markets want to trade “events.” This could be a new asset class.

Second, prediction markets will centralize

Truly liquid markets will only be concentrated in a few platforms. Polymarket and Kalshi are currently the strongest front-end entries.

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

Market-making, data, indices, risk control, settlement, compliance tools will become the profit pools of this industry. 5© doesn’t need to judge who will ultimately win between Polymarket and Kalshi. It only needs to judge: will this industry grow? If yes, then infrastructure investment opportunities will emerge.

This is also why the CEOs of these two competitors can simultaneously become investors.

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