Author: Anita
In Wall Street, there is a classic signal: when competitors start betting on the same infrastructure, the industry has entered the next stage.
This is the current 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 few event contract exchanges licensed by U.S. regulators.
The two paths are completely different:
One is global, on-chain, decentralized narrative.
One is compliant, CFTC, traditional financial track.
But the CEOs of these two 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 bet on this fund. These two companies are the two most important players in the prediction market and the most 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 origin of 5(c) is not a traditional fund investing in projects since 2020, but rather a group of people who have explored the underlying issues in Kalshi's early market structure, capitalizing their experience. 5(c) is not a traditional thematic fund. It is more like a capital tool organized by insiders in the industry.
5(c) is not betting on platforms but on the arms warehouse behind the platform wars.
Public materials show 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 indexes;
Who does cross-platform data;
Who creates trading tools;
Who handles risk control and monitoring;
Who defines outcome settlement;
Who turns prediction markets from retail betting into institutional asset classes.
Platforms can compete, but infrastructure can be shared. Polymarket needs depth, and so does Kalshi; Polymarket needs more credible prices, and Kalshi needs it too; Polymarket needs institutional entry, and Kalshi needs it even more.
It is betting on the whole prediction market ecosystem, not a specific entry point.
Why are people from Kalshi doing this?
The lineage of 5(c) is clear: Kalshi.
The path of Kalshi is entirely different from Polymarket. Polymarket is a crypto-native growth machine that quickly breaks out through globalization, on-chain assets, and event narratives. Kalshi opts for the U.S. regulatory path, long-term dealing with CFTC, state regulators, and event contract boundaries.
Therefore, people coming out of Kalshi naturally care about several issues:
What events can be designed as contracts;
What events should not be traded;
What markets are prone to manipulation;
Why market makers are reluctant to come in;
How traders exploit non-public information;
Where regulators will tighten boundaries ultimately.
This perspective is different from that of a traditional crypto fund. A traditional crypto fund sees growth curves, while people from Kalshi see market structure.
The biggest problem with prediction markets has never been “Is there anyone who wants to bet?” Humanity has always wanted to bet. The question is: can this betting behavior be packaged as a financial market and withstand regulation, liquidity, manipulation, settlement disputes, and institutional scrutiny? 5(c)'s choice to invest in infrastructure is addressing this question.
Will prediction markets be monopolized by a few giants?
It is highly likely.
Prediction markets seem infinitely expandable, as new events arise every day. However, very few markets can form effective transactions. Most events do not have enough traders, do not have sufficient liquidity, and lack clear settlement standards.
This leads to one outcome: the more liquidity is concentrated, 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 in; the more willing market makers are to come in, the further liquidity concentrates. This is a typical exchange network effect.
Stock trading, options trading, and futures trading are all like this. In the end, the market will not be evenly distributed across 100 platforms but rather concentrated 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-layer monopoly:
First layer: Front-end platform monopoly
Polymarket and Kalshi are currently closest to this position.
Polymarket occupies the crypto-native mindshare of global users; Kalshi occupies the U.S. compliant entry. The two paths are different, but both are vying for the default position of “event contract exchange.”
Second layer: Liquidity monopoly
The real valuable thing may not be the platform but the market maker network.
If an institution can simultaneously serve Polymarket, Kalshi, and other trading venues, providing cross-market making, arbitrage, and price stabilization, it would become the Jane Street or Citadel of the prediction markets.
This is likely what 5(c) is most eager to invest in.
Third layer: Data monopoly
When prediction market prices are used by the 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;
The war risk index;
Election volatility;
The probability of breakthroughs in AI technology;
The probability of corporate events.
This will become the Bloomberg of prediction markets. Whoever controls data distribution holds the interpretive power.
Insider trading is not a marginal issue but the "original sin" of prediction markets.
Prediction markets cannot do without insider trading, but insider trading is killing it.
In traditional finance, insider trading is a market defect, while in prediction markets, insider information is almost part of the product temptation. Because what prediction markets sell is “who knows the future sooner.”
The problem is, if those who know the future early start to bet, is this market discovering information or rewarding corruption?
Recent regulatory pressures have already highlighted the issue. The AP reported that prediction markets are under greater scrutiny due to concerns about insider trading and illegal gambling, including cases where military personnel are accused of using non-public information to bet on sensitive military operations and political figures participating in markets related to their own elections.
Recently, Kalshi has also penalized and suspended three congressional candidates who bet on their own election-related markets. Although the bet amounts were small, the incidents themselves hit at the most vulnerable aspects of prediction markets: if candidates, government employees, military personnel, regulators, and corporate executives can trade events involving non-public information, the market price is no longer just “collective wisdom,” but may become “monetizing power.”
Several U.S. states 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 in prediction markets, with the governor of New York signing an executive order prohibiting state employees from profiting on Kalshi, Polymarket, and other prediction markets 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 grow relying on gray information dividends.
There is a paradox here.
The value of prediction markets lies in their ability to absorb decentralized information. But within this decentralized information, there inevitably exists a portion of non-public information.
Company employees know the 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 are completely barred from participation, the market will lose part of its 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 most difficult to solve.
Economists like prediction markets because they can aggregate information. Regulators dislike prediction markets because they can potentially 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 traders 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 reports, and penalty mechanisms.
This will sacrifice some level of “openness” in exchange for mainstream acceptance.
5(c)'s opportunity also comes from this tightening regulation.
Many people see regulation as a negative for prediction markets. In the short term, it is. In the long term, it may not be. The stricter the regulatory environment, the more beneficial it is for infrastructure companies.
Why?
Because once the industry starts to comply, platforms will need:
Identity verification;
Trading 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 entirely solved by Polymarket or Kalshi alone.
This is precisely 5(c)'s opportunity. Its ecological bet is not just about “getting more people to bet.” More importantly, it is about enabling the prediction market to meet the conditions to enter the financial system.
If the early prediction markets relied on topics, traffic, political events, and crypto funds for growth, then the next phase will rely on institutionalization. Institutionalization means slower growth but also that significant money can enter.
It bets on three things.
First, events will become an asset class.
In the past, financial markets traded company profits, interest rates, commodities, currencies, volatility. Prediction markets aim to trade “events.” This could be a new asset class.
Second, prediction markets will become centralized.
Truly liquid markets will only be concentrated in a few platforms. Polymarket and Kalshi are currently the two strongest front-end entry points.
Third, after the front end, the most significant value lies in the back end.
Market making, data, indexes, risk control, settlement, compliance tools will become the profit pool for this industry. 5(c) does not need to determine which of Polymarket and Kalshi will ultimately win. It just needs to assess whether the industry will grow. If the answer is yes, then investment opportunities will appear at the infrastructure level.
This is also why the CEOs of two competitors can simultaneously become investors.
They are not collectively supporting a rival; they are insuring the market foundation they will both need in the future.
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