Author: Anita
On Wall Street, there's a classic signal: when competitors start betting on the same infrastructure, the industry has entered the next phase.
This is what the prediction market is like now.
On one side is Polymarket—the most influential event marketplace in the crypto world; on the other side is Kalshi—one of the only event contract exchanges to have received regulatory approval in the United States.
The two paths are completely different:
One is a narrative of globalization, on-chain, and decentralization.
One is compliance, CFTC, and traditional financial tracks.
However, the CEOs of both companies simultaneously invested their money in the same fund, 5(c) Capital.
This matter is more unusual than it appears on the surface.
5(c) Capital is a small fund aiming to raise approximately $35 million. Polymarket CEO Shayne Coplan and Kalshi CEO Tarek Mansour both invested in the fund . These two companies are among the most important players in the prediction market and are also its most direct competitors.
The fund was driven by two early Kalshi employees: Adhi Rajaprabhakaran and Noah Zingler-Sternig . The former was a Kalshi trader, and the latter was the former head of operations at Kalshi.
Polymarket was founded in 2020. The true origins of 5(c) are not from an established fund that started investing in projects in 2020, but rather from a group of people who had explored the underlying issues in Kalshi's early market structure and turned their 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) The investment isn't in the platforms themselves, but in the arsenal behind the platform wars.
Publicly available information indicates that 5(c) plans to invest in approximately 20 companies, with a focus on market makers, index design, and prediction market infrastructure.
It's not about investing in "the next Polymarket" or "the next Kalshi".
It bets on:
Who provides liquidity to the prediction market?
Who designed the event index?
Who handles cross-platform data?
Who makes the trading instruments?
Who is responsible for risk control and monitoring?
Who defines the settlement result?
Who transformed the prediction market 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 credible pricing, and so does Kalshi; Polymarket needs institutional participation, and Kalshi needs it even more.
It's betting on the entire prediction market ecosystem, not just a single entry point.
Why was it someone from the Kalshi faction who did this?
5(c)’s lineage is very clear: Kalshi.
Kalshi's path is completely different from Polymarket's. Polymarket is a growth machine for crypto-natives, rapidly expanding its reach through globalization, on-chain assets, and event narratives. Kalshi, on the other hand, chose the US regulatory path, dealing with the CFTC, state regulations, and the boundaries of event contracts for a long time.
Therefore, people who come from Kalshi will naturally be concerned about a few things:
What events can be designed as contracts?
What events should not be traded?
Which markets are easily manipulated?
Why are market makers unwilling to join?
How traders utilize non-public information;
At what boundaries will regulation eventually tighten?
This is different from the perspective of a typical crypto fund. Typical crypto funds see the growth curve, while those in the Kalshi group see the market structure.
The biggest problem with predicting markets has never been "whether anyone wants to bet." Humans have 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) Choosing to invest in infrastructure answers this question.
Will the prediction market be monopolized by a few giants?
Very likely.
Prediction markets seem to expand infinitely because new events occur every day. However, very few markets actually generate effective trading. Most events lack sufficient traders, sufficient liquidity, and clear enough settlement standards.
This leads to the following result: the more concentrated the liquidity, the more credible the price; the more credible the price, the more concentrated the user base; the more concentrated the user base, the more market makers are willing to participate; the more market makers are willing to participate, the more concentrated the liquidity becomes. This is a typical exchange network effect.
This applies to stock trading, options trading, and futures trading as well. Ultimately, the market won't be evenly distributed across 100 platforms, but rather concentrated in the hands of a few exchanges, clearinghouses, market makers, and data terminals.
The prediction market will be no exception. In the next 12–24 months, the prediction market will likely form a three-tiered monopoly:
First layer: Front-end platform monopoly
Polymarket and Kalshi are currently the closest to this location.
Polymarket holds sway over the crypto-native market and the global user mindshare; Kalshi dominates the compliant gateway in the US. Their paths differ, but both are vying for the default position as the "event-based futures exchange."
Second layer: Liquidity monopoly
The real value may not lie in the platform itself, but in the market-making network.
If an institution can simultaneously serve Polymarket, Kalshi, and other exchanges, providing cross-market making, arbitrage, and price stabilization, it will become the Jane Street or Citadel of prediction markets.
This is probably what 5(c) most wants to vote for.
Third level: Data monopoly
When market price predictions are used by media, funds, businesses, and AI agents, probability itself becomes a data product.
Someone will sell it in the future:
Probability of a US recession;
Probability of interest rate cut;
War risk index;
Election volatility;
The probability of a breakthrough in AI technology;
Company event probability.
This will become a prediction market version of Bloomberg. Whoever controls data distribution controls the interpretation of that data.
Insider trading is not a marginal issue, but rather the "original sin" of predicting the market.
Predicting markets relies on insider trading, but insider trading is killing it.
In traditional finance, insider trading is a market flaw; in prediction markets, insider information is almost part of the product's allure. This is because prediction markets are essentially selling "who knows the future first."
The question is, if people who know the future start placing bets, is this market discovering information or rewarding corruption?
Recent regulatory pressures have already illustrated the problem. AP reports that prediction markets are facing increased scrutiny due to concerns about insider trading and illegal gambling, including cases of military personnel allegedly using non-public information to bet on sensitive military operations and politicians interfering in markets related to their own elections.
Kalshi recently penalized and suspended three congressional candidates who placed bets on their own campaign-related markets. Although the bets were small, the incident itself hit the most vulnerable spot in prediction markets: if candidates, government employees, military personnel, regulators, and corporate executives can all trade events for which they have non-public information, market prices are no longer just a matter of "collective wisdom," but could become a "monetization of power."
Several US states have also begun taking action. New York, California, Illinois, and other states have recently implemented restrictions on government employees 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 in their positions to profit from prediction markets such as Kalshi and Polymarket.
This is the regulator telling the market: if the market wants to enter mainstream finance, it can no longer rely on the growth of gray information dividends.
There is a paradox here.
The value of market prediction lies in its ability to absorb fragmented information. However, fragmented information inevitably includes some non-public information.
Company employees are aware of the project's progress.
Government employees are aware of policy trends.
The campaign team was aware of the internal polls.
Military personnel were aware of the operational plan.
Supply chain personnel are aware of changes in production capacity.
Traders know the order flow.
If these individuals are completely excluded, the market loses some of its informational advantage. If they are allowed to participate, the market will be accused of encouraging corruption and insider trading. This is the most difficult institutional dilemma to resolve in market prediction.
Economists like predicting markets because it aggregates information. Regulators dislike predicting markets because it may reward illegal access to information.
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 can trade insensitive events;
Institutions can trade events that have undergone compliance review;
Government employees, candidates, and insiders are restricted from participating;
War, assassination, death, military operations and other such events are strictly prohibited;
The platform must establish monitoring, KYC, abnormal transaction reporting, and penalty mechanisms.
This will sacrifice some "openness" but in exchange for mainstreaming.
The opportunity presented by 5(c) also stems from this tightening of regulations.
Many people view regulation as a negative factor for the market. In the short term, yes. In the long term, not necessarily. Stricter regulations are generally more beneficial to infrastructure companies.
Why?
Because once the industry begins to comply with regulations, the platform will need to:
Identity verification;
Transaction monitoring;
Insider trading detection;
Identification of market manipulation;
Contract review;
Settlement dispute resolution;
Cross-platform risk control;
Institutional-level data recording;
Audit and reporting system.
These are not things that Polymarket or Kalshi alone can completely solve internally.
This is precisely the opportunity for 5(c). Its bet on an ecosystem that goes beyond simply "getting more people to bet." More importantly, it's about making prediction markets ready to enter the financial system.
If the early prediction market grew based on buzz, traffic, political events, and crypto funding, the next phase will rely on institutionalization. Institutionalization means slower growth, but it also means that large sums of money can enter.
It bets on three things.
First, the event will become an asset class.
In the past, financial markets traded company profits, interest rates, commodities, currencies, and volatility. Predicting what the market wants to trade involves "events." This could be a new asset class.
Second, the market is expected to become more centralized.
A truly liquid market 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 greatest 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) There's no need to determine who will ultimately win between Polymarket and Kalshi. It only needs to determine: Will this industry grow? If the answer is yes, then investment opportunities will emerge at the infrastructure layer.
This is why two rival CEOs can become investors at the same time.
They are not supporting a competitor; they are insuring themselves on the market foundation they will need in the future.
