Author: Changan I Biteye Content Team

At Charles de Gaulle Airport in Paris, a man stands by the runway, holding a portable heat source, heating a meteorological sensor.

Minutes later, the Polymarket weather market settles at 22°C, and the position he built at an extremely low price turns into $34,000.

The whole process does not involve any advanced quantitative strategy, nor is there any technical threshold; he simply did one thing: he knew where the entire market's settlement data came from and influenced it.

What this article aims to discuss is not a specific loophole, but a more fundamental issue: when a market aims to "reflect reality," does it also provide participants with the incentive to influence reality?

In this article, we will answer three questions:

• Which type of market is easiest to manipulate at the source in prediction markets?

• How do these "loopholes" occur in reality?

• What are the true attitudes of Polymarket and Kalshi facing these issues?

I. You think you are betting on reality, but you are actually betting on data sources

When most people discuss prediction markets, they focus on the rules themselves, such as: how does this market determine a win? But these belong to the first layer; the settlement logic of prediction markets has two layers:

• The first layer is platform rules, which determine "what kind of result counts as a win."

• The second layer is data sources, which determine "what happened in the real world."

The market indeed bets on reality itself, but reality must first be "recorded" to settle. So in the past, people studied the rules, looked for the specific sources cited in the rules, confirmed which website was actually used, and even emailed the upstream data providers to try to obtain the data earlier.

This step essentially competes over who "knows the outcome sooner"; for example, some people attend a live sports game and place bets before the score synchronizes with the official data system.

However, there is another point here that is easily overlooked: when everyone is trying to find ways to "get data faster," some people begin to bypass this step and directly influence the outcome itself. As long as reality ultimately enters the market through a specific data source, influencing reality equals influencing the settlement.

From "checking rules," to "finding data sources," and then to "influencing outcomes," these are three stages on the same path; the first two still involve exploiting information asymmetry, while the last step is already actively manufacturing results.

This also fundamentally changes the risks associated with prediction markets. The issue is no longer just whether the rules are rigorous or whether the data is timely, but whether reality has already been intervened in by someone before it is recorded.

• When you cannot influence the data source, you are predicting.

• When you can influence the data source, you are changing the outcome.

The competition in prediction markets is essentially about one thing: who can determine "the reality read by the market" sooner or directly.

II. Differences in Manipulability Among Different Types of Markets

Not all markets carry the same risks. Based on manipulation logic, they can be roughly divided into four categories.

Category 1: Markets relying on single physical data sources

Weather markets are often considered the easiest to be influenced by manipulation, as settlements depend on specific readings from certain weather stations, which are physical devices with public locations and sometimes insufficient maintenance. Under certain conditions, attackers can physically influence sensor readings.

The deeper issue is that weather data itself has multi-source discrepancies; measurements from Weather Underground (WU) and aviation METAR data for the same location often do not match, and sometimes the market rules do not clearly specify which source to use, or the rules themselves leave room for interpretation. This ambiguity is a risk in itself.

Category 2: Markets where insiders can know the outcome in advance

Content creator markets naturally have information asymmetries. Polymarket and Kalshi have hosted many markets related to MrBeast's videos, betting on which words he will say in the next video, video length, and views. The entire production team knows this information before the video is released.

Kalshi publicly dealt with its first insider trading case in February 2026: MrBeast's editor Artem Kaptur had nearly perfect success rates betting on markets associated with MrBeast, and the bets targeted extremely low-odds, niche options , which drew the attention of the platform's anti-fraud system.

Kalshi determined he utilized non-public information from the video for betting, profiting over $5,000, and was ultimately fined $20,000 and banned for two years, while also being reported to the CFTC.

In a similar type of market, several members of the Israeli Air Force were scrutinized or prosecuted for betting on the timing of military strikes against Iran on Polymarket. One officer revealed information about the 2025 strike operation to a colleague, resulting in a combined profit of approximately $244,000, and were ultimately prosecuted for "leaking confidential information." Another crew member said during interrogation, "The entire squadron is betting on Polymarket."

Similar signals emerged from the Venezuela side as well: in January 2026, a newly created Polymarket account profited over $400,000 from markets betting on Maduro's resignation and U.S. military actions.

The structural issue in these types of markets is that anyone who knows the content can treat the prediction market as a monetization channel. KOLs, celebrities, and athletes' entourage are all potential parties with information asymmetry.

Category 3: Markets where the involved parties have the motivation to manipulate the outcome

This is a more concealed layer than insider trading: the involved parties know of the market's existence and can directly manipulate the course of events.

The most typical case is the Andrew Tate tweet count market, where Polymarket hosted multiple markets asking, "How many tweets will Andrew Tate post this week?", with peak single-session trading volumes exceeding $240,000.

On March 10, 2026, trader @Euanker released on-chain analysis, accusing at least seven associated accounts of coordinating bets in six such markets, with a total profit of approximately $52,000. On-chain evidence shows these accounts used the same exchange and Gnosis Safe wallet, highly associated with Tate himself.

The issue revealed by this case is more fundamental than ordinary insider trading: Tate himself is the controller of the variable, wishing to win a certain range, he just needs to tweet more or less, acting as both athlete and referee.

Another version of this logic occurred when Coinbase's CEO Brian directly mentioned "Bitcoin, Ethereum, blockchain, Staking, Web3" during an earnings call; he later said on X this was "a spontaneous joke" meant to ensure that all markets on Polymarket and Kalshi settled as Yes.

Category 4: Markets where a single person's actions can change real outcomes

In August 2025, a series of incidents occurred in the WNBA where spectators threw green sex toys onto the court, prompting Polymarket to open a series of betting markets. One user "gigachadsolana" bet $13,000 that such an event would happen about two hours before it occurred, netting over $6,000 after the event.

The core issue of this case is not whether this user knew in advance, but whether the market structure itself provided an incentive: any participant holding enough betting positions can lock in profits by personally carrying out that action, with a cost that is merely a ticket and a prop.

Using Domer's counterparty identification framework: new account, single market, large bets, price insensitivity (market price trading), betting and immediately withdrawing. This combination meets all the characteristics of insider trading. It just came too quickly, and by the time others reacted, the market had already settled.

III. The Fundamental Differences Between Kalshi and Polymarket

Whether the loopholes in prediction markets are punished largely depends on which platform you operate on. Two leading platforms in the industry faced the same issues but took entirely different paths.

Kalshi's approach treats law enforcement as brand-building. In the cases of MrBeast's editor and congressional candidates, every handling result is publicly released, with clear details on the penalty amounts, account suspension durations, and whether it was reported to the CFTC. Kalshi directly states in its advertisements throughout Washington, "We ban insider trading."

Polymarket’s attitude is much more complex. In November 2025, Polymarket's CEO Shayne Coplan said during an interview with CBS's "60 Minutes," when asked about insider trading, "I believe it's a good thing that people enter the market with an informational advantage. Obviously, you need to manage this, and the boundaries and ethical standards need to be very clear and strict... we have spent a lot of time on this."

The logic behind this statement is: insider information entering the market can actually make prices more accurate; this is the value of prediction markets. Those who know the timing of military operations bet, those who know the video content bet, this information originally had no outlet, and prediction markets provided an exit, while also making market prices closer to the truth.

This logic has some academic basis, but it also means that Polymarket has, for quite some time, maintained a tacit attitude toward what happens on the platform.

The turning point was the "Van Dyke case," where Polymarket stated in a press release that when they found users using confidential government information to trade, they proactively referred the matter to the Justice Department and cooperated with the investigation, stating, "Insider trading has no place in Polymarket; today's arrests prove the system is functioning normally."

Identity verification and accountability: the same person, two different outcomes

Understanding the differences between the two platforms can most straightforwardly be pictured by imagining what would happen if the same insider trader operated on both platforms.

Registering an account on Kalshi requires submitting real identity information to complete KYC certification. The platform's AI system continuously scans for abnormal trading patterns; once a problem is detected, Kalshi knows who is behind the account and can directly contact the person, or even transfer the identity information to the CFTC.

Process: System detects anomaly → Platform confirms identity → Public penalties → Report to CFTC.

Registering on Polymarket only requires a cryptocurrency wallet address and does not necessitate any real identity information. When a community analyst focused on the account "ricosuave666," it earned $155,000 betting on markets related to Israeli strikes against Iran.

Polymarket's response was to delete the account, but after deletion, the person behind it could immediately return using a new wallet address, and the platform has no mechanism to identify this as the same person.

The Van Dyke case is a special situation. He registered a Polymarket account using a personal email, leaving a traceable digital footprint that was ultimately tracked down by the FBI via on-chain records. Polymarket's Chief Legal Officer Neal Kumar later stated, "This is not anonymous; you will be found, just like this person."

This illustrates the fundamental differences in accountability capabilities between the two platforms:

• Kalshi’s KYC allows the platform to identify and address problematic accounts on its own;

• Polymarket relies on on-chain transparency plus post-intervention by law enforcement, leaving a gap in between that no one manages.

IV. The Reflexivity Paradox of Prediction Markets

The real contradiction of prediction markets lies in the fact that they are designed as a "tool for discovering truth," yet their incentive mechanisms also influence reality.

This is not simply a design flaw of a platform nor a problem that can be resolved solely through regulation; it is the intrinsic contradiction of prediction markets. As long as an event can be traded, it is no longer merely an observed object, but becomes a market that participants can influence.

This issue has long existed in financial markets; Soros referred to it as "reflexivity": the market's expectations of reality can inversely affect reality itself.

• Falling stock prices can lead to financing difficulties.

• Financing difficulties can further deteriorate the company's fundamentals.

The market was originally reflecting reality, but reflecting itself also changes that reality; prediction markets push this reflexivity to a more extreme position.

Because it is not trading stock prices or the future value of an asset, but directly betting on whether real events will happen. A person can not only bet that "something will happen," but may also gain motivation to make that thing happen due to this bet.

Weather sensors, sports events, video content, tweet counts, military actions; these cases appear completely different on the surface, but they all point to the same issue: when reality becomes financialized, reality itself becomes part of the transaction.

Thus, the most dangerous aspect of prediction markets is not that they might predict incorrectly, but that they may predict so valuably that people begin to act around those predictions.

The more successful it is, the more it can attract those with informational advantages. The more important it becomes, the more likely it is to change participants' behaviors. The closer it is to reality, the more likely it is to shape reality in return.

This is the deepest paradox of prediction markets: it wants to be a mirror of reality, but when the mirror becomes valuable enough, people will start to change the world in front of the mirror.

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