Can a hairdryer earn $34,000? Interpreting the reflexivity paradox of prediction markets

Author: Changan I Biteye Content Team

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

A few 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 entire process does not involve sophisticated quantitative strategies, nor is there any technical threshold; he simply did one thing: he knew where the settlement data for the entire market came from and influenced it.

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

In this article, we will answer three questions:

• Which types of markets are most easily manipulated from the source in prediction markets?

• How do these "loopholes" occur in reality?

• What is the true attitude of Polymarket and Kalshi towards these issues?

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

Most people focus on the rules themselves when discussing prediction markets, 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, determining "what kind of outcome counts as a win."

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

The market is indeed betting on reality itself, but reality must first be "recorded" to settle. Therefore, in the past, people studied the rules, looking up the specific sources cited in the rules to confirm which website was used, even directly emailing the upstream data providers to try to obtain data earlier.

This step is essentially a competition to see who "knows the result earlier," such as someone attending a live sports event and placing a bet before the score is synchronized to the official data system.

But there is another layer that is easily overlooked: while everyone is trying to "get data faster," some people start to bypass this step and directly influence the outcome itself. As long as reality eventually enters the market through a certain data source, then influencing reality is equivalent to influencing settlement.

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

This also fundamentally changes the risk of prediction markets. The question is no longer just whether the rules are rigorous or whether the data is timely, but whether reality has already been intervened in before it is recorded.

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

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

The competition in prediction markets is essentially about one thing: who can more quickly or directly determine "the reality that the market reads."

2. Differences in manipulability among different types of markets

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

First category: Markets relying on single-point physical data sources

Weather markets are often considered the most easily manipulated type, as settlements depend on specific readings from particular weather stations, which are physical devices, publicly located, and sometimes poorly maintained. Under certain conditions, attackers can physically influence sensor readings.

A deeper issue is that weather data itself has multi-source discrepancies; Weather Underground (WU) and aviation METAR data often yield inconsistent measurements for the same location. Sometimes the market rules do not clearly specify which source to use, or the rules themselves have interpretative space, and this ambiguity is a risk in itself.

Second category: Markets where insiders can know the results in advance

Content creator markets naturally have information asymmetry. Polymarket and Kalshi have hosted numerous markets around MrBeast's videos, betting on which words he will say in his next video, video length, and view counts. The entire production team knows this information before the video is released.

Kalshi publicly handled its first insider trading case in February 2026: MrBeast's editor Artem Kaptur had a nearly perfect success rate in betting on markets related to MrBeast, and the bets were all on low-odds obscure options, which caught the attention of the platform's anti-fraud system.

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

Similar signals have emerged in the direction of Venezuela: in January 2026, a newly created Polymarket account profited over $400,000 in markets regarding Maduro's resignation and U.S. military actions.

The structural problem of this type of market is that anyone who knows the content can use the prediction market as a monetization channel. KOLs, artists, and athletes' associates are all potential sources of information asymmetry.

Third category: Markets where the parties involved have motives to manipulate the outcome

This is a more covert layer than insider trading: the parties involved know of the market's existence and can directly manipulate the event's direction.

The most typical case is the market for the number of tweets by Andrew Tate. Polymarket has opened multiple markets asking "How many tweets will Andrew Tate post this week?" with 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 across six such markets, collectively profiting about $52,000. On-chain evidence showed these accounts used the same exchange and Gnosis Safe wallet, highly linked to Tate himself.

The issue revealed by this case is more fundamental than ordinary insider trading: Tate himself is the controller of the variable; he can decide to post more or fewer tweets to win in a certain range, effectively being both the athlete and the referee.

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

Fourth category: Markets where a single person's actions can change the outcome of reality

In August 2025, incidents occurred at WNBA games where spectators threw green sex toys onto the court, prompting Polymarket to open a series of betting markets. One user, "gigachadsolana," placed a $13,000 bet about two hours before the incident, netting over $6,000 after it occurred.

The core issue of this case is not whether this user knew in advance, but that the market structure itself created an incentive: anyone holding a sufficient betting position can lock in profits by personally carrying out the act, with costs being just a ticket and a prop.

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

3. The essence of the divergence between Kalshi and Polymarket

Whether the loopholes in prediction markets will be punished largely depends on which platform you operate on. The two leading platforms in the industry have taken entirely different paths in facing the same issues.

Kalshi's approach treats enforcement as part of brand building. Each handling result of the MrBeast editor case and congressional candidate case is publicly released, clearly stating the penalty amounts, account bans, and whether it is reported to the CFTC. In advertisements placed throughout Washington, Kalshi directly states, "We ban insider trading."

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

The logic behind this statement is that insider information flowing into the market actually makes prices more accurate, which is the value of prediction markets. Bets placed by those who know military action timelines or video content are information that has no other outlet, and prediction markets provide them with an exit, while also bringing market prices closer to the truth.

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

The turning point was the "Van Dyke case," where Polymarket stated that when they discovered users were trading using confidential government information, they proactively referred the matter to the Department of Justice and cooperated with the investigation, stating, "Insider trading has no place on Polymarket; today's arrests prove the system is functioning properly."

Identity verification and accountability: the same person, two outcomes. The most direct way to understand the differences between the two platforms is to imagine 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 verification. 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 party involved or pass the identity information to the CFTC.

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

Registering on Polymarket only requires a cryptocurrency wallet address, with no real identity information needed. When community analysts focused on the account "ricosuave666," which made $155,000 in the market regarding Israel's strike on Iran.

Polymarket's handling method was to delete that account, but after the account was deleted, the person behind it could immediately return by using a new wallet address; the platform has no mechanism to identify that this is the same person.

The Van Dyke case is a special situation. He registered a Polymarket account with a personal email, leaving a traceable digital footprint, and was ultimately found by the FBI following the 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 highlights the essential difference in accountability capabilities between the two platforms:

• Kalshi's KYC allows the platform to identify and address problematic accounts itself;

• Polymarket relies on on-chain transparency plus post-fact intervention from law enforcement, leaving a gap in which no one is managing.

4. The reflexivity paradox of prediction markets

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

This is not merely a matter of a single platform's design being inadequate, nor is it a problem that can be solved solely through regulation; rather, it is an intrinsic contradiction of prediction markets. As long as an event can be traded, it is no longer just an observed object but becomes a market that can be influenced by participants.

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

• A decline in stock prices may lead to financing difficulties.

• Financing difficulties further worsen the company's fundamentals.

The market was originally reflecting reality, but the reflection itself changed reality, and prediction markets have pushed this reflexivity to a more extreme position.

Because they are not trading company stock prices or the future prices of certain assets, but directly betting on whether real events will occur. A person can not only bet on "something happening," but they may also gain the motivation to make that thing happen because of this bet.

Weather sensors, live 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 is financialized, reality itself becomes part of the transaction.

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

The more successful it is, the more it attracts those with information advantages. The more important it becomes, the more likely it is to change participants' behavior. The closer it gets 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.