Author: Changan | Biteye Content Team
At Paris Charles de Gaulle Airport, a man stands beside the runway, holding a portable heat source and heating a weather sensor.
A few minutes later, the Polymarket weather market settled at 22°C, and his position, which he had built in advance at an extremely low price, turned into $34,000.
The entire process involved no sophisticated quantitative strategies or even any technical barriers. He simply did one thing: he knew where the settlement data for the entire market came from and how to influence it.
This article doesn't actually discuss a specific vulnerability, but rather a more fundamental question: 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 most easily manipulated from the source in the prediction market?
How do these "loopholes" occur in reality?
What are Polymarket and Kalshi's true attitudes toward these issues?
I. You think you're imprisoning reality, but you're actually imprisoning the data source.
When most people discuss prediction markets, they focus on the rules themselves, such as: how exactly is winning calculated in this market? But these only belong to the first level. The settlement logic of prediction markets has two levels:
The first layer is the platform rules, which determine "what kind of result counts as a win".
The second layer is the data source, which determines "what happened in the real world".
The market is indeed betting on reality itself, but reality must be "recorded" before it can be settled. So in the past, people studied the rules, looked up the specific sources cited in the rules, confirmed which website was used, and even sent emails directly to the upstream data providers to try to get the data earlier.
This step is essentially a competition of who "knows the result earlier," such as someone going to watch the game live and placing bets before the score is synchronized to the official data system.
But there's another point that's easier to overlook: while everyone is trying to "get data faster," some people are starting to bypass this step and directly influence the outcome itself. As long as reality eventually enters the market through a certain data source, influencing reality is equivalent to influencing the settlement.
From "finding the rules" to "finding the data source" and then to "influencing the result," these are three stages on the same path. The first two are still taking advantage of information asymmetry, while the last step is actively creating the result.
This has fundamentally changed the risks associated with market prediction. The issue is no longer just whether the rules are rigorous or the data is timely, but whether reality has been interfered with before it is even recorded.
You are making predictions when you cannot influence the data source.
When you can influence this data source, you are changing the outcome.
Competition in the prediction market is essentially a contest over one thing: who can determine "the reality read by the market" earlier or more directly.
II. Differences in Maneuverability Among Different Types of Markets
Not all market risks are the same. Based on manipulation logic, they can be roughly divided into four categories.
Category 1: Markets that rely on single-point physical data sources
Weather-related markets are generally considered among the most vulnerable to manipulation, as settlements rely on specific readings from particular weather stations. These stations are physical facilities, publicly accessible, and sometimes poorly maintained. Under certain conditions, attackers can physically influence sensor readings.
A deeper problem is that weather data itself has multiple sources and discrepancies. Weather Underground (WU) and aviation METAR data often have inconsistent measurements of the same location. Market rules sometimes do not explicitly specify which source to use, or the rules themselves have room for interpretation. This ambiguity is itself a risk.
The second type: markets where insiders can know the outcome in advance.
The content creator market inherently suffers from information asymmetry. Polymarket and Kalshi have both launched numerous video marketplaces surrounding MrBeast, allowing users to bet on his next video's lyrics, length, and view count. This information is known to the entire production team before the video is even released.
Kalshi publicly addressed its first such insider trading case in February 2026: Mr. Beast editor Artem Kaptur had a near-perfect success rate in betting on Mr. Beast-related markets, consistently betting on obscure options with extremely low odds. This pattern caught the attention of the platform's anti-fraud system.
Kalshi determined that he used non-public information from the video to place bets, winning over $5,000. He was ultimately fined $20,000 and had his account suspended for two years. He was also reported to the CFTC.
Similar cases include Israeli Air Force members being investigated or prosecuted for betting on the timing of a military strike against Iran on Polymarket. One officer leaked information about a 2025 strike to a colleague, and the two profited a combined $244,000, ultimately being charged with "leaking classified information." Another crew member stated during interrogation, "The entire squadron was betting on Polymarket."
Similar signals have emerged from Venezuela: In January 2026, a newly created Polymarket account profited over $400,000 in the market surrounding Maduro's downfall and US military action.
The structural problem in this type of market is that anyone who knows the content can use the prediction market as a monetization channel. KOLs, celebrities, and people close to athletes are all potential parties with information asymmetry.
The third category: markets where the parties involved have an incentive to manipulate the outcome.
This is a more covert layer than insider trading: the parties involved know the market exists and can directly manipulate the course of events.
The most typical example is the Andrew Tate tweet count market. Polymarket launched multiple markets for "How many tweets will Andrew Tate post this week?", with the highest transaction volume in a single market exceeding $240,000.
On March 10, 2026, trader @Euanker published on-chain analysis alleging that at least seven linked accounts coordinated bets across six such markets, generating a total profit of approximately $52,000. On-chain evidence showed that these accounts used the same exchanges and Gnosis Safe wallet, and were highly linked to Tate himself.
This case reveals a more fundamental problem than ordinary insider trading: Tate himself is the controller of the variables, and he can win a certain range by posting more or fewer tweets, which is equivalent to being both the player and the referee.
Another version of the same logic: Coinbase CEO Brian read out "Bitcoin, Ethereum, Blockchain, Staking, Web3" during an earnings call. He later said on X that it was a "spontaneous joke" to get all the markets on Polymarket and Kalshi to settle as Yes.
Category 4: Markets where individual action can alter the actual outcome.
In August 2025, following a series of incidents where WNBA spectators threw green sex toys onto the court, Polymarket launched a series of betting markets. One user, "gigachadsolana," placed a $13,000 bet about such an event approximately two hours before it occurred, and subsequently netted over $6,000.
The core issue in this case is not whether the user knew in advance, but rather that the market structure itself constitutes an incentive: anyone holding a sufficient betting position can lock in profits by personally carrying out this action, with the cost being nothing more than a ticket and a prop.
Using Domer's counterparty identification framework: new account, single market, large bets, price insensitivity (market trading), and immediate withdrawal after betting. This combination meets all the characteristics of insider trading. It just happens too quickly; by the time others realize it, the market has already settled.
III. What is the essence of the disagreement between Kalshi and Polymarket?
Whether a vulnerability in the prediction market will be punished largely depends on which platform you operate on. Two leading platforms in the industry faced the same problem but took drastically different paths.
Kalshi's approach was to treat law enforcement as a brand-building strategy. In the MrBeast editor case and the congressional candidate case, the outcomes were always publicly released, clearly outlining the fines, account suspension periods, and whether the CFTC was notified. In advertisements placed throughout Washington, D.C., Kalshi directly stated, "We ban insider trading."
Polymarket's stance is far more complex. In November 2025, when Polymarket CEO Shayne Coplan was asked about insider trading on CBS's "60 Minutes," he said, "I think it's a good thing that people come into the market with an informational advantage. Obviously, you need to regulate that, you need to draw very clear and strict lines... and ethical standards, and we've spent a lot of time on that."
The logic behind this statement is that insider information flowing into the market actually makes prices more accurate; this is the value of prediction markets. People who know the timeline of military operations make bets, people who know the content of videos make bets—this information would otherwise have nowhere to be monetized, but prediction markets provide an outlet for it, while simultaneously bringing market prices closer to the truth.
This logic has some basis in academic circles, but it also means that Polymarket tacitly approved of what happened on its platform for a considerable period of time.
The turning point was the "Van Dyke case." In a statement, Polymarket said that when they discovered that a user was using confidential government information for trading, they proactively handed the matter over to the Department of Justice and cooperated with the investigation. "Insider trading has no place at Polymarket, and today's arrest proves that 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 transaction patterns. Once a problem is detected, Kalshi knows who is behind the account and can directly contact the person involved or transfer the identity information to the CFTC.
Process: System detects anomaly → Platform confirms identity → Publicly announces penalty → Report to CFTC.
Registering on Polymarket only requires a crypto wallet address and no real identity information. Community analysts tracked the account "ricosuave666," which reportedly made $155,000 in the market related to Israel's attack on Iran.
Polymarket's solution is to delete the account, but after the account is deleted, the person behind it can immediately return with a new wallet address, as the platform has no mechanism to identify that it is the same person.
The Van Dyke case is a special one. He registered a Polymarket account using a personal email address, leaving a traceable digital trail that ultimately led the FBI to him via the blockchain. Polymarket's Chief Legal Officer, Neal Kumar, later said, "It's not anonymous; you'll be found, just like this guy."
This is the fundamental difference between the two platforms in their ability to hold people accountable:
Kalshi's KYC allows the platform to identify and handle problematic accounts on its own;
Polymarket relies on on-chain transparency and subsequent intervention by law enforcement agencies, leaving a gap in the chain where no one is in charge.
IV. The Reflexivity Paradox of Predicting Markets
The real paradox of prediction markets is that they are designed as a "tool for discovering the truth," but their incentive mechanisms can also affect reality.
This isn't a problem of poor platform design, nor can it be solved simply through regulation; rather, it's an inherent contradiction in the prediction market. Once an event can be traded, it ceases to be merely an object of observation and transforms into a market that can be influenced by participants.
This problem has long existed in financial markets, which Soros calls "reflexivity": market expectations of reality will, in turn, affect reality itself.
A drop in stock price may lead to financing difficulties.
Financing difficulties further worsened the company's fundamentals.
The market is supposed to reflect reality, but the reflection itself changes reality. The market foresees pushes this reflexivity to an even more extreme position.
Because it doesn't trade company stock prices or the future price of a certain asset, but rather directly bets on whether a real event will happen. A person can not only bet on "something will happen," but may also gain the motivation to make that thing happen because of that bet.
Weather sensors, live sports events, video content, number of tweets, military operations—these cases appear completely different on the surface, but they all point to the same problem: when reality is financialized, reality itself becomes part of the transaction.
Therefore, the most dangerous aspect of market prediction is not that it might be wrong, but that it might be too valuable, to the point that people start acting based on that prediction.
The more successful it is, the more it attracts those with informational advantages. The more important it is, the more likely it is to change participants' behavior. The closer it is to reality, the more likely it is to shape reality in turn.
This is the deepest paradox of prediction markets: they want to be a mirror of reality, but when the mirror becomes valuable enough, someone will start to change the world in front of it.
