Prediction markets are essentially betting on reality. When participants can obtain or even influence this path earlier, the market no longer merely reflects reality but begins to shape it. At Paris Charles de Gaulle Airport, a man stands beside the runway, holding a portable heat source and heating a weather sensor. Minutes later, the Polymarket weather market settles at 22°C, turning his pre-established position at an extremely low price into $34,000. The entire process involved no sophisticated quantitative strategies, not even any technical barriers; he simply did one thing: knew where the market's settlement data came from and influenced it. This article doesn't discuss a specific loophole, but rather a more fundamental question: when a market aims to "reflect reality," does it also provide participants with an incentive to influence reality? This article will answer three questions: Which type of prediction market is most easily manipulated from the source? How do these "loopholes" occur in reality? And what is Polymarket and Kalshi's true attitude towards these questions? I. You think you're betting on reality, but you're actually betting on the data source. Most people discussing prediction markets focus on the rules themselves, such as: how exactly does this market calculate a win? But these only belong to the first layer. The settlement logic of prediction markets has two layers: 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 first be "recorded" before settlement. So in the past, people studied the rules, looking through the specific sources cited in the rules to confirm which website was used, and even directly emailing the upstream data providers to try to get the data earlier. This step is essentially a competition of who "knows the result earlier," like someone going to watch a game live and placing a bet before the score is synchronized to the official data system. But there is another layer that is more easily overlooked: while everyone is trying to "obtain data faster," some people start to bypass this step and directly influence the result itself. As long as reality will eventually enter the market through a certain data source, then influencing reality is equivalent to influencing the settlement. From "finding the rules" to "finding the data source" to "influencing the outcome," these are three stages on the same path. The first two still utilize information asymmetry, while the final step actively creates the outcome. This fundamentally changes the risks of 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 recorded.When you cannot influence the data source, you are making predictions. When you can influence the data source, you are changing the outcome. Competition in the prediction market is essentially a battle over one thing: who can determine the "reality read by the market" earlier or directly. II. Differences in Manipulation Potential Among Different Types of Markets Not all markets carry the same risk. Based on manipulation logic, they can be broadly categorized into four types. The first type: Markets relying on a single physical data source. Weather markets are generally considered the most susceptible to manipulation. Settlements depend on specific readings from a particular weather station, which is a physical facility with a publicly known location and sometimes inadequate maintenance. Under certain conditions, attackers can physically influence sensor readings. A deeper problem is the inherent discrepancies in weather data itself. Weather Underground (WU) and aviation METAR data often yield inconsistent measurements for 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. Content creator markets naturally suffer from information asymmetry. Polymarket and Kalshi both operated numerous video marketplaces surrounding MrBeast, allowing users to bet on his next video's lyrics, length, and view count. This information was known to the entire production team before the video's release. Kalshi publicly handled its first such insider trading case in February 2026: MrBeast editor Artem Kaptur's bets on MrBeast-related marketplaces showed a near-perfect success rate, consistently betting on low-odds, obscure options. This pattern caught the attention of the platform's anti-fraud system. Kalshi determined he used non-public information from the videos to make bets, profiting over $5,000, ultimately resulting in a $20,000 fine, a two-year account suspension, and a report to the CFTC. Similar marketplaces have seen several Israeli Air Force members 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 prosecuted for "leaking classified information." Another crew member testified during interrogation, "The entire squadron was betting on Polymarket." Similar signals emerged from Venezuela: in January 2026, a newly created Polymarket account profited over $400,000 in betting on Maduro's downfall and US military action. The structural problem with these markets is that anyone with access to the content can monetize prediction markets. Key opinion leaders (KOLs), celebrities, and people close to athletes are all potential parties with information asymmetry.The third type: 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 case is the Andrew Tate tweet count market. Polymarket launched multiple markets asking "How many tweets will Andrew Tate post this week?", with the highest trading volume in a single market exceeding $240,000. On March 10, 2026, trader @Euanker published on-chain analysis, accusing at least seven related accounts of coordinating bets in six such markets, with a total profit of approximately $52,000. On-chain evidence showed that these accounts used the same exchange and Gnosis Safe wallet, highly linked to Tate himself. This case reveals a problem more fundamental than ordinary insider trading: Tate himself is the controller of the variable, posting more or fewer tweets to win in a certain range, essentially being both player and referee. Another version of the same logic: Coinbase CEO Brian directly 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 Polymarket and Kalshi markets settled as "Yes." The fourth type: Markets where a single person's action can change the outcome. In August 2025, after several incidents of spectators throwing green sex toys into WNBA arenas, Polymarket launched a series of betting markets. One user, "gigachadsolana," bet $13,000 about such an event about two hours before it occurred, netting over $6,000 afterward. The core issue in this case isn't whether the user knew beforehand, but rather that the market structure itself constitutes an incentive: anyone holding a sufficient betting position can lock in profits by personally performing this action; the cost is merely a ticket and a prop. Using Domer's counterparty identification framework: new account, single market, large bet, price insensitivity (market price trading), immediate withdrawal after betting. This combination fulfilled all the characteristics of insider trading. It just happened too fast; by the time others reacted, the market had already settled. Third, the disagreement between Kalshi and Polymarket essentially boils down to whether loopholes in the prediction market will be punished, which largely depends on which platform you operate on. These two leading platforms faced the same problem but took drastically different paths. Kalshi treated enforcement as brand building. In the MrBeast editor case and the congressional candidate case, every outcome was publicly released, clearly stating the penalty amount, account suspension period, and whether it was reported to the CFTC. In advertisements placed throughout Washington, Kalshi directly stated, "We ban insider trading." Polymarket's attitude was much 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, which is the value of prediction markets. People who know the timeline of military operations bet, people who know the content of videos bet—this information would otherwise have nowhere to be monetized, but prediction markets give them an outlet, while also making market prices closer to the truth. This logic has some academic basis, but it also means that Polymarket tacitly condoned 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 users were using classified government information for trading, they proactively transferred the matter to the Department of Justice and cooperated with the investigation. "Insider trading has no place on Polymarket, and today's arrest proves that the system is functioning properly." Identity Verification and Accountability: One 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 for 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 person or transfer the identity information to the CFTC. The process is: system detects anomalies → platform confirms identity → public penalty → CFTC report. Registering on Polymarket only requires a crypto wallet address and no real identity information. When community analysts targeted the account "ricosuave666," which earned $155,000 in the Israeli-Iranian trade war, Polymarket deleted the account. However, after deletion, the person behind it could immediately reappear with a new wallet address; the platform had no mechanism to identify the same person. The Van Dyke case is a special case. He registered a Polymarket account with a personal email address, leaving traceable digital records, which were eventually traced by the FBI through on-chain records. Polymarket's Chief Legal Officer, Neal Kumar, later said, "This isn't anonymous; you'll be found, just like this person." This is the fundamental difference between the two platforms in terms of accountability: Kalshi's KYC allows the platform to identify and handle problematic accounts on its own; Polymarket relies on on-chain transparency plus post-incident intervention by law enforcement agencies, leaving a gap in the chain where no one is in charge.IV. The Reflexivity Paradox of Prediction Markets The real contradiction of prediction markets lies in the fact that while they are designed as "tools for discovering the truth," their incentive mechanisms can influence reality. This isn't a problem of poor platform design or regulation alone; it's an inherent contradiction within prediction markets. Once an event can be traded, it ceases to be merely an object of observation and becomes a market that can be influenced by participants. This problem has long existed in financial markets, which Soros calls "reflexivity": market expectations of reality can, in turn, affect reality itself. A drop in stock prices can lead to financing difficulties, which in turn worsen a company's fundamentals. The market is supposed to reflect reality, but the reflection itself alters reality. Prediction markets push this reflexivity to an even more extreme position. Because they don't trade company stock prices or the future price of an asset, but rather directly bet on whether a real event will occur. A person can not only bet on "something will happen," but they can also gain the motivation to make that event happen through that bet. Weather sensors, live sports events, video content, tweet counts, military operations—these examples appear completely different, 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 prediction markets is not their potential for error, but rather their potential for becoming so valuable that people begin to act accordingly. The more successful they are, the more they attract those with informational advantages. The more important they are, the more likely they are to alter participant behavior. The closer they are to reality, the more likely they are to shape reality in turn. This is the deepest paradox of prediction markets: they aspire to be a mirror of reality, but when the mirror becomes valuable enough, someone will begin to alter the world in front of it. [Biteye]