OpenClaw, which gambles with humans on Polymarket, has already earned tens of thousands of dollars per month Author: Li Nan Source: Silicon Star Pro Some people say OpenClaw is a toy lobster, while others want to turn it into a money making machine. Sending lobsters to Polymarket is a new gameplay that many people are starting to try. On Xiaohongshu, someone offered 1000 yuan to help deploy OpenClaw. One of the main uses is to use OpenClaw for quantitative trading on Polymarket. And this is not a sudden idea. On February 13th, the official blog post of OpenClaw mentioned that a robot driven by OpenClaw has demonstrated the powerful potential of autonomous agents in predicting the market - earning a weekly profit of $115000. At the end of January, Polymarket also posted an interesting post: Agents are trading on Polymarket, trying to subsidize their token costs. This looks a bit unbelievable. Some lobsters constantly devour their owners' wallets, while others can not only feed themselves but also their owners. Robots are mining in Polymarket while human traders are still being influenced by fear and greed. A robot account called "0x8dxd" quietly completed over 20000 trades on Polymarket, earning a total profit of over 1.7 million US dollars. Let's first introduce Polymarket, a place where everything can be traded. It is the world's largest decentralized prediction marketplace platform, allowing users to trade Yes or No contracts around future verifiable events. The contract price fluctuates between 0 and 1 US dollar and directly corresponds to the market consensus probability. And users can exchange the retrieval report for the accuracy of the prediction. for instance. Between 2024 and 2025, fans and investors worldwide are eyeing Taylor Swift's relationship with rugby star Travis Kelce. Polymarket launched a predictive trade: 'Will the two announce their engagement before the end of 2025?' When the market was generally inclined towards' NO ', someone bought a large amount of' Yes' and later made a big profit. In other words, if you have a more accurate insight into an event, then there is a chance to make money in Polymarket. However, for robots like 0x8dxd, predictive ability is not important. Their way of making money relies on a bug catching mechanism and a quick response that humans cannot reach. In summary, robots mainly rely on several core techniques. Firstly, there is mathematical parity arbitrage. This exploits a bug in predicting the market. In the binary option trading of Polymarket, regardless of whether the result is "Yes" or "No", the final settlement price of the winning party's contract is always $1. When market sentiment fluctuates or liquidity suddenly changes, the total cost on both sides of the market (Yes and No) may be less than $1. At this point, the robot quickly buys both long and short shares simultaneously, achieving risk-free arbitrage profits. Another focus is on the extremely short-term cryptocurrency volatility market. BTC, ETH, and other 5-minute and 15 minute short-term prediction markets experience intense volatility, especially during extreme market conditions such as forced liquidation on exchanges, which can easily lead to price misalignment. This provides a perfect breeding ground for high-frequency intervention by robots. The third is to act as a digital market maker, earning price differentials through high-frequency two-way orders. For example, when the fair price of a result fluctuates around 80 cents, the robot will buy at 80 cents and quickly sell at 81 or 82 cents. This type of single profit is extremely small, but when accumulated, it can be very substantial. Overall, robots have ruthlessly harvested Polymarket with their extremely high speed advantage and iron like machine discipline. This corresponds to the disadvantages of humans as carbon based organisms, such as slow reactions, lack of rationality, and the need for sleep. The emergence of OpenClaw greatly reduces the threshold for deploying automated trading robots and promotes the further explosion of silicon-based power. Compared to traditional Python robots, traders can configure OpenClaw trading agents for automated trading without the need for deep programming. The capabilities of OpenClaw itself also make it suitable for trading scenarios. Lobsters can continuously monitor market prices and trading volumes, ensuring that traders do not miss opportunities and warning of risks in a timely manner. In fact, many people have already linked the previously mentioned 0x8dxd with OpenClaw. Although there is no direct evidence to suggest that it was built on OpenClaw, it happened to be active since the birth of OpenClaw. Moreover, when the story of 0x8dxd turning Polymarket into an ATM spread, the OpenClaw community saw a surge in creating skills such as Polymarket trading. In recent Polymarket predictions, OpenClaw has become a high-frequency term in discussions about automated trading. However, relying solely on some general strategies to execute trades is clearly not reliable. Can this also make money? A simple conclusion is that once the formula for stable arbitrage is made public, it becomes invalid. If everyone uses the same routine, the routine itself will not hold true. So when it comes to any tutorial that shares such experiences, it's best to be careful. In fact, Polymarket has made adjustments to combat robot arbitrage behavior. For example, introducing transaction fees, increasing transaction friction costs, and changing the underlying delay mechanism of order execution to restrict automated transactions that specifically exploit time difference vulnerabilities for rush buying. This forces traders to explore the greater potential of AI and seek more secretive opportunities. So intentional traders combined general strategies with unique scenarios and discovered some unexpected gameplay. For example, trading weather. Predicting weather is currently one of the most widely circulated cases in Polymarket, where some robots specialize in trading weather data. An account named 'automatedAItrangbot' will join Polymarket in January 2025. It is enthusiastic about betting on weather forecasts, earning over $70000 in profits. Someone also discovered that a robot that only trades in the London weather market has turned $1000 into $24000 in less than a year. The core logic is that predicting the market's response to sudden weather changes often lags behind. In theory, if you have a sensitive and reliable AI agent, such as installing a weather plugin on OpenClaw, you can place bets on odds that have not been adjusted in a timely manner after the official weather forecast is updated. But this is not enough for AI. With the evolution of large models, robots should not only recognize obvious signals such as weather forecasts, but should at least do something in some intelligent dimension that humans cannot do. In fact, AI has indeed demonstrated more enticing abilities in predicting the market. A paper on 'LiveTradeBench' conducted 'simulated trading' based on real-time real-world data. On the Polymarket's "2025 Russia Ukraine ceasefire" market, big models have the opportunity to make a big profit by relying on their own reasoning and predictions. The case is as follows: In October last year, Zelensky visited the White House and proposed a trade proposal for "drones for Tomahawk missiles". Grok-3 conducted "belief based reasoning" and dynamically increased the internal estimated ceasefire probability from 0.15 to 0.22. At the same time, it noticed that the price of the "YES" contract had jumped significantly to 0.18 at that time. This forms cross validation, so Grok-3 determines that there is undervalued arbitrage space in the contract and establishes a firm long and hold strategy. And ultimately, the market price of the contract steadily increased, giving it the opportunity to profit. But Grok is not yet the best performer. The above paper tested the performance of 21 mainstream big language models in the financial market, covering both the US stock market and the Polymarket prediction market. Among them, Claude Sonnet-3.7's performance in Polymarket was unparalleled. It achieved a cumulative return rate of 20.54% over 50 trading days of observation. Its maximum drawdown is 10.65%, which is also significantly ahead of the market average. The experiments behind the story of "picking up money" are more noteworthy than the wealth story of robot arbitrage, as they at least suggest a new possibility. If 0x8dxd relies on speed and sprinting, then the emergence of large models has put another trump card on the table, which is reasoning itself, and can also become a weapon. The future division of labor for automated trading robots is likely to be that the large model is responsible for making judgments and compressing scattered information into probabilistic conclusions; Tools like OpenClaw are responsible for executing and turning this conclusion into actual ordering operations and position management. What used to be affordable only for quantitative funds can now be built by individual developers. This means that the competitive dimension of the prediction market is undergoing changes. In the traditional prediction market, humans rely on experience and intuition. In the era of high-frequency arbitrage, machines rely on speed and discipline. Nowadays, reasoning ability has also been programmed, and the real threshold has become who is better at transforming complex information into accurate probabilities. So some people have a new fantasy: if they have a smart and reliable lobster, they have a chance to turn Polymarket into a money printer. Unfortunately, there is still a significant gap between theory and practice. Prophet Arena is a platform used to evaluate AI predictive capabilities, and research based on it reveals some significant risks that cannot be ignored. Firstly, the predictive ability of large models is not stable. Top models can approach or even exceed market consensus in open domain prediction, but 'guessing accurately' and 'earning' are two different things. Improved prediction accuracy will not automatically result in sustained excess returns. Secondly, the time window is a realistic challenge. As an event approaches a conclusion, the impact of sudden information becomes more intense, and models tend to be conservative at this stage, with slower probability adjustments and faster response times in the human market. Furthermore, large models are easily biased by noise. An emotional news article or a wave of social media turbulence can cause a significant fluctuation in the probability judgment of the model. In contrast, experienced human traders have a stronger sense of anchoring and are less easily overwhelmed by short-term noise. In addition, OpenClaw frameworks typically require the import of private keys and transaction permissions, and various security issues may quietly drain accounts. So, instead of expecting AI+OpenClaw to have a dimensionality reduction impact on the prediction market, it's better to focus on the deep impact it will have on this market. As more and more AI driven agents become available, the response of price changes to information becomes faster, which may actually eliminate the illusion of automatic arbitrage. Once robots or lobsters flood the market, the window for arbitrage will only become narrower. Whether you can sustain profitability at that time will not depend on whether you have a smarter lobster, but on whether you understand the risks you are taking on. AI can place bets for humans, but it is humans themselves who have to bear the consequences.