OpenClaw, betting against humans on Polymarket, is now earning tens of thousands of dollars per month.
Original author: Li Nan, Silicon Star Pro
Some people say OpenClaw is just a toy, while others want to turn it into a money-making machine. Sending the lobster to Polymarket has become a new trend many are trying.
On Xiaohongshu, someone is offering 1,000 yuan to hire someone to deploy OpenClaw, one of the main purposes being to use OpenClaw for quantitative trading on Polymarket—and this is not a spontaneous idea.
On February 13, OpenClaw's official blog post mentioned that a robot powered by OpenClaw demonstrated the powerful potential of autonomous agents in predicting markets—earning a profit of $115,000 in a single week.
At the end of January, Polymarket also posted an interesting update: Agents are trading on Polymarket to subsidize their token costs.
This seems a bit unbelievable. Some lobsters keep devouring their owner's wallet, while others have not only become self-sufficient but are now supporting their owners.
Bots mining on Polymarket
While human traders are still driven by fear and greed, a bot account named «0x8dxd» has quietly executed over 20,000 trades on Polymarket, generating total profits exceeding $1.7 million.
Let’s start with Polymarket, a place where everything can be traded.
It is the world's largest decentralized prediction market platform, allowing users to trade Yes or No contracts on future verifiable events. Contract prices fluctuate between $0 and $1, directly reflecting the market's consensus probability. Users can earn rewards based on the accuracy of their predictions.
For example.
Between 2024 and 2025, fans and investors worldwide kept their eyes on the relationship between Taylor Swift and NFL star Travis Kelce. Polymarket capitalized on this by launching a prediction market: "Will Taylor Swift and Travis Kelce be together by 2025?"
"Engagement announced by year-end?" When the market overwhelmingly leaned toward "NO," someone bought heavily on "Yes" and made a substantial profit.
In other words, if you have a more precise insight into an event, you have the opportunity to profit on Polymarket. However, for bots like 0x8dxd, predictive ability doesn’t matter—they profit through a system designed to exploit bugs and respond faster than any human can.
In summary, bots primarily rely on several core strategies.
First is mathematical arbitrage. This exploits a bug in prediction markets. In Polymarket’s binary options trading, regardless of whether the outcome is "Yes" or "No," the final settlement price of the winning contract is always $1. When market sentiment fluctuates or liquidity changes abruptly, the combined cost of both sides (Yes and No) may fall below $1. At this point, bots rapidly buy positions on both sides simultaneously to achieve risk-free arbitrage profits.
Also, focus on the highly volatile short-term cryptocurrency markets. Short-term price predictions for BTC, ETH, and other assets on 5-minute and 15-minute timeframes experience extreme fluctuations, especially during extreme market conditions such as mass liquidations on trading platforms, which easily lead to price discrepancies—creating an ideal environment for high-frequency bot interventions.
Third, it acts as a digital market maker, earning spreads by placing high-frequency bid and ask orders. For example, when the fair price of a certain outcome fluctuates around 80 cents, the bot buys at 80 cents and quickly sells at 81 or 82 cents. Although the profit per trade is minimal, it accumulates into a substantial amount over time.
Overall, bots, with their extreme speed advantage and ironclad machine discipline, ruthlessly exploited Polymarket. This directly reflects the disadvantages of humans as carbon-based life forms—slower reactions, limited rationality, and the need for sleep. The emergence of OpenClaw has significantly lowered the barrier to deploying automated trading bots, further fueling the surge of silicon-based forces.
Compared to traditional Python bots, traders can configure the OpenClaw Trading Agent for automated trading without deep programming knowledge. OpenClaw’s inherent capabilities also make it well-suited for trading scenarios—its agents can continuously monitor market prices and trading volumes, ensuring traders never miss opportunities while promptly alerting them to risks.
In fact, many people have already connected the previously mentioned 0x8dxd with OpenClaw. Although there is no direct evidence that it was built on OpenClaw, it became active precisely when OpenClaw was launched. Moreover, after 0x8dxd’s exploits of turning Polymarket into an ATM became widely known, the OpenClaw community saw a surge in interest in creating Skills such as Polymarket-trading.
In recent Polymarket prediction markets, OpenClaw has become a frequently mentioned term in discussions about automated trading. However, relying solely on generic strategies for trading is clearly not reliable.
Can you really make money this way?
A simple conclusion is that once a formula for stable arbitrage is made public, it ceases to work. If everyone uses the same approach, that approach itself will no longer be valid. Therefore, exercise caution when encountering any tutorials sharing such strategies.
In fact, Polymarket has made adjustments to combat arbitrage by bots, such as introducing trading fees, increasing transaction friction costs, and modifying the underlying latency mechanism for order execution to limit automated trading that exploits time-delay vulnerabilities for front-running.
This pushes traders to explore AI's greater potential and uncover more hidden opportunities. As a result, thoughtful traders have combined general strategies with unique scenarios, uncovering some unexpected approaches—such as trading the weather.
Predicting the weather is one of the most popular use cases on Polymarket, with some bots specifically trading weather data.
An account named "automatedAItradingbot" joined Polymarket in January 2025. It specializes in betting on weather forecasts and has earned over $70,000. Others have discovered that a bot trading only London weather markets turned $1,000 into $24,000 in less than a year.
The core logic is that market reactions to sudden weather changes are often delayed. In theory, if you have a sensitive and reliable AI agent—such as equipping OpenClaw with a weather plugin—you could place bets on betting markets that haven't yet adjusted their odds after official weather forecasts are updated.
But that’s not enough for AI. As large models evolve, bots should not merely recognize obvious signals like weather forecasts—they should, at least in some intelligent dimension, do things humans cannot.
In fact, AI has indeed demonstrated more compelling capabilities in predicting markets.
A paper on "LiveTradeBench" conducted simulated trading based on real-world live data. On the Polymarket market for "2025 Russia-Ukraine Ceasefire," large models could have profited significantly through their own reasoning and predictions.
The case is as follows:
Last October, Zelensky visited the White House and proposed a "drone-for-Tomahawk-missile" trade deal. Grok-3 performed belief-based reasoning, dynamically increasing its internal estimated probability of a ceasefire from 0.15 to 0.22, while noting that the price of the "YES" contract had surged sharply to 0.18 at the time. This cross-verification led Grok-3 to conclude that the contract was undervalued and presented an arbitrage opportunity, prompting it to adopt a firm long-term holding strategy. Ultimately, the market price of the contract rose steadily, allowing it to realize a profit.
But Grok is not yet the best performer.
The above paper tested 21 leading large language models on financial markets, covering both the U.S. stock market and the Polymarket prediction market. Among them, Claude-Sonnet-3.7 outperformed all others on Polymarket, achieving a cumulative return of 20.54% over 50 trading days, with a maximum drawdown of just 10.65%, significantly outperforming the market average.
Behind the "free money" story
The experiments above are more worth attention than the wealth stories of arbitrage bots, as they at least point to a new possibility. If those like 0x8dxd rely on speed and front-running, the emergence of large models has laid another card on the table: reasoning itself can also become a weapon.
The future division of labor for automated trading bots will likely involve large models making judgments by compressing fragmented information into probabilistic conclusions, while tools like OpenClaw handle execution, turning those conclusions into actual order placement and position management. What was once only affordable for quantitative funds is now within reach for individual developers.
This means the competitive landscape of prediction markets is changing.
In traditional prediction markets, humans rely on experience and intuition. In the era of high-frequency arbitrage, machines rely on speed and discipline. Now that reasoning has been programmed, the real barrier becomes who is better at transforming complex information into accurate probabilities.
Then another fantasy emerged: if one had a smart and reliable lobster, they could turn Polymarket into a money printer.
Unfortunately, there is still a significant gap between theory and practice. Prophet Arena is a platform designed to evaluate AI forecasting capabilities, and research based on it has revealed some notable risks.
First, the predictive capability of large models is not stable. Top models can approach or even surpass market consensus in open-domain predictions, but being right and making money are two different things. Improved prediction accuracy does not automatically translate into consistent excess returns.
Second, the time window presents a practical challenge. The closer an event is to its outcome, the more densely packed the sudden information becomes, and models tend to be conservative during this phase, adjusting probabilities slowly—while human markets react more swiftly.
Moreover, large models are easily misled by noise. A single emotional news article or a surge of social media activity can cause significant fluctuations in the model’s probability judgments. In contrast, experienced human traders exhibit stronger anchoring and are less susceptible to being overwhelmed by short-term noise.
Additionally, the OpenClaw class framework typically requires importing private keys and transaction permissions, and various security issues may quietly drain your account.
So instead of expecting AI + OpenClaw to deliver a game-changing disruption to prediction markets, focus on the deeper impacts it will bring. As AI-driven agents become more numerous and price movements respond to information faster, this may actually dispel the illusion of automated arbitrage.
Once bots or lobsters become widespread, arbitrage windows will only narrow further. At that point, whether you can sustain profitability won’t depend on whether you own a smarter lobster, but on whether you understand the risks you’re taking.
AI can place bets on behalf of humans, but the consequences must still be borne by humans themselves.
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