Recently, on the prediction market platform Polymarket, some automated robots built on OpenClaw have been trading around the clock, making profits through arbitrage, market making, and information capture. Developers claim that these robots can now generate tens of thousands of dollars in monthly income.
The recent popularity of Openclaw has also sparked infinite imagination in the iGaming industry, especially in the fields of poker, betting, or PVP games, where the entire gambling ecosystem might undergo changes.
🤖 First, it's important to understand what OpenClaw is
OpenClaw is an AI Agent Framework, not a traditional large model. A complete OpenClaw system usually consists of several core parts:
🟢 First is the Large Language Model (LLM), such as Claude, GPT, etc. This part acts as the "brain" of the AI, responsible for understanding information, reasoning, and strategizing.
🟢 Next is the Agent execution framework. This part is the real core of OpenClaw, responsible for translating the model's judgments into actual actions. For example: reading data, calling APIs, executing trades, sending instructions, etc.
🟢 Third is the Tools and Plugins system. OpenClaw can connect to various external tools, such as market data interfaces, trading platform APIs, weather data sources, or game servers. These tools provide real-time information to the AI, allowing it to perform operations across different systems.
🟢 Lastly, the Memory and Strategy module. AI agents can save historical data, trade records, or behavioral feedback, thus optimizing future decision-making.
Under this architecture, the large model is responsible for thinking, and then OpenClaw handles the integration of actions.
On Polymarket, similar agents have long been used for automated trading.
Currently, the active AI trading robots on Polymarket generally rely on three strategies.
High-frequency arbitrage
For example, the contract for the ultimate winner will settle at $1.
If at any moment:
YES price = 0.48
NO price = 0.47
The total is only 0.95.
The robot will buy both YES and NO, locking in $0.05 of risk-free profit.
This opportunity usually lasts only a few seconds, nearly impossible for humans to catch, but AI can complete the trade instantly.
Market making for profit margins
Another common strategy is acting as a market maker.
For example, if the fair price of a contract fluctuates around 0.80, the robot will place orders simultaneously:
Buy: 0.80
Sell: 0.82
Lag arbitrage
Utilizing the market's delayed response to information, such as in weather prediction markets.
The robot can read meteorological data in real-time. When the official weather forecast updates, if the market odds have not yet adjusted, they will immediately place bets.
There is a robot specifically trading the London weather market that turned $1,000 into $24,000 in less than a year.
A study called LiveTradeBench tested 21 mainstream large models in real markets. The results showed that Claude Sonnet 3.7 led other models in the prediction market, achieving a cumulative return of 20.54% over 50 trading days.
Besides helping practitioners with OpenClaw, when connected to iGaming platforms, it can also play a completely new role: AI player.
Online poker platforms have always faced the problem of not having enough full tables. In the past, the platform's solution was to use simple robot players. Thus, OpenClaw could complete many tasks of AI poker players or real human opponents in PVP games.
The biggest imagination space is even building an AI gambling ecosystem, allowing AI to have its own accounts and capital pools for hedge management.
🤖 How to deploy OpenClaw?
Deploying OpenClaw is not complicated, and the general process includes several steps.
🟢 First is choosing the AI model, usually using a large language model or a specialized strategy model for decision-making.
🟢 The second step is configuring the OpenClaw Agent.
At this stage, it is necessary to define input data, decision logic, and execution actions. For example, reading poker game information, calculating probabilities, deciding bet amounts.
🟢 The third step is connecting to platform interfaces, where AI needs to be able to read data status and execute operations.
The last step is continuous training. Through historical data and reinforcement learning, AI strategies can be continuously optimized.
🔴 The emergence of OpenClaw, while bringing more convenience to practitioners, similar AI agents like OpenClaw also bring industry risks.
As more and more AI agents enter the market, the gambling industry might see a new ecological structure: human players, AI players, and AI agent players together forming a new casino world.
This also brings new problems, such as fairness and security risks. In the foreseeable future, the situation of humans using AI agents for arbitrage gambling will become increasingly common.
If AI agents collude with real player accounts, they might form new cheating modes, such as through data sharing or coordinated betting to affect odds. At that time, platforms need to establish monitoring systems for AI behavior, such as detecting abnormal decision frequencies, abnormal profit curves, and associated transactions between accounts.
Then there are system security issues. AI agents like OpenClaw, if they access platform APIs and manage multiple permissions, could cause significant losses if private keys or permissions are stolen.
From an industry trend perspective, as AI agent technology continues to mature, the situation of humans using AI to assist in betting or even fully automated gambling is likely to become increasingly common. Future online gambling platforms will also have to manage more and more algorithm players and AI agents.
The proliferation of OpenClaw could both drive industry efficiency and gameplay innovation, while also potentially amplifying risks related to fairness, regulation, and security—opportunities and challenges are likely to arrive simultaneously.
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