AI tools are increasingly empowering coding, research, and travel planning. A viral post on X has put the spotlight on a different use case: automated trading strategies.
A user who goes by 0x_Discover has claimed that an AI-driven trading agent helped him capture time-zone arbitrage opportunities on Polymarket, a prediction platform. The result of using an Agent in this case was significant overnight profits.
In the post, the user said he received a notification at around 3:47 AM seeking approval to deploy $12,000 (about Rs. 11.08 lakh) across six prediction markets nearing resolution. After authorising the trades, he went back to sleep. By morning, he claimed the positions had generated profits of $43,800, roughly Rs. 40.47 lakh.
He added that the agent had been operational for about nine days and was designed specifically to identify pricing inefficiencies linked to time-zone differences.
Time-zone arbitrage occurs when real-world outcomes become known in one part of the world while market prices elsewhere have not fully reacted. According to the user, many Polymarket participants are based in the United States, which can create brief windows of opportunity during US night hours.
The AI agent reportedly monitored global data streams, including Japanese government RSS feeds, European Parliament livestreams, Australian financial news wires, Middle East flight-tracking updates, and policy announcements from Asian central banks.
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The system was programmed to send alerts when markets were set to resolve between 2 AM and 6 AM Eastern Time. On the night in question, it identified six markets linked to developments such as a Japanese interest-rate decision, a European Parliament vote, policy signals from South Korea, Australian trade updates, OPEC-related production notes involving the UAE, and a regulatory vote in Singapore.
The user claimed trades were entered at prices between 15 cents and 31 cents, with payouts later settling between 95 cents and $1.
The post has triggered intense discussion on X over whether AI agents can consistently exploit market inefficiencies, or if such gains remain anecdotal in volatile prediction markets.