Analysis: AI Trading Agents Could End Exchange High-Frequency Trading Models, Bringing Fair Incentives to Retail Investors

Odaily Odaily reports that as AI trading agents enter financial markets, structural problems in retail trading are facing potential transformation. The current business models of exchanges and brokerages rely on customers trading frequently. Regardless of whether the customer profits or loses, the platform profits through commissions, spreads, and order flow. Research shows that 74% to 89% of retail traders ultimately lose money, and the Payment for Order Flow (PFOF) mechanism hidden behind zero-commission trades ensures that the platform's profits are unrelated to customer returns.

Independent, programmable AI trading agents can change this structural contradiction: by linking the agent's returns to the customer's portfolio returns, they encourage disciplined trading rather than trading frequency. Agents can choose to reduce positions, avoid impulsive moves, and protect customer assets in highly volatile markets, achieving true alignment of interests.

As the US eliminates minimum asset requirements for day trading and the EU prepares to implement a PFOF ban, traditional exchange models are facing regulatory pressure. Meanwhile, AI agents are restructuring trading infrastructure through innovative channels such as on-chain payments, gas-free transactions, and decentralized exchanges, providing retail investors with transparent, fair, and verifiable trading intermediaries. (CoinDesk)