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

On May 31, according to CoinDesk, as AI trading agents enter financial markets, structural issues in retail trading are facing potential changes. Current exchange and brokerage business models rely on frequent customer trading, profiting from commissions, spreads, and order flow regardless of profit or loss. Research shows that 74% to 89% of retail traders ultimately lose money, while the Payment for Order Flow (PFOF) mechanism hidden behind zero-commission trading disconnects platform profits from customer gains. Independent, programmable AI trading agents can alter this structural conflict by linking agent revenue to customer portfolio returns, encouraging disciplined trading rather than trading frequency. Agents can choose to reduce positions, avoid impulsive actions, and protect customer assets in volatile markets, achieving true alignment of interests.